40 lines
2.0 MiB
40 lines
2.0 MiB
<!DOCTYPE html> <html lang=en-US style><!--
|
||
Page saved with SingleFile
|
||
url: https://www.sciencedirect.com/science/article/pii/S0022169421005552
|
||
saved date: Mon Oct 09 2023 09:21:46 GMT-0400 (hora de verano de Cuba)
|
||
--><meta charset=utf-8>
|
||
<meta name=citation_pii content=S0022169421005552>
|
||
<meta name=citation_id content=126508>
|
||
<meta name=citation_issn content=0022-1694>
|
||
<meta name=citation_volume content=600>
|
||
<meta name=citation_publisher content=Elsevier>
|
||
<meta name=citation_firstpage content=126508>
|
||
<meta name=citation_fulltext_world_readable content>
|
||
<meta name=citation_journal_title content="Journal of Hydrology">
|
||
<meta name=citation_type content=JOUR>
|
||
<meta name=citation_doi content=10.1016/j.jhydrol.2021.126508>
|
||
<meta name=dc.identifier content=10.1016/j.jhydrol.2021.126508>
|
||
<meta name=citation_article_type content="Full-length article">
|
||
<meta property=og:description content="The complexity of karst groundwater flow modelling is reflected by the amount of simulation approaches. The goal of the Karst Modelling Challenge (KMC…">
|
||
<meta property=og:image content=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-ga1.jpg>
|
||
<meta name=citation_title content="Karst modelling challenge 1: Results of hydrological modelling">
|
||
<meta property=og:title content="Karst modelling challenge 1: Results of hydrological modelling">
|
||
<meta name=citation_publication_date content=2021/09/01>
|
||
<meta name=citation_online_date content=2021/06/06>
|
||
<meta name=robots content=INDEX,FOLLOW,NOARCHIVE,NOODP,NOYDIR>
|
||
<title>Desafía de modelado de Karst 1: Resultados de la modelización hidrológica - ScienceDirect</title>
|
||
<link rel=canonical href=https://www.sciencedirect.com/science/article/pii/S0022169421005552>
|
||
<meta property=og:type content=article>
|
||
<meta name=viewport content="initial-scale=1">
|
||
<meta name=SDTech content="Proudly brought to you by the SD Technology team in London, Dayton, and Amsterdam">
|
||
<link rel="shortcut icon" href="data:image/vnd.microsoft.icon;base64,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" type=image/x-icon>
|
||
<style>@font-face{font-family:elseviersans;src:url(data:binary/octet-stream;base64,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)format("woff2")}@font-face{font-family:elseviersans;src:url(data:binary/octet-stream;base64,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)format("woff2");font-weight:700}@font-face{font-family:elseviersans;src:url(data:binary/octet-stream;base64,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)format("woff2");font-style:italic}@font-face{font-family:elseviergulliver;src:url(data:binary/octet-stream;base64,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)format("woff2")}@font-face{font-family:elseviergulliver;src:url(data:binary/octet-stream;base64,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)format("woff2");font-weight:700}@font-face{font-family:elseviergulliver;src:url(data:binary/octet-stream;base64,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)format("woff2");font-style:italic}/*!normalize.css v5.0.0 | MIT License | github.com/necolas/normalize.css*/html{font-family:sans-serif;-ms-text-size-adjust:100%;-webkit-text-size-adjust:100%}article,aside,header,section{display:block}a{background-color:transparent;-webkit-text-decoration-skip:objects}a:active,a:hover{outline-width:0}strong{font-weight:bolder}sub,sup{font-size:75%;line-height:0;position:relative;vertical-align:baseline}sub{bottom:-.25em}sup{top:-.5em}img{border-style:none}svg:not(:root){overflow:hidden}button{font-size:100%;font-variant-numeric:lining-nums;line-height:1.15;margin:0}button{overflow:visible}button{text-transform:none}html [type=button],[type=submit]{-webkit-appearance:button;border-radius:0}button::-moz-focus-inner,[type=button]::-moz-focus-inner,[type=reset]::-moz-focus-inner,[type=submit]::-moz-focus-inner{border-style:none;padding:0}button:-moz-focusring,[type=button]:-moz-focusring,[type=reset]:-moz-focusring,[type=submit]:-moz-focusring{outline:1px dotted ButtonText}::-webkit-file-upload-button{-webkit-appearance:button;font:inherit}template{display:none}h1,h2,h3,h4,p,dl,dd,ol,ul,form,figure,table,th,td{margin:0;padding:0}html{-webkit-box-sizing:border-box;box-sizing:border-box}*,*:before,*:after{-webkit-box-sizing:inherit;box-sizing:inherit}html{font-size:1.25rem;font-variant-numeric:lining-nums;line-height:1.4}@supports (--css:variables){html{line-height:var(--sd-ui-line-height)}}body{font-family:ElsevierSans,Arial,Helvetica,Roboto,Lucida Sans Unicode,Microsoft Sans Serif,Segoe UI Symbol,STIXGeneral,Cambria Math,Arial Unicode MS,sans-serif;color:#1f1f1f;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}*,*:after,*:before{margin:0;padding:0;-webkit-box-sizing:border-box;box-sizing:border-box}.grid{width:100%}.grid:before,.row:before,.grid:after,.row:after{content:" ";display:table}.grid:after,.row:after{clear:both}.grid img{width:auto;max-width:100%;height:auto}[class*=col-]{float:left;width:100%;min-height:1px}@media only screen and (min-width:48em){.row>.col-md-8{width:33.3333333333%}.row>.col-md-16{width:66.6666666667%}}@media only screen and (min-width:62em){.row>.col-lg-6{width:25%}.row>.col-lg-12{width:50%}}.move-left{float:left;left:0}.move-right{float:right;right:0}h3,h4{font-weight:400}@supports (--css:variables){h1.h1-3xl{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){h1.h1-2xl{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){h1.h1-xl{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){h1{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){h2{line-height:var(--sd-ui-line-height)}}h3{line-height:1.333}@supports (--css:variables){h3{line-height:var(--sd-ui-line-height)}}h4{font-size:1rem;line-height:1.4}@supports (--css:variables){h4{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){h5{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){h1.alt-xl{line-height:var(--sd-ui-line-height)}}.button-alternative{background-color:transparent;color:#1f1f1f;border:none;text-decoration:none;font-family:inherit;display:inline-block}.button-alternative-icon svg.icon{border:2px solid;height:40px;width:40px;padding:6px;-webkit-transition:background-color .3s,border-color .3s,color .3s,fill .3s;-o-transition:background-color .3s,border-color .3s,color .3s,fill .3s;transition:background-color .3s,border-color .3s,color .3s,fill .3s;vertical-align:middle}.button-alternative svg.arrow-external-link{vertical-align:middle}.button-alternative-text{margin:0 .5em;-webkit-transition:border-color .3s;-o-transition:border-color .3s;transition:border-color .3s;padding-bottom:2px;border-bottom:2px solid transparent;vertical-align:middle;text-align:center;color:#1f1f1f}.button-alternative-secondary:not(.on-dark-background) .button-alternative-icon svg.icon{background-color:#fff;border-color:#0272b1;fill:currentColor}.button-alternative:hover,.button-alternative:focus-visible{cursor:pointer}.button-alternative:disabled{cursor:default}.button-alternative:hover:not(:disabled):not(.on-dark-background) .button-alternative-icon svg.icon,.button-alternative:active:not(:disabled):not(.on-dark-background) .button-alternative-icon svg.icon{background-color:#fff;border:2px solid #eb6500;fill:#1f1f1f}.button-alternative:hover:not(:disabled):not(.on-dark-background) .button-alternative-text,.button-alternative:active:not(:disabled):not(.on-dark-background) .button-alternative-text{border-bottom:2px solid #eb6500;color:#1f1f1f}.button-alternative:focus-visible:not(:disabled):not(.on-dark-background){outline:2px solid #eb6500;outline-offset:1px}.button-alternative-secondary:disabled .button-alternative-icon svg.icon,.button-alternative-tertiary:disabled .button-alternative-icon svg.icon{background-color:#fff;border-color:#b8b8b8;fill:#b8b8b8}.button-alternative:disabled .button-alternative-text{color:#b8b8b8}.anchor{color:#0272b1;-webkit-transition:color .3s ease,border-bottom-color .3s ease;-o-transition:color .3s ease,border-bottom-color .3s ease;transition:color .3s ease,border-bottom-color .3s ease;text-decoration:none;display:inline-block}.anchor:active{color:#1f1f1f}.anchor:active:hover .anchor-text{border-bottom:2px solid #eb6500}.anchor:focus-visible{outline:2px solid #eb6500;outline-offset:1px}.anchor:hover{color:#1f1f1f}.anchor:hover .anchor-text{border-bottom:2px solid #eb6500}.anchor svg.icon{fill:currentColor;vertical-align:middle}.anchor-icon-left svg.icon{margin-right:8px}.anchor svg.icon.arrow-external-link{margin-left:2px}.anchor-text{border-bottom:2px solid transparent;-webkit-transition:border-bottom-color .3s;-o-transition:border-bottom-color .3s;transition:border-bottom-color .3s}.anchor-has-colored-icon svg.icon{fill:#0272b1}.anchor-has-colored-icon:active svg.icon,.anchor-has-colored-icon:focus-visible svg.icon,.anchor-has-colored-icon:hover svg.icon{fill:#eb6500}.anchor-navigation{color:#1f1f1f}.button-link{background-color:transparent;border:none;-webkit-transition:color .3s ease;-o-transition:color .3s ease;font-family:inherit}.button-link:active,.button-link:hover{color:#1f1f1f;cursor:pointer}.button-link:active .button-link-text,.button-link:hover .button-link-text{border-bottom:2px solid #eb6500}.button-link:active svg.icon,.button-link:hover svg.icon{fill:#eb6500}.button-link:focus-visible{outline:2px solid #eb6500;outline-offset:2px}.button-link:focus-visible:hover .button-link-text{border-bottom:2px solid #eb6500}.button-link:focus-visible:hover svg.icon{fill:#eb6500}.button-link:disabled{color:#b8b8b8;cursor:default}.button-link:disabled .button-link-text{border-bottom:none}.button-link:disabled svg.icon{fill:currentColor}.button-link svg.icon{fill:#0272b1;vertical-align:middle;-webkit-transition:fill .3s ease;-o-transition:fill .3s ease;transition:fill .3s ease}.button-link-icon-left svg.icon{margin-right:8px}.button-link-icon-only svg.icon{margin:0}.button-link-icon-right svg.icon{margin-left:8px}.button-link-text{border-bottom:2px solid transparent;-webkit-transition:border-bottom-color .3s ease;-o-transition:border-bottom-color .3s ease;transition:border-bottom-color .3s ease;vertical-align:middle}.button-link-primary{color:#1f1f1f}.button-link-secondary{color:#0272b1}.button{border:2px solid;font-family:inherit;overflow:hidden;-webkit-transition:background-color .3s,border-color .3s,color .3s;-o-transition:background-color .3s,border-color .3s,color .3s;transition:background-color .3s,border-color .3s,color .3s;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;white-space:nowrap;-o-text-overflow:ellipsis;text-overflow:ellipsis}@media only screen and (min-width:36em){.button{max-width:100%}}.button-text{border-bottom:2px solid transparent;-webkit-transition:border-bottom-color .3s ease,color .3s;-o-transition:border-bottom-color .3s ease,color .3s}.button-anchor{background-color:transparent}.button-anchor:disabled{color:#b8b8b8}.button-anchor:hover:not(:disabled) .button-text{border-bottom:2px solid #eb6500}.button-anchor:focus-visible:not(:disabled){outline:2px solid #eb6500}.button:hover,.button:focus-visible{cursor:pointer}.button:disabled{cursor:default}@-webkit-keyframes icon-spinner-anim{0%{opacity:1}100%{opacity:0}}@keyframes icon-spinner-anim{0%{opacity:1}100%{opacity:0}}.popover{position:relative;display:inline-block}@supports (--css:variables){.badge-sm{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){.badge-md{line-height:var(--sd-ui-line-height)}}.link-button{border:2px solid;display:inline-block;font-family:inherit;height:40px;font-size:20px;overflow:hidden;padding:0 10px;-webkit-transition:background-color .3s,border-color .3s,color .3s;-o-transition:background-color .3s,border-color .3s,color .3s;transition:background-color .3s,border-color .3s,color .3s;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;white-space:nowrap;max-width:250px;-o-text-overflow:ellipsis;text-overflow:ellipsis;text-decoration:none;text-align:center}@media only screen and (min-width:36em){.link-button{max-width:100%}}.link-button svg.icon{fill:currentColor;vertical-align:middle;-webkit-transition:fill .3s ease;-o-transition:fill .3s ease;transition:fill .3s ease;height:36px;width:36px;padding:6px}.link-button-text-only{line-height:36px}.link-button-icon-left{padding-left:0}.link-button-icon-right{padding-right:0}.link-button-icon-left .link-button-text,.link-button-icon-right .link-button-text{vertical-align:middle}.link-button-primary:not(.on-dark-background){background-color:#0272b1;border-color:#0272b1;color:#fff}.link-button-secondary:not(.on-dark-background){background-color:#fff;border-color:#0272b1;color:#1f1f1f}.link-button:hover,.link-button:active{color:#1f1f1f;background-color:#fff;background:#fff}.link-button:hover:not(.on-dark-background),.link-button:active:not(.on-dark-background){border-color:#eb6500}.link-button:hover svg.icon,.link-button:active svg.icon{fill:#1f1f1f}.link-button:focus-visible:not(.on-dark-background){outline:2px solid #eb6500;outline-offset:1px}.link-button:hover,.link-button:focus-visible{cursor:pointer}:root{--sd-ui-right-side-panel-width:calc(496px)}@media (min-width:calc(512px)){.side-panel{width:var(--sd-ui-right-side-panel-width)}}@media (max-width:calc(512px)){.side-panel-border{width:100vw;border-left:32px solid rgba(46,46,46,.55);background:0 0}}.u-margin-0-top{margin-top:0!important}.u-margin-0-right{margin-right:0!important}.u-padding-xs{padding:8px!important}.u-padding-xs-top{padding-top:8px!important}.u-margin-xs-top{margin-top:8px!important}.u-padding-xs-bottom{padding-bottom:8px!important}.u-margin-xs-bottom{margin-bottom:8px!important}.u-padding-s-ver{padding-top:16px!important;padding-bottom:16px!important}.u-margin-s-ver{margin-top:16px!important;margin-bottom:16px!important}.u-padding-s-hor{padding-right:16px!important;padding-left:16px!important}.u-padding-s-top{padding-top:16px!important}.u-margin-s-top{margin-top:16px!important}.u-padding-s-right{padding-right:16px!important}.u-margin-s-right{margin-right:16px!important}.u-padding-s-bottom{padding-bottom:16px!important}.u-margin-s-bottom{margin-bottom:16px!important}.u-padding-s-left{padding-left:16px!important}.u-margin-s-left{margin-left:16px!important}.u-margin-m-top{margin-top:24px!important}.u-margin-m-right{margin-right:24px!important}.u-margin-m-bottom{margin-bottom:24px!important}.u-padding-l-ver{padding-top:32px!important;padding-bottom:32px!important}.u-margin-l-ver{margin-top:32px!important;margin-bottom:32px!important}.u-margin-l-top{margin-top:32px!important}.u-margin-l-bottom{margin-bottom:32px!important}@media only screen and (min-width:20em){.u-margin-m-top-from-xs{margin-top:24px!important}}@media only screen and (min-width:36em){.u-margin-0-bottom-from-sm{margin-bottom:0!important}.u-padding-m-hor-from-sm{padding-right:24px!important;padding-left:24px!important}.u-margin-m-right-from-sm{margin-right:24px!important}.u-margin-xl-top-from-sm{margin-top:40px!important}}@media only screen and (min-width:48em){.u-margin-0-top-from-md{margin-top:0!important}.u-margin-0-bottom-from-md{margin-bottom:0!important}.u-margin-m-right-from-md{margin-right:24px!important}.u-padding-l-hor-from-md{padding-right:32px!important;padding-left:32px!important}.u-margin-l-top-from-md{margin-top:32px!important}.u-margin-l-right-from-md{margin-right:32px!important}}@media only screen and (min-width:62em){.u-margin-l-right-from-lg{margin-right:32px!important}}@media only screen and (min-width:75em){.u-padding-l-hor-from-xl{padding-right:32px!important;padding-left:32px!important}}.u-clr-grey6{color:#707070!important}.u-clr-grey8{color:#1f1f1f!important}.u-bg-white{background-color:#fff!important}.u-bg-grey1{background-color:#f5f5f5!important}@supports (--css:variables){:root{--sd-ui-line-height:calc(1em + 8px)}}.u-font-serif{font-family:ElsevierGulliver,Georgia,Times New Roman,Times,STIXGeneral,Cambria Math,Lucida Sans Unicode,Microsoft Sans Serif,Segoe UI Symbol,Arial Unicode MS,serif!important}@supports (--css:variables){.u-font-serif{--sd-ui-line-height:calc(1em + 10px)}}.u-font-sans{font-family:ElsevierSans,Arial,Helvetica,Roboto,Lucida Sans Unicode,Microsoft Sans Serif,Segoe UI Symbol,STIXGeneral,Cambria Math,Arial Unicode MS,sans-serif!important}@supports (--css:variables){.u-font-sans{--sd-ui-line-height:calc(1em + 8px)}}@supports (--css:variables){.text-2xs{line-height:var(--sd-ui-line-height)}}.text-xs{font-size:.7rem;line-height:1.57}@supports (--css:variables){.text-xs,.switch-small{line-height:var(--sd-ui-line-height)}}.text-s{font-size:.8rem;line-height:1.5}@supports (--css:variables){.text-s,.switch{line-height:var(--sd-ui-line-height)}}.text-m{line-height:1.4}@supports (--css:variables){.text-m,.alert-text{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){.text-l{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){.text-xl{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){.text-2xl{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){.text-3xl{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){.text-4xl{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){.text-5xl{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){.u-h1-3xl{line-height:var(--sd-ui-line-height)!important}}@supports (--css:variables){.u-h1-2xl{line-height:var(--sd-ui-line-height)!important}}@supports (--css:variables){.u-h1-xl{line-height:var(--sd-ui-line-height)!important}}@supports (--css:variables){.u-h1{line-height:var(--sd-ui-line-height)!important}}.u-h2{font-size:32px!important;font-size:1.6rem!important;line-height:1.333!important;font-weight:400!important}@supports (--css:variables){.u-h2{line-height:var(--sd-ui-line-height)!important}}.u-h3{font-size:24px!important;font-size:1.2rem!important;line-height:1.333!important;font-weight:400!important}@supports (--css:variables){.u-h3{line-height:var(--sd-ui-line-height)!important}}.u-h4{font-size:20px!important;font-size:1rem!important;line-height:1.4!important;font-weight:400!important}@supports (--css:variables){.u-h4{line-height:var(--sd-ui-line-height)!important}}@supports (--css:variables){.u-h5{line-height:var(--sd-ui-line-height)!important}}@supports (--css:variables){.u-h1-alt{line-height:var(--sd-ui-line-height)!important}}.u-text-center{text-align:center!important}.u-text-truncate{max-width:100%!important;white-space:nowrap!important;overflow:hidden!important;-o-text-overflow:ellipsis!important;text-overflow:ellipsis!important}.u-text-italic{font-style:italic!important}.u-display-block{display:block!important}.u-display-inline{display:inline!important}.u-display-inline-block{display:inline-block!important}.u-position-relative{position:relative}.u-show-from-sm{display:none!important}@media only screen and (min-width:36em){.u-show-from-sm{display:block!important}}.u-show-from-md{display:none!important}@media only screen and (min-width:48em){.u-show-from-md{display:block!important}}.u-show-from-lg{display:none!important}@media only screen and (min-width:62em){.u-show-from-lg{display:block!important}}.u-show-inline-from-sm{display:none!important}@media only screen and (min-width:36em){.u-show-inline-from-sm{display:inline!important}}.u-show-inline-from-md{display:none!important}@media only screen and (min-width:48em){.u-show-inline-from-md{display:inline!important}}@-webkit-keyframes gh-mobile-menu{0%{right:-320px}1%{right:-300px}99%{right:-20px}100%{right:0}}@keyframes gh-mobile-menu{0%{right:-320px}1%{right:-300px}99%{right:-20px}100%{right:0}}.gh-move-to-spine>.anchor:hover{cursor:pointer;color:#323232!important}.button-link{-webkit-transition:border-bottom-color .3s ease,color .3s ease;-o-transition:border-bottom-color .3s ease,color .3s ease}.icon-help,.icon-search{-webkit-transition:all .3s ease!important;-o-transition:all .3s ease!important;transition:all .3s ease!important}.u-flex-center-ver{display:-webkit-box;display:-ms-flexbox;display:flex;-webkit-box-align:center;-ms-flex-align:center;align-items:center}.u-list-reset{list-style:none;padding-left:0!important}.gh-move-to-spine>.anchor:hover{cursor:pointer;color:#323232!important}.button-link{-webkit-transition:border-bottom-color .3s ease,color .3s ease;-o-transition:border-bottom-color .3s ease,color .3s ease;transition:border-bottom-color .3s ease,color .3s ease}.icon-help,.icon-search{-webkit-transition:all .3s ease!important;-o-transition:all .3s ease!important;transition:all .3s ease!important}#gh-branding{-ms-flex-negative:0;flex-shrink:0;-ms-flex-positive:1;margin-right:64px;text-decoration:none}#gh-branding .gh-logo{-webkit-transition:height .3s,width .3s;-o-transition:height .3s,width .3s;transition:height .3s,width .3s;height:48px;width:54px}#gh-branding .gh-logo+.gh-wordmark{margin-left:16px}@supports (--css:variables){#gh-branding h1{line-height:var(--sd-ui-line-height)}}#gh-branding .gh-wordmark{-webkit-transition:height .3s,margin .3s,width .3s;-o-transition:height .3s,margin .3s,width .3s;transition:height .3s,margin .3s,width .3s;-webkit-transform:translateY(2px);-ms-transform:translateY(2px);transform:translateY(2px)}#gh-branding:hover,#gh-branding:focus{border-bottom:none}#gh-branding:focus{outline:2px solid #eb6500;outline-offset:1px}@supports (--css:variables){#gh-mob-inst-cnt .gh-inst-cnt{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){.gh-lib-banner.gh-lb-legacy{line-height:var(--sd-ui-line-height)}}.gh-nav-cnt{position:relative;width:77%}.gh-nav-cnt .gh-nav-links-container-h{display:-webkit-box;display:-ms-flexbox;display:flex;-webkit-box-align:center;-ms-flex-align:center;align-items:center;float:right}.gh-nav-cnt .gh-nav-links-container-h nav+nav{margin-left:64px;margin-right:32px}@media only screen and (max-width:getBreakpointDownValue(48em)){.gh-nav-cnt .gh-nav-utilities{margin-left:0}}.gh-nav-cnt .gh-nav-h{z-index:1;float:left}.gh-nav .gh-nav-item{display:block}.gh-nav .gh-nav-item:before{content:none!important}.gh-nav .gh-nav-item .gh-icon{max-width:initial!important}.gh-nav .gh-nav-action{position:relative;color:#1f1f1f}.gh-nav .gh-nav-action .gh-icon{margin:0!important}.gh-nav .gh-nav-action:focus,.gh-nav .gh-nav-action:hover{color:#1f1f1f}.gh-nav-h{display:-webkit-box;display:-ms-flexbox;-webkit-box-align:center;-ms-flex-align:center;align-items:center;display:inline-block;overflow:visible;-webkit-transition:margin-left .4s ease;-o-transition:margin-left .4s ease;transition:margin-left .4s ease}.gh-nav-h>.gh-nav-list{height:100%;display:-webkit-box;display:-ms-flexbox;display:flex;-webkit-box-align:center;-ms-flex-align:center;align-items:center}.gh-nav-h>.gh-nav-list>.gh-nav-item+.gh-nav-item{-webkit-transition:padding-left .2s;-o-transition:padding-left .2s;transition:padding-left .2s}.gh-nav-h.gh-nav-links>.gh-nav-list>.gh-nav-item{line-height:26px}.gh-nav-h.gh-nav-utilities>.gh-nav-list>.gh-nav-item+.gh-nav-item{padding-left:8px}.gh-nav .gh-icon-btn:active,.gh-nav .gh-icon-btn:hover,.gh-nav .gh-icon-btn:focus{color:#eb6500!important}.gh-nav .gh-icon-btn:active .gh-icon,.gh-nav .gh-icon-btn:hover .gh-icon,.gh-nav .gh-icon-btn:focus .gh-icon{fill:#eb6500!important}.gh-nav .gh-icon-btn .gh-icon{-webkit-transition:height .3s,width .3s;-o-transition:height .3s,width .3s;transition:height .3s,width .3s}@media only screen and (max-width:getBreakpointDownValue(48em)){.gh-search-cnt{top:0;left:0!important;right:0!important;bottom:0}}@media only screen and (max-width:getBreakpointDownValue(48em)){.gh-search-cnt .gh-nav-search-icon{left:16px}}@media only screen and (max-width:getBreakpointDownValue(48em)){.gh-search-cnt .gh-search-keyword .gh-search-input-field{padding:0 40px 0 48px}}@media only screen and (max-width:getBreakpointDownValue(48em)){.gh-search-cnt .gh-search-keyword .search-input-clear{right:16px}}.gh-nav-help-icon{display:inline-block}.gh-nav-help-icon:focus svg{fill:#eb6500!important}.gh-nav-help-icon svg.icon{fill:#4c4c4c}#gh-main-cnt .gh-profile-container{-ms-flex-negative:0;flex-shrink:0;display:-webkit-box;display:-ms-flexbox;display:flex}#gh-signin-btn,#gh-cta-btn{-ms-flex-negative:0;flex-shrink:0;position:relative}@supports (--css:variables){#gh-drawer .gh-nav h2{line-height:var(--sd-ui-line-height)}}.search-button-link svg.icon:not(:hover){fill:#4c4c4c}.search-button-link>.link-button{margin-left:24px;margin-right:-16px!important;display:block;vertical-align:text-bottom;color:#1f1f1f!important}@media only screen and (max-width:1350px){.search-button-link>.link-button{display:none}}.search-button-link>.link-button .icon{vertical-align:middle;fill:#1f1f1f!important}.search-button-link>.link-button.search-button-outline{background-color:#fff;border:1px solid #707070;border-bottom:2px solid #1f1f1f;color:#4c4c4c!important;margin-right:0;padding-top:1px}.search-button-link>.link-button.search-button-outline .link-button-text{margin-right:2px!important;border-right:1px solid #707070;padding:0 30px 0 0!important}.search-button-link>.link-button.search-button-outline .link-button-text::after{content:" ScienceDirect"}@media only screen and (max-width:1440px){.search-button-link>.link-button.search-button-outline .link-button-text::after{content:"…"}}.search-button-link>.link-button.search-button-outline .icon{fill:#0272b1!important}.search-button-link>.link-button.search-button-outline:hover{outline:2px solid #1f1f1f;outline-offset:-2px}.search-button-link>.link-button.search-button-outline:hover .link-button-text{color:#1f1f1f;border-right:1px solid #1f1f1f}#gh-main-cnt{-webkit-transition:padding .3s,height .3s;-o-transition:padding .3s,height .3s;transition:padding .3s,height .3s;height:80px;width:100%;-webkit-box-pack:justify;-ms-flex-pack:justify;justify-content:space-between}#gh-cnt{width:100%;background:#fff;font-family:ElsevierSans,Arial,Helvetica,Roboto,Lucida Sans Unicode,Microsoft Sans Serif,Segoe UI Symbol,STIXGeneral,Cambria Math,Arial Unicode MS,sans-serif!important;color:#4c4c4c;position:relative}@media only screen and (max-width:1120px){#gh-cnt .gh-nav-h+.gh-nav-h{margin-left:48px!important}#gh-cnt .gh-nav-h.gh-nav-utilities>.gh-nav-list>.gh-nav-item+.gh-nav-item{padding-left:2px}@supports (--css:variables){#gh-cnt #gh-main-cnt .gh-profile-container>.button{line-height:var(--sd-ui-line-height)}}#gh-cnt #gh-main-cnt .gh-profile-container>.link-button,#gh-cnt #gh-main-cnt .search-button-link>.link-button{margin-left:24px!important}@supports (--css:variables){#gh-cnt #gh-main-cnt .gh-profile-container>.link-button,#gh-cnt #gh-main-cnt .search-button-link>.link-button{line-height:var(--sd-ui-line-height)}}#gh-cnt .gh-nav-h+.gh-nav-h{margin-left:32px!important}#gh-cnt .gh-nav-h>.gh-nav-list>.gh-nav-item{font-size:.9rem;line-height:1.5555555556}@supports (--css:variables){#gh-cnt .gh-nav-h>.gh-nav-list>.gh-nav-item{line-height:var(--sd-ui-line-height)}}#gh-cnt .gh-nav-h>.gh-nav-list>.gh-nav-item .gh-icon-btn svg{height:20px!important}#gh-cnt .gh-move-to-spine{display:none!important}@supports (--css:variables){#gh-cnt.gh-reduce-v-space #gh-main-cnt .gh-profile-container>.button{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){#gh-cnt.gh-reduce-v-space #gh-main-cnt .gh-profile-container>.link-button,#gh-cnt.gh-reduce-v-space #gh-main-cnt .search-button-link>.link-button{line-height:var(--sd-ui-line-height)}}@supports (-ms-high-contrast:none){#gh-cnt.gh-reduce-v-space .gh-lib-banner.gh-lb-legacy{width:0}}@supports (--css:variables){#gh-cnt.gh-reduce-v-space #gh-branding h1{line-height:var(--sd-ui-line-height)}}@supports not (-ms-high-contrast:none){#gh-cnt.gh-reduce-v-space #gh-branding .gh-wordmark{width:auto}}#gh-cnt #gh-main-cnt{height:48px}@supports (--css:variables){#gh-cnt #gh-main-cnt .gh-profile-container>.button{line-height:var(--sd-ui-line-height)}}#gh-cnt #gh-main-cnt .gh-profile-container>.link-button,#gh-cnt #gh-main-cnt .search-button-link>.link-button{font-size:.8rem;line-height:1.75;height:32px;margin-left:24px!important}@supports (--css:variables){#gh-cnt #gh-main-cnt .gh-profile-container>.link-button,#gh-cnt #gh-main-cnt .search-button-link>.link-button{line-height:var(--sd-ui-line-height)}}#gh-cnt #gh-main-cnt .gh-nav-h>.gh-nav-list>.gh-nav-item .gh-icon-btn svg{height:20px!important}@supports (-ms-high-contrast:none){#gh-cnt .gh-lib-banner.gh-lb-legacy{width:0}}#gh-cnt #gh-branding .gh-logo,#gh-cnt #gh-branding svg{height:32px!important}@supports (--css:variables){#gh-cnt #gh-branding h1{line-height:var(--sd-ui-line-height)}}#gh-cnt #gh-branding .gh-wordmark{margin-left:0!important;height:15px!important}@supports not (-ms-high-contrast:none){#gh-cnt #gh-branding .gh-wordmark{width:auto}}#gh-cnt #gh-branding{margin-right:0!important}#gh-cnt .gh-nav-utilities.gh-nav-h{margin-left:0!important;margin-right:0!important}}.els-footer-links{display:-webkit-box;display:-ms-flexbox;display:flex;-webkit-box-orient:vertical;-webkit-box-direction:normal;-ms-flex-direction:column;flex-direction:column;-webkit-box-align:start;-ms-flex-align:start;align-items:flex-start;-ms-flex-wrap:wrap;flex-wrap:wrap}@media only screen and (min-width:36em){.els-footer-links{-webkit-box-orient:horizontal;-webkit-box-direction:normal;-ms-flex-direction:row;flex-direction:row}}.sd-flex-container{display:-webkit-box;display:-ms-flexbox;display:flex;-webkit-box-orient:vertical;-webkit-box-direction:normal;-ms-flex-direction:column;flex-direction:column;height:100vh}.sd-flex-content{-webkit-box-flex:1;-ms-flex:1 0 auto;flex:1 0 auto}.els-footer{border-top:.1rem solid #eb6500;display:-webkit-box;display:-ms-flexbox;display:flex;-webkit-box-orient:vertical;-webkit-box-direction:normal;-ms-flex-direction:column;flex-direction:column;-ms-flex-negative:0;flex-shrink:0}@media only screen and (min-width:48em){.els-footer{-webkit-box-orient:horizontal;-webkit-box-direction:normal;-ms-flex-direction:row;flex-direction:row}}.els-footer-content{-webkit-box-flex:1;-ms-flex:auto;flex:auto}a{word-break:break-word}.App{position:relative;margin:auto}.App .Article p{margin:0 0 16px}.App .page{position:relative;height:100%}table{margin-left:0;margin-right:0}table th{font-weight:700;font-size:14px}table td{font-size:14px}html{--page-auto-margin:0px}@media only screen and (min-width:1440px){html{--page-auto-margin:(100vw - 1440px)*0.25}}ol,ul{list-style:none}u{text-decoration:underline}.accessbar-utility-component{-webkit-box-align:center;-ms-flex-align:center;align-items:center;display:-webkit-box;display:-ms-flexbox;display:flex;font-size:16px;height:48px;line-height:var(--sd-ui-line-height);margin:0 auto;max-width:100%;width:-webkit-fit-content;width:-moz-fit-content;width:fit-content}.accessbar-utility-component svg.icon{-ms-flex-negative:0;flex-shrink:0}.accessbar-utility-button.button-anchor{padding:0;border-color:transparent;color:#0272b1}.accessbar-utility-link.link-button-anchor:hover,.accessbar-utility-link.link-button-anchor:active,.accessbar-utility-button.button-anchor:hover,.accessbar-utility-button.button-anchor:active{color:#1f1f1f;cursor:pointer}.accessbar-utility-link .link-button-text,.accessbar-utility-button .button-text{display:block;-o-text-overflow:ellipsis;text-overflow:ellipsis;overflow:hidden}.accessbar-utility-link.link-button-anchor:hover .link-button-text,.accessbar-utility-link.link-button-anchor:active .link-button-text,.accessbar-utility-button.button-anchor:hover .button-text,.accessbar-utility-button.button-anchor:active .button-text{border-bottom:2px solid #eb6500}.accessbar-utility-button.button-anchor:hover:not(.on-dark-background),.accessbar-utility-button.button-anchor:active:not(.on-dark-background){border-color:transparent}.accessbar-utility-button .button-text{-webkit-transition:none;-o-transition:none;transition:none}.accessbar-utility-link .link-button-text{border-bottom:2px solid transparent}.accessbar-label{display:none}@media only screen and (min-width:62em){.accessbar-label{display:-webkit-box;display:-ms-flexbox;display:flex;-ms-flex-negative:0;flex-shrink:0;-webkit-box-pack:end;-ms-flex-pack:end;justify-content:flex-end;padding:0 1.6rem;width:calc(25% + 0.8rem + var(--page-auto-margin));-webkit-box-align:center;-ms-flex-align:center;align-items:center}}.accessbar{border:solid #1f1f1f;border-width:.2rem 0 .05rem;display:-webkit-box;display:-ms-flexbox;display:flex;-webkit-box-orient:vertical;-webkit-box-direction:normal;-ms-flex-direction:column;flex-direction:column;padding:.4rem .8rem}@media only screen and (min-width:36em){.accessbar{padding:.4rem 1.2rem}}@media only screen and (min-width:48em){.accessbar{-webkit-box-align:center;-ms-flex-align:center;align-items:center;-webkit-box-orient:horizontal;-webkit-box-direction:normal;-ms-flex-direction:row;flex-direction:row;height:3.2rem}}@media only screen and (min-width:62em){.accessbar{padding:0 1.6rem}}.accessbar>ul{display:contents}.accessbar>ul>li{margin:0 0 .2rem;overflow:hidden;padding:3px;white-space:nowrap}@media only screen and (min-width:48em){.accessbar>ul>li{margin:0 1.2rem 0 0}}.accessbar>ul>li:last-child{margin:0}.screen-reader-only{border-width:0;clip:rect(0,0,0,0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;white-space:nowrap;width:1px}.figure:first-of-type{border-top:1px solid #b8b8b8}.author-group .button-link .react-xocs-author-icon{height:1em;margin:0 0 0 4px}.author-group .button-link .react-xocs-author-icon:last-child{margin:0 0 0 4px}.author-group .button-link .react-xocs-alternative-link{-webkit-text-decoration:underline currentColor;text-decoration:underline currentColor;text-decoration-thickness:1px;text-underline-position:from-font}.author-group .anchor:hover .react-xocs-alternative-link,.author-group .button-link:hover .react-xocs-alternative-link{text-decoration:none}.author-group .button-link{line-height:var(--sd-ui-line-height)}@supports ((-o-object-fit:cover) or (object-fit:cover)){.article-biography.article-biography-has-image .article-biography-image img{width:162px;height:224px;-o-object-fit:cover;object-fit:cover}}.captions{color:#1f1f1f}.e-component{display:block;border-top:2px solid #f0f0f0;padding-top:16px;font-size:16px}.figure{display:block;padding-top:8px;margin:0 0 20px;border-top:0;border-bottom:1px solid #b8b8b8}.figure:first-of-type{border-top:1px solid #b8b8b8}.figure img{height:auto;max-width:100%;margin-bottom:16px}.inline-figure{display:inline-block;margin:0}.list{margin-bottom:24px}.list .react-xocs-list-item{display:-webkit-box;display:-ms-flexbox;display:flex}.list .list-label{width:24px;-ms-flex-negative:0;flex-shrink:0}.list .react-xocs-list-item:last-child p:last-child{margin-bottom:0}.references .title{display:block;font-size:18px}.references .label{float:left;padding-right:10px}.references .label>a{cursor:pointer}.references .reference{margin-bottom:16px;margin-left:48px;display:block}.references .reference .host{color:#707070}.bibliography{color:#1f1f1f;margin-bottom:32px}.bibliography .section-title{font-size:24px;margin-bottom:16px}.tables{margin:16px 0;font-size:14px;border-top:2px solid #8e8e8e;border-bottom:2px solid #8e8e8e}.tables .label{font-size:14px}.tables table{border-collapse:collapse;border-spacing:0;text-align:left;width:100%}.tables.frame-topbot table{border-top:1px solid #8e8e8e;border-bottom:1px solid #8e8e8e}.tables tr.rowsep-1 th{border-bottom:1px solid #8e8e8e}.tables.rowsep-0 td,.tables.rowsep-0 th{border-bottom:0}.tables.colsep-0 td,.tables.colsep-0 th{border-right:0}.tables thead{vertical-align:bottom}.tables thead th{font-weight:700}.tables tbody{vertical-align:top}.tables td,.tables th{padding:5px}.tables th{position:relative}.tables th.rowsep-1::after{content:"";border-bottom:1px solid #8e8e8e;position:absolute;bottom:-1px;left:0;right:0;display:block;margin:0 4px}.tables td:last-child,.tables th:last-child{border-right:0}.tables .valign-top{vertical-align:top}.tables .align-left{text-align:left}.tables .captions{margin-top:16px}.tables .groups{overflow-x:auto;margin:16px 0}.download-all-supplemental-data .article-attachment{font-size:18px;margin:16px 0;display:-webkit-box;display:-ms-flexbox;display:flex;-webkit-box-align:start;-ms-flex-align:start;align-items:flex-start;-webkit-box-pack:justify;-ms-flex-pack:justify;justify-content:space-between}.download-all-supplemental-data .download-all-button{display:grid;grid-template-columns:auto auto;text-align:left;line-height:var(--sd-ui-line-height)}.download-all-supplemental-data .help-link{min-width:-webkit-fit-content;min-width:-moz-fit-content;min-width:fit-content}@supports (--css:variables){.ec-research-data-card{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){.ec-research-data-card .ec-research-data-card-title,.ec-research-data-card .ec-research-data-card-name{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){.ec-interactive-case-insights{line-height:var(--sd-ui-line-height)}}.Abstracts .abstract{margin-bottom:8px}.Abstracts .author-highlights{background-color:#f5f5f5;padding:32px;max-width:680px}.Abstracts .author-highlights h2{margin-top:0!important}.accessbar-sticky{background-color:#fff;z-index:2}@media only screen and (min-width:48em){.accessbar-sticky{position:sticky;top:-4px}}.Article .article-wrapper{max-width:1440px;margin:auto}.Article .sticky-table-of-contents{overflow:hidden;position:sticky;top:60px}.Article .topic-link{-webkit-text-decoration:underline #1f1f1f;text-decoration:underline #1f1f1f;text-decoration-thickness:1px;color:#1f1f1f;text-underline-offset:1px}.Article .topic-link:hover{text-decoration:underline;-webkit-text-decoration:underline #eb6500;text-decoration:underline #eb6500;text-decoration-thickness:2px;color:#1f1f1f;text-underline-offset:2px}.Article .topic-link:focus-visible{outline:2px solid #eb6500;outline-offset:1px}.ArticleIdentifierLinks{display:-webkit-box;display:-ms-flexbox;display:flex;-webkit-box-orient:vertical;-webkit-box-direction:normal;-ms-flex-direction:column;flex-direction:column;-webkit-box-pack:justify;-ms-flex-pack:justify;justify-content:space-between}@media only screen and (min-width:36em){.ArticleIdentifierLinks{-webkit-box-orient:horizontal;-webkit-box-direction:normal;-ms-flex-direction:row;flex-direction:row}}.Banner{margin-bottom:8px}.Banner .banner-options{border-bottom:1px solid #000}.Banner .wrapper.truncated .author-group{margin-bottom:0}.Body section{margin-bottom:8px}.Copyright{margin:32px 0;font-size:13px}.DownloadFullIssue{padding-left:0}@supports (--css:variables){.DownloadIssueModal .download-full-issue-article .download-full-issue-article-title{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){.EntitledRecommendationsModal .entitled-recommendation-article .entitled-recommendation-title{line-height:var(--sd-ui-line-height)}}.ExportCitation .icon-cited-by-66{-webkit-transform:rotate(180deg);-ms-transform:rotate(180deg);transform:rotate(180deg)}.Footnotes .footnote{display:-webkit-box;display:-ms-flexbox;display:flex}.Footnotes .footnote-label{-webkit-box-flex:0;-ms-flex:0 0 48px;flex:0 0 48px}.Footnotes .footnote-detail{-webkit-box-flex:1;-ms-flex-positive:1;flex-grow:1}.Footnotes sup{top:0;font-size:100%}.Head{word-break:break-word}.Head .article-dochead{font-size:.7rem;line-height:2;color:#707070;font-family:ElsevierSans,Arial,Helvetica,Roboto,Lucida Sans Unicode,Microsoft Sans Serif,Segoe UI Symbol,STIXGeneral,Cambria Math,Arial Unicode MS,sans-serif}@supports (--css:variables){.Head .article-dochead{line-height:var(--sd-ui-line-height)}}.issue-navigation{overflow:hidden;font-size:16px;line-height:24px}.Keywords .keywords-section .section-title{margin-top:16px;font-size:24px}.Keywords .keywords-section .keyword{display:inline;margin:0;padding:0}.Keywords .keywords-section .keyword span::after{content:", "}.Keywords .keywords-section .keyword span:last-child::after{content:""}.Keywords .keywords-section .keyword::after{content:"; "}.Keywords .keywords-section .keyword:last-child::after{content:""}.Keywords:not(:last-child),.article-textbox .keywords-section{margin-bottom:32px;border-bottom:2px solid #f0f0f0;padding-bottom:32px}.LicenseInfo{display:-webkit-box;display:-ms-flexbox;display:flex;-webkit-box-pack:justify;-ms-flex-pack:justify;justify-content:space-between}.ListArticles a:hover,.ListArticles a:focus{text-decoration:none}.ListArticles h3{font-size:.9rem}.RelatedContentPanel .plum-sciencedirect-theme{font-family:ElsevierSans,Arial,Helvetica,Roboto,Lucida Sans Unicode,Microsoft Sans Serif,Segoe UI Symbol,STIXGeneral,Cambria Math,Arial Unicode MS,sans-serif!important}.RelatedContentPanel .plum-sciencedirect-theme .PlumX-Summary{font-family:ElsevierSans,Arial,Helvetica,Roboto,Lucida Sans Unicode,Microsoft Sans Serif,Segoe UI Symbol,STIXGeneral,Cambria Math,Arial Unicode MS,sans-serif!important}.RelatedContentPanel .plum-sciencedirect-theme .PlumX-Summary .pps-seemore{color:#0272b1}.RelatedContentPanel .plum-sciencedirect-theme .PlumX-Summary img.plx-logo{height:16px}.OpenAccessLabel{font-style:italic}.OpenAccessLabel .access-indicator{background:#53b848;border-radius:8px;display:inline-block;width:8px;height:8px;margin-right:8px}@supports (--css:variables){.paginatedReferences h3{line-height:var(--sd-ui-line-height)}}@supports (--css:variables){.paginatedReferences .host .series h3.title{line-height:var(--sd-ui-line-height)}}.PageDivider{margin-bottom:24px;border-bottom:2px solid #f0f0f0}.CitedSection{border-left:1px solid}.CitedSection p::after,.CitedSection p::before{content:"…"}.CitedSection .cite-header{margin-bottom:4px}.Publication{margin-bottom:8px;border-bottom:2px solid #f0f0f0;padding-bottom:12px;width:100%;min-height:106px;display:-webkit-box;display:-ms-flexbox;display:flex;-webkit-box-pack:justify;-ms-flex-pack:justify;justify-content:space-between}@media screen and (max-width:599px){.Publication{padding-bottom:24px;min-height:auto}}.Publication .publication-brand,.Publication .publication-volume,.Publication .publication-cover{-webkit-box-flex:0;-ms-flex:0 1 auto;flex:0 1 auto}.Publication .publication-brand{min-width:60px}.Publication .publication-volume{vertical-align:middle;margin:auto;max-width:500px;padding:0 4px}.Publication .publication-cover{min-width:60px;vertical-align:top;text-align:right}.Publication .publication-brand-image,.Publication .publication-cover-image{display:block;max-height:88px}.Publication .publication-cover-image{border:1px solid #b8b8b8}.ReferencedArticles{display:-webkit-box;display:-ms-flexbox;display:flex;overflow:hidden}.ReferenceLinks{margin-top:4px}.ReferenceLinks .link{cursor:pointer;margin-right:24px;white-space:nowrap;display:inline-block}.ReferenceLinks els-view-pdf-element::part(getftr-tooltip){display:none}@media only screen and (min-width:62em){.ReferenceLinks els-view-pdf-element::part(getftr-tooltip){display:initial}}@media screen and (max-width:599px){.pad-left{padding-left:16px}}@media screen and (max-width:599px){.pad-right{padding-right:16px}}.sr-only-focusable:active,.sr-only-focusable:focus{clip:auto;height:auto;margin:0;overflow:visible;position:static}.u-clamp-2-lines{display:-webkit-box;-webkit-box-orient:vertical;-webkit-line-clamp:2}.RelatedContent{color:#1f1f1f}.RelatedContentPanelItem{margin-bottom:16px}.RelatedContentPanelItem .related-content-panel-list-entry-outline-padding{padding:3px;margin-left:-3px}.RelatedContentPanelItem .related-content-panel-list-entry-outline-padding .anchor{vertical-align:top}.RelatedContentPanelItem .article-source .source{max-width:100%!important;white-space:nowrap!important;overflow:hidden!important;-o-text-overflow:ellipsis!important;text-overflow:ellipsis!important}.RelatedContentPanel{border-bottom:2px solid #f0f0f0}.RelatedContentPanel .related-content-panel-toggle{padding:0;width:100%;position:relative;text-align:left;line-height:32px}.RelatedContentPanel .related-content-panel-toggle .button-link-text{font-size:16px}.RelatedContentPanel .related-content-panel-toggle .icon{position:absolute;right:0;top:4px;height:22px;width:22px}.RelatedContentPanel .related-content-panel-toggle:hover .icon,.RelatedContentPanel .related-content-panel-toggle:hover .related-content-panel-title-text{border-bottom-color:#eb6500;fill:#eb6500}.RelatedContentPanel .related-content-panel-toggle.is-up .icon{-webkit-transform:scaleY(-1);-ms-transform:scaleY(-1);transform:scaleY(-1)}.RelatedContentPanel .related-content-panel-title-text{border-bottom:2px solid transparent;position:relative;top:2px}.RelatedContentPanel.hidden{visibility:hidden;height:0}@supports (width:clamp(0px,1px,2px)){.side-panel{--sd-ui-right-side-panel-width:clamp(330px,calc(var(--right-column-width) + var(--page-auto-margin)),560px)}}@supports (--css:variables){.substance-info-panel-title{line-height:var(--sd-ui-line-height)}}@media screen and (max-width:599px){.pad-left{padding-left:16px}}@media screen and (max-width:599px){.pad-right{padding-right:16px}}.sr-only-focusable:active,.sr-only-focusable:focus{clip:auto;height:auto;margin:0;overflow:visible;position:static}.u-clamp-2-lines{display:-webkit-box;-webkit-box-orient:vertical;-webkit-line-clamp:2}.Outline li:not(:last-child){padding-bottom:8px}.Figures ol{display:grid;grid-gap:12px;grid-template-columns:repeat(auto-fill,80px)}.Figures img{border:1px solid #dcdcdc;height:80px;-o-object-fit:cover;object-fit:cover;padding:1px;width:100%}.Tables .icon{width:20px;height:20px}.Extras .icon{width:20px;height:20px}.TableOfContents{margin-bottom:32px;padding-left:16px}.TableOfContents h2{padding:16px 0}.TableOfContents h2:first-child{padding-top:0}.TableOfContents .PageDivider{margin-bottom:16px;clear:both}.TableOfContents .toc-list-entry-outline-padding{padding:3px;margin-left:-3px}.TableOfContents .toc-list-entry-outline-padding>.anchor{vertical-align:top}els-view-pdf-element.universal-pdf-button::part(container){line-height:normal;color:#0272b1}els-view-pdf-element.universal-pdf-button::part(container):active,els-view-pdf-element.universal-pdf-button::part(container):hover{color:#1f1f1f}els-view-pdf-element.universal-pdf-button::part(container):focus-visible{color:#0272b1;outline:2px solid #eb6500;outline-offset:1px}els-view-pdf-element.universal-pdf-button::part(externallinkarrow){fill:currentColor;height:1em;vertical-align:middle;width:1em}@supports (--css:variables){div.able-modal-dialog{line-height:var(--sd-ui-line-height)}}.pad-left{padding-left:32px}@media screen and (max-width:599px){.pad-left{padding-left:16px}}.pad-right{padding-right:32px}@media screen and (max-width:599px){.pad-right{padding-right:16px}}.sr-only{border:0;clip:rect(0 0 0 0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px}.sr-only-focusable:active,.sr-only-focusable:focus{clip:auto;height:auto;margin:0;overflow:visible;position:static}.u-clamp-2-lines{overflow:hidden;display:-webkit-box;-webkit-box-orient:vertical;-webkit-line-clamp:2}html,body{margin:0;padding:0;height:100%;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}.inline-figure>img{vertical-align:middle}.icon-pdf-multicolor-adjusted,els-view-pdf-element.universal-pdf-button::part(icon){position:relative;top:-2px}#gh-cnt{z-index:3}#MathJax_Message{position:fixed;left:1px;bottom:2px;background-color:#E6E6E6;border:1px solid #959595;margin:0px;padding:2px 8px;z-index:102;color:black;font-size:80%;width:auto;white-space:nowrap}.plx-logo{margin:0 0 0 4px!important;vertical-align:top;border:none!important;-ms-interpolation-mode:bicubic}.PlumX-Summary{font-weight:400;overflow:hidden}.PlumX-Summary *{text-align:left}.PlumX-Summary .pps-branding{height:14px;line-height:14px;color:#6e1a62}.PlumX-Summary .pps-cols{overflow:hidden}.PlumX-Summary .pps-col-empty{font-size:90%;text-align:center;color:#333}.PlumX-Summary .pps-container{border-top:none}.PlumX-Summary .pps-seemore{color:#007dbb!important}.PlumX-Summary .pps-container-vertical .pps-seemore{text-align:center!important;position:relative;top:-0.7em}.PlumX-Summary .pps-container{position:relative}.plum-sciencedirect-theme{-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:auto}.plum-sciencedirect-theme .PlumX-Summary{font-size:16px}.plum-sciencedirect-theme .PlumX-Summary .pps-container{float:none;border:0}.plum-sciencedirect-theme .PlumX-Summary .pps-cols{margin-bottom:16px}.plum-sciencedirect-theme .PlumX-Summary .pps-col{float:none;width:auto;padding:4px 0;border-top:none}.plum-sciencedirect-theme .PlumX-Summary .pps-seemore{text-decoration:none;font-size:13px}.plum-sciencedirect-theme .PlumX-Summary .pps-seemore .svg-arrow{width:8px;height:8px;margin-left:8px;transform:rotate(270deg)}.plum-sciencedirect-theme .PlumX-Summary .pps-seemore:active,.plum-sciencedirect-theme .PlumX-Summary .pps-seemore:focus,.plum-sciencedirect-theme .PlumX-Summary .pps-seemore:hover,.plum-bigben-theme .PlumX-Summary .pps-seemore:active,.plum-bigben-theme .PlumX-Summary .pps-seemore:focus,.plum-bigben-theme .PlumX-Summary .pps-seemore:hover{color:#e9711c;text-decoration:underline;outline:0}.plum-sciencedirect-theme .PlumX-Summary .pps-container-vertical .pps-seemore{display:inline;float:right;text-align:left;margin-top:12px}.plum-sciencedirect-theme .PlumX-Summary .pps-container-vertical .pps-branding{width:auto}.plum-sciencedirect-theme .PlumX-Summary .pps-container-vertical .pps-col{margin-bottom:8px}.plum-sciencedirect-theme .PlumX-Summary .pps-container-vertical .pps-branding-bottom{display:inline}.sf-hidden{display:none!important}</style><meta http-equiv=content-security-policy content="default-src 'none'; font-src 'self' data:; img-src 'self' data:; style-src 'unsafe-inline'; media-src 'self' data:; script-src 'unsafe-inline' data:; object-src 'self' data:; frame-src 'self' data:;"><style>img[src="data:,"],source[src="data:,"]{display:none!important}</style></head>
|
||
<body><div id=MathJax_Message style=display:none></div>
|
||
<noscript class=sf-hidden>
|
||
JavaScript is disabled on your browser.
|
||
Please enable JavaScript to use all the features on this page.
|
||
<img src="https://smetrics.elsevier.com/b/ss/elsevier-sd-prod/1/G.4--NS/1696857653727?pageName=sd%3Aproduct%3Ajournal%3Aarticle&c16=els%3Arp%3Ast&c2=sd&v185=img&v33=ae%3AANON_GUEST&c1=ae%3A228598&c12=ae%3A12975512">
|
||
</noscript>
|
||
<a class="anchor sr-only sr-only-focusable u-display-inline anchor-default" href=#screen-reader-main-content data-reactroot><span class=anchor-text data-moz-translations-id=0>Saltar al contenido principal</span></a><a class="anchor sr-only sr-only-focusable u-display-inline anchor-default" href=#screen-reader-main-title data-reactroot><span class=anchor-text data-moz-translations-id=0>Saltar al artículo</span></a>
|
||
<div data-iso-key=_0><div class=App id=app data-aa-name=root data-reactroot><div class=page><div class=sd-flex-container><div class=sd-flex-content><header id=gh-cnt><div id=gh-main-cnt class="u-flex-center-ver u-position-relative u-padding-s-hor u-padding-l-hor-from-xl"><a id=gh-branding class=u-flex-center-ver href=https://www.sciencedirect.com/ aria-label="ScienceDirect home page" data-aa-region=header data-aa-name=ScienceDirect><img class=gh-logo src="data:image/svg+xml;base64,<svg
    xmlns="http://www.w3.org/2000/svg"
    version="1.1"
    viewBox="0 0 54 48"
  >
    <path
      xmlns="http://www.w3.org/2000/svg"
      d="M15.04 22.48c.58-.3.91-.68.97-1.11.09-.4-.26-.36-.35-.28-.32.26-.74.66-1.14.93a4.19 4.19 0 0 0-.56.07c-.05-.02-.1-.1-.08-.12.37-.23.77-.46 1.03-.77.17-.2.1-.37-.23-.41-.37-.06-.8.1-1.13.39-.57.48-.84 1.55-.9 2.23-.11.12-.15.12-.27.17a2.83 2.83 0 0 1 .3-2.2c.05-.14.01-.37-.2-.43-.11-.05-.17-.02-.22.1-.28.81-.77 1.15-1.45 1.66-.06.05-.68.39-.83.48-.54-.17-1.15-1.17-1.66-1.93a.47.47 0 0 0-.45-.2c-1.11.13-1.94.05-2.54-.83-.27-.36-.39-.43-.72-.11-.38.39-.75.69-1.12 1-.2.13-.34.09-.42-.16a7.04 7.04 0 0 1-.37-2.35c0-.33.04-.84.05-1.16a2.87 2.87 0 0 0-.71-.48C1.1 16.53 0 15.5 0 15.17c0-.07.06-.25.32-.4 1.36-.6 2.43-.1 2.83-.16.35-.03.46-.28.4-.77a2.7 2.7 0 0 1 .88-2.42c.46-.5.43-1.04.32-1.65-.06-.28-.12-.35-.4-.32-.75.16-.71.18-.85.83-.08.42-.23.85-.47 1.2-.3.47-.7.87-1.05 1.29-.12.17-.26.17-.44.05A2.44 2.44 0 0 1 .6 9.74c.12-.32.3-.67.49-.84l.1.03c0 .06 0 .13-.03.19l-.22.69c-.28.8-.23 1.57.3 2.26.18.22.34.29.44.19.34-.35.7-.67 1-1.05.36-.42.47-.93.36-1.46a9.27 9.27 0 0 0-.1-.6c-.05-.3-.18-.27-.32-.1-.33.34-.49.61-.65 1.23a.48.48 0 0 1-.15.23.65.65 0 0 1-.08-.33c.1-.56.22-1.05.58-1.41.23-.26.17-.4-.05-.46-.8-.22-1.49-.45-2.1-1.2a.67.67 0 0 1-.16-.4c0-.16.08-.28.26-.41.7-.48 1.42-.99 2.11-1.14l.57-.09C2.62 4.1 2.3 2.9 2.1 1.81c0-.23.16-.84.25-1.02.15-.02.34-.02.62.08.32.12.62.32.92.5.29.17.37.17.56-.09.32-.47.7-.89 1.19-1.14.17-.08.28-.14.39-.14.16 0 .28.1.44.34.27.43.5.93.74 1.4.28.47.58.5 1 .19.48-.32.91-.63 1.35-.99.4-.32.52-.32.97 0 .4.27.83.78 1.05 1.23.04.08.97-.42 1-.64.04-.4.15-.7.3-1.02.2-.4.47-.51.73-.51.23 0 .67.26 1 .47l.7.4c.37.2.39.2.61-.13.25-.4.47-.47.99-.47 1.02 0 1.95.26 2.66 1.1.13.17.25.2.41.04.29-.3.56-.41 1.1-.41h.95c.21 0 .35.01.55-.26.26-.38.55-.74.97-.74.67 0 1.55.94 2 1.75l-.24.12a4.76 4.76 0 0 0-1.75-1.1c-.4-.17-.58 0-.53.42.06.5.19 1 .3 1.49.15.46.55.86 1 1.05l.02.17c-.07-.02-.16-.05-.28-.06a2.04 2.04 0 0 0-.87.03c-.56.14-1.07.38-1.57.6-.25.12-.25.18-.04.39.28.26.54.56.86.79.9.68 1.8.54 2.79.16l.46-.16c.42-.08.87.1 1.21.4l1 .91c.32.3.4.58.2.96-.33.64-.7 1.3-1.06 1.92a.62.62 0 0 1-.23.24c-.12-.1-.23-.33-.11-.53.44-.56.72-1.34.82-1.87.09-.52-.19-.77-.68-.64l-.42.16c-.18.07-.24 0-.28-.18-.07-.16-.15-.26-.2-.43-.12-.28-.28-.37-.52-.19-.42.26-.71.52-.96.93a3.53 3.53 0 0 0-.4 2.17c.01.1.09.16.14.22a.4.4 0 0 0 .19-.2c.13-.32.21-.64.27-1 .03-.2.06-.54.12-.72.05-.1.2-.06.28-.03.21.08.3.19.33.34-.42.53-.68 1.32-.96 2.02-.05.1-.23.22-.34.22-.13-.01-.3-.12-.34-.23-.17-.45-.47-.89-.59-1.34-.3-1.51-.4-1.6-.46-1.69a9.32 9.32 0 0 0-2.26-2.57c-.07-.07-.1-.16-.16-.22.08-.04.15-.11.23-.14l.88-.34c.38-.13.64-.37.8-.71.1-.2.25-.4.36-.59.16-.28.14-.43-.07-.67a1.44 1.44 0 0 0-1.47-.45c-.57.14-.92.54-1.1 1.08-.07.2-.09.4-.09.61 0 .3.1.37.35.21.41-.34.88-.62 1.33-.92-.2.48-.5.85-.92 1.12-.28.18-.55.32-.83.48-.24.17-.32.1-.4-.18-.06-.3-.03-.6-.03-.9A1.97 1.97 0 0 0 18 .9l-.74-.3a.7.7 0 0 0-.28.64c.06.8.18 1.4.32 2.08.13.28.44.4.75.45.19-.1.34-.36.09-.76l-.37-.72c-.03-.1-.08-.18-.1-.28-.02-.04-.04-.1 0-.15.04.02.09.02.18.11.46.5.84 1.07 1.08 1.78.1.28 0 .39-.26.42-.62.05-1.24.1-1.84.22-.97.2-1.3.56-1.65 1.21-.15.28-.23.46-.24.62.35.03.74.01 1.3-.09a3.7 3.7 0 0 0 1.34-.44c.26-.3.46-.61.85-.89.12-.08.38-.11.48-.05a5.12 5.12 0 0 1 2.56 2.5c.04.12.15.23.2.32.11.13.07.18-.09.17-.22-.06-.47-.12-.67-.2-.08-.04-.15-.17-.19-.26l-.2-.58c-.11-.23-.17-.23-.4-.15l-.45.19c-.17.06-.25.09-.29-.1-.05-.28-.13-.55-.2-.8-.1-.31-.22-.38-.52-.23a2.8 2.8 0 0 0-1.25 1.28c-.26.54-.36.9-.12 2.34.06.28.28.62.54.7l2.42.75c.12 0 .2.14.05.24-.3.2-.63.38-.94.57-.15.1-.24.05-.31-.1a1.9 1.9 0 0 0-1-.9c-.16-.08-.24-.05-.3.12-.12.26-.24.64-.34.91-.06.14-.12.2-.28.1-.69-.58-1.41-.61-2.26-.5-.42.06-.61.17-.51.59.23 1.16 1.25 1.7 2.11 2.07a.59.59 0 0 0 .3-.23c.13-.19.1-.37-.1-.54-.33-.29-1.04-.62-1.39-.94-.06-.19.34-.35.56-.21.63.28 1.11.8 1.62 1.3.26.25.38.22.46-.14.1-.28.19-.7.27-1.03.05-.16.15-.1.19 0 .06.24.14.55.14.83 0 .3-.1.56-.19.84-.05.28-.27.43-.5.37-1.07-.1-1.88.4-2.54.42-.15 0-.47-.1-.62-.17-.24-.18.1-.2.18-.2.28 0 .7 0 .92-.12-.06-.1-.18-.2-.29-.27-.6-.49-1.26.03-1.77.29-.3.16-.31.2-.19.5.19.46.52.64.98.64.26 0 .51.04.78.03a.93.93 0 0 0 .37-.1c.35-.12.65-.3.97-.43.4-.06.38.14.2.33l-.79.49c-.08.04-.17.07-.23.13-.11.08-.25.22-.23.28.05.1.17.21.3.26.37.11.74.17 1.1.25.57.11.81-.05.97-.62.08-.31.19-.6.34-.9.09-.18.2-.2.31-.02.4.73.8 1.44 1.22 2.14.08.16 0 .24-.15.22-.9-.1-1.8-.2-2.7-.34-.3-.04-.6-.24-.9-.37a3.2 3.2 0 0 0-1.47-.39c-.45 0-.72.28-.56.72.2.62.44 1.26.7 1.89.17.42.59.58 1.04.62.22.03.14-.12.09-.22l-.75-1.61c-.1-.27-.02-.31.13-.09.41.62.84 1.2 1.21 1.83.23.39-.05.5-.33.5-.94-.09-1.8-.41-2.7-.76-.1-.03-.22-.23-.2-.31.17-.16.38 0 .56 0 .22.04.3-.06.22-.22a.86.86 0 0 0-.45-.42 5.56 5.56 0 0 0-1.34-.49c-.28-.05-.47.07-.41.32.07.32.22.61.39.95.05.1.05.22.02.31-.17 0-.37.05-1.07-.25-.7-.29-.7-.48-.7-1.34 0-.18 0-.55.03-.71.07-.24.1-.28.3-.25.73.15 1.47.35 2.18.7.22.09.42.27.65.36.18.06.27 0 .25-.19a.65.65 0 0 0-.41-.56c-.77-.32-1.53-.69-2.3-1-.18-.07-.24-.15-.14-.35.3-.5.46-1.09.33-1.72 0-.05.06-.11.12-.17a.47.47 0 0 1 .34.43c.06.54.42.91.77 1.28.72.74.72.74 1.72.5.13-.02.23-.11.34-.19-.1-.07-.19-.2-.32-.26-.18-.1-.39-.15-.6-.23-.66-.25-.86-.73-.64-1.43a24.2 24.2 0 0 0 .63-1.95c.07-.3.1-.63.11-.96 0-.18.14-.18.25-.2l1.46-.1c.37-.02.8-.46.94-.71l.49-.8a1.01 1.01 0 0 0-1-.14c.11-.36.26-.7.26-1.05 0-.23-.05-.5-.15-.73-.1-.22-.25-.26-.44-.12-.35.3-.65.65-.76 1.14-.11.48-.31 1.36-.21 1.52.22-.17.52-.39.71-.45.1-.02.26 0 .34.06-.33.3-.72.56-1.08.75-.27.12-.37-.04-.33-.3.32-1.21.12-1.3.02-1.9a6.98 6.98 0 0 0-.9-2.09c-.13-.16-.34-.1-.61.23-.45.54-.56 1-.8 1.92l-.05.19-.32.28-.14-.16c.12-.43.25-.68.25-1.02 0-.42-.18-.72-.46-.95-.35.22-.66.5-.75.65a.92.92 0 0 0 .02 1.06c.36.62.3 1.25.19 1.98l-.1-.03a8.26 8.26 0 0 0-.25-1.41c-.08-.3-.27-.55-.43-.8-.16-.24-.3-.74-.17-.95.19-.32.77-.5 1-.79.23-.35.12-.5.41-.82.18-.21.56-.65.84-.5.84.42 1.73.51 2.67.39.4-.06.49-.05.6-.45.1-.33.14-.1.47-.22.57-.19.87-.7.87-1.3 0-.34.02-.68-.02-1.03-.03-.3-.14-.58-.23-.85-.08-.18-.2-.14-.28-.01-.23.3-.63.9-.84 1.24l-.33.6c-.05.16-.13.2-.27.05a3.03 3.03 0 0 0-.49-.43c-.3-.2-.33-.81-.21-1.2.13.07.22.22.3.42.12.26.17.56.38.65.27.14.54-.42.5-.8a.6.6 0 0 0-.1-.28C14.77.83 14.1.4 13.68.4c-.48 0-.8.6-.6 1.16.06.15.12.26.2.4-1.15.6-1.46.99-1.92 1.52-.05.07-.1.25-.05.34.21.41.13.55-.31.8-.2.1-.46.21-.67.27-.03.02-.08-.02-.14-.03a.28.28 0 0 1 .06-.11c.28-.63.58-1.22.84-1.86.28-.68-.33-1.3-.88-1.52-.36-.14-.65-.06-.9.27-.6.72-.73 1.9.12 3.02.13.19.1.26-.12.28-.49.04-.97.1-1.47.12a1.95 1.95 0 0 0-1.5.8c-.1.16-.25.2-.41.09-.38-.24-.7-.18-1.1.05a4.9 4.9 0 0 1-1.84.62c-.07 0-.15-.06-.2-.12.03-.05.09-.13.23-.18.4-.1.72-.2 1.08-.3.06-.06.09-.22.13-.29-.07-.1-.2-.21-.31-.22a4.91 4.91 0 0 0-1.02 0c-.88.08-1.53.5-2.09 1.01-.26.26-.28.53 0 .75.65.46 1.35.93 2.23.7.62-.2 1-.66 1.4-1.1.33-.36.74-.77 1.13-.33.57.65.68 1.33 1.61 1.21.36-.04.5-.16.6-.5l.07-.4c.03-.25-.1-.38-.36-.28-.17.06-.31.15-.48.19-.1.02-.2-.01-.3-.04 0-.1.02-.22.08-.28.17-.11.38-.2.56-.29.7-.18 1.36-.32 2.05-.46a.6.6 0 0 1 .21 0l-.06.2c-.25.57-.59.75-1.04.86-.06.05-.17.18-.25.44a2.6 2.6 0 0 0 .5 2.57c.22.25.5.46.79.63.48.28.61.75.79 1.24.11.31.23.62.37.92-.39.28-.71.6-.94 1-.1.1-.17.06-.22-.05l-.27-.66c-.24-.58-.65-.96-1.3-1.01-.24-.02-.47-.06-.7-.09-.26-.02-.44.1-.53.4-.37 1.12-.02 2.09 1.05 2.68-.86.22-1.56.56-2 1.16.04.17.3.39.64.53.84.34 1.7.3 2.49.04.46-.17.48-.06.56.28l.24 1.04c.05.25-.09.39-.32.3-1.28-.37-2.8-.98-3.9-1.72a5.77 5.77 0 0 0-1.43-.43c-.17.1-.31.22-.46.2a8.16 8.16 0 0 1-1.38-.5c-.22-.12-.23-.19.05-.23.43 0 1.21.19 1.61.19.4-.12.4-.19.15-.35-.94-.75-2-.57-3.05-.4-.38.08-.4.26-.19.56 1.36 1.53 2.36 1.52 4.02 1.33 1.08.68 2.16 1.3 3.42 1.7l.02.18c-.9.06-1.75.21-2.56.38-.41.08-.5.33-.27.7.33.54.86.8 1.47.94.43.13.87.1 1.3-.08.5-.23.83-.65 1.3-.91.28-.15.75.02 1.03.1.42.13.83.32 1.27.38 1.73.24 3.57.4 5.18.26-.2.38-.33.73-.46 1.1-.09.22-.01.27.22.25.34 0 .67-.2.81-.46.03-.26.03-.5.08-.76.03-.14.07-.33.16-.35.07-.01.47.06.42.17-.14.4-.26.71-.43 1.11.15.03.31.06.47.06.65 0 .75-.1.72-.74a2.7 2.7 0 0 0-.08-.55c-.08-.49.02-.82.44-1.05.4-.2.84-.14 1.25-.09a7.63 7.63 0 0 1 3.52 1.6c.51.4.07.97.07 1.74 0 .49-.1.99-.09 1.24.02.4.19.61.34.73.45.34.94.68 1.41 1 .49.3.73.5 1.3.7.14.04.3.04.42.03a1.49 1.49 0 0 0-.03-.6c-.28-.19-.67-.28-1.12-.43-.08-.03-.17-.14-.21-.24a.84.84 0 0 1 .32-.19l.73-.15c.05-.02.15-.1.18-.16-.03-.07-.09-.15-.14-.16-.3-.1-.7-.16-.94-.22-.1-.03-.14-.12-.22-.2a.91.91 0 0 1 .36-.19l.74-.09a.4.4 0 0 0 .2-.12c-.03-.07-.07-.17-.13-.2-.15-.07-.92-.35-1-.43-.07-.07-.08-.16-.11-.28.1-.05.25-.1.35-.08l.62.01a.4.4 0 0 0 .26-.12.56.56 0 0 0-.17-.2c-.3-.18-.41-.3-.75-.46-.15-.1-.23-.18-.26-.38.12-.03.28-.05.42-.02.23.06.35.13.59.17.06.02.18-.01.26-.03a.26.26 0 0 0-.07-.18c-.09-.1-.21-.14-.32-.23-.17-.11-.46-.39-.63-.53-.05-.04-.02-.18-.03-.23h.18c.23.05.56.15.75.18.33.06.29.01.15-.23-.2-.18-.42-.3-.62-.52-.05-.07-.03-.2-.03-.3.32.02.65.17.94.17.02-.05.04-.1.04-.17a4.75 4.75 0 0 0-.23-.55c-.08-.22.03-.3.19-.24.22.1.57.28.81.34.1.03.22 0 .34-.05-.11-.22-.24-.4-.4-.62-.02-.06 0-.14.06-.2.38.1.77.37 1.11.46.16.06.26 0 .3-.08.03-.09-.25-.37-.23-.48 0-.07.02-.1.06-.14l.65.2c.48.13.75.17.82.06.1-.03 0-.25-.15-.51.38-.03.42.15 1.08.28.25.06 3.74-.43 5.16-.87.56-.18 1.2-.46 1.73-.7l.17.03c.4 1.03 1.24 1.34 2.1 1.44l.8.05c.25 0 .3-.06.23-.31a4.47 4.47 0 0 0-1.3-2.2l-.55-.35c-.24-.25-.21-.54.05-.74.44-.3 1.04-.59 1.49-.87.65-.41.87-1.06.83-1.74a.58.58 0 0 0-.24-.45.73.73 0 0 0-.33.23c-.18.37-.36.68-.51 1-.13.07-.26.1-.37.15-.03-.12-.1-.23-.06-.34.2-.58.7-1.04 1.04-1.41.14-.24-.07-.33-.17-.31-.37.08-.78.22-1.06.54a7.5 7.5 0 0 0-1.47 3.04c-.06.29-.19.4-.28.66-.12.41-.11.5.24.76.52.33 1.19.64 1.67.98.2.22.1.3-.11.3a1.93 1.93 0 0 1-1.4-.63c-.18-.2-.34-.34-.56-.37-.5.14-1.2.34-1.72.41-.3.06-.53.18-.69.46-.1.23-.31.28-.56.19-.37-.1-.74-.24-1.11-.32a4.19 4.19 0 0 0-1.86.13c-.25.04-.31-.02-.24-.26.07-.3.17-.6.23-.9 0-.13.03-.33-.1-.4-.12.06-.3.09-.35.17-.2.3-.34.6-.53.92-.12.25-.17.25-.43.14a4.75 4.75 0 0 1-1.05-.65c-.25-.4-.52-.73-.65-1.08-.05-.09.1-.16.21-.16.28.22.62.5.87.74.17.19.22.37.33.61.03.06.3.05.35-.05.04-.1.08-.23.12-.43.1-.37.03-.88-.3-1.22-.42-.42-.7-.5-1.26-.9-.71-.48-.81-.45-1.07.03-.2.39-.35.82-.35 1.3.19 1.14.46 1.6 1.83 2.3a.38.38 0 0 1-.1.2c-.1.07-.15.11-.3.11-.49-.01-1.17-.02-1.7-.07-.15 0-.24-.06-.28-.14a6.98 6.98 0 0 1 .62-5.02.84.84 0 0 1 .33-.37c.29-.15.75-.34 1.03-.46.16-.08.25-.02.32.15.12.44.17.76.2 1.14.02.23.03.47.13.51.22.1.42-.09.53-.28.42-.68.53-1.44.22-2.26-.03-.07.04-.22.12-.27.95-.38 1.83-.77 2.8-1.14.53-.03 1.22.03 2.18.18.16.02.5.3.84.5-.44.22-1.08.69-1.53 1.11-.12.13-.28.3-.17.66.38-.02.56-.13.84-.2.43-.13.7-.2 1.04-.2a1.86 1.86 0 0 1 .28.04.51.51 0 0 1-.13.29c-.12.11-.31.1-.45.17-.14.1-.28.23-.19.4.07.11.25.24.38.22.45-.03.86-.08 1.32-.22.2-.17.99-1.2.54-1.29-.25-.05-.79-.09-1.02-.21a.47.47 0 0 1 .28-.32c.24.04.52.11.9.15a5.5 5.5 0 0 0 1.83-.08c.25-.1.33-.19.25-.35a2.88 2.88 0 0 0-1.7-1.48c-.34-.1-.75-.02-1.1-.02l-3-.03c-.06 0-.13-.06-.2-.1.06-.04.12-.1.17-.12.28-.17.6-.31.89-.5.42-.3.7-.71.81-1.25a.51.51 0 0 0 0-.28c-.28-1.03-.83-1.77-2-1.9l-.57-.1c-.22-.01-.3.08-.3.27 0 .37.02.56 0 .94 0 .18.1.25.3.27a7.17 7.17 0 0 1 1.59.46c.3.12.37.3 0 .62l-.84.6c-.15.1-.21.02-.23-.12-.02-.22-.02-.47-.06-.68a.47.47 0 0 0-.12-.21.61.61 0 0 0-.45.37c-.1.3-.18.57-.23.86a.91.91 0 0 1-.64.75c-.17.04-.32-.02-.29-.18a4 4 0 0 1 .12-.56c.27-.65.41-1.2.36-1.82a13.12 13.12 0 0 1-.1-1.29.38.38 0 0 1 .28-.4c.5-.2.98-.4 1.5-.56.19-.07.33 0 .47.18l1.07 1.25c.27.3.35.29.6-.05.21-.31.46-.6.81-.8.19-.09.19-.27-.03-.35-.4-.15-.8-.23-1.22-.37-.09-.03-.2-.03-.3-.07-.06-.03-.14-.11-.14-.17 0-.05.08-.14.14-.16.77-.27 1.4-.52 2.08-.79.19-.08.28-.23.25-.42-.02-.25-.1-.47-.13-.71-.04-.22-.19-.4-.42-.42-.93-.1-1.49.17-2.12.82a.58.58 0 0 0-.15.51c.11.02.2.02.29 0a3.8 3.8 0 0 0 1-.43c.16-.18.3-.37.39-.37.05 0 .15.23.18.32-.09.1-.18.22-.92.54a21.25 21.25 0 0 1-4.07 1.4c-.2.02-.18-.1-.13-.18.26-.46.55-.93.84-1.42.28-.17.36-.1.25.17a5 5 0 0 1-.3.58c-.03.18.05.23.16.21a1.56 1.56 0 0 0 1.16-1.39c0-.23-.11-.31-.36-.3-.35.02-.74.05-1.08.16-.66.16-1.05.62-1.26 1.21-.1.28-.15.56-.2.85-.06.3-.32.42-.6.57-.45.28-.68.22-1.07.3a6.42 6.42 0 0 0-.98.22c0-.11-.03-.21 0-.28.22-.35.42-.7.65-1.02.33-.48.24-.75-.28-.93a1.94 1.94 0 0 0-2.23.78c-.28.44-.48.98-.68 1.46-.12.29 0 .46.3.53.48.08 1.05-.1 1.48-.21-.03.14-.04.67-.09.79l-.27.6c-.18.3-.38.4-.7.24a14.7 14.7 0 0 1-1.46-.79 3.37 3.37 0 0 0-1.73-.56.84.84 0 0 1-.18-.01c-.2-.05-.22-.12-.1-.25.16-.15.17-.31.08-.5l-.64-1.2a3.26 3.26 0 0 1-.3-.7c.15-.03.74.17.74.28.04.65.24 1.12.58 1.53.26.26.5.1.53-.17.05-.34-.42-.9.19-2.19C26.7 1 27.5 0 28.02 0c.28 0 .51.12.68.53.1.19.16.63.24.82.08.19.17.28.37.26.5-.07 1.2-.37 1.77-.45.36-.05.64.85.82 1.18.28.48.41.9.73.38.54-.75.79-1.58 1.36-1.85a4.03 4.03 0 0 1 2.35-.4c.25.03.33.13.27.38-.05.15-.06.3-.1.46-.05.18.1.24.3.1.27-.16.47-.38.66-.62.48-.52.96-.78 1.91-.78.45 0 .8.1 1.2.23.3.11.35.16.26.47l-.27.9c-.09.4-.02.46.34.66.32.19.57.32.76.56a.65.65 0 0 0 .4-.14A4.81 4.81 0 0 1 43.8.74c.3-.16.38-.14.5.14.25.64.39 1.19.43 1.78.34-.39.73-.8 1-1.12C46.74.6 47.74.6 49.09.94c.33.1.07.43-.12.69l-.26.39c-.09.14-.15.15-.26.03-.17-.2-.34-.54-.5-.71-.31-.3-.85-.13-1.22.1a5 5 0 0 0-1.81 1.84 9.77 9.77 0 0 1-1.58 1.88c-.24.09-.24.08-.19-.17a6.65 6.65 0 0 0 1.1-3.45.83.83 0 0 0-.64.14c-.93.52-1.4 2.37-1.53 3.6-.03.27-.03.47-.26.6-.39.22-.54.32-.93.5-.1.01-.15 0-.23-.01.18-.82.05-1.4-.42-2.17-.08-.15-.19-.23-.36-.17-.1.06-.19.13-.4.3a3.5 3.5 0 0 0-1.1 2.69c0 .18.18.18.31.09.17-.14.26-.25.36-.43.23-.39.43-.8.65-1.19.04-.1.15-.16.23-.19.05.1.05.2.03.28l-.17.75a2.33 2.33 0 0 1-.74 1.02c-.07.1-.11.26.07.28.8.04 1.27-.05 1.96-.42.72-.37 1.2-.68 1.52-.64.22.03 1.3.34 1.4.42-.06.11-.14.15-.2.22-.12.1-.65.38-.88.58-.37.3-.5.45-.43 1.07.04.28.09.54.15.8.03.1.1.21.15.23.1 0 .19-.07.24-.14l.9-1.15a.7.7 0 0 1 .19-.11c0 .07.02.14 0 .18-.07.46-.3.8-.58 1.14l-.18.23c-.24.35-.26.4-.02.75.28.4.32.86.23 1.3-.06.32-.17.8-.23 1.28-.59.27-1.18.65-1.68.99-.31.28-.37.5-.37.57.15.18.29.21.58.33.4.13 1.17-.03 1.31-.46.05-.1.1-.3.1-.48.14.03.27.03.31.15.15.47.28.78.58 1.09-.04.12-.1.21-.23.4-.17.3-.32.55-.57.5-.1-.02-.2-.04-.31-.04-.19 0-.37 0-.35.33.04.43.1.62 0 1.06-.05.24-.13.5-.37.5-.58-.05-.97-.48-1.53-.47a2.2 2.2 0 0 1-1.07-.19c-.3-.15-.51-.32-.65-.4-2.36.38-4.96.86-6.97 1.3-.22.04-.2.18-.25.4-.1.52-.3 1.02-.39 1.53-.02.09.1.19.15.26.06-.05.14-.09.2-.16.12-.18.2-.39.34-.56.24-.26.88.02.99.37.1.21-.2.32-.19.56.02.2.32.39.32.65 0 .23-.1.38-.13.5.87.56 1.09 1.03 1.11 1.75.03.22.03.34.15.47.12.12.32.3.4.4.41.48.79 1.23 1.06 1.3.14.06.5.08.77.06l.05.14c-.09.22-.33.48-.56.55-.64.2-.95-.75-1.3-1.3a4.65 4.65 0 0 0-.54-.72c-.28.31-.74 1.55-.7 2.13.18.8.65 1.58.95 2.15.22.35.64.47 1.05.63.34.12.6.18.95.08.18-.04.25-.05.33-.12v-.72c.08-1.3.29-2.6.37-3.72 0-.3.23-.8.6-1.06.4-.28.57-.65.55-1.13a.88.88 0 0 1 .09-.46c.13-.26-.12-.26-.37-.3-.28-.04-.35-.15-.2-.4.25-.44.63-.8.92-1.23.19-.3.28-.5.35-.69-.08-.36-.05-.9.31-.92 1.19-.14 1.68-.03 2.43.53.05.08.06.19.06.28 0 .1.21.17.61.57.31.29.6 1.1.09 2.35.26.15.25.4.22.68-.02.24.06.27.28.36.28.1.43.26.66.84.2.55.81 2.01 1.16 5.6a14.92 14.92 0 0 1-.48 2.87c.24 1.72.51 3.84.73 6.73.17 2.74.22 4.14.27 5.27.49.04.64.14.74-.27l.1-.38c.09-.27.36-.18.37-.03l.3.87c.06.26.13.41.23.66.32.44.46.05.46-.27.04-.28.04-.52.04-.77.05-.32.22-.34.34-.12l.43 1.02c.16.35.33.44.4.23.05-.29.06-.8.05-1.21-.07-.23-.17-.49-.2-.69 0-.28.22-.22.27-.14a32.48 32.48 0 0 1 1.52 1.98c.28.17.45-.12.37-.23-.02-.11-.16-.25-.16-.47.04-.36.37-.2.45-.07.3.3.51.7.69 1.03a.65.65 0 0 0 .28.14c.06-.09.09-.23.08-.32-.02-.11-.15-.38-.19-.5.13-.21.4.06.48.14.1.1.19.25.3.45.17.26.45.48 1.07.57l.03.18c-.08.06-.22.12-.6.19a7.6 7.6 0 0 1-2.32.05 2.7 2.7 0 0 0-1.71.37c-.37.21-.74.18-1.18.17-.84-.04-1.68-.04-2.53-.06-1.77-.05-2.66.3-4.66.41-.65 0-2-.34-2-.75 0-.3.16-.53.16-.68 0-.19-.04-.24-.2-.28-1.43-.25-2.83.07-4.13.31-1.1.17-1.67 0-2.25-.46a2.91 2.91 0 0 1-.8-.85 11.37 11.37 0 0 0-2.22-2.11c-.93-.74-2.11-2.1-2.85-3.02-.17-.2-.46-1.15-.56-1.42a2.85 2.85 0 0 1-.13-1.23c.11-.1.25-.2.36-.21.62-.02 1.4-.32 1.42-.5.06-.14-.04-.26-.16-.23-.33.06-.77.06-1.1 0-.18-.03-.4-.1-.5-.2.12-.14.2-.24.32-.31a3.27 3.27 0 0 0 1.45-.56.35.35 0 0 0-.04-.18c-.4-.02-.72-.02-1.18-.13a1.05 1.05 0 0 1-.45-.26.72.72 0 0 1 .34-.28c.52-.17.9-.42 1.25-.75v-.2a7.1 7.1 0 0 1-1.42-.15c-.21-.11-.23-.13 0-.4.34-.08.96-.13 1.3-.24.24-.13.2-.35-.05-.42a7.63 7.63 0 0 1-1.21-.13c-.25-.23-.28-.25.04-.4l1.07-.22c.41-.14.35-.4 0-.5-.32-.04-.55-.11-.87-.17-.43-.21-.42-.23-.08-.37l.68-.19c.19-.04.38-.12.41-.26a.2.2 0 0 0-.02-.14c-.28-.08-.87-.02-1.13-.1-.1-.03-.19-.13-.24-.25l.25-.14c.32-.07.67-.21.92-.35a.81.81 0 0 0 .1-.24.6.6 0 0 0-.18-.09c-.34-.1-.53-.17-.87-.3-.19-.23-.18-.28.08-.38l.48-.13a3.1 3.1 0 0 0-.24-.48.48.48 0 0 0-.17-.17c-.28-.17-.68-.33-.98-.53-.56-.34-1.14-.68-1.58-1.12a3.63 3.63 0 0 1-1.17-1.91c-.09-.47-.1-.96-.01-1.42.05-.35.25-.72.44-1.06.07-.12.13-.23.05-.33-.28-.36-.51-.6-.86-.93a1.47 1.47 0 0 0-.55-.28c-1.11-.2-1.79-.42-2.3-.36a1.77 1.77 0 0 0-.42.64c.1.38.28.95.29 1.43.4.04.9.18 1.4.46.4.26.49.4.65.9.34 1.11.43 2.54.68 4.06.2 1.46-.55 2.66-2.02 2.71-.8.04-1.46-.12-1.67-.66-.26-.76-.46-1.4-.7-2.17-1.05.26-2.15.62-3.26 1.16-.98.46-1.78.9-2.81 1.33-1.97.8-4.14 1.3-6.08 1.3a8.6 8.6 0 0 1-2.96-.52c-.7-.28-.96-.57-1.12-1.46a8.48 8.48 0 0 1-.03-2.3c.09-.74.21-1.26.72-2.65.22-.62.51-.9.99-1.15a5.21 5.21 0 0 1 2.57-.5c.3.01.7.06.98.18.56.2.64.65.66 1.2 0 .33.02.66 0 .98-.03.26-.02.37-.02.47.7.1 2.18.06 2.88-.1.58-.11.81-.2 1.86-.7.73-.33 1.71-1.06 2.86-1.66zm22.28 12.45c.06.13.22.83.22 1.07 0 .57-.05 1.1-.11 1.98-.1 1.15.03 2.32-.13 3.46-.07.56-.2 1.42-.16 2.19 0 .3.06.39.23.42.56.13.93.19 1.21.19.86 0 1.42-.39 1.34-.87a11.63 11.63 0 0 1-.4-3.2l.23-.01c.31 1.3.58 1.94.67 2.62.12.61 0 1.67.13 2.2 1.43.16 3.14.02 3.66-.09.13-1.17.1-2.37-.42-2.82-.21-.2-.85-.68-1.13-.86a6.41 6.41 0 0 1-1.53-1.7c-.61-.96-.78-1.27-1.32-2.21-.5-.88-.84-1.63-1.18-2.14-.22-.35-.4-.56-.5-.65a6.58 6.58 0 0 0-1.3-.25c-.6-.06-1-.13-1.35-.5-.3-.3-.42-.55-.24-.95.1-.21-.03-.36-.18-.54-.18-.22-.13-.46.09-.63a3.55 3.55 0 0 1 1.72-.97l-.11-.48c-.28.04-.59.12-1.15.12-.77 0-1.47-.32-1.75-1.33-.1-.36-.55-1.39-.68-1.84-.1-.28 0-.63.14-1.03-.25-.3-.45-.63-.66-.91-.41-.18-.92-.35-1.25-.76-.14-.17-.2-.18-.33-.1-.11.09-.14.2-.3.35-.14.13-.45.14-.74.02-.28-.11-.42-.4-.41-.68.03-.37.21-.7.05-.85-.3-.3-.56-.65-.22-.9.1-.08.31-.06.44-.12a.93.93 0 0 0 .45-1.04c-.08-.31-.25-.45-.55-.48-.44-.03-.64-.25-.64-.54 0-.37.33-.53.63-.46.12.02.29.09.39.18.03.14.1.34.2.32.5-.06.9-.58.9-1.07 0-.17-.11-.25-.44-.18-.6.1-1.34.32-2.08.57-.4.14-.46.46-.24.84.08.08.13.18.19.28.1.14.05.26-.1.34l-.18.14c-.22.17-.25.35-.11.6.04.1.1.16.17.23.16.15.16.28 0 .46a.7.7 0 0 0-.14.76c.06.21.23 1.42.23 1.65-.01.63.11 1.25.07 1.84-.02.42.19.8.6 1.26.45.5.77.94 1.38 1.53.71.68 1.1 1.49 1.17 2.48.03.47.25.64.7.88.94.26 1.26.79 1.37 1.5.19 1.28-.43 2.52-1.58 2.4-.58-.05-.87-.4-.87-.71 0-.33.16-.65.43-.74.05-.02.22 0 .26 0a.56.56 0 0 1 .02.2c0 .08-.1.13-.15.3-.02.1-.02.33.09.47.12.16.33.22.56.14.26-.08.43-.3.55-.54.54-.96.27-2.1-.75-2.34-.32-.1-.79-.14-1.09.03-.63.4-1.17.62-1.7 1.1a.4.4 0 0 0-.13.3 52.05 52.05 0 0 0 .45 6.34c0 .45.33 1.02.46 1.1.37-.27.47-.54.8-1.06.15-.09.22-.03.27.1 0 .48-.1.94-.05 1.35.03.12.17.23.27.31.22-.2.23-.5.32-.74.25-.26.3.03.32.1-.02.3 0 .59.03.75 0 .12.12.14.22.17.27-.54.61-1.02 1.05-1.32.26-.19.43-.03.28.23a6.6 6.6 0 0 0-.6 1.58.93.93 0 0 0 .26-.03c.06-.02.14-.04.17-.19.19-.46.29-.52.5-.18.1.31.24.54.42.63.28-.08.3-.41.41-.65.3.02.32.34.47.5.1.08.95.11 1.1.1l-.02-.5c-.02-.99 0-1.78.06-2.76.08-1.02.19-2.07.24-2.97.04-.75.13-1.88.2-2.77zM51.26 15.9c-.23-.03-1.16-.09-1.4-.09-.21 0-.77 0-.79.29 0 .08.11.23.24.32.68.46 2.58.54 3.35.41.02-.18 0-.4-.06-.56-.19-.57-.89-1.07-1.53-1.28a5.26 5.26 0 0 0-2.06-.07l-.14-.35c.23-1 .2-1.93.06-2.67-.08-.4-.19-.6-.4-.93-.14-.18-.29-.18-.46-.04-.1.1-.22.22-.41.47-.65.78-.62 1.3-.5 2.48.07.05.15.1.37.1.2 0 .3-.09.32-.37.02-.39.11-.8.17-1.16.1.03.26.14.28.2.04.61-.16 1.24-.3 1.81.03.07.47.26.4.45-.05.14-.74.31-1.22.35a2.46 2.46 0 0 0-1.14-.94c-.28-.11-.59-.2-.36-.39l.38-.15c.65-.27.43-.64.4-1.27-.02-.24-.12-.31-.34-.3-.33.02-.54.05-.8.19-.5.3-.87.79-1.22 1.19-.06.06-.2.03-.32.03l.62-1.75c.06-.18.15-.37.35-.32 1 .23 2.03.04 2.74-.67.3-.32.3-.47-.09-.74a2.36 2.36 0 0 0-2.14-.46c-.3.12-.5.3-.55.61-.03.19.9-.07 1.22-.04.1 0 .16.09.23.14-.6.26-1.29.39-1.86.55-.19.05-.26 0-.3-.18a.81.81 0 0 1 .3-.82c.43-.37.78-.81.87-1.4.06-.31-.2-.79-.34-1.06-.02-.07.02-.13.02-.18.37.09.88.33 1.19.55a2.63 2.63 0 0 0 1.83 2.03c.33.11.66.24 1.01.34.32.1.56 0 .54-.34l-.01-.46c.27-.12.6-.22.67-.2.53.07 1.07.33 1.61.5.38.2.2.32-.07.28-.4-.06-.81-.18-1.2-.28-.25-.05-.46.06-.5.3.17.3.94.65 1.22.77.4.14 1.95.32 1.93-.1a5.45 5.45 0 0 0-.18-1.23c-.1-.34-.4-.54-.72-.62a8.38 8.38 0 0 0-2.07-.24c-.16 0-.46.1-.74.24l-.09-.46c-.14-.62-.44-.77-.9-.93.02-.18.02-.3.02-.5.41-.23.91-.62 1.1-.95-.16-.16-.46-.24-.62-.3-.63-.22-.7-.46-1.12-.65a2.8 2.8 0 0 0-1-.25c-.32 0-.75.08-.84.2-.05.08 0 .21.18.34.28.22.87.37 1.16.58.18.1.09.15-.03.17a2.88 2.88 0 0 1-1.14-.33c-.21-.11-.28-.2-.43-.26-.28-.1-.33-.02-.52.3-.14.24.03.36.23.47.67.37 1.16.59 2 .68.1.1.12.25.15.44-.84-.11-1.12-.12-1.64-.3-.83-.45-1.49-.8-2.29-1.07-.15-.06-.2-.16-.04-.26.84-.5 1.64-1.12 2.49-1.58.12-.07.31-.04.46-.01.41.07.55.2.96.32.24.1.48.16.52-.06.12-.61.32-1.07.5-1.33.37-.57.9-.87 1.6-1.3C50.33.9 51.18 0 51.49 0c.84 0 1.67 1.53 1.84 2.1.11.4-.56.76-.1.76.38 0 .74.06.74.4a2.9 2.9 0 0 1-.46 1.6c-.32.44-.65.9-1.08 1.19l.03.15c.72.54 1.51.93 1.51 1.75 0 .43-.12.74-.37.96-.2.16-.35.33-.33.58.03.57.06.89.03 1.44-.03.38-1.17.45-1.33.62a4.65 4.65 0 0 1-.31 2.83l.23.43c1.15.8 1.3 1.54 1.47 2.17-.32.31-.84.57-1.02.57-.75 0-2.4-.56-2.72-.6-.13.23-.06.44.11.9.15.43.43.72.43 1.1 0 .2-.03.49-.56.57a3.74 3.74 0 0 1-2.48-.68c-.15-.08-.28-.18-.43-.25-.25-.14-.57-.03-.65.25l-.17.56c-.1.31-.25.34-.67.25-1.18-.25-1.73-1.52-1.73-2.65.05-.23.24-.14.28-.03.07.55.31 1.08.49 1.53.22.43.5.66.97.72.1 0 .22-.18.33-.38.25-.53.49-.88.41-1.5-.03-.22-.09-.52-.25-.72-.07-.1-.2-.16-.33-.28.06-.1.16-.21.25-.18.52.18.91.18 1.24 0 .2-.1.3-.25.43-.42.45-.12 1.18-.28 1.8-.37.84 0 1.58.12 2.2.4zM19.4 36.32c.45 0 .99.22.98.7l-.1.25a.25.25 0 0 1-.1-.03c-.17-.36-.44-.57-.88-.48-.4.08-.76.29-1.02.58-.18.18.18.3.4.41l.47.28c0 .25-.63 2.01-.31 2.07.8.14 1.86-.96 1.97-2.51-.03-.56.29-.84.9-1.05.73-.27 1.07-.6.95-1.4-.06-.38.17-.27.38-.6l.24-.26c.04.11.12.38.1.49-.17.64-.04 1.14.28 1.72l.6 1.54c.13.15.26.03.4.19.51 1.03 1.09 2.58 1.87 3.24.64.53 1.32 1.46 1.93 2.02.35.31.6 1.1 1.04 1.25.32.12.3.28.26.57-.06.35.1.42.22.42.22-.03.42-.08.65-.1.48.1.37.44.01.5-.24.02-.47-.03-.71-.03-.09.02-.17.06-.25.1.02.09.06.18.13.24.39.21.67.24 1.05.32.07 0 .13.06.21.09-.03.06-.1.21-.18.25-.12.02-.28.02-.43 0-.35-.08-.66-.23-.99-.37-.11-.04-.18-.16-.29-.24l-.28-.08c0 .11-.03.21-.02.3.07.16.23.42.16.47-.02.05-.25.19-.31.15-.28-.13-.5-.45-.77-.6-.33-.2-.64-.2-.95.01-.33.23-.62.42-.93.6-.38.25-.79.65-1.24.65-.25 0-.88-.38-1.16-.46-.52-.17-.69-.19-1.24-.31-.47-.11-.88.06-1.35.22-.58.18-1.21.25-1.84.37-.35.06-.76 0-1.13-.02-.56-.02-1.1-.07-1.65-.06-.37 0-.76.07-1.12.19-.16.04-.34.07-.49.07-.25 0-.5-.06-.72-.22a1.06 1.06 0 0 0-.8-.15c-.26.05-.77.15-.98-.05-.26-.26-.89-.78-1.24-.71-1.1.18-1.64.81-2.72.53-.76-.2-1.26-.38-2-.13-.2.07-.68-.04-.87-.01-.28.03-.37-.02-.34-.3l.03-.15c.05-.29-.23-.5-.51-.4-.3.1-.49.41-.77.56-.53.26-1.03.18-1.6-.04-.4-.17-.8-.1-1.17.1-.24.12-.63.7-.9.7-.08 0-.22-.16-.27-.29v-.27a1.86 1.86 0 0 1 .61-.56 3.49 3.49 0 0 1 2.13-.28c.33.05.7.09 1.01-.04.25-.11.51-.13.74-.25.42-.23.79-.42 1.26-.25.35-.16.53-.27.7-.53.1-.42-.06-.86.05-1.27.09-.37.25-.84.37-1.2.07-.18.2-.16.32-.14.34.5.75.96.54 1.41-.14.32.59.54.47.86-.12.24-.07.4.18.42l.18.02c.22 0 .22-.14.22-.41 0-1.53-.49-2.59-1.01-3.79a1.77 1.77 0 0 1 0-1.4l.3-.94c.18-.45.55-.84.97-1 .05-.02.21.05.25.12.68.96.93 2.12.6 3.28-.1.34-.17.7-.24 1.08-.02.09.01.18.03.28.06-.06.15-.12.2-.19.34-.5.68-1 .95-1.54.26-.52.7-.73 1.23-.83.59-.12.75-.05.6.52-.09.33-.19.67-.3.97a1.93 1.93 0 0 1-.99 1.2c-.33.18-.64.43-.93.65-.11.15-.14.14-.02.29.11.14.28.05.42-.03.55-.4 1.12-.72 1.68-1.03.2-.09.27-.01.25.23-.03.28-.07.66-.35.76-.3.1-.71.15-.98.28a1.86 1.86 0 0 0-.47.4c-.18.28-.18.47-.33.76-.03.07 0 .18 0 .28.1-.03.2-.03.26-.07.36-.26.56-.56.92-.84.28-.21.34-.19.45.16.04.11.13.2.24.3.1-.07.23-.13.31-.25.33-.48.53-1.45.96-2.09.08 0 .17.06.23.08.09.22-.03.95-.01 1.11 0 .45.15.54.55.34.32-.16.6-.33.92-.5.33-.16.6-.03.4.23-.1.16-.25.28-.35.44-.05.1-.04.26-.05.38.09 0 .22.01.31-.03.25-.07.49-.18.73-.28.32-.15.66-.1 1.02 0 .35.12.7.2 1.03.3.27.06.56-.06.8-.17a8.64 8.64 0 0 1 1.48-.57 4.75 4.75 0 0 0 1.83-1.05c.12-.13.3-.2.45-.33.1-.09.2-.53.21-.67.1-.37-.06-1.24.02-1.63.05-.1.12-.3.18-.4l.16.04c.05.14.11.36.11.47-.02.67-.03 2.16-.1 2.8 0 .16-.27.4-.42.46-1.17.42-2.35.87-3.53 1.2-.53.15-1.12.07-1.68.08-.44 0-.88-.07-1.32-.04-.34.03-.84.35-1.19.4-.97.15-1.8.09-2.76.26-.89.17-1.74.41-2.58.65-.23.06-.35.13-.53.26-.07.05-.09.15-.11.22.09.05.18.11.27.1.22-.03.4.04.62.01.52-.08 1.04-.14 1.58-.19.11 0 .26.08.36.15a1 1 0 0 0 .82.17c.32-.07.6.01.88.21.48.4.6.41 1.16.1.43-.23.9-.23 1.38-.2 1.06.05 2.05.2 3 .38a2.05 2.05 0 0 0 1.98-.76c.27-.34.53-.68.74-1.01.18-.24.37-.4.65-.4.6 0 1.21-.14 1.8-.1.33.02.5.33.5.52-.03.2-.1.32-.33.31-.46-.03-.9-.05-1.34-.11-.47-.04-.84.1-1.12.45-.15.16-.11.28.11.33.1.01.22.06.32.07.24.02.35.14.42.36.04.14.33.28.82.3.62.04.94.04 1.48-.13.23-.08.38-.34.53-.53.36-.51.6-.65 1.23-.51.45.08.75-.4 1.15-.37.73.1-.1-1.38-.56-1.87-.64-.47-1.15-1.41-1.71-2.11-.41-.51-.9-.7-1.26-1.33-.68-1.13-1.24-2.5-2-3.4-.29-.31-.93-.1-1.25.07a.88.88 0 0 0-.37.26c.91.77.95 1.84.63 2.63-.09.25-.18.24-.44.14a9.6 9.6 0 0 1-.72-.29c-.16-.08-.3-.04-.46.06-.7.42-1.54.98-1.68.84-.28-.27-.73-1.11-.7-1.54.02-.35.14-.65.27-.85a6.98 6.98 0 0 1-.9-.57v-.31c.58-.56 1.36-1.1 1.96-1.1zm-17.42-8.1c.08.55.3 1 .84 1.2.76.32 1.77.5 2.62.52 1.02.03 2.13-.06 3.32-.28a21.59 21.59 0 0 0 5.36-1.87 14.83 14.83 0 0 1 3.25-1.24 7.37 7.37 0 0 1 3.6-.1c.54.13.65.13.57-.25-.03-.26-.1-.5-.15-.77-.13-.89-.09-1.73-.42-2.5a1.03 1.03 0 0 0-.5-.45 2.94 2.94 0 0 0-.85-.26 6.05 6.05 0 0 0-1.8 0 9.06 9.06 0 0 0-2.73.84c-.93.46-1.86.97-2.55 1.39a7.35 7.35 0 0 1-3.78 1.09c-.89 0-1.47-.03-2.36-.13a8.41 8.41 0 0 1-3.47-.95c-.24-.12-.37-.09-.46.16a7.79 7.79 0 0 0-.47 3.6zM43.38 38c-.3.04-.94.13-1.32.15a1.77 1.77 0 0 1-.36-.05c.06-.11.1-.15.16-.18.45-.23.94-.45 1.27-.7.19-.15.15-.28.1-.4-.63.27-1.21.4-1.87.5-.05 0-.08-.04-.1-.07.51-.38 1.2-.69 1.57-1.02.25-.2.06-.26-.02-.35-.54.19-1.08.37-1.65.52a.65.65 0 0 1-.26-.02c0-.11.11-.16.2-.22a8.68 8.68 0 0 0 1.59-1.04l-.03-.14-.19-.02c-.44.08-1.4.22-1.93.27-.26 0-.22-.15-.17-.18.42-.19.74-.41 1.05-.74.11-.12.1-.23.06-.29-.5.24-1.08.24-1.42.28-.06-.08.02-.18.06-.22.4-.16.5-.29.7-.43.14-.1.44-.3.5-.4.04-.05.03-.16 0-.2a6.51 6.51 0 0 1-1.24.3.62.62 0 0 1-.22-.03c.03-.1.05-.17.1-.2.59-.3.9-.42 1.3-.67a1 1 0 0 0 .28-.36l-.04-.06-1.42.44c-.1-.02-.14-.08-.22-.14a.57.57 0 0 1 .16-.17 7.6 7.6 0 0 0 2.64-1.52c.22-.18.42-.4.5-.56l-.06-.11a13.7 13.7 0 0 1-2.9 1.63c-.22-.02-.18-.15-.1-.2.1-.1.37-.41.6-.7a8.4 8.4 0 0 0 2.43-1.41c.06-.06.09-.22.05-.32-.48.21-1.2.62-1.83.86l-.12-.04c.02-.11.08-.22.14-.33a8.11 8.11 0 0 0 1.53-1.24c.02-.07.05-.15 0-.23-.4.22-.85.37-1.26.51-.14-.09.03-.33.05-.39.59-.48.87-.8 1.15-1.2.06-.1.06-.18 0-.33-.32.23-.79.37-1.15.51l-.1-.03c-.02-.12 0-.1.04-.18.42-.4.79-.7 1.05-1.15.02-.05.02-.17-.03-.25-.35.12-.62.22-1 .32a.3.3 0 0 1-.14-.03c.02-.12 0-.15.05-.24.26-.23.52-.53.76-.9.07-.09.06-.18.01-.25-.18.05-.52.1-.69.11-.07.02-.16-.06-.21-.15l.65-.46a.56.56 0 0 0 .11-.18c-.08-.04-.15-.08-.2-.06-.28.03-.54.06-.82.12-.61.13-.61.21-.67.39a13.61 13.61 0 0 1-.68 2.03c-.08.18-.19.36-.37.43-.26.14-.2.27-.28.53-.11.5-.19.46-.4.95a2.7 2.7 0 0 1-.57.94h-.14l-.05-.7c-.05-.47-.14-.37-.22-.84-.05-.42.06-.87.11-1.23.05-.42.1-1.14.1-1.55-.04-.03-.08-.1-.13-.13a.4.4 0 0 0-.13.18c-.09.32-.24.82-.28 1.15-.1.76.05 1.5 0 2.27a8.65 8.65 0 0 0-.1 2.4c.25-.03.24-.06.59-.2.46-.18.8-.25 1.27-.67.34-.28.28-.85.4-1.32.16-.56.38-.76.6-.85.07-.02.13 0 .2.02l-.33.82c-.11.3-.22.77.1.82l.23.04c.01.06.01.14-.03.19-.3.28-.54.63-1.91 1.02-.16.05-.5.46-.52.62-.03.22-.03.47.02.82.14 1.37.82 2.65 1.58 3.92.64 1.08 1.12 1.9 1.81 2.6.33.3.67.53 1.21.27.3-.13.66-.36.75-.44.04-.06.05-.15.04-.23zm-18.6-19.7c.03.54-.07.68-.76.5-.73-.22-1.06-.14-1.53-.67-.28-.33-.63-.33-.93-.02-.14.17-.24.33-.46.37-.04-.07-.09-.18-.06-.26.08-.32.27-.56.57-.7.21-.12.47-.2.7-.26.64-.25.95-.63.95-1.38 0-.43.14-.86.14-1.3 0-.32-.12-.43-.42-.32-.53.2-1.03.4-1.56.63a.64.64 0 0 0-.37.35c-.15.28-.24.56-.37.84-.12.16-.23.08-.26.02-.25-.4-.56-.82-.82-1.16-.23-.28-.23-.38.03-.57l.86-.76c.43-.4.6-.93.64-1.52v-.81c-.01-.35.06-.46.41-.41.25.03.5.1.71.2.4.17.78.4 1.17.6.2.13.36.3.38.57 0 .3-.2.5-.47.39a8.56 8.56 0 0 0-1.31-.46c-.23-.05-.45.07-.49.28-.09.51.35.54.31.68-.11.36-.7.47-.78.8-.07.3-.07.5.2.45.74-.15 1.46-.6 2.02-.84.49-.2.74-.13.8.41.26 1.58.5 2.94.68 4.35zm11.44-5.02c.14.04.32.12.43.22.27.63.84 1.32 1.57 1.7.23.12.46.16.65.18.07-.28.19-.74.19-1.25 0-.4-.12-.84-.5-1.21a4.84 4.84 0 0 0-1.11-.84.47.47 0 0 0-.55.05c-.01.09-.01.23.04.31.18.3.6.59.8.88.06.3.04.28-.2.18a4.7 4.7 0 0 1-1.33-1.16c-.21-.24-.35-.24-.5.02a4.5 4.5 0 0 0-.67 2 2.05 2.05 0 0 0 1.11 2.14c.35.15.6.11.77-.21.17-.33.3-.61.4-.97.06-.19.06-.26-.08-.43a6.15 6.15 0 0 0-.56-.62.81.81 0 0 0-.28-.23.37.37 0 0 0-.07.24c.01.28.06.58.07.87 0 .07-.01.19-.06.25a.57.57 0 0 1-.32-.37c-.18-.69-.15-1.3.2-1.75zM4.06 3.15l.54.64c.22.42.34.8.45 1.13.1.34.24.34.45.19a.76.76 0 0 0 .32-.65 2.17 2.17 0 0 0-.12-.59c-.2-.72-.06-1.34.57-1.8.22-.14.37-.39.3-.65-.1-.36-.19-.7-.33-1.04C6.2.23 5.98.2 5.84.27c-.7.37-1.54 1.44-.9 2.34.03.05 0 .22-.05.31-.6-.58-1.74-1.78-2.09-1.75-.1.17-.17.51-.1 1.21.1.68.27 1.38.78 2.33.13.21.25.34.44.5.12.1.37.22.56.22.06-.14.07-.39.03-.55-.17-.64-.34-1.12-.55-1.7zm47 2.68c.49-.1.89-.24 1.07-.31.8-.3 1.33-.82 1.5-1.7.07-.23-.07-.36-.33-.42l-.52-.14c-.5-.15-.61.06-.8.56-.16.37-.31.8-.71 1.11-.12.1-.54-.2-.66-.28a4.26 4.26 0 0 0 1.68-2.1c.1-.17 0-.34-.18-.4-.25-.07-.5-.1-.8-.07a4.01 4.01 0 0 0-2.88 2.14c-.04.1 0 .31.06.34.11.05.4.02.52-.03.07 0 .1-.11.15-.18.33-.58.88-.93 1.51-1.21.19-.1.25-.03.15.18-.12.16-1.26.96-1.28 1.07-.15.33.07.37.26.37l.34-.09c.12.6.48.99.9 1.16zm-17.22-.1c-.02.28-.12.44-.38.35-.52-.23-.83-.8-1.42-.99-.21-.07-.34.12-.37.32-.1.6-.19 1.12-.19 1.35 0 .82.44 1.26.88 1.7.13.11.13.19-.01.25l-1.8 1c-.17.12-.37.14-.56 0-.17-.15-.23-.35-.17-.54.21-.9.36-1.84.6-2.72.36-1.4.97-1.87 1.98-2 .37-.06.64-.06.92-.06.3 0 .42.06.46.33.04.18.05.39.05 1.01zm1.36 6.17c0-.68-.55-.87-.79-1.45-.22-.57-.42-.57-.88-.16-.52.5-.84.92-1.02 1.63a3.9 3.9 0 0 0 .01 1.96c.05.2.16.41.38.35a.37.37 0 0 0 .27-.37c0-.58.05-1.14.34-1.51.13-.17.19-.2.26-.25.1.14.06.3.06.33-.09.43-.15.87-.24 1.3-.06.28-.06.44.09.7.17.26.3.3.42.12.53-.85 1.1-2.07 1.1-2.65zM6.14 13.78a5.21 5.21 0 0 0-.37-1.67c-.22-.53-.56-.84-.78-.84-.2 0-.68.56-.84 1.01-.4 1.15-.19 2.08.48 3.18.09.14.27.4.53.14.2-.24.36-.4.55-.65.26-.31.44-.75.43-1.17zm25.07 17.96c-.12-.96-.84-1.89-2.05-2.7-.25-.1-.27.07-.27.28.12 1.33.27 2.67.44 3.86.05.26.17.33.41.2.36-.17.7-.34 1.05-.54.32-.19.52-.36.42-1.1zm10.96-10.89c-.2-.46-.5-.66-.97-.7-.47-.05-.86-.11-1.35-.19-.28.04-.47.26-.63.63.01.08.04.17.1.19.16.06.44.11.48.14.06.04.24.23.28.38-.04.13-.2.27-.42.3-.28.06-.28.29-.23.58.04.28.14.5.36.8.19-.1.28-.15.42-.3a.63.63 0 0 0 .19-.54c-.04-.6.3-1.04.71-1.44l.1.05c-.07.35-.25.6-.3.94-.08.7-.32 1.3-1.02 1.78.22.28.75.07.92-.1.29-.26.52-.58.63-.98l.11-.35a.28.28 0 0 1 .1.02c.13.26-.06.8.03 1.18a.87.87 0 0 0 .35-.28c.26-.62.39-1.53.14-2.1zm8.76-14.33c-.12 0-.6.01-.74.04-.11.03-.17.29-.11.34.13.09.22.16.33.2.44.05.63.05 1.14.13.08.01.18.06.25.14-.05.04-.1.1-.19.12-.93.11-1.2.1-1.86-.34-.18-.08-.24.05-.19.22.1.32.36.58.84.77.38.17.75.37 1.15.37.6-.02 1.02-.06 1.51-.14.26-.04.52-.19.56-.32.05-.13 0-.42-.15-.6l-.35-.36c-.56-.59-.92-.96-1.75-.87-.11 0-.28.3-.44.3zm.58 6.2c0-.34-.05-.6-.14-.9a1.58 1.58 0 0 0-1.38-1.14c-.34 0-.51.06-.37.38.04.1.34.43.46.7.16.31.31.64.31.91a.93.93 0 0 1-.08.38c-.2-.11-.32-.37-.37-.49-.09-.3-.1-.47-.2-.64-.08-.15-.19-.19-.23-.19-.13 0-.19.05-.2.23a2.67 2.67 0 0 0 1.46 2.67c.26.1.33.03.41-.2a5.58 5.58 0 0 0 .33-1.7zM5.58 9.12c-.13.5-.15.83-.14 1.1.01.36.19.66.52.8a6.23 6.23 0 0 0 3.1.5c.03 0 .13-.07.13-.1-.02-.22-.14-.25-.33-.3-.44-.16-1.61-.47-1.77-.66-.12-.17-.06-.34.13-.37a1 1 0 0 1 .51.08c.3.14.54.33.84.5.1.05.2.15.32 0 .08-.14.16-.27-.02-.4l-.69-.55c-.6-.47-1.49-.7-2.6-.6zm21.29 7.71c-.05.1-.14.2-.25.33-.1-.11-.22-.19-.27-.3a22.36 22.36 0 0 1-1.12-3.73c-.02-.24.07-.33.28-.32.53.06 1 .3 1.46.54.07.06.13.22.14.33.1 1.05-.04 2.23-.24 3.15zM3.95 18.97l-.08-.03c.05-.64.37-1.54.25-1.77a1.3 1.3 0 0 0-.64.06c-.22.1-.32.32-.36.7-.06.82.07 1.67.25 2.41.05.28.17.31.37.11.53-.48 1.03-1.1 1.43-1.57.28-.36.33-.7.22-1.05-.1-.29-.29-.4-.5-.4-.24 0-.32.18-.38.35-.13.46-.34.79-.56 1.19zm42.97-1.9c.42 0 .76.08.88.12.5.18.72.4.81.58.1.17.1.26.1.34a.9.9 0 0 1-.83-.17c-.45-.42-1.1-.6-1.49-.42.03.3.3.65.54.84.78.6 1.47.7 2.3.51.15-.05.2-.25.2-.42-.03-.54 0-1.11-.5-1.58-.57-.53-1.37-.51-1.92-.28-.09.05-.23.11-.3.19.02.06.09.2.21.28zM27.56 2.32c.17-.04.1.03.05.18-.1.48-.37.85-.7 1.19-.2.16-.23.42-.1.55.24.23.52.03.7-.15.38-.41.61-.93.83-1.43.26-.62.2-1.4-.1-1.92-.15-.22-.4-.25-.58-.08a3.23 3.23 0 0 0-1.14 2.23c0 .13.01.35.1.4.42-.2.68-.66.93-.96zm11.36 22.52c.04-.04.12 0 .15.02 0 .5.12 1.1.18 1.28a.58.58 0 0 0 .19-.13l.14-.68c.31-.96.08-1.72-.32-2.6-.07-.16-.18-.18-.35-.1-.12.05-.23.13-.12.3.09.14.08.25 0 .4-.13.24-.23.51-.37.81-.05.19-.06.33-.08.56l-.14 3.44c.06.19.17.15.23.04.08-.19.13-.36.17-.56.1-.54.25-.56.28-1.1.05-.56.05-1.11.03-1.67zM7.39 3.57c-.01-.05 0-.18.02-.21l.57.1c.28.05.42.01.52-.2.15-.41.07-.78-.36-.87-.65-.12-1.3.1-1.86.22-.36.13-.45.5-.18.77.41.4.81.8 1.25 1.15.36.29.77.36 1.18.28.19-.05.35-.17.35-.33a.47.47 0 0 0-.14-.34 1.26 1.26 0 0 0-.33-.18zM6.8 28.63c-.03.19-.12.35-.43.33a3.83 3.83 0 0 1-.83-.12c-.3-.08-.38-.2-.4-.3-.1-.57-.04-1.33.09-1.85.04-.1.35-.28.48-.35.4.04.72.1 1 .18.12.07.34.4.36.45.06.18.06.37.06.56-.11.46-.25.86-.33 1.1zm31.08 16.8c.75.28 1.33.36 1.55.32a.45.45 0 0 0 .34-.5c0-.2-.1-.38-.37-.45-.57-.12-1.15-.2-1.7-.4-.3-.1-.55-.09-.83.13-.43.32-.84.5-1.37.6a.65.65 0 0 0-.34.12c-.15.2-.11.33.12.46.92.08 1.79-.1 2.6-.28zm3.07 1.83c.46 0 .8-.1 1.12-.3a2.8 2.8 0 0 1 1.35-.6c.2-.04.41-.25.43-.42 0-.23-.19-.26-.34-.31-.45-.14-.98-.12-1.44-.24-.15-.02-.36-.02-.48.07-.96.97-1.15.74-1.6 1.22-.12.16-.12.35.21.44.26.06.6.12.75.14zM25 10.86c0-.23.43-.6.67-.57.04.02.09.05.13.1l.09.5c.03.2.1.37.2.37.48.04 1.05-.3 1.32-.72.28-.44.49-.95.73-1.44.22-.53.57-.2.45.12-.31.92-.7 1.86-.99 2.77-.1.33-.14.35-.46.24l-1.54-.55a.8.8 0 0 1-.6-.81zM36.8 33.85c.25.12.23.07.56-.12a.76.76 0 0 0 .3-.54l-.1-2.01c0-.07-.09-.17-.12-.17-.28 0-.62-.06-.84.06-.43.24-.8.57-1.18.88-.15.15-.01.3.21.28.19-.08.4-.17.59-.27l.35-.19c0 .07 0 .12-.03.17-.34.37-.6.82-1.05 1.06-.12.02-.09.32.09.33.14-.02.42-.17.5-.25.29-.22.4-.36.69-.6.1.15.16.26.02.38l-.75.67c-.11.11-.03.22.1.2.2-.02.38-.1.53-.2.2-.12.35-.29.52-.47 0 .02.1.13.09.13-.14.22-.3.42-.48.66zm-23.82-23.4c-.11 1.12-.39 2.25-.9 2.77-.4-.79-.68-1.7-.96-2.5.22-.28.5-.7.62-1.02.06-.16.15-.19.3-.08.33.31.62.56.94.82zm1.33 16.04a.93.93 0 0 1-.5.64c-.2.1-.52.23-.88.23-.17 0-.3-.04-.32-.19l-.3-1.5c0-.12.1-.28.2-.38.32-.3.74-.39 1.18-.41.1 0 .4.13.45.2.09.47.14.9.17 1.4zm-4.06-6.03c-.6.1-1.12.63-1.03.84.01.47.1.91.36 1.31.15.24.27.3.52.13a4.47 4.47 0 0 0 1.78-1.82c.12-.22-.12-.29-.34-.4-.3-.14-.61-.14-.78-.06-.17.09-.23.27-.26.4-.19.68-.23.7-.43.94a1.8 1.8 0 0 1 .18-1.34zM14.28 46c.03-.08.05-.13.12-.14.1-.02.37-.02.4 0 .53.15 1.02-.23 1.51-.3.11-.02.24-.04.37-.02.32.01.65.02 1.01-.07.15-.06.34.02.51.1.57.24 1.12.38 1.67.06.29-.2.58-.38.88-.53.51-.2.56.14.58.33a.48.48 0 0 1-.28.29c-1.77.63-3.3.64-5.28.4a4.47 4.47 0 0 1-1.5-.12zM3.2 27.39c-.04.24.04.6.23.8l.09.08-.05.15a4.19 4.19 0 0 1-.91-.12c-.13-.05.23-.24.26-.47.06-.45.19-1.2.19-1.75 0-.1-.24-.2-.24-.28a.34.34 0 0 1 .11-.19c.66.16.75.15.88.46.13.28.25.74.45 1.08.03-.02.09-.04.12-.07.03-.3.02-.75.03-.96.01-.17.09-.25.27-.25.13 0 .48.01.62.04.02.03.02.05.02.1 0 .08-.11.11-.14.12-.31.11-.33.14-.47 1l-.15.98c-.02.08-.03.23-.08.33-.08 0-.23 0-.26-.08-.21-.52-.5-1.08-.76-1.55h-.1c-.07.2-.07.4-.12.59zm20.47 4.59c.03-.2.03-.4 0-.57-.1-.32-.25-.63-.2-.9.14-.49.24-1 .2-1.5a6.27 6.27 0 0 1 .05-1.86c.17.06.3.17.34.28.04.2.09.4.09.57.06.53.02 1.04-.01 1.56-.08 1-.17 1.93-.14 2.92.02.15.05.34.1.48.12.3.06.63-.18.97l-.15-.07c-.11-.41-.4-.98-.51-1.43-.02-.08 0-.15.1-.17.2-.03.31-.15.32-.28zm9.68-7.4c-.4-.04-.83-.4-.8-1.05-.2.27-.45.55-.68.66.02.22.57.54.95.7.22.28.37.42.65.74.17-.43.38-.8.44-1.03a3.3 3.3 0 0 0-.32-1.51c-.31-.35-.8-.63-1.27-.94-.23.25.06.79.64 1.11-.13.36-.11.9.46 1.21zm-27.82-1c.29-.02.6-.06.94-.11.22-.06.47-.11.48-.4.02-.24-.28-.38-.48-.44a4.43 4.43 0 0 0-2.93.3c-.25.13-.46.27-.6.42-.22.24-.18.39 0 .52.18.14.45.29.8.37.04-.2.08-.54.15-.74.32-.52 1.21-.62 1.83-.64.15.03.15.22.03.26-.18.14-.53.1-.62.26 0 .15.15.21.4.2zm1.99 5.4c-.01-.11.12-.25.13-.33.03-.15.03-.35.03-.5.02-.4-.07-1.06-.07-1.45l.22-.14c.07-.03.13.02.21.1.19.18.6.64.8.82.02 0 .05-.05.06-.06v-.3c0-.2-.04-.35-.1-.57-.07-.25-.25-.43-.05-.45l.76-.05c-.07.15-.13.26-.16.48-.03.3-.1.59-.1.88.04.44.13.89.17 1.26 0 .03-.06.07-.11.1a.7.7 0 0 1-.22-.14c-.37-.38-.63-.72-1.11-1.18a.4.4 0 0 0-.07.2c.1.3.21.62.41.9.12.16.15.21-.16.33-.17.05-.45.13-.63.09zm13.6.08c.44-.27.62-1.17.5-1.6a.58.58 0 0 0-.14-.31c-.14-.14-.3-.1-.37.14-.09.35-.13.72-.17 1.05a3.26 3.26 0 0 0-.99-.03c-.33.05-.98.24-.98.64 0 .65 1.5.5 2.15.11zm2.63-4.5a.74.74 0 0 0 .18.52c.67.7 1.6 1.42 2.18 1.56.52.14.96.45 1.28.87l.3.42c.23.28.52.46.9.52.07.02.16 0 .23 0-.06-.23-.06-.26-.22-.43-1.38-1.23-2.84-2.19-4.26-3.18-.14-.09-.4-.25-.6-.28zM9.36 40.38v-.02c-.14-.59-.3-1.15-.44-1.72-.12-.2-.32-.06-.3.03-.15.41-.3.83-.43 1.27a.61.61 0 0 0 0 .4c.22.6.43 1.21.67 1.83.04.08.15.13.21.18.06-.07.14-.16.16-.23zm2.44-14.6c.04.12.1.26.11.39.01.1.01.17-.02.22 0 .03-.06.02-.12 0-.29-.14-.61-.32-.87-.15-.1.07-.06.24.06.3l.82.33c.37.14.47.3.4.74-.07.42-.38.59-.74.69-.15.04-.31.06-.51.04-.03.13 0 .28-.09.33-.06.04-.08.03-.15.06a3.6 3.6 0 0 1-.28-1.02c0-.08.03-.09.06-.12.12.04.23.2.42.38.22.08.6.14.66.08.11-.14.16-.23.16-.28 0-.1-.13-.19-.25-.23-.28-.16-.44-.22-.74-.34-.28-.11-.33-.36-.33-.6 0-.25.14-.52.54-.65l.46-.13c.04-.08.07-.36.07-.41.06-.01.18-.03.2.01zM6 8.26c-.57 0-.92-.14-1.48-.09-.4.03-.79.2-1.18.35-.23.12-.19.22.02.3.2.1.34.15.54.14l3.6-.33c.07-.1.12-.17.19-.32-.44-.29-1-.23-1.54-.39zm13.24 17.23c-.05.03-.13.1-.27.24a.73.73 0 0 1-.23.17l-.16-.1.26-.6c.09-.23.18-.26.4-.11l.2.17c.18.14.4.25.6.16.2-.1.1-.38-.1-.48-.3-.19-.53-.24-.9-.46-.21-.13-.3-.33-.3-.61 0-.23.15-.51.45-.54.11-.03.25-.03.4-.03.2 0 .33-.03.45-.2.1-.11.2-.19.23-.06.07.34.03.7-.06.91-.06-.01-.1-.01-.17-.04-.17-.28-.4-.41-.6-.38-.19.02-.28.16-.14.31.25.26.6.4.9.57.43.23.4.56.35.8a.6.6 0 0 1-.63.5c-.22 0-.43-.06-.68-.22zm-.84-.86c.04.72-.54.98-.95.97-.38 0-.6-.08-.74-.46-.06-.23-.14-.9-.18-1.16-.08-.1-.2-.13-.4-.18-.1-.02-.1-.1-.12-.18.14-.07.39-.14.56-.17.41-.06.41-.04.48.34.05.42.12.87.2 1.3.14.42.59.33.59-.03 0-.33.02-.71 0-1.07-.07-.08-.19-.26-.32-.38-.16-.19-.16-.24.06-.33.52-.2.56-.33.63.36.03.26.19.66.19 1zm.48 3.67l1.3-.3c.35-.1.54-.47.52-.8-.04-.25-.19-.39-.45-.44a3.54 3.54 0 0 0-1.68.23c-.04.44.12.93.31 1.3zM6.91 23.66c-.16 0-.39.04-.55.09-.33.08-.5.11-.82.08-.19-.02-.49-.14-.66-.18-.2.01-.4.2-.47.47-.04.28.13.48.44.53.62.14 1.2.25 1.79.25.22 0 .25-.03.26-.21.02-.35.01-.65.01-1.03zm14.9-13.3a3.77 3.77 0 0 1-.82-.25c-.09-.03-.15-.26-.15-.38.02-.32-.06-.65 0-1 .02-.18.13-.23.3-.2l.73.19c.32.13.79 1 .65 1.31-.14.28-.39.32-.7.34zM16.42 25.6a.93.93 0 0 1-.26.25c-.3.2-.86.33-1.24.58-.19.13-.1-.32-.11-.49l-.2-1.04a1.36 1.36 0 0 0-.34-.56c-.1-.13.2-.18.37-.23l.6-.19c.23-.07.28.09.22.14-.31.22-.28.32-.25.6.05.29.05.6.06.83.05.11.11.26.2.36.18-.05.39-.2.39-.25l.04-.6c.23-.27.26-.14.3-.01.1.22.14.36.22.61zm.93-6.6c-.13-.4-.26-.83-.36-1.24-.03-.17.11-.27.27-.24.56.12 1.1.25 1.67.4.18.04.18.19.06.31-.36.37-.74.69-1.24.91-.26.14-.26.04-.4-.15zm24.1-14.77a4.1 4.1 0 0 0-.63-1.54c-.26-.32-.56-.35-.84-.06-.1.1-.18.19-.24.28-.28.36-.26.47.16.67.64.19 1 .56 1.45 1.03zM11.9 40.4a2.6 2.6 0 0 0-.83.74l-.58 1c-.07.16-.18.55-.22.8l.1.1a4.65 4.65 0 0 0 .83-.6c.47-.46.58-.9.84-1.83l.05-.11zM52.9 2.13c-.25-.52-.71-1.22-.9-1.47-.26-.32-.38-.34-.66-.06l-.25.28c-.28.35-.45.78 0 .84.4.07.93 0 1.18.18l.54.36zm-47.18 25c-.07.69-.05.69-.05 1.16 0 .26.12.26.35.28.21 0 .28-.19.33-.44l.14-.94c.03-.3.05-.48-.13-.51-.35-.05-.6.17-.64.46zm3.63-12.58a3.6 3.6 0 0 1-.87-.3c-.47-.24-.91-.75-.84-1.21.04-.22.16-.28.34-.18.36.26.66.65.92.93.21.22.37.47.46.76zm3.5 11.06c0 .19.16.74.22 1.05.02.17.06.3.3.27.32-.05.43-.18.38-.44-.03-.28-.1-.57-.16-.87-.06-.28-.18-.44-.43-.43-.26.03-.35.12-.32.42zM26.66 8.05c-.2.35-.37.7-.6 1.01-.18.26-.46.41-.71.63-.24.06-.25.04-.17-.2.36-.6.7-1.06 1.1-1.57.28-.15.38-.03.37.13zm18.67 9.66c0 .22-.04.43-.11.64l-.06.03c-.29-.73-.99-1.9-.94-2.02-.02-.08.17-.13.25-.11l.32.13c.18.43.37.84.54 1.33zm2.03-15.42c.03 0 .15.04.23.11.14.11.16.24-.03.32-.44.2-1.17.6-1.65.77-.09.05-.2.06-.15-.11.09-.28 1.2-1.1 1.6-1.1zM31.44 22.5c-.3 0-.56.24-.56.48 0 .27.35.56.66.56.28 0 .47-.18.47-.46.02-.34-.22-.58-.57-.58zM6.91 46c-.06 0-.18.2-.28.21-.38.03-.5.07-.5.23 0 .21.1.32.28.27.56-.17.69-.15 1.42-.1l.25-.3c-.09-.07-.15-.16-.25-.19A4.16 4.16 0 0 0 6.9 46zM39.7 19.21c-.12.07-.22.15-.19.29l.1.22c.21-.04.45-.06.68-.04.3.1.8.2 1.11.26.08.02.17-.06.23-.14l-.07-.12c-.43-.43-1.14-.44-1.86-.46zM19.19 9.8l-.76-.34c-.05-.02-.08-.16-.03-.22.17-.25.34-.48.52-.7.1-.1.17-.09.21.03l.38.95c.08.19 0 .26-.32.28zM9.18 19.15c.18-.04.27.08.25.24-.02.1-.05.13-.2.2-.58.19-1.1.18-1.7.06-.18-.02-.3-.06-.33-.1a.35.35 0 0 1 .22-.16zm5.34-14.93c-.06.01-.12.06-.16.03-.5-.28-1.2-.65-1.65-.94-.1-.15-.04-.2.1-.18.37.12 1 .3 1.36.44.24.12.31.33.35.65zm7.58 10.86c.06.09.11.17-.04.42-.26.4-.32.8-.49 1.33-.04.12-.11.24-.17.32-.05.06-.14.08-.2.11-.02-.06-.06-.15-.05-.2l.5-1.64c.08-.2.23-.3.45-.34zM40.28 9.6a9.03 9.03 0 0 1-1.96-.68c-.21-.19-.17-.18.1-.19l1.84.5c.35.26.28.25.02.37zM32.7 21.25a.62.62 0 0 0-.28-.35c-.1-.05-.27-.03-.37.06-.1.06-.22.16-.3.26-.14.2-.05.39.17.55.18.13.39.12.51-.06.11-.14.21-.24.27-.46zm-12.26-9c-.07.15-.09.29-.18.39-.25.3-.53.58-.8.87-.27.12-.28-.04-.25-.17.25-.47.6-.86.93-1.24.03-.04.14-.07.18-.04.05.03.08.1.12.19zM9.93 9.7c-.07.04-1.05-1.5-1.17-1.8 0-.06.16-.18.24-.24.06.04.14.1.17.15.31.67.86 1.8.82 1.85zm-.42 6.13l-.42-.19c-.3.07-.56.2-.9.25-.36.02-.51-.05-.65-.14.05-.12.25-.23.54-.28.17-.03.9-.1 1.54-.04zM47.4 8l1.08.81c.1.08.09.23.13.32l-.24-.03c-.46-.23-.95-.42-1.25-.87-.26-.39.16-.34.28-.23zM20.04 9.34l-.12.19c-.04-.06-.1-.11-.14-.19-.09-.18-.09-.4-.1-.6-.06-.46-.04-.7.24-.99.12-.13.2-.05.2.07.03.58 0 .95-.08 1.52zm-6.92-1.7c.1.05.28 1.15.4 1.72.03.13.02.3 0 .42-.05.13-.1.16-.2.05-.19-.59-.28-1.18-.38-1.74-.03-.17-.03-.35.18-.44zm31.57 6.74c.44.25.96.7 1.18.96.05.05.03.2 0 .31-.08-.03-.16-.03-.22-.08-.37-.23-.71-.5-1.06-.74-.04-.03-.05-.3-.09-.36a.4.4 0 0 1 .19-.1zM24.34 4.79c.07.05.1.1.15.16a.4.4 0 0 1-.15.17c-.32.12-1.06.12-1.36-.06a.95.95 0 0 1-.16-.18l.21-.12c.44-.1.93-.06 1.3.04zm8.37 1.79c.3.46.43.91.56 1.41 0 .03-.02.04-.02.12-.07-.05-.11-.06-.14-.1a2.6 2.6 0 0 1-.74-1.23c-.04-.1-.02-.2.12-.28a.5.5 0 0 1 .22.08zm-5.79 31.85c.36-.2.46-.36.77-.55.13-.08.12-.1.05-.22-.07-.14-.14-.14-.32-.1-.35.13-.49.21-.84.35-.15.06-.2.12-.05.24zm-10.9-33c.43-.3.9-.45 1.37-.63.08-.02.19.01.27.09l-.16.14c-.38.27-.75.47-1.1.68-.11 0-.28-.03-.38-.06a.28.28 0 0 1 0-.22zM4.9 14.55c-.1-.5-.24-1.11-.15-1.56.02-.1.09-.19.2-.28.08.09.18.15.19.23.09.53-.06 1.12-.1 1.6zM29.13 3.32c.32-.26.86-.54 1.25-.73a4.7 4.7 0 0 1-1.28 1.37c-.03-.2-.1-.53.03-.64zm-5.96 19.44c0 .2.1.43.26.59.1-.08.17-.22.17-.28.04-.4.06-.8.06-1.2.05-.16-.18-.2-.24-.04-.09.3-.24.62-.25.93zm20.44 16.1l-1.72.58c.4.45 1.56.08 1.72-.58zM18.26 7.6c.03.46-.06 1.03-.3 1.45-.04.05-.1.09-.18.14-.02-.08-.1-.14-.09-.2.1-.47.21-1.02.33-1.49.01-.03.09-.05.13-.06zm24.1 32.9c.37 0 .73 0 1-.08.26-.16.25-.49.33-.71l-.1-.03-.4.37a5.8 5.8 0 0 1-.84.17c-.05.06-.1.1-.13.17.05.06.1.11.15.12zm-3.44-18.43c.12-.27.23-.52.32-.77.02-.05-.06-.2-.16-.29a.73.73 0 0 0-.2.1 3.72 3.72 0 0 0-.55.82c.04.06.22.06.58.14zm-28.81-7.3l.36-.92c.06-.02.12-.02.17 0 .05.29 0 .73-.02.91-.06.25-.15.4-.37.51a1.52 1.52 0 0 1-.14-.5zm21 6.84c-.25 0-.45.15-.47.37 0 .19.2.36.43.36.24-.01.41-.18.43-.39 0-.22-.14-.34-.39-.34zm-.22 2.29c0-.25-.15-.42-.42-.42-.22 0-.37.14-.37.33 0 .23.23.45.46.43.2 0 .33-.15.33-.34zm-.94-2.35c0 .2-.13.33-.33.33-.26 0-.46-.16-.46-.4a.4.4 0 0 1 .38-.37c.2-.02.4.16.4.44zm.16.93c-.22 0-.37.14-.37.33 0 .2.2.37.42.37.17 0 .33-.18.33-.37 0-.18-.16-.33-.38-.33zm-2.44 16.64c-.3 0-.54.14-.53.3.01.16.24.37.41.37.2 0 .3-.2.3-.44-.02-.19 0-.23-.18-.23zM9.93 3.9a3.23 3.23 0 0 1 .06-1.63c.24.48.26 1.27-.06 1.63zm20.94 16.3c-.15.1-.28.25-.26.31.02.11.14.21.21.33a.6.6 0 0 0 .25-.3c.03-.07-.1-.19-.21-.34z"
      fill="#53565a"
    />
  </svg>" alt="Elsevier logo" height=48 width=54><svg xmlns=http://www.w3.org/2000/svg version=1.1 height=15 viewBox="0 0 190 23" role=img class="gh-wordmark u-margin-s-left" aria-labelledby=gh-wm-science-direct focusable=false aria-hidden=true alt="ScienceDirect Wordmark"><title id=gh-wm-science-direct>CienciaDirect</title><g><path fill=#EB6500 d="M3.81 6.9c0-1.48 0.86-3.04 3.7-3.04 1.42 0 3.1 0.43 4.65 1.32l0.13-2.64c-1.42-0.63-2.97-0.96-4.78-0.96 -4.62 0-6.6 2.44-6.6 5.45 0 5.61 8.78 6.14 8.78 9.93 0 1.48-1.15 3.04-3.86 3.04 -1.72 0-3.4-0.56-4.72-1.39l-0.36 2.64c1.55 0.76 3.57 1.06 5.15 1.06 4.26 0 6.7-2.48 6.7-5.51C12.59 11.49 3.81 10.76 3.81 6.9M20.27 9.01c0.23-0.13 0.69-0.26 1.72-0.26 1.72 0 2.41 0.3 2.41 1.58h2.38c0-0.36 0-0.79-0.03-1.09 -0.23-1.98-2.15-2.67-4.88-2.67 -3 0-6.7 2.31-6.7 7.76 0 5.22 2.77 7.99 6.63 7.99 1.68 0 3.47-0.36 4.95-1.39l-0.2-2.31c-0.99 0.82-2.84 1.52-4.06 1.52 -2.14 0-4.55-1.71-4.55-5.91C17.93 10.2 20.01 9.18 20.27 9.01"></path><rect x=29.42 y=6.97 fill=#EB6500 width=2.54 height=14.95></rect><path fill=#EB6500 d="M30.67 0.7c-0.92 0-1.65 0.92-1.65 1.81 0 0.93 0.76 1.85 1.65 1.85 0.89 0 1.68-0.96 1.68-1.88C32.35 1.55 31.56 0.7 30.67 0.7M48.06 14.13c0-5.18-1.42-7.56-6.01-7.56 -3.86 0-6.67 2.77-6.67 7.92 0 4.92 2.97 7.82 6.73 7.82 2.81 0 4.36-0.63 5.68-1.42l-0.2-2.31c-0.89 0.79-2.94 1.55-4.69 1.55 -3.14 0-4.88-1.95-4.88-5.51v-0.49H48.06M39.91 9.18c0.17-0.17 1.29-0.46 1.98-0.46 2.48 0 3.76 0.53 3.86 3.43h-7.46C38.56 10.27 39.71 9.37 39.91 9.18zM58.82 6.57c-2.24 0-3.63 1.12-4.85 2.61l-0.4-2.21h-2.34l0.13 1.19c0.1 0.76 0.13 1.78 0.13 2.97v10.79h2.54V11.88c0.69-0.96 2.15-2.48 2.48-2.64 0.23-0.13 1.29-0.4 2.08-0.4 2.28 0 2.48 1.15 2.54 3.43 0.03 1.19 0.03 3.17 0.03 3.17 0.03 3-0.1 6.47-0.1 6.47h2.54c0 0 0.07-4.49 0.07-6.96 0-1.48 0.03-2.97-0.1-4.46C63.31 7.43 61.49 6.57 58.82 6.57M72.12 9.01c0.23-0.13 0.69-0.26 1.72-0.26 1.72 0 2.41 0.3 2.41 1.58h2.38c0-0.36 0-0.79-0.03-1.09 -0.23-1.98-2.15-2.67-4.88-2.67 -3 0-6.7 2.31-6.7 7.76 0 5.22 2.77 7.99 6.63 7.99 1.68 0 3.47-0.36 4.95-1.39l-0.2-2.31c-0.99 0.82-2.84 1.52-4.06 1.52 -2.15 0-4.55-1.71-4.55-5.91C69.77 10.2 71.85 9.18 72.12 9.01M92.74 14.13c0-5.18-1.42-7.56-6.01-7.56 -3.86 0-6.67 2.77-6.67 7.92 0 4.92 2.97 7.82 6.73 7.82 2.81 0 4.36-0.63 5.68-1.42l-0.2-2.31c-0.89 0.79-2.94 1.55-4.69 1.55 -3.14 0-4.88-1.95-4.88-5.51v-0.49H92.74M84.59 9.18c0.17-0.17 1.29-0.46 1.98-0.46 2.48 0 3.76 0.53 3.86 3.43h-7.46C83.24 10.27 84.39 9.37 84.59 9.18zM103.9 1.98h-7.13v19.93h6.83c7.26 0 9.77-5.68 9.77-10.03C113.37 7.33 110.93 1.98 103.9 1.98M103.14 19.8h-3.76V4.1h4.09c5.38 0 6.96 4.39 6.96 7.79C110.43 16.87 108.19 19.8 103.14 19.8zM118.38 0.7c-0.92 0-1.65 0.92-1.65 1.81 0 0.93 0.76 1.85 1.65 1.85 0.89 0 1.69-0.96 1.69-1.88C120.07 1.55 119.28 0.7 118.38 0.7"></path><rect x=117.13 y=6.97 fill=#EB6500 width=2.54 height=14.95></rect><path fill=#EB6500 d="M130.2 6.6c-1.62 0-2.87 1.45-3.4 2.74l-0.43-2.37h-2.34l0.13 1.19c0.1 0.76 0.13 1.75 0.13 2.9v10.86h2.54v-9.51c0.53-1.29 1.72-3.7 3.17-3.7 0.96 0 1.06 0.99 1.06 1.22l2.08-0.6V9.18c0-0.03-0.03-0.17-0.06-0.4C132.8 7.36 131.91 6.6 130.2 6.6M145.87 14.13c0-5.18-1.42-7.56-6.01-7.56 -3.86 0-6.67 2.77-6.67 7.92 0 4.92 2.97 7.82 6.73 7.82 2.81 0 4.36-0.63 5.68-1.42l-0.2-2.31c-0.89 0.79-2.94 1.55-4.69 1.55 -3.14 0-4.89-1.95-4.89-5.51v-0.49H145.87M137.72 9.18c0.17-0.17 1.29-0.46 1.98-0.46 2.48 0 3.76 0.53 3.86 3.43h-7.46C136.37 10.27 137.52 9.37 137.72 9.18zM153.23 9.01c0.23-0.13 0.69-0.26 1.72-0.26 1.72 0 2.41 0.3 2.41 1.58h2.38c0-0.36 0-0.79-0.03-1.09 -0.23-1.98-2.14-2.67-4.88-2.67 -3 0-6.7 2.31-6.7 7.76 0 5.22 2.77 7.99 6.63 7.99 1.69 0 3.47-0.36 4.95-1.39l-0.2-2.31c-0.99 0.82-2.84 1.52-4.06 1.52 -2.15 0-4.55-1.71-4.55-5.91C150.89 10.2 152.97 9.18 153.23 9.01M170 19.44c-0.92 0.36-1.72 0.69-2.51 0.69 -1.16 0-1.58-0.66-1.58-2.34V8.95h3.93V6.97h-3.93V2.97h-2.48v3.99h-2.71v1.98h2.71v9.67c0 2.64 1.39 3.73 3.33 3.73 1.15 0 2.54-0.39 3.43-0.79L170 19.44M173.68 5.96c-1.09 0-2-0.87-2-1.97 0-1.1 0.91-1.97 2-1.97s1.98 0.88 1.98 1.98C175.66 5.09 174.77 5.96 173.68 5.96zM173.67 2.46c-0.85 0-1.54 0.67-1.54 1.52 0 0.85 0.69 1.54 1.54 1.54 0.85 0 1.54-0.69 1.54-1.54C175.21 3.13 174.52 2.46 173.67 2.46zM174.17 5.05c-0.09-0.09-0.17-0.19-0.25-0.3l-0.41-0.56h-0.16v0.87h-0.39V2.92c0.22-0.01 0.47-0.03 0.66-0.03 0.41 0 0.82 0.16 0.82 0.64 0 0.29-0.21 0.55-0.49 0.63 0.23 0.32 0.45 0.62 0.73 0.91H174.17zM173.56 3.22l-0.22 0.01v0.63h0.22c0.26 0 0.43-0.05 0.43-0.34C174 3.28 173.83 3.21 173.56 3.22z"></path></g></svg></a><div class="gh-nav-cnt u-hide-from-print"><div class="gh-nav-links-container gh-nav-links-container-h u-hide-from-print gh-nav-content-container"><nav aria-label=links class="gh-nav gh-nav-links gh-nav-h"><ul class="gh-nav-list u-list-reset"><li class="gh-nav-item gh-move-to-spine" data-moz-translations-id=0><a class="anchor gh-nav-action anchor-default" href=https://www.sciencedirect.com/browse/journals-and-books data-aa-region=header data-aa-name="Journals & Books" data-moz-translations-id=1><span class=anchor-text data-moz-translations-id=2>Diarios y libros</span></a></ul></nav><nav aria-label=utilities class="gh-nav gh-nav-utilities gh-nav-h"><ul class="gh-nav-list u-list-reset"><li class="gh-move-to-spine gh-help-button gh-help-icon gh-nav-item" data-moz-translations-id=0><div class=popover id=gh-help-icon-popover data-moz-translations-id=1><div id=popover-trigger-gh-help-icon-popover data-moz-translations-id=2><button class="button-link gh-nav-help-icon gh-icon-btn button-link-primary button-link-icon-only" type=button aria-expanded=false aria-label="ScienceDirect Support Center links" data-moz-translations-id=4><svg focusable=false viewBox="0 0 114 128" aria-hidden=true alt="ScienceDirect help page" width=21.375 height=24 class="icon icon-help gh-icon" data-moz-translations-id=5><path d="m57 8c-14.7 0-28.5 5.72-38.9 16.1-10.38 10.4-16.1 24.22-16.1 38.9 0 30.32 24.68 55 55 55 14.68 0 28.5-5.72 38.88-16.1 10.4-10.4 16.12-24.2 16.12-38.9 0-30.32-24.68-55-55-55zm0 1e1c24.82 0 45 20.18 45 45 0 12.02-4.68 23.32-13.18 31.82s-19.8 13.18-31.82 13.18c-24.82 0-45-20.18-45-45 0-12.02 4.68-23.32 13.18-31.82s19.8-13.18 31.82-13.18zm-0.14 14c-11.55 0.26-16.86 8.43-16.86 18v2h1e1v-2c0-4.22 2.22-9.66 8-9.24 5.5 0.4 6.32 5.14 5.78 8.14-1.1 6.16-11.78 9.5-11.78 20.5v6.6h1e1v-5.56c0-8.16 11.22-11.52 12-21.7 0.74-9.86-5.56-16.52-16-16.74-0.39-0.01-0.76-0.01-1.14 0zm-4.86 5e1v1e1h1e1v-1e1h-1e1z" data-moz-translations-id=6></path></svg></button></div></div><li class="gh-search-toggle gh-nav-item search-button-link search-with-button-link" data-moz-translations-id=7><a class="anchor button-link-primary gh-nav-action gh-icon-btn search-input-fallback-link anchor-default anchor-icon-only sf-hidden" href=https://www.sciencedirect.com/search data-aa-button=search-in-header-opened-from-article aria-label="Search ScienceDirect" role=button data-moz-translations-id=8><svg focusable=false viewBox="0 0 100 128" aria-hidden=true alt=Search width=18.75 height=24 class="icon icon-search gh-icon" data-moz-translations-id=9><path d="m19.22 76.91c-5.84-5.84-9.05-13.6-9.05-21.85s3.21-16.01 9.05-21.85c5.84-5.83 13.59-9.05 21.85-9.05 8.25 0 16.01 3.22 21.84 9.05 5.84 5.84 9.05 13.6 9.05 21.85s-3.21 16.01-9.05 21.85c-5.83 5.83-13.59 9.05-21.84 9.05-8.26 0-16.01-3.22-21.85-9.05zm80.33 29.6l-26.32-26.32c5.61-7.15 8.68-15.9 8.68-25.13 0-10.91-4.25-21.17-11.96-28.88-7.72-7.71-17.97-11.96-28.88-11.96s-21.17 4.25-28.88 11.96c-7.72 7.71-11.97 17.97-11.97 28.88s4.25 21.17 11.97 28.88c7.71 7.71 17.97 11.96 28.88 11.96 9.23 0 17.98-3.07 25.13-8.68l26.32 26.32 7.03-7.03" data-moz-translations-id=10></path></svg></a><a class="link-button link-button-small search-button-outline link-button-primary link-button-icon-right" href=https://www.sciencedirect.com/search data-aa-button=search-in-header-opened-from-article aria-label="Search ScienceDirect" role=button data-moz-translations-id=11><span class=link-button-text data-moz-translations-id=12>Búsqueda</span><svg focusable=false viewBox="0 0 100 128" aria-hidden=true alt=Search width=18.75 height=24 class="icon icon-search" data-moz-translations-id=13><path d="m19.22 76.91c-5.84-5.84-9.05-13.6-9.05-21.85s3.21-16.01 9.05-21.85c5.84-5.83 13.59-9.05 21.85-9.05 8.25 0 16.01 3.22 21.84 9.05 5.84 5.84 9.05 13.6 9.05 21.85s-3.21 16.01-9.05 21.85c-5.83 5.83-13.59 9.05-21.84 9.05-8.26 0-16.01-3.22-21.85-9.05zm80.33 29.6l-26.32-26.32c5.61-7.15 8.68-15.9 8.68-25.13 0-10.91-4.25-21.17-11.96-28.88-7.72-7.71-17.97-11.96-28.88-11.96s-21.17 4.25-28.88 11.96c-7.72 7.71-11.97 17.97-11.97 28.88s4.25 21.17 11.97 28.88c7.71 7.71 17.97 11.96 28.88 11.96 9.23 0 17.98-3.07 25.13-8.68l26.32 26.32 7.03-7.03" data-moz-translations-id=14></path></svg></a></ul></nav></div></div><div class="gh-profile-container gh-move-to-spine u-hide-from-print"><a class="link-button link-button-secondary link-button-small u-margin-s-right link-button-text-only" href="https://www.sciencedirect.com/user/register?targetURL=%2Fscience%2Farticle%2Fpii%2FS0022169421005552" id=gh-cta-btn data-aa-region=header data-aa-name=Register data-moz-translations-id=0><span class=link-button-text data-moz-translations-id=1>Registro</span></a><a class="link-button link-button-primary link-button-small link-button-text-only" href="https://www.sciencedirect.com/user/login?targetURL=%2Fscience%2Farticle%2Fpii%2FS0022169421005552&from=globalheader" id=gh-signin-btn data-aa-region=header data-aa-name="Sign in"><span class=link-button-text data-moz-translations-id=0>Inicia sesión</span></a></div><div id=gh-mobile-menu class="mobile-menu u-hide-from-print sf-hidden"></div></div></header><div class=Article id=mathjax-container role=main><div class=accessbar-sticky><div id=screen-reader-main-content></div><div role=region aria-label="Download options and search"><div class=accessbar><div class=accessbar-label></div><ul aria-label="PDF Options"><li class=ViewPDF data-moz-translations-id=0><a class="link-button accessbar-utility-component accessbar-utility-link link-button-primary link-button-icon-left" aria-label="View PDF. Opens in a new window." data-moz-translations-id=1 href="https://www.sciencedirect.com/science/article/pii/S0022169421005552/pdfft?md5=801518b61a07b041b082f0ebe29517bb&pid=1-s2.0-S0022169421005552-main.pdf" rel=nofollow><svg focusable=false viewBox="0 0 32 32" height=24 width=24 class="icon icon-pdf-multicolor pdf-icon" data-moz-translations-id=2><path d="M7 .362h17.875l6.763 6.1V31.64H6.948V16z" stroke=#000 stroke-width=.703 fill=#fff data-moz-translations-id=3></path><path d="M.167 2.592H22.39V9.72H.166z" fill=#da0000 data-moz-translations-id=4></path><path fill=#fff9f9 d="M5.97 3.638h1.62c1.053 0 1.483.677 1.488 1.564.008.96-.6 1.564-1.492 1.564h-.644v1.66h-.977V3.64m.977.897v1.34h.542c.27 0 .596-.068.596-.673-.002-.6-.32-.667-.596-.667h-.542m3.8.036v2.92h.35c.933 0 1.223-.448 1.228-1.462.008-1.06-.316-1.45-1.23-1.45h-.347m-.977-.94h1.03c1.68 0 2.523.586 2.534 2.39.01 1.688-.607 2.4-2.534 2.4h-1.03V3.64m4.305 0h2.63v.934h-1.657v.894H16.6V6.4h-1.56v2.026h-.97V3.638" data-moz-translations-id=5></path><path d="M19.462 13.46c.348 4.274-6.59 16.72-8.508 15.792-1.82-.85 1.53-3.317 2.92-4.366-2.864.894-5.394 3.252-3.837 3.93 2.113.895 7.048-9.25 9.41-15.394zM14.32 24.874c4.767-1.526 14.735-2.974 15.152-1.407.824-3.157-13.72-.37-15.153 1.407zm5.28-5.043c2.31 3.237 9.816 7.498 9.788 3.82-.306 2.046-6.66-1.097-8.925-4.164-4.087-5.534-2.39-8.772-1.682-8.732.917.047 1.074 1.307.67 2.442-.173-1.406-.58-2.44-1.224-2.415-1.835.067-1.905 4.46 1.37 9.065z" fill=#f91d0a data-moz-translations-id=6></path></svg><span class=link-button-text data-moz-translations-id=7>Vista<strong data-moz-translations-id=8>PDF</strong></span></a><li class=DownloadFullIssue data-moz-translations-id=9><button type=button class="button accessbar-utility-component accessbar-utility-button button-anchor" aria-disabled=false aria-label="Download full issue" data-moz-translations-id=10><span class=button-text data-moz-translations-id=11>Descargar edición completa</span></button></ul><form class="QuickSearch sf-hidden" action=/search#submit aria-label=form></form></div></div></div><div class="article-wrapper grid row"><div role=navigation class="u-show-from-lg col-lg-6 u-padding-s-top sticky-table-of-contents" aria-label="Table of contents"><div class="TableOfContents text-s" lang=en><div class=Outline id=toc-outline><h2 class=u-h4>Esquema</h2><ol class=u-padding-xs-bottom><li class=toc-list-entry-outline-padding data-moz-translations-id=0><a class="anchor u-text-truncate anchor-default" href=#ab010 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title=Highlights data-moz-translations-id=1><span class=anchor-text data-moz-translations-id=2>Aspectos destacados</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=3><a class="anchor u-text-truncate anchor-default" href=#ab015 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title=Abstract data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Resumen</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=6><a class="anchor u-text-truncate anchor-default" href=#ab005 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title="Graphical abstract" data-moz-translations-id=7><span class=anchor-text data-moz-translations-id=8>Resumen gráfico</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=9><a class="anchor u-text-truncate anchor-default" href=#kg005 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title=Keywords data-moz-translations-id=10><span class=anchor-text data-moz-translations-id=11>Palabras clave</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=12><a class="anchor u-text-truncate anchor-default" href=#s0005 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title="1. Introduction" data-moz-translations-id=13><span class=anchor-text data-moz-translations-id=14>1. Introducción</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=15><a class="anchor u-text-truncate anchor-default" href=#s0055 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title="2. Modelling approaches compared in KMC" data-moz-translations-id=16><span class=anchor-text data-moz-translations-id=17>2. Enfoques de modelado en comparación en KMC</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=18><a class="anchor u-text-truncate anchor-default" href=#s0125 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title="3. Study site and data" data-moz-translations-id=19><span class=anchor-text data-moz-translations-id=20>3. Sitio de estudio y datos</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=21><a class="anchor u-text-truncate anchor-default" href=#s0130 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title="4. Methods" data-moz-translations-id=22><span class=anchor-text data-moz-translations-id=23>4. Métodos</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=24><a class="anchor u-text-truncate anchor-default" href=#s0180 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title="5. Results" data-moz-translations-id=25><span class=anchor-text data-moz-translations-id=26>5. Resultados</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=27><a class="anchor u-text-truncate anchor-default" href=#s0215 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title="6. Discussion and model comparison" data-moz-translations-id=28><span class=anchor-text data-moz-translations-id=29>6. Discusión y comparación de modelos</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=30><a class="anchor u-text-truncate anchor-default" href=#s0220 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title="7. Conclusion and outlook" data-moz-translations-id=31><span class=anchor-text data-moz-translations-id=32>7. Conclusión y perspectivas</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=33><a class="anchor u-text-truncate anchor-default" href=#s0240 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title="CRediT authorship contribution statement" data-moz-translations-id=34><span class=anchor-text data-moz-translations-id=35>Declaración de contribución de autoría de CRediT</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=36><a class="anchor u-text-truncate anchor-default" href=#coi005 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title="Declaration of Competing Interest" data-moz-translations-id=37><span class=anchor-text data-moz-translations-id=38>Declaración de Intereses Competisionantes</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=39><a class="anchor u-text-truncate anchor-default" href=#s0245 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title=Acknowledgements data-moz-translations-id=40><span class=anchor-text data-moz-translations-id=41>Agradecimientos</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=42><a class="anchor u-text-truncate anchor-default" href=#s0255 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title="Appendix. Supplementary data" data-moz-translations-id=43><span class=anchor-text data-moz-translations-id=44>Apéndice. Datos complementarios</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=45><a class="anchor u-text-truncate anchor-default" href=#bi005 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title=References data-moz-translations-id=46><span class=anchor-text data-moz-translations-id=47>Referencias</span></a></ol><button class="button-link button-link-secondary u-margin-xs-top u-margin-s-bottom button-link-icon-right" type=button aria-expanded=false data-aa-button="sd:product:journal:article:type=menu:name=show-full-outline"><span class=button-link-text data-moz-translations-id=0>Mostrar todo el esquema</span><svg focusable=false viewBox="0 0 92 128" height=20 width=17.25 class="icon icon-navigate-down" data-moz-translations-id=1><path d="m1 51l7-7 38 38 38-38 7 7-45 45z" data-moz-translations-id=2></path></svg></button><div class=PageDivider></div></div><div class=CitedBy id=toc-cited-by><h2 class=u-h4><a class="anchor anchor-default" href=#section-cited-by data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Citado por (30)</span></a></h2><div class=PageDivider></div></div><div class=Figures id=toc-figures><h2 class=u-h4>Figuras (12)</h2><ol class=u-margin-s-bottom><li><a class="anchor u-display-block anchor-default" href=#f0060 data-aa-button="sd:product:journal:article:type=anchor:name=figure"><span class=anchor-text><div><img alt="Unlabelled figure" class=u-display-block height=114px src="data:image/gif;base64,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" width=219px></div></span></a><li><a class="anchor u-display-block anchor-default" href=#f0005 data-aa-button="sd:product:journal:article:type=anchor:name=figure"><span class=anchor-text><div><img alt="Fig. 1. Conceptual model of a karst hydrological system (left)" class=u-display-block height=94px src=data:image/gif;base64,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 width=219px></div></span></a><li><a class="anchor u-display-block anchor-default" href=#f0010 data-aa-button="sd:product:journal:article:type=anchor:name=figure"><span class=anchor-text><div><img alt="Fig. 2. Parameterization used in KARSTFLOW methodology" class=u-display-block height=128px src=data:image/gif;base64,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 width=219px></div></span></a><li><a class="anchor u-display-block anchor-default" href=#f0015 data-aa-button="sd:product:journal:article:type=anchor:name=figure"><span class=anchor-text><div><img alt="Fig. 3. Conceptualization on karst aquifers by MODFLOW CFPv2 (See Reimann et al" class=u-display-block height=164px src="data:image/gif;base64,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" width=136px></div></span></a><li><a class="anchor u-display-block anchor-default" href=#f0020 data-aa-button="sd:product:journal:article:type=anchor:name=figure"><span class=anchor-text><div><img alt="Fig. 4. Map of the Milandre catchment with its main characteristics" class=u-display-block height=164px src=data:image/gif;base64,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 width=187px></div></span></a><li><a class="anchor u-display-block anchor-default" href=#f0025 data-aa-button="sd:product:journal:article:type=anchor:name=figure"><span class=anchor-text><div><img alt="Fig. 5. Workflow applied for testing the effect of resampling and of using daily…" class=u-display-block height=43px src="data:image/gif;base64,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" width=219px></div></span></a></ol><button class="button-link button-link-secondary u-margin-xs-top u-margin-s-bottom button-link-icon-right" type=button data-aa-button="sd:product:journal:article:type=menu:name=show-figures"><span class=button-link-text data-moz-translations-id=0>Mostrar 6 cifras más</span><svg focusable=false viewBox="0 0 92 128" height=20 width=17.25 class="icon icon-navigate-down" data-moz-translations-id=1><path d="m1 51l7-7 38 38 38-38 7 7-45 45z" data-moz-translations-id=2></path></svg></button><div class=PageDivider></div></div><div class=Tables id=toc-tables><h2 class=u-h4>Cuadros (6)</h2><ol class=u-padding-s-bottom><li class=toc-list-entry-outline-padding data-moz-translations-id=0><a class="anchor u-text-truncate anchor-default anchor-has-colored-icon anchor-icon-left" href=#t0005 data-aa-button="sd:product:journal:article:type=anchor:name=table" title="Overview of the main characteristics of the 13 models applied for the Karst Modelling Challenge step 1." data-moz-translations-id=1><svg focusable=false viewBox="0 0 98 128" width=20 height=20 class="icon icon-table" data-moz-translations-id=2><path d="m54 68h32v32h-32v-32zm-42 0h32v32h-32v-32zm0-42h32v32h-32v-32zm42 0h32v32h-32v-32zm-52 84h94v-94h-94v94z" data-moz-translations-id=3></path></svg><span class=anchor-text data-moz-translations-id=4>Cuadro 1</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=5><a class="anchor u-text-truncate anchor-default anchor-has-colored-icon anchor-icon-left" href=#t0010 data-aa-button="sd:product:journal:article:type=anchor:name=table" title="Quality classes for VCC, NASH and KGE criteria, and for the necessary effort." data-moz-translations-id=6><svg focusable=false viewBox="0 0 98 128" width=20 height=20 class="icon icon-table" data-moz-translations-id=7><path d="m54 68h32v32h-32v-32zm-42 0h32v32h-32v-32zm0-42h32v32h-32v-32zm42 0h32v32h-32v-32zm-52 84h94v-94h-94v94z" data-moz-translations-id=8></path></svg><span class=anchor-text data-moz-translations-id=9>Cuadro 2</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=10><a class="anchor u-text-truncate anchor-default anchor-has-colored-icon anchor-icon-left" href=#t0015 data-aa-button="sd:product:journal:article:type=anchor:name=table" title="Comparison of the efficiency criteria for HDIVs and HDAVs." data-moz-translations-id=11><svg focusable=false viewBox="0 0 98 128" width=20 height=20 class="icon icon-table" data-moz-translations-id=12><path d="m54 68h32v32h-32v-32zm-42 0h32v32h-32v-32zm0-42h32v32h-32v-32zm42 0h32v32h-32v-32zm-52 84h94v-94h-94v94z" data-moz-translations-id=13></path></svg><span class=anchor-text data-moz-translations-id=14>Cuadro 3</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=15><a class="anchor u-text-truncate anchor-default anchor-has-colored-icon anchor-icon-left" href=#t0020 data-aa-button="sd:product:journal:article:type=anchor:name=table" title="Comparison of the model performances. All models produced acceptable results. Color ratings are given in Table 2." data-moz-translations-id=16><svg focusable=false viewBox="0 0 98 128" width=20 height=20 class="icon icon-table" data-moz-translations-id=17><path d="m54 68h32v32h-32v-32zm-42 0h32v32h-32v-32zm0-42h32v32h-32v-32zm42 0h32v32h-32v-32zm-52 84h94v-94h-94v94z" data-moz-translations-id=18></path></svg><span class=anchor-text data-moz-translations-id=19>Cuadro 4</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=20><a class="anchor u-text-truncate anchor-default anchor-has-colored-icon anchor-icon-left" href=#t0025 data-aa-button="sd:product:journal:article:type=anchor:name=table" title="Part of the total hydrograph in the respective components." data-moz-translations-id=21><svg focusable=false viewBox="0 0 98 128" width=20 height=20 class="icon icon-table" data-moz-translations-id=22><path d="m54 68h32v32h-32v-32zm-42 0h32v32h-32v-32zm0-42h32v32h-32v-32zm42 0h32v32h-32v-32zm-52 84h94v-94h-94v94z" data-moz-translations-id=23></path></svg><span class=anchor-text data-moz-translations-id=24>Cuadro 5</span></a><li class=toc-list-entry-outline-padding data-moz-translations-id=25><a class="anchor u-text-truncate anchor-default anchor-has-colored-icon anchor-icon-left" href=#t0030 data-aa-button="sd:product:journal:article:type=anchor:name=table" title="Table showing the three criteria applied to the four components of the 13 models compared for the challenge. Models are sorted according to their global performance (see Table 4)." data-moz-translations-id=26><svg focusable=false viewBox="0 0 98 128" width=20 height=20 class="icon icon-table" data-moz-translations-id=27><path d="m54 68h32v32h-32v-32zm-42 0h32v32h-32v-32zm0-42h32v32h-32v-32zm42 0h32v32h-32v-32zm-52 84h94v-94h-94v94z" data-moz-translations-id=28></path></svg><span class=anchor-text data-moz-translations-id=29>Cuadro 6</span></a></ol><div class=PageDivider></div></div><div class=Extras id=toc-extras><h2 class=u-h4>Extras (2)</h2><ol class=u-padding-s-bottom><li><span class=download-all-supplemental-data><span class=in-toc><form method=post action=/sdfe/pdf/download/S0022169421005552/attachments target=download_all><button class="button-link download-all-button button-link-secondary u-font-sans u-padding-xs-bottom button-link-icon-left" type=submit><svg focusable=false viewBox="0 0 98 128" width=18.375 height=24 class="icon icon-download" data-moz-translations-id=0><path d="m77.38 56.18l-6.6-7.06-16.78 17.24v-40.36h-1e1v40.34l-17.72-17.24-7.3 7.08 29.2 29.32 29.2-29.32m10.62 17.82v2e1h-78v-2e1h-1e1v3e1h98v-3e1h-1e1" data-moz-translations-id=1></path></svg><span class=button-link-text data-moz-translations-id=2><span class=download-all-title data-moz-translations-id=3>Descargar todos</span></span> </button></form></span></span><li class=toc-list-entry-outline-padding><a class="anchor u-text-truncate anchor-default anchor-has-colored-icon anchor-icon-left" href=#m0005 data-aa-button="sd:product:journal:article:type=anchor:region=outline:name=sdf" title="Supplementary Data 1" data-moz-translations-id=0><svg focusable=false viewBox="0 0 95 128" width=20 height=20 class="icon icon-pdf-download" data-moz-translations-id=1><path d="m82 108h-7e1v-49c0-6.08 4.92-11 11-11h17v-2e1h-6c-2.2 0-4 1.8-4 4v6h-7c-3.32 0-6.44 0.78-9.22 2.16 2.46-5.62 7.28-11.86 13.5-17.1 2.34-1.98 5.3-3.06 8.32-3.06h46.4v4e1h1e1v-5e1h-56.4c-5.38 0-10.62 1.92-14.76 5.4-9.1 7.68-18.84 20.14-18.84 32.1v70.5h9e1v-18h-1e1v8zm-25.94-39.4c-0.84-0.38-1.84-0.56-2.98-0.56h-9.04v23.6h5.94v-9h2.18c2.48 0 4.36-0.62 5.66-1.84s1.92-3.06 1.92-5.5c0-1.04-0.12-2-0.4-2.88-0.26-0.88-0.66-1.64-1.22-2.3s-1.22-1.14-2.06-1.52zm-3.12 8.94c-0.4 0.46-0.98 0.7-1.74 0.7h-1.22v-5.76h1.22c1.56 0 2.34 0.96 2.34 2.88 0 0.98-0.2 1.72-0.6 2.18zm21.84-8.52c-0.96-0.66-2.32-0.98-4.06-0.98h-8.72v23.6h8.72c1.74 0 3.1-0.32 4.06-0.98s1.7-1.52 2.18-2.62 0.78-2.34 0.88-3.76 0.16-2.9 0.16-4.44-0.06-3.02-0.16-4.44-0.4-2.68-0.88-3.76c-0.48-1.1-1.2-1.96-2.18-2.62zm-2.78 14.66c-0.06 0.96-0.18 1.72-0.38 2.24s-0.48 0.88-0.84 1.04-0.86 0.24-1.48 0.24h-1.32v-14.74h1.32c0.62 0 1.12 0.08 1.48 0.24s0.64 0.52 0.84 1.04 0.32 1.28 0.38 2.24c0.06 0.98 0.08 2.24 0.08 3.84s-0.02 2.9-0.08 3.86zm13.98-6.58v-4.02h7.74v-5.04h-13.7v23.6h5.96v-9.72h7.26v-4.82z" data-moz-translations-id=2></path></svg><span class=anchor-text data-moz-translations-id=3>Datos complementarios 1</span></a><li class=toc-list-entry-outline-padding><a class="anchor u-text-truncate anchor-default anchor-has-colored-icon anchor-icon-left" href=#m0006 data-aa-button="sd:product:journal:article:type=anchor:region=outline:name=sdf" title="Supplementary Data 2" data-moz-translations-id=0><svg focusable=false viewBox="0 0 95 128" width=20 height=20 class="icon icon-document" data-moz-translations-id=1><path d="m35.6 1e1c-5.38 0-10.62 1.92-14.76 5.4-9.1 7.68-18.84 20.14-18.84 32.1v70.5h9e1v-108zm0 1e1h46.4v88h-7e1v-49c0-6.08 4.92-11 11-11h17v-2e1h-6c-2.2 0-4 1.8-4 4v6h-7c-3.32 0-6.44 0.78-9.22 2.16 2.46-5.62 7.28-11.86 13.5-17.1 2.34-1.98 5.3-3.06 8.32-3.06z" data-moz-translations-id=2></path></svg><span class=anchor-text data-moz-translations-id=3>Datos complementarios 2</span></a></ol><div class=PageDivider></div></div></div></div><article class="col-lg-12 col-md-16 pad-left pad-right u-padding-s-top" lang=en><div class=Publication id=publication><div class="publication-brand u-show-from-sm"><a class="anchor anchor-default" href=https://www.sciencedirect.com/journal/journal-of-hydrology title="Go to Journal of Hydrology on ScienceDirect"><span class=anchor-text><img class=publication-brand-image src="data:image/png;base64,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" alt=Elsevier></span></a></div><div class="publication-volume u-text-center"><h2 class="publication-title u-h3" id=publication-title><a class="anchor publication-title-link anchor-navigation" href=https://www.sciencedirect.com/journal/journal-of-hydrology title="Go to Journal of Hydrology on ScienceDirect" data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Diario de Hidrología</span></a></h2><div class=text-xs><a class="anchor anchor-default" href=https://www.sciencedirect.com/journal/journal-of-hydrology/vol/600/suppl/C title="Go to table of contents for this volume/issue" data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Volumen 600</span></a>, Septiembre de 2021, 126508</div></div><div class="publication-cover u-show-from-sm"><a class="anchor anchor-default" href=https://www.sciencedirect.com/journal/journal-of-hydrology/vol/600/suppl/C><span class=anchor-text><img class=publication-cover-image src="data:image/gif;base64,R0lGODlhcQCWAPcAACIeHyMfICYiIyglJi0pKiknKDEtLjQzMzw6Ozg3OEE+Pj0/QEM/QD1BQT1aVj1kXz1rZTxqZD5yaz55cUFFRUVLS0xLS0lGR0VQT0xUVEdXVlNcW1hXV1FOT0ZgXVZgX0NrZkRuaEppZVRjYlpkY1xsalhraURybElybVlxbkt2cEp8dV51c1V8dmRiY2JtbGpra2Vwb2R0cmp1dGV4dmx4d2R8em18enR0dHF9e359fXBub4B/fxyLfhSKfAiHeT6HfU+EfUGHflWDfWuBfmSBfnSAfySOgSuPgy6QhDSRhjuShzyTiDaUiR6Ogk2LgkWJgFWNhVuNhkOUikuWjE2Yj0eYjlSVjFuTi1OYj1qYj02akEWakFSbkVyck2yEgW2JhWWKhXSDgnmEg3WJh3yIh3WMinuMimKUjX2RjnWRjmOelmyZlGefmGyWkXSWknyVknSalnuZlXqemXadmFmhmGagmWuimmSgl3KknnyinXamoHmnoXaoonyppH6uqHOqpISDg4KNjIyMjIaHh4KQj4WUkomVlISYlomYl4SdmoycmpKSkpGdnJucnJGPkISino2hnpGgn4SloYylooKspouppoWuqYmvqpSlo5Wpppmop5Wtqputq52iooawq4yxrZGzr5yxr461sJW2spq2s5a4tJq5tp68uKSlpaqrq6OuraKxr6K1s62zs6S5tqK+u6m9u7S3t57AvKbBvqjCv7fAv6bEwKzEwq7IxbDHxLzBwbPJxrbLyLrNy77Qzr/S0MTIyMDSz83Qz8LT0cvX1cnY1s3b2cfY1tHd3NDT09Xg39bi4Nvl5N7o5+Pq6eDp5/P29gAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACH5BAAAAAAALAAAAABxAJYAAAj+AJcIXKKEoJKDTZZMUThlChUqV7JkuXLFi0Uva9CsWdOmzZ2PeULm2cOHjx8/kyqpvHQJEyZQoELJDEWKFM2aNnHi5FTzZk6fP3XeLEUzFNGeOkmVUjhwIBCBTBo2fAixy5UuXipa3Lix40c6IvOUNHlyZcuXMWUmXYsTKNu3QnO+LWpT6sKmA6VShXg1i5aLGLt6/HhH5B6SZc2ehZlWLk5RcCPHTVoK59HIdqh4ySI1r12qEq16+btV8OCwh/f4Iauy0mKYM91KZouKstLZpSqvLYXsVC5aVxoKlGqFSnEqWboo7/IXj3M7HL2CDHlYz8nrihk3xmlqtk7dSWv+W65ZuTx427qjQWvmaw0QIUDivxdCP4h9+1gtBhniZUjGrmEE6EYeYbCRBx1x7KGHHnP4UckkcrwhhyKTzETebbbdllt5pIi3myVwIKLIHJRQAokiE3KiiCWKKAJJKd11V0o0zyTDSx5C2DXVQ1VsMZFWfw0RBAoeDOEBkWig0IIJQ+xhgwkssJDCkzbQUAINephRBBGKqKFWTd2RYkplp5xSU5mmxCjjed+VMgcZN5hBwxlwxGAGGWQoAocZRBDBgpiAkvJMM8K80kYUVEwwQQQRSHACBCeAcAJFUZB2ERZrFLEGG27cwcanbuzBBh1svEEHHSYtmNIkrL7k05j+aZpiJnmzigkrjDiJ52FNlIRiiU2hgGIJJZbMxMmvYlbWXTPH/ALLJ3FccUKjjUIKAQQhBKHcaFpw1dFg0410GHaKXaKdWrKlqeyYtsqaVJq21oRKmPSGqVOtYMZaijPLIJMLKnqsMS2jETwgAQgg2NdfadGBC5ZhJJlUiYMrveQSYzmVZya88cbbnZkg62bvbRvnJuOYtYp3ipqkHFOMML2gEocWKzwAgc0PhBBpwgtz1fBpEJNFsUqYtHQusPnqBKu6HnusL3exJvv0KbV5aKZulfmiCy20gOKGFyo8KsEKkqqwwhD88eetdHkUFlJJ1pFb8cWMoQuju2VuGOv+ysmuHKu6MFa9Moy4JssmyIOrW8srwfrRqQpChB3C5DrXYZF/poH0sLh6CJ1SJUW7hFZaahGOt1J3KxXy33ePabKyuG6MCuFJzcp6KbScAgofeWikwhVPPBFEwh54UMdGgQlWmNvUIVbWH62dNbqF+ZJ59d5irm6y362XuXKZpPg95rwdqrzxKaWgP28ol1QSRx6bBUHRCUGoAAJG/XXFBmHhisVaa6B7Td2Q8jFZpc9vs5PV97y3wFLMyxTzultuECc+CMoLfGjy2ylGAZNLxMEPecCCA+xzgiONEHMb4V/bwlISlMQEFK4KhcVgUxmZiEyBDJzdvNDHw7x5L3D+DpwgA3FItSCWL3wMzFtlRvGJJvZhEkUYAhCIFwQPOEADQyiCF6QDErfpIWKVsBvVYGEmmZDOMURU4t4OiMQk4jB9sqpah8J3wCT28I6oAAUTVWKEPqBNSPb5436UR5iwiKVzfjCX6lABC66REVigQCOM0PdGVMzOjj6sox3naMn0sdGOBvyhKYpWiU9UggiACIIDhOSB/dgHBf4ZggqZ5z/sYKJDsEAFLXDhSNvByChpWuAQIYgK7+GQiAckYgSL6cMh3jGDVMOESpr4CVPgwQ5EuCba6oAHTXGRlmMRGuhw2Uhd4KIWtLAkLNaZuGDyEGWzEyIom4kmBQaOgUj+1CQDNVmK9rlmFKwyAhGKgAch9SeWBO3f28ZCsVvqsha10IU5uca1W9yiFmQcHD0xecB56bBDnrxjKIvoQAw+046oYIm5RjEKTPjhD4CoAyDsUISZ2gEQhWReOPkwMQeFYoy1wIVEg0rRWNBinZbE5A83ykmqdahMzMRkmqKqVH2WwjVFayIgAOGcO0AnOnYoJAvF0ofOteaWp2ikUM15Tl4e9ahITapSoZrDIDqwNknUZxHnmldjVsIkfPgIHgAxBDx85FttCOvyWNiHkvx1YmGk2imCKtHKopMWt4gFRteZS1S8gq5Ui2pUn1q1u3qvmKLdJzNXK9nTQpV3XpX+qR3sgNiO7E+sIhnLX3m6EpqUaa28kCguzlmLW5zTqOt8BWtzGNrQ6rCTO4Tqczv6XMmitoiWFC0q/kCEOtihoGGtLbjCxbux+BOAaI2oLnjB3uG6FxcW3Wx2ratc66qvutnNb2hLCl3+rva6820uKrjazSKElbZeGa8hx6KHSsAQvZgIhSmAW1n3BtWit+AsagGc3+dakph3TWqHRTzi/5bYuUz0wx1siuBvqZCFjr1Ed0xJtAhPthe+CC5b4VuLWGQ2riX+r3Odm98QZ9fIHiYyapGMWlPCdLbhHS8tF+rYW5ICE5+YoVJ04YtesNecF4XoLeDa2RErWcnQDbL+motM4jTPtw99wOlsD/tNcI4lJbc0RbBcIuFTxAIXvfBycIcL0eLKt8Nonu+aF81mRnc4D0b4CCAMTNvbto2WnWshxUIxi1bA0MawGC57dVxorkG0zPp1NJODHOJVrzmIA57pHbw729sqNJyraU1MZgFDPZZpl2tl62W5FotcKlm5J3a0m5XNaBZDuaY4JUwfxKVbAIJuz66SVStmYepCD5sWr5jFmlEdZHIzu8PmHvFsKa1Yr/aBlodxrGMhPL1TzOLepqYoLbi9bzUjW53oPjfABz5udd802vyT9B/gHE5rg050LxkFTWZBinvz++LbTue5zd3ZXKYbFbFQtsf+GcnIjnsVyoTBKZz70MfGzjt6oqubxEVhik6TohXbzninwZ1djVtyFsj2eJlhodyh5zeXPrekxjuO7o+TXNKK3SpO/9Byhk+CYpf4BAd1dy4J16QV3ZlFJyy+7U5nl9vcJjnB1a52pgdc6ZYMOds7G/Tsuh0VKwaEEbZqhDwA4g9/4EMfKjF4AH5iFrXwRS1sQYRIkKJutgv7vcfeaYuLO9zKvfe/iW50gCN15HcfMZBLvna7w2LaeeiDg/zwiT58on0pyfLDMYF4WxADogVwRC1A0ac+GSENYiKCKLgdiTTc2wjIR36HDBEJjSMiEuykhBjiKgZRWLLoHleEGIz+IAYxkMGBkeh+9wtB8pGTvuRuJzxkmfh6re8xWKEII+ILze3c08IIBuCBEXTgAgPYYhYCsAuLZwQWMAytAAA6QAiE0ACD8AobQABGsE4b4AgetwALMAgkJwC2AHoltwAuoAMgmACDQAsygACBwAM84Am8wFlCh34lJ3fr1ER7ZAqjUAmmYIO30AuVwDeYYG+00AqGwAkrMwCeQAtEcAHIAAqzgH/EAIDBYAu1YAQdcAyzAACqEAmQUAGMQAsbwAEGQAKnkAEUCAs54AKuYAA8F4As+HkHoApKRwEUGAMWsAmLwIIu2IJCx4Kj8Ao4FgqjMAuV8Ap7eAvEUAl7CAr+pqCEs0AJAOAKryAKRLiEF9CEs1AIBpAMtiAAwQBRUlgMsxAALsAABYADw0ALHDAInYAADVABFFgKBLADM2AAMEALpRCAd7hOBuAKuoQKcEgLM3AAMIADyHCLn7eGGkZ0weULsFALv9ALsJAL1XQLnwAKt9CDoFAmogAAG5ABGRAAqcAJRpAARhADMrABl0gLAxAIyPcBHdCEATAMpUABHFAKs7ABI+iAAUCBI+ACjMAIg1AAnYAKAsADN3ADObCGC7ADNVADN3AAjkCCHIAMjmSMFJmH61QLuQB0upB4yZALvRArKnEKweJguWMEg3CSjlAMaUIIJ3mSrtCEhzD+CI3ACI+wC//HA8oAbkbgCbVQCALYSGcQDK/AA8nAWWngCrQwBi35CMWAfnDQkoPACDmJCKnQC+lUkVjJgr8wCr+wMrBwC7mwCx+AAEYgiHlQCbUwBAZgBH94b5E0C5xQC4gHQ1zDbRCFb/tGC+jEba9AUX1JZmS2hm/VSLoEV7rEgoBZmOk0kbt4leu0mOjnC8SwkUr4Cq9gDEMQAJ7AQalnCiMAAIMwCrXQJ5Nwb6ZQC2lwBqcwCb9HBMinCGJncYdgBIbwe2RgBpqQdolwBndCBmdQBojQSLRACWQgCmtoSYZwBsq5nM3HSKVQCGXgm7yZBg6UCWdwCIs5nGf+QAmLyVm+YCO8MAufoAt8aAQA4AhEUIN9NwIB8Ah/iH8J0Ar2tgECoAq9AAcAUAAHkAAJIAADMAP3xgkNIAAMwAAKgAADUAC8QAuFAAAEYAAQagAEEACEsE4xAAC2MJiN9AIAEKEQOqGicFQUEAAeagABAAO1EI8BYATphAgDwAHM4EiO9AvgyQt+0Auv0AlEAAAdEAQYwAFA4AAJ0J415wtGMAAI4Ar0qQrEYAqIAJq3h04dAACNcG8NsADM8AtaOgMAUAy0kAONKAxiWigBEAjodKEbyFlHRZ/JoKVa2qCq0EgHYAFt6qY74Aq+QIsjWgaLUAAc0AwTyVm3wB7+xGALojkLtuABADAABPCgEjoA7fkL8xcJA2AA9YkMv1ALb1Cm+3YJofCkOlBxCHAAqfB8iJABALAMs1ADjVhsjaQJABAIvHShu9BIjwkL9JmTtioGAaAKXDOnwEALZzADM1ADZ1CYKuqngKqmRxULs+AMzlALVJh4tFACAOAJxIAMtrAMjNeetYeR5jkIy9ALcgkHZXpvoCMHAIADFKcAAjChACAABeACqrqjrgALyKUJFDqrGPqYzUqfxSCcqDAGArAK6QSstPACHdABAuACznJUrWAACLCsFHWrsxAN0VAL0FALwCCXJaCZcnmXn/kI89dlRpCPzSia+EkIbgn+ChpghTVxoMtACwnQhhJJCzjQiPi6TvpKCLgAC7Q6mLQQCxswAF4qnGJQn7/aAcCAConnCwTgsG9FCwdwAcVgVHDlqs/IDM/wDLWwDBhpC+ypCohncYpKsmVrC42Qj7eHlmoAAAhAAQ3QABMKA8lAEw2AABJJChQgADOgl+ZJAYIruAsQAIPArxdQARWQuDVACyQgAB3AAAsguP0pC1yDAARwAYJ7AQwwADDwC/pWtRLJrG8ll87wDLPQDLWgra5JDMCgeMgADMgHhZ/Asb/ACkawC8AADLMwCpCwAzqAfDzACLtADDdBBIMgDPcmCjigAwa7hCgIgij4CMnANXD+AII6EL2qwAudMAbRywPZm5Jcowjgi4IoSAg5CW5cMwOMgAy2Wmz6Bg2pawzQELuKNwu3xx6pewzG0AzJYAx/wJG1UAynAAy2oLyg4KmkQ3EyMQsbgjt4SVHx1WOndpV6+W1wpW8avMEcTFGXJZxcc5jQ4AuzsAzQAAzRapfL4Ay/0C9d6wzNgAyzQK5NmAyIp621wGkBlBZdYxTlsXNAp77NmrXLOMTIVbEyKqMgrMG3iq/dqaFJTAvHoKrHgAzGMAsyfG+5wAzNAA2/8AzQAA3McAxyCQxNSsZySQrCABOYYAkDRBQUhzrDF255qZdLvMQdvMF4TFGKucFYm8f+tADGEPULzjB/FgcMyOAMvsAMtdC1qou/cql4Fbcys2AubvxCDVxxRlFx/AbInvzJoBzK+8YMrzsLx9CEvDsLfIAMViytEJUMp6yl3sZBtBAKtOCpEWZGwTILm0yP9EjHgIzBGvzHwZxvbrXBwybMppwLwPALxNAMWGwLMlELznAMHduEyEDKxsC7kiqelYB2V4YWMgR/nFYTlefJ6IROx/zJvyHKHvzOF0xRqVvCtcAMJ9wsZesLZmzKs+DMxtCEcol4D6KIgDg6MPF4orAhOQfPGizM68zQHJwLwtzB6ayX76yxs8AMvtDFyFC//3fDEAUMaGzAh5rDs5ASwSL+CpdwyTFxS2rhwLNgb51s0RcMURq8zulcasKczHlc0fnmbXqZDB0rxtHKDMzQhPtme4jcsX9YC3wwChVHe3zgKr0GG+Q8FJqnb9621bu0wedU01wN1jatzrw0XF2d0xZdanqJDDY8xf3SyGhctrv7CyT8zao8C2ihEjpcNDAULDEhFw5smR681WG9S0BN2IhN2Fzz0Dk91oTNzBybDMxgw5ONDHc5C4icqbPgILXgEjf4za5iNHXzlkpxzp2c2IVm1mKN2qzd1Ytt2IaN2vf2tSK9DMnAytZcC1Fqyhm62ZgACUVAB3NQBEWACXNQCXMwCTEXSZEknxW3bZz1Z8b+FWaKXdYQRVxEVdarfd2ITVzYjd3cfZchXcXfiQzHkAy+AIVUOAvJIJfWUQlu0CdFYAIm0HtFYARNRDdi8odl0mlf+ZXw9WcQtZEYed1d7d3ZbdhtheDcveCETWhtldpBBQhyidsGPMbcKpdNCAp6GQp6kGV6IAhvsCVFIAdGoAcn7mDnYiatMHzc9pUYNlxg6W0STVTAFlRB9dUR3lY3DuES7uMOvuMQdQerm81mnKnm3bEQRYly2USVzGlZx3oRV4PaoROzICvhhq8+hgux8GcSXeOLR9Z6GeQQXtYWtuBnDuQNjuMO7gvPwMXHsN61kAyAWmihYNOlkBaWwMv+KtEHL9FEoKN1j/dT9uaDr5BhYmZhA07jY57ja57jhHbd3o3mwIbgZW7duJCpRo2pAY0My4DGcrlvzBhhLfEGpYkJdOAqWbbqFAfVNxfT+KYJrQCWEs1junALr4BOmqDOle7oZs7m7xXsOB7bab7j77V4mNosiYd4p1wLIUpxlwBRSug+GDAHs2AJFaAGKaAlRlACRPACRnDfRLAByEcIadCXRRAJRiAJmrAIkSAJkqALu9kKyKcJYpAIh3DskA7px47mwV7saC7pw6V4EGUMWmrZ/azblnd4m901c4ABRhAJcPABH1AEH5ABGFACGVAEGVACJFABL/ACxE0Cwyf+CMgnCGlQCIUgCUZQC4nAp4gwm0aQA4Zw4P+e5jm+kf5e6TfP74oe5hzLjIinparcB4AYRpUw6G8ACaAwdpagB6wiBxsgB24MCZxACZyQ9VmfCbSgCa/QCpZZXEZVC6zg7LXQCnoZC6xw82x/7DvW9nDvXrPNsfMnzSXMbWf5Enmwy5xWyQR9CRUHCp8wE0pICuEW0C8eUYkNhakd92xvC2z/9o7vXqbUz9Cs2aHwCdw6C3eA16HgB+V87S5h1zARoFG950IYxHVc4ItuC4wP9Lkw+bgA+W1P+7I/XLaPC5UAPYbMu5yml7NgB59ACi2BqEqICURQLKQAA+tOCnv+bggUZ3HFdG9pkAYhujixMFS5UOM4Bwe7nvv/Lvnh717if/u6zwdaV7v4Owt/wMuzIBKbPc1dMwlYJAqdQPEvYAIbUAL7GAMdUAQAwaAIEVGvjBQioigRkYNGIhkxQuRVoUKvJEEUJMaIoTGFOi7ahEvkyFojcZUsOZKXSZG6Rupy6fIlriCVZgEaBWrWJ1t/ZlUCZSfPHUyYgM6aRatIihKJSn0gQuKFkRdEYBgxUWSDkQ+IZqWBCOdgIUFGBAlalKhWokxpDhlR1ClRokZiDGmKFIOlyJW8SvbdO9JWYMJ1fs6qlWdUpVqf+vTxk2fNmj6V7uhMOgsTUqS0ZnH+6oy0E1JKlFrRqpU6V61cuVpRWq3LL2vVuQh34gv4b63ZJlcGlokr+HBAoD4hzSN0Fqg+d5y3sePFThtQoSr1+WNpVqvqmTlzZkXr1ffUqW3RtqUrdS/VtmzlgqmSb+CVvHn//j1fePyY+1sKx6WX4wD5JI8g6jiuEjvw8CIIQNa4I4/jIqsklJ8snEWUDGtBrBbPOEMtKVpQq+W8Ek0scTUTAcQFsP5cwi8/v/ZaqT+TXnwJseX6mOUPQBSbJQ887LgDEDvWyCOPWSBzY4MP3JiFKkQWmYQSRChZBKlCaOlElFZm2WQWTbZrpbxFEBFFlESSyqS1GuWbKT84AQP+7D+VbGzJJR+F6LGxOu7445Po6sADEC+QvEOUytygAANEaiEihq0yiIiED4yIkhaGIDKiDIgSKcQQXljJxAhCijjojERay7PFVofjRU5XWaoxpjdlgom/WezgYpZRiPHlJjwq8UKILvC4o4/J1gAlj0omIYKIREKBQwZIXojLAoiImCUStEotRJNNCsnklEZIoaSWTDpZ5BBJWklDlPdcDa7W3HyLdb46eYFxvlxzzZOLP4E5hphlcvnDiz68uMMOIf7www47+gDlkks208nCUL7MzJbOaEGxvNRiXc0vX1JkzT344nPRP4DpbNEWmPmFkb9+/9VFiDwAmQUYYJj+QcoxOwDBAw9Ck/zkEuNCAaUSo3QE2eNfkOJQ5PLOa03mWnxR2a9ceGH15RZxznXme12Ubb9/x/6XC7cN68WYXnyt5Y/HhvaiDT/8OC6US+Kg46dOIqHaPKQ20VCU1MK8euTyckHsvRI9pBmmysl2Nd8W96WXbMvJ7kIIgQE5hsNZ+BhlyT+GxKONNdhQ8pM3TCDCjz4uVaMIOYh4y4gSZojWCA8hoqQTRFphpRFJNDmkFU4MKSMTRBDppJNDPMe+L+35Spvmy7H/twchehCYQ58wgfyPP4ZYvQ8h3a9kDg+KKMKPD2R44YMXiuhkgwxiyB8RXpAaQUBKEJIoRBn+AnEQIjCiEYc4AyJeYAhDIMIQn1tbrTDHr8uxTYMYxBm/mkC+HgTBDn8YhceMUYvH1IFolKGMHy5RCTpgog+YmEQNWWELUKSGFqwQjWfMQyLEsKVqHkoNYjpRi1eUzYOx+lzlYqXBKcqmiv+q1eWa4LYeAEFgfwDGLCB3h0/8STp4cJ8fKqHGilXiEzKchYlMhBqQnWxrJjqZe07GtRJx7Ty+4KDlPDi2vljORRzEDxZlY0Uohq8HjxTCn35lk1p0ARBCQJbEPpGsClWnEhb7BCWR4jGPmedqf7zjHlNzsl+kppWLROTXrthI71URkYH0Xve+ZkWYcAEIjxRYHez+cBxf/KEOQdgCIO6AhzVcZxJ+mIQRiuCYIshgM5yxBSKM8KHwmOdkrTCEJDaBRFv8Yoer3JotpwhFdq7Tnbc05DoZSTJ5PpJ8QnDQFuzQs1EYqQ516MId/BAozWhCAxoAhR9EIAOopGAEiIkIRCSRFrgkYhORMARqjHCINJCLCIughS9EerJ5qvOd69zcSVVa0nWSrw49KBQgHAQ5QPzBDsJE0hqQopNIpKEIbwAFAIswAgE+CiyKiEQmBBEJ6xWCEqwQRRoywTxDtEKbkShRK0+mUq521ate7cFLw3oHSY5imAiywxCONAnMzMIPmlmOTTiRsQ7FsUQ7PE8mysP+tVbaQpUirYVWvzpYwnbVnm4DBCAQpCBADMEUf+rDEEaRi1vcAhSTMMUoMJG0UGACFKQgBWcmt7WSATKdsQKsKlsp2F70QmaFXWcv3inbWNGWtiolIRfEV4dR4MGmxvTRJ+w2C1+8AhSmMMUnlAuK45qiubN4hSiE+B6/8AKVgPyjVgGrVZn1IhfdtS4vbitb235VlrOtLXlj5TYf/BIIvO1DGYlGxn4O4TizOMUpKKGBIVACEhsgAv2MMAm3wAEGRXDEOGthBEkcYhW0OI0RWFGLTtDCCCcbA0gKYQvp9cIQDEagJG5b21yot7zkRXFtxbviKapXvF205zETawr+PBRBmcPU5CdOYYpcbAIDcCgNBaTpAhJkgAglgAoHopWaQ5ShEBCcy1kIcQhDCMI9GYWIukRlCAhCJBLiRXFrV2ziE3v3xGAes4p7MMKw/hMQfRjFUHwU30BVwiav8IUuOvEKSFgCEpCghFgQYYlFZOIMpXHEInKRiVZsohOn8QwnXkELUWxCE7ZIU4cUh1VJaMgVmgizX0vMYtuK2bWtRTWqT5pqLoiuCQ5z0CRCubM6GKEPPLPzKUbBC1/cYhQ7fsUpgj0LU5yicKsxj4rd41e/cg0YrvTr1lBti9ZSW9VgTnW2w4ztXujC1L0AJCDDTD4vytRIl3gMZISSh8r+xPe4vIjFjnVxiljcAnK1QEVnJufdUaNapM0+zy+Yzddyglvb1tZ2qrGN8IRf+9uoJnckXUikxTQnWZyM7ydAcQtTGAESsLiFIihxi1dwaDwlOgUTTcTr1n6zEZ6xxZie7U1qW9sWI2JPwxnecJ5nexepFoLo/umjZO2tEuyehJ0/CYpRpA4iRTjFLUhAhDKcoQhXKYIkFrwII9DlDJsgxCYWIYnzqEXCRhhnJhphiEpvQhJpiITb3eXgHfI83Nzued7/+U8HDc05lbmOY+xsFOXOAhJDwIAhZlGEDmzLCBzoQAHTsPWNRuQMtIgEyBqxiEUEosq2aAREtHkIskj+oiyh30Gn/b162foi1a53vbZj3/NjHoisiiXCY56l9KI4jbk7ToUojG2KV5whFiSi8ImwpnxfoGbmv7BjHnPBR9dWF9x/zLvBsw372P9c+y3PtjLdPAQXAsLOBaqMJZSm2U+YAhamaEWxdwwLXCBbZK1x0/Rj32xgjDQXvxA46PMFYNC/74u9AwQ32DM4BJw92QM/BuyFvUusNyMC9amEUYiQUGi/48KEUSA+WBgP6HoFkFMPkSEZfgMz14M+/RupkaqF/hMp7YPAGAS/1/s+f5u9GXw9TFIrQOCbUZizC3Qjz2KaDhyF8ZC/EewQZDsv6wIkbOOakXo2gZNCkfr+hRJLwAXMQi3UwgNUwC98wC+kwTK6KfP7hNQJpaY7Q40TtswCtlmABVioBViYhXpDjxI7r3AzuBUELBgELL/KQgWUQQNsOS/kQjEcRDEMlOSyDMXIrIg5P+b6BFR4Pw+ELpCLQ1u4hcdpDzfhthYExcCywhgkxQUsxZFKQO57wEBMRDCMvZt6GL4pkF8bhVrgG6PwwGJzrudCvvujDfyzQRkERV9gQUBUxVNERu5DxWNsLegTxiyEPhdyoT+QkE/ywF9DBeQCBWE7BVRgIlrYMVrwRhTJmqwBNyxMxWVkQVD0Q1QEt18IRGVMRVMcxkJswXkEt/HBiTywGz+oGF3+88BXyCymsxhQoEQPGRHRShnWQDV0PMZhhMh7lMhlrEeKpMc9hEdSLMVWG5pRKAJO0ixMKLZfy6/mcLdXeIVspDer4cTt8wUBBEWYjMhhzMiZjEh4rEmcbEaaBDcuuKTEspsZwoRT4KwODEH88sBQcD8kyjcOIRFkcy2NtMmpjMmXTMcWpMKbxMpeqMmqhKTyg7PqYK5L+LVKKLZW6AVvlD9YQA1YQIVTkEORobbpw0KI7D+ZpMqZhL69/AVhgEi8VEGuHMZggL5HUixhKgL48ayme0vNKjal7EZKdMs4DBHVYA08XMdhtIW7hEGYBMCR2ktUDE0BDECsHMCXfEn+0vxM0gTNXgiGsFIsxRICgbqY9nOaxbgYkswvU0AFWmBL6EK+9wBGewTNUcRLmRzN1ETN5FxNvzRNnETNUYxO1fyBHlCmIBCCmnKjM5yhM3TDpotMfItLH7qar3Etb7tK0my21fSZ6UzN0PQF54xPkZLPYPBL+YxP/CRMmRQG59zLHqjOQEksx/iEzboYGQIFUWg6U7AQt5TMOXwcXViN1ng9K+TLAbzL1Cwnq+RLv2LN+XTO+8TL/qxK5fzMkRKGYBCpUagDH4BNAtkbFBqFjbnNUGAa5HK/ksM3ltSFGKzL6FzOC4XP93QPnwHAI51PrYS+ELVQvhRM1vTPpgP+BEiqKb5xowU9BabLLEvwwDkktjmMw8sUzgIETSS9ywvtzL000iP9hc70BfvMz/dETRVl0tU00dBkUje6wPF5mOAikFE4jvgzBV9RQ2FjSfP4rowsxBMFUTY9zfYU0s/c0OQEUr7ESUf1SzttUjaNL1OwCbECykBpv1Ayy1bIxT/YgwvEUfxChZPbq3Q80vtcTtTkzAG0Uza1VFx11F19SVlFTWFgzpe8jppCipfKTtt5DPUBQhsbmqHYA92zhMVUSt4snFrwtvdc0vhE00c9U1y9U10FV14V0flcTUtlt8Sqgz+4hSkVAvv6wT8IAvJzISO4gz3QA8C7GCPML7j+LA+YSMByRdKX7L8z/dZwxVY5RdLPBBZgwVVgDVe1OkNf+YRaCDrrrINWkzFhKhJQMAqlO0N+TQ05RE+sTNhwNVls1VVfYFhv3VUzNVlcVR8Xco5L+IRcEJ2gqwOJ+8jjuAVkIIZeeAVAlbZ4RE6UfdmjDdeVRVpHBQak9YWGQZb4otlcqAMvwllAuIRZ6AU6RIy4MQZiUFR/7dFu+9cjBVuwXdrVPNukbVq0bVM2BVaH/UyfaVpcdVsAJIY6SJK/cyOkwFie6T8O2UY4HM8eXQ+x5RfiXFiV/QWwjVuzbVyzVVrIBcC6xds2bdqm7c++jFxiABbN1VViqFtfUKv+4IIY4VpXLpisW8AvestGOAzZ9ejXXHm9y71ctMXdu8XcyIU+z1VZ3K3cta3cvuxPv/RdIwXWs9Vdn8nbnJUYUfVO0SG2X8NReoPdWtixwPoXsrVHNs1d3hXdxvXctlVe3/Xc21XexjXSs+1P5QWG841bYSDf9z1bicMnI8GJ48BZECG+HSO+WDiinKOZb1vO3BXd8wVb+u3c98Vbzz3fzlVf4W3TB3bgBB5fYhCGtXXb9/UFQPDJW5vAxLLZY+qDzEJC6MJRJuoQ9cCgdExfBEbf2+3ctf3ds6VbDXbgBu5clVVZYNjcAxZfBPZJt/GCoZjASgi62Sw2lGzdQZX+Q1ygPwBZpJiEXAfO4OLNYBwO4gre4gqG4YX14hq+YizOYPJVXh9wmJytg8z6hCGgNRmrXq4djxG8BZDbD5qphZaT3MgdYwz24hk2XzAW5GDgYgRe2C7GYCz24vIlhh7Ygve6KQcRFDI6kH9quqDF0ViIQ5m4BZGwVpbT45dc5PbNYEJmZLA15UUOZFWu4Bq2YgweBj9O3xkOKy6oZIUBwkD5g0galmt0LriMwziMBZG4Be+S0EVF5CuW5T9mZQR25RyGZlY25Sz23FRe5C5qgn/igolZI3abBYmzA/DURWGDLjjs5JNQDy9s4GZ2YGv2Yndm53euYHiOZwfuAl7+EZ/RsZjK+ITGEqYzPEJdE+gRrLeYkNBrjc56VuiFRuSFjufE+iXr/Mjzww5jGpI/YC5xJj44NGf16DZ/FSlGJuRhoOeFLmmHrmBqnmfPzWIu8JGqTVfAqzizEiYi+CzkIslfQ8k43I880+NTdmiVRmmFjmWHtk5luiQEoUZ2u9KPpK/UyUXiG49YiAXbYKQEHOoKLoasRumGbmaJQ6NPwANLgp/KOAVdaBj6YlCmCYUQrCx7k1DUEuVF9lnPrWti2Gpi+FrP3Wu95msH7mtiUAbA9uLAvmsHruu8Dmy9roNkuqmxJiMCUa5FtCkyusBp/TWOLugWScXCRmy7Jga9ZPDZvPZsv/5aY+jrvUZtwl5sv67r1/7r0w5tn/0lBImkZRqC+FoMP+jAW+BbTkCuHcuvqe5kRhqpv/7s0FbuCk7teiZtwvZrvjZs2MZrdh6fV8tZgWEY5Tq/UcCFTXIWIuRGYavjYb4FkD7u5PZinxVt1mZl1Xbg515t5l7u9QZtvTaGrWbvLRKfLdiCxiarZDkhgNYFPcgDf8Rp+YPLt04PuYZu9p7tCqbu6J7v5kbu1l7t5mZvCI/uRQ4IADs=" alt="Journal of Hydrology"></span></a></div></div><h1 id=screen-reader-main-title class="Head u-font-serif u-h2 u-margin-s-ver"><div class=article-dochead data-moz-translations-id=0><span data-moz-translations-id=1>Documentos de investigación</span></div><span class=title-text data-moz-translations-id=2>Desafía de modelización de Karst 1: Resultados de la modelización hidrológica</span></h1><div class=Banner id=banner><div class="wrapper truncated"><div class="AuthorGroups text-s"><div class=author-group id=author-group><span class=sr-only data-moz-translations-id=0>Autor enlaza abierta panel de superposición</span><button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au005 data-moz-translations-id=1><span class=button-link-text data-moz-translations-id=2><span class=react-xocs-alternative-link data-moz-translations-id=3><span class=given-name data-moz-translations-id=4>Pierre-Yves</span> <span class="text surname" data-moz-translations-id=5>Jeannin</span></span> <span class=author-ref id=baf005 data-moz-translations-id=6><sup data-moz-translations-id=7>un</sup></span> <svg focusable=false viewBox="0 0 106 128" title="Correspondence author icon" width=20 height=20 class="icon icon-person react-xocs-author-icon" data-moz-translations-id=10><path d="m11.07 1.2e2l0.84-9.29c1.97-18.79 23.34-22.93 41.09-22.93 17.74 0 39.11 4.13 41.08 22.84l0.84 9.38h10.04l-0.93-10.34c-2.15-20.43-20.14-31.66-51.03-31.66s-48.89 11.22-51.05 31.73l-0.91 10.27h10.03m41.93-102.29c-9.72 0-18.24 8.69-18.24 18.59 0 13.67 7.84 23.98 18.24 23.98s18.24-10.31 18.24-23.98c0-9.9-8.52-18.59-18.24-18.59zm0 52.29c-15.96 0-28-14.48-28-33.67 0-15.36 12.82-28.33 28-28.33s28 12.97 28 28.33c0 19.19-12.04 33.67-28 33.67" data-moz-translations-id=11></path></svg><svg focusable=false viewBox="0 0 102 128" title="Author email or social media contact details icon" width=20 height=20 class="icon icon-envelope react-xocs-author-icon" data-moz-translations-id=12><path d="m55.8 57.2c-1.78 1.31-5.14 1.31-6.9 0l-31.32-23.2h69.54l-31.32 23.19zm-55.8-24.78l42.94 32.62c2.64 1.95 6.02 2.93 9.4 2.93s6.78-0.98 9.42-2.93l40.24-30.7v-10.34h-102zm92 56.48l-18.06-22.74-8.04 5.95 17.38 21.89h-64.54l18.38-23.12-8.04-5.96-19.08 24.02v-37.58l-1e1 -8.46v61.1h102v-59.18l-1e1 8.46v35.62" data-moz-translations-id=13></path></svg></span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au010 data-moz-translations-id=14><span class=button-link-text data-moz-translations-id=15><span class=react-xocs-alternative-link data-moz-translations-id=16><span class=given-name data-moz-translations-id=17>Guillaume</span> <span class="text surname" data-moz-translations-id=18>Artigue</span></span> </span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au015 data-moz-translations-id=21><span class=button-link-text data-moz-translations-id=22><span class=react-xocs-alternative-link data-moz-translations-id=23><span class=given-name data-moz-translations-id=24>Christoph</span> <span class="text surname" data-moz-translations-id=25>Butscher</span></span> </span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au020 data-moz-translations-id=28><span class=button-link-text data-moz-translations-id=29><span class=react-xocs-alternative-link data-moz-translations-id=30><span class=given-name data-moz-translations-id=31>Yong</span> <span class="text surname" data-moz-translations-id=32>Chang</span></span> </span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au025 data-moz-translations-id=35><span class=button-link-text data-moz-translations-id=36><span class=react-xocs-alternative-link data-moz-translations-id=37><span class=given-name data-moz-translations-id=38>Jean-Baptiste</span> <span class="text surname" data-moz-translations-id=39>Charlier</span></span> <span class=author-ref id=baf025 data-moz-translations-id=40><sup data-moz-translations-id=41>e</sup></span> </span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au030 data-moz-translations-id=44><span class=button-link-text data-moz-translations-id=45><span class=react-xocs-alternative-link data-moz-translations-id=46><span class=given-name data-moz-translations-id=47>Lea</span> <span class="text surname" data-moz-translations-id=48>Duran</span></span> </span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au035 data-moz-translations-id=51><span class=button-link-text data-moz-translations-id=52><span class=react-xocs-alternative-link data-moz-translations-id=53><span class=given-name data-moz-translations-id=54>Laurence</span> <span class="text surname" data-moz-translations-id=55>Gill</span></span> </span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au040 data-moz-translations-id=58><span class=button-link-text data-moz-translations-id=59><span class=react-xocs-alternative-link data-moz-translations-id=60><span class=given-name data-moz-translations-id=61>Andreas</span> <span class="text surname" data-moz-translations-id=62>Hartmann</span></span> </span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au045 data-moz-translations-id=65><span class=button-link-text data-moz-translations-id=66><span class=react-xocs-alternative-link data-moz-translations-id=67><span class=given-name data-moz-translations-id=68>Anne</span> <span class="text surname" data-moz-translations-id=69>Johannet</span></span> </span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au050 data-moz-translations-id=72><span class=button-link-text data-moz-translations-id=73><span class=react-xocs-alternative-link data-moz-translations-id=74><span class=given-name data-moz-translations-id=75>Hervé</span> <span class="text surname" data-moz-translations-id=76>Jourde</span></span> </span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au055 data-moz-translations-id=79><span class=button-link-text data-moz-translations-id=80><span class=react-xocs-alternative-link data-moz-translations-id=81><span class=given-name data-moz-translations-id=82>Alireza</span> <span class="text surname" data-moz-translations-id=83>Kavousi</span></span><span class=author-ref id=baf045 data-moz-translations-id=84><sup data-moz-translations-id=85>i</sup></span></span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au060 data-moz-translations-id=86><span class=button-link-text data-moz-translations-id=87><span class=react-xocs-alternative-link data-moz-translations-id=88><span class=given-name data-moz-translations-id=89>Tanja</span> <span class="text surname" data-moz-translations-id=90>Liesch</span></span> </span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au065 data-moz-translations-id=93><span class=button-link-text data-moz-translations-id=94><span class=react-xocs-alternative-link data-moz-translations-id=95><span class=given-name data-moz-translations-id=96>Yan</span> <span class="text surname" data-moz-translations-id=97>Liu</span></span> </span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au070 data-moz-translations-id=100><span class=button-link-text data-moz-translations-id=101><span class=react-xocs-alternative-link data-moz-translations-id=102><span class=given-name data-moz-translations-id=103>Martin</span> <span class="text surname" data-moz-translations-id=104>Láthi</span></span> </span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au075 data-moz-translations-id=107><span class=button-link-text data-moz-translations-id=108><span class=react-xocs-alternative-link data-moz-translations-id=109><span class=given-name data-moz-translations-id=110>Arnauld</span> <span class="text surname" data-moz-translations-id=111>Malard</span></span> </span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au080 data-moz-translations-id=114><span class=button-link-text data-moz-translations-id=115><span class=react-xocs-alternative-link data-moz-translations-id=116><span class=given-name data-moz-translations-id=117>Naomi</span> <span class="text surname" data-moz-translations-id=118>Mazzilli</span></span> </span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au085 data-moz-translations-id=121><span class=button-link-text data-moz-translations-id=122><span class=react-xocs-alternative-link data-moz-translations-id=123><span class=given-name data-moz-translations-id=124>Eulogio</span> <span class="text surname" data-moz-translations-id=125>Pardo-Igúzquiza</span></span> </span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au090 data-moz-translations-id=128><span class=button-link-text data-moz-translations-id=129><span class=react-xocs-alternative-link data-moz-translations-id=130><span class=given-name data-moz-translations-id=131>Dominique</span> <span class="text surname" data-moz-translations-id=132>Thiéry</span></span> </span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au095 data-moz-translations-id=135><span class=button-link-text data-moz-translations-id=136><span class=react-xocs-alternative-link data-moz-translations-id=137><span class=given-name data-moz-translations-id=138>Thomas</span> <span class="text surname" data-moz-translations-id=139>Reimann</span></span> </span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au100 data-moz-translations-id=142><span class=button-link-text data-moz-translations-id=143><span class=react-xocs-alternative-link data-moz-translations-id=144><span class=given-name data-moz-translations-id=145>Philip</span> <span class="text surname" data-moz-translations-id=146>Schuler</span></span> </span></button><span data-moz-translations-id=149>...</span><button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au110 data-moz-translations-id=150><span class=button-link-text data-moz-translations-id=151><span class=react-xocs-alternative-link data-moz-translations-id=152><span class=given-name data-moz-translations-id=153>Andreas</span> <span class="text surname" data-moz-translations-id=154>Wunsch</span></span> </span></button></div></div></div><button class="button-link button-link-secondary u-margin-s-ver text-s show-more-button button-link-icon-right" type=button id=show-more-btn data-aa-button=icon-expand><span class=button-link-text data-moz-translations-id=0>Mostrar más</span><svg focusable=false viewBox="0 0 92 128" height=20 width=17.25 class="icon icon-navigate-down" data-moz-translations-id=1><path d="m1 51l7-7 38 38 38-38 7 7-45 45z" data-moz-translations-id=2></path></svg></button><div class="banner-options u-padding-xs-bottom text-s"><div class="toc-button-wrap u-display-inline-block u-hide-from-lg u-margin-s-right sf-hidden"></div><button class="button-link AddToMendeley u-margin-s-right u-show-inline-from-md button-link-primary button-link-icon-left" type=button><svg focusable=false viewBox="0 0 86 128" height=16 width=16 class="icon icon-plus" data-moz-translations-id=0><path d="m48 58v-38h-1e1v38h-38v1e1h38v38h1e1v-38h38v-1e1z" data-moz-translations-id=1></path></svg><span class=button-link-text data-moz-translations-id=2>Añadir a Mendeley</span></button><div class="Social u-display-inline-block" id=social><div class="popover social-popover" id=social-popover><div id=popover-trigger-social-popover><button class="button-link u-margin-s-right button-link-primary button-link-icon-left" type=button aria-expanded=false aria-haspopup=true><svg focusable=false viewBox="0 0 128 128" height=16 width=16 class="icon icon-share" data-moz-translations-id=0><path d="m9e1 112c-6.62 0-12-5.38-12-12s5.38-12 12-12 12 5.38 12 12-5.38 12-12 12zm-66-36c-6.62 0-12-5.38-12-12s5.38-12 12-12 12 5.38 12 12-5.38 12-12 12zm66-6e1c6.62 0 12 5.38 12 12s-5.38 12-12 12-12-5.38-12-12 5.38-12 12-12zm0 62c-6.56 0-12.44 2.9-16.48 7.48l-28.42-15.28c0.58-1.98 0.9-4.04 0.9-6.2s-0.32-4.22-0.9-6.2l28.42-15.28c4.04 4.58 9.92 7.48 16.48 7.48 12.14 0 22-9.86 22-22s-9.86-22-22-22-22 9.86-22 22c0 1.98 0.28 3.9 0.78 5.72l-28.64 15.38c-4.02-4.34-9.76-7.1-16.14-7.1-12.14 0-22 9.86-22 22s9.86 22 22 22c6.38 0 12.12-2.76 16.14-7.12l28.64 15.38c-0.5 1.84-0.78 3.76-0.78 5.74 0 12.14 9.86 22 22 22s22-9.86 22-22-9.86-22-22-22z" data-moz-translations-id=1></path></svg><span class=button-link-text data-moz-translations-id=2>Comparte</span></button></div></div></div><div class="ExportCitation u-display-inline-block" id=export-citation><div class="popover export-citation-popover" id=export-citation-popover><div id=popover-trigger-export-citation-popover><button class="button-link button-link-primary button-link-icon-left" type=button aria-expanded=false aria-haspopup=true><svg focusable=false viewBox="0 0 106 128" height=16 width=16 class="icon icon-cited-by-66" data-moz-translations-id=0><path xmlns=http://www.w3.org/2000/svg d="m2 58.78v47.22h44v-42h-34v-5.22c0-18.5 17.08-26.78 34-26.78v-1e1c-25.9 0-44 15.12-44 36.78zm1e2 -26.78v-1e1c-25.9 0-44 15.12-44 36.78v47.22h44v-42h-34v-5.22c0-18.5 17.08-26.78 34-26.78z" data-moz-translations-id=1></path></svg><span class=button-link-text data-moz-translations-id=2>Cite</span></button></div></div></div></div></div><div class="ArticleIdentifierLinks u-margin-xs-bottom text-xs" id=article-identifier-links><a class="anchor doi anchor-default" href=https://doi.org/10.1016/j.jhydrol.2021.126508 target=_blank rel="noreferrer noopener" aria-label="Persistent link using digital object identifier" title="Persistent link using digital object identifier" data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>https://doi.org/10.1016/j.jhydrol.2021.126508</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=2><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=3></path></svg></a><a class="anchor rights-and-content anchor-default" href="https://s100.copyright.com/AppDispatchServlet?publisherName=ELS&contentID=S0022169421005552&orderBeanReset=true" target=_blank rel="noreferrer noopener" data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Obtenga derechos y contenido</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=6><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=7></path></svg></a></div><div class="LicenseInfo text-xs u-margin-xs-bottom"><div class=License><span data-moz-translations-id=0>Bajo licencia Creative Commons</span><a class="anchor anchor-default" href=http://creativecommons.org/licenses/by-nc-nd/4.0/ target=_blank rel="noreferrer noopener" data-moz-translations-id=1><span class=anchor-text data-moz-translations-id=2>license</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=3><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=4></path></svg></a></div><div class=OpenAccessLabel><span class=access-indicator data-moz-translations-id=0></span>acceso abierto</div></div><section class=ReferencedArticles></section><section class=ReferencedArticles></section><div class=PageDivider></div><div class="Abstracts u-font-serif text-s" id=abstracts><div class="abstract author-highlights" id=ab010 lang=en><h2 class="section-title u-h4 u-margin-l-top u-margin-xs-bottom">Aspectos destacados</h2><div id=as010><p id=sp0010><ul class=list>-<li class=react-xocs-list-item data-moz-translations-id=4><span class=list-label data-moz-translations-id=5>-</span><span data-moz-translations-id=6><p id=p0010 data-moz-translations-id=7>, los modelos de depósito, se comparaban directamente los modelos semi- y completamente distribuidos.</p></span><li class=react-xocs-list-item data-moz-translations-id=8><span class=list-label data-moz-translations-id=9>-</span><span data-moz-translations-id=10><p id=p0015 data-moz-translations-id=11>Cómo se pudieron evaluar los resultados? Los criterios de evaluación de Kling-Gupta fueron reconocidos como los mejores criterios, aunque no perfectos.</p></span><li class=react-xocs-list-item data-moz-translations-id=12><span class=list-label data-moz-translations-id=13>-</span><span data-moz-translations-id=14><p id=p0020 data-moz-translations-id=15>Los pasos de tiempo por hora tanto para los datos como para la modelización son más adecuados que los diarios para la mayoría de los sistemas kárstico.</p></span><li class=react-xocs-list-item data-moz-translations-id=16><span class=list-label data-moz-translations-id=17>-</span><span data-moz-translations-id=18><p id=p0025 data-moz-translations-id=19>La mayoría de los modelos son razonablemente buenos, pero mal predecir los bajos caudales de agua.</p></span></ul><p></p></div></div><div class="abstract author" id=ab015 lang=en><h2 class="section-title u-h4 u-margin-l-top u-margin-xs-bottom">Resumen</h2><div id=as015><p id=sp0015>La complejidad de la modelización del flujo de aguas subterráneas kársticas se refleja en la cantidad de enfoques de simulación. El objetivo del Karst Modelling Challenge (KMC) es comparar diferentes enfoques en un solo sistema utilizando el mismo conjunto de datos. Trece equipos con diferentes modelos computacionales para simular variaciones de descarga en los muelles kársmicos han aplicado sus respectivos modelos en un solo conjunto de datos procedentes del Sistema Hidrogeológico Milandre Karst (MKHS). Los enfoques incluyen redes neuronales, modelos de embalse, modelos semidistribuidos y modelos de aguas subterráneas totalmente distribuidos. Se proporcionaron cuatro años y medio de entrada meteorológica por hora o diario y datos de descarga por hora para la calibración modelo. La validación comunífice de un año de alta, sin los datos de descarga observados. El rendimiento del modelo se evaluó utilizando la conservación del volumen, la eficiencia de Nash-Sutcliffe (NSE) y la eficiencia de Kling-Gupta (KGE) aplicadas en el vertido total y componentes de flujo individuales. Como resultado, la comparación de los rendimientos de los modelos es una tarea difícil debido a las diferencias en la arquitectura del modelo, pero también requiere pasos de tiempo: algunos de los modelos requieren pasos diarios agregados mientras que otros podrían ejecutarse utilizando datos por hora, lo que proporcionó algunas diferencias interesantes dependiendo de cómo se transformaron los datos. El uso de datos instantáneos (por ejemplo, valor al mediodía) produce menos sesgo que promediando los datos por hora a lo largo de un día. La transformación de los datos por hora en diarios produce una disminución de Nash y KGE de 0,05 a 0,05 a 0,058 (es decir, de 1 a 0,73). Las simulaciones resultantes (valores predecidos para el año 2016) produjeron KGE que oscilaban entre 083 y 0,37 (0,83 a 0,24 euros para NSE). Aunque las simulaciones coinían con los flujos monitoreados razonablemente bien, la mayoría de los modelos lucharon para simular las condiciones del flujo de base con precisión. En general, los modelos que mejor realizaron para este ejercicio fueron los globales (Jardeia y Varkarst), con un número limitado de parámetros, que se pueden calibrar mediante procedimientos de calibración automática. Los modelos de red neuronal también mostraron un potencial justo, con uno proporcionando resultados razonables a pesar del conjunto de datos relativamente corto disponible para el calentamiento (4,5 años). Los modelos semi-y completamente distribuidos también sugirieron que con algún esfuerzo más podrían funcionar bien. La precisión de las predicciones de los modelos no parece aumentar mediante el uso de modelos con más de 9-12 parámetros de calibración. Una evaluación de los errores relativos entre los valores previstos y los observados reveló que para la mayoría de los modelos, el 50% de los valores previstos contenían más del 50% de la diferencia con la tasa de descarga observada, con un 25% con una diferencia superior al 100%. Una parte significativa de los valores mal previstados correspondió al flujo de base, lo que fue sorprendente dado que como flujo de base es generalmente mucho más fácil de predecir que el flujo máximo. Por lo tanto, esto demuestra que los enfoques de modelización y los criterios para la calibración están demasiado orientados a las secciones de flujo máximo de los hidrógrafos, y que las mejoras podrían obtenerse más centradas en el flujo de base.</p></div></div><div class="abstract graphical" id=ab005 lang=en><h2 class="section-title u-h4 u-margin-l-top u-margin-xs-bottom">Resumen gráfico</h2><div id=as005><p id=sp0005><span class=display></span><figure class="figure text-xs" id=f0060><span><img src=data:image/jpeg;base64,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 height=200 alt><ol class=u-margin-s-bottom><li data-moz-translations-id=0><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-ga1_lrg.jpg target=_blank download title="Download high-res image (159KB)" data-moz-translations-id=1><span class=anchor-text data-moz-translations-id=2>Descargar: <span class=download-link-title data-moz-translations-id=3>Descargar imagen de alta resolución (159KB)</span></span></a><li data-moz-translations-id=4><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-ga1.jpg target=_blank download title="Download full-size image" data-moz-translations-id=5><span class=anchor-text data-moz-translations-id=6>Descargar Descargar <span class=download-link-title data-moz-translations-id=7>imagen a tamaño completo</span></span></a></ol></span></figure><p></p></div></div></div><ul id=issue-navigation class="issue-navigation u-margin-s-bottom u-bg-grey1"><li class="previous move-left u-padding-s-ver u-padding-s-left" data-moz-translations-id=0><li class="next move-right u-padding-s-ver u-padding-s-right" data-moz-translations-id=8></ul><div class="Keywords u-font-serif text-s"><div id=kg005 class=keywords-section><h2 class="section-title u-h4 u-margin-l-top u-margin-xs-bottom">Palabras clave</h2><div id=k0005 class=keyword><span data-moz-translations-id=0>Modelización</span></div><div id=k0010 class=keyword><span data-moz-translations-id=0>Karst</span></div><div id=k0015 class=keyword><span data-moz-translations-id=0>Comparación</span></div><div id=k0020 class=keyword><span data-moz-translations-id=0>Criterios de eficiencia</span></div><div id=k0025 class=keyword><span data-moz-translations-id=0>Recargo</span></div><div id=k0030 class=keyword><span data-moz-translations-id=0>Flujo de base</span></div></div></div><div class="Body u-font-serif text-s" id=body><div><section id=s0005><h2 id=st025 class="u-h4 u-margin-l-top u-margin-xs-bottom">1. Introducción</h2><section id=s0010><h3 id=st030 class="u-h4 u-margin-m-top u-margin-xs-bottom">1.1. Idea</h3><p id=p0030>El objetivo del reto de la modelización era invitar a equipos de investigación de todo el mundo a comparar sus enfoques y herramientas de modelización aplicándolos en el mismo conjunto de datos. Había que definir un método de evaluación común y los resultados de cada equipo han sido analizados con los mismos criterios. De esta manera todos los resultados podrían compararse directamente. Se han probado varias funciones objetivas (varias formas de calcular la diferencia entre las series temporales previstas y las series temporales observadas), con tres posteriormente utilizadas para la comparación final.</p></section><section id=s0015><h3 id=st035 class="u-h4 u-margin-m-top u-margin-xs-bottom">1.2. Definición de los problemas</h3><div><p id=p0035>Para comenzar con una pregunta bastante simple (Pase 1) se decidió centrarse en el comportamiento hidrológico de los sistemas hidrogeológicos kárstico (KHS), es decir, la relación entre los parámetros que controlan la entrada de agua en el kárstico (principalmente precipitación y temperatura) y la tasa de descarga de los muelles <a class="anchor u-display-inline anchor-paragraph" href=#f0005 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0005 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>kárstico</span></a> a la salida del sistema (<a class="anchor u-display-inline anchor-paragraph" href=#f0005 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0005 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Fig. </span></a>1). La cuestión que deben resolver los respectivos equipos es pronosticar con la mayor precisión posible las tasas de alta en el jarlet kárstico de los datos de entrada meteorológica.<figure class="figure text-xs" id=f0005><span><img src=data:image/jpeg;base64,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 height=343 alt aria-describedby=cn0005><ol class=u-margin-s-bottom><li data-moz-translations-id=0><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr1_lrg.jpg target=_blank download title="Download high-res image (346KB)" data-moz-translations-id=1><span class=anchor-text data-moz-translations-id=2>Descargar Descargar <span class=download-link-title data-moz-translations-id=3>imagen de alta resolución (346KB)</span></span></a><li data-moz-translations-id=4><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr1.jpg target=_blank download title="Download full-size image" data-moz-translations-id=5><span class=anchor-text data-moz-translations-id=6>Descargar Descargar <span class=download-link-title data-moz-translations-id=7>imagen a tamaño completo</span></span></a></ol></span><span class="captions text-s"><span id=cn0005><p id=sp0020><span class=label data-moz-translations-id=0>Fig. </span>1. Modelo conceptual de un sistema hidrológico kárstico (izgato). El agua de precipitación (alógena y autógena) fluye a través del macizo kárstico a lo largo de conductos (flejas rojas) y fisuras (flejas azules) con el pico de descarga correspondiente en la primavera después de unas pocas horas o días. Para KMC step 1 sólo consideramos la relación entre precipitación y descarga. Los subsistemas pueden o no ser considerados en los enfoques de modelización aplicados. (Para la interpretación de las referencias al color en esta leyenda figura, el lector es referido a la versión web de este artículo.)</p></span></span></figure></div><p id=p0040>Cabe señalar que se prevén nuevas medidas, con la simulación del flujo y la distribución de la cabeza en el espacio y el tiempo (Pase 2), y la simulación de los procesos de transporte (Pase 3). Sin embargo, la evaluación de este paso 1 del reto no tiene en cuenta si los modelos podrán abordar o no los objetivos de los nuevos pasos.<p id=p0045>En este trabajo, la palabra "Modelo" se refiere a los enfoques y las herramientas numéricas correspondientes utilizadas por los equipos respectivos para su ejercicio de modelado. La simulación de la palabra se utiliza para nombrar los resultados del trabajo de modelado.</p></section><section id=s0020><h3 id=st040 class="u-h4 u-margin-m-top u-margin-xs-bottom">1.3. Fluen a través de un macizo kárstico</h3><p id=p0050>Como se describe en la mayoría de los libros de texto (por ejemplo, <a class="anchor u-display-inline anchor-paragraph" href=#b0190 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0190 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Ford y Williams, 1989</span></a><span data-moz-translations-id=2>), los macizos kársticos se caracterizan por una <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/geomorphological-response title="Learn more about geomorphology from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=3>geomorfología</a> e hidrología específicas (</span><a class="anchor u-display-inline anchor-paragraph" href=#f0005 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0005 data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Fig. </span></a><span data-moz-translations-id=6><span data-moz-translations-id=7>1). Las <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/landform title="Learn more about landforms from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=8>formas de tierra</a> kratra se deben a la disolución de la roca en el agua de precipitación. Las formas terrestres de superficie como karrenfields, dolines y cuevas se producen por</span> <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/infiltration title="Learn more about infiltration from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=9>infiltración</a> de en la roca. Los ejes verticales, las cuevas (vainas o fratic) y los grandes muelles están relacionados con el flujo y la salida de agua.</span><p id=p0055>Desde un punto de vista hidrológico la precipitación (llugar y derretimiento de la nieve) se infiltra a través de la cubierta vegetal y los suelos en la capa superior de la roca, que es más o menos climatizado (epikarst). Una parte de las aguas de precipitación regresa de vuelta a la atmósfera a través de <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/evapotranspiration title="Learn more about evapotranspiration from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=0>evapotranspiración</a>. En algunos casos, las aguas superficiales pueden formar un pequeño arroyo o incluso un río, que se traga en una ubicación concentrada (aguño de traga). Si el arroyo drena una zona no kárstica esta parte de la cuenca se llamará "allogenic".<p id=p0060><span data-moz-translations-id=0>Epikarst distribuye el agua parcialmente hacia ejes verticales que conducen el líquido directamente a la <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/phreatic-zone title="Learn more about phreatic zone from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=1>zona freática</a><span data-moz-translations-id=2>. Otra parte del agua está atrapada en el fondo del <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/epikarst title="Learn more about epikarst from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=3>epikarst</a> y se filtra lentamente hacia abajo a través de grietas y articulaciones. En algunos puntos el agua es drenada de nuevo por la red de canales kársticos, que drena eficientemente la masa de roca, y conduce el agua en la salida del sistema kárstico: el manantial kárstico (</span></span><a class="anchor u-display-inline anchor-paragraph" href=#b0405 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0405 data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Mangin, 1975</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0570 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0570 data-moz-translations-id=6><span class=anchor-text data-moz-translations-id=7>Williams, 1983</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0515 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0515 data-moz-translations-id=8><span class=anchor-text data-moz-translations-id=9>Smart y Friederich, 1986</span></a>). El volumen de agua almacenada en fisuras es significativo en comparación con el almacenado en los canales kársticos (<a class="anchor u-display-inline anchor-paragraph" href=#b0305 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0305 data-moz-translations-id=10><span class=anchor-text data-moz-translations-id=11>Király, 1975</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0165 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0165 data-moz-translations-id=12><span class=anchor-text data-moz-translations-id=13>Drogue,</span></a> 1980, <a class="anchor u-display-inline anchor-paragraph" href=#b0520 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0520 data-moz-translations-id=14><span class=anchor-text data-moz-translations-id=15>Smart y Hobbs, 1986</span></a>).<p id=p0065>La relación entre la recarga y la descarga en los muelles parece bastante directa en la mayoría de los sistemas kárstico, ya que los eventos de lluvia son seguidos por picos claros de las tasas de descarga en los muelles kárstico generalmente en unas pocas horas a días. Por lo tanto, la respuesta es bastante similar a la que se suele observar en <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/surface-water-hydrology title="Learn more about surface hydrology from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=0>la hidrología superficial</a>.<p id=p0070>El grado de flujo difuso en la matriz rocosa en comparación con el flujo concentrado en conductos, así como la cantidad de recarga concentrada en agujeros de golondrina vs difamar difuso a través de suelos y epikarst puede ser significativamente diferente dependiendo de las características de la roca y de contextos geomorfológicos y climatológicos.<p id=p0075>Se han desarrollado varios modelos para simular la relación funcional entre los <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/peak-discharge title="Learn more about discharge peaks from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=0>picos</a> de precipitación y <span data-moz-translations-id=1>. Algunos de ellos incluyen procesos físicos que tienen lugar en cada subsistema atravesado por el agua, otros simplifican la naturaleza en subsistemas lineales, y otros sólo consideran la relación matemática entre una señal de entrada y una salida. El objetivo del paso 1 del Karst Modelling Challenge es comparar estos enfoques, y su capacidad para simular un <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/hydrograph title="Learn more about hydrograph from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=2>hidrografo</a> de una serie de tiempos de precipitación.</span></p></section><section id=s0025><h3 id=st045 class="u-h4 u-margin-m-top u-margin-xs-bottom">1.4. Perspectivas cortas sobre el modelado del flujo de aguas subterráneas en kárstico</h3><p id=p0080>El análisis de los hidrografos comenzó principalmente después de la publicación de <a class="anchor u-display-inline anchor-paragraph" href=#b0390 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0390 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Maillet (1905)</span></a>. Sin embargo, la monitoreo continuo de los muelles kársticos realmente comenzó sólo desde la década de 1950. Para el análisis de los manantiales kársticos, los primeros modelos ocurrieron a lo largo de la década de 1960 (<a class="anchor u-display-inline anchor-paragraph" href=#b0490 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0490 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Schoeller, 1962</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0195 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0195 data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Forkasiewicz y Paloc,</span></a> 1967, <a class="anchor u-display-inline anchor-paragraph" href=#b0120 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0120 data-moz-translations-id=6><span class=anchor-text data-moz-translations-id=7>Castany, 1968</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0120 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0120 data-moz-translations-id=6><span class=anchor-text data-moz-translations-id=7>Brown</span></a><a class="anchor u-display-inline anchor-paragraph" href=#b0075 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0075 data-moz-translations-id=8><span class=anchor-text data-moz-translations-id=9>, 1970</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0400 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0400 data-moz-translations-id=12><span class=anchor-text data-moz-translations-id=13>Brown</span></a><a class="anchor u-display-inline anchor-paragraph" href=#b0085 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0085 data-moz-translations-id=10><span class=anchor-text data-moz-translations-id=11>, 1973</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0400 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0400 data-moz-translations-id=12><span class=anchor-text data-moz-translations-id=13>Mangin, 1970</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0405 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0405 data-moz-translations-id=14><span class=anchor-text data-moz-translations-id=15>Mangin, 1975</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0110 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0110 data-moz-translations-id=16><span class=anchor-text data-moz-translations-id=17>Chemin, 1974</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0040 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0040 data-moz-translations-id=18><span class=anchor-text data-moz-translations-id=19>Bezes, 1976</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0425 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0425 data-moz-translations-id=20><span class=anchor-text data-moz-translations-id=21>Milanovic, 1976</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0015 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0015 data-moz-translations-id=22><span class=anchor-text data-moz-translations-id=23>Atkinson, 1977</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0150 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0150 data-moz-translations-id=24><span class=anchor-text data-moz-translations-id=25>Dreiss, 1982</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0155 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0155 data-moz-translations-id=26><span class=anchor-text data-moz-translations-id=27>Dreiss, 1983</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0135 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0135 data-moz-translations-id=28><span class=anchor-text data-moz-translations-id=29>Dodge, 1983</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0060 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0060 data-moz-translations-id=30><span class=anchor-text data-moz-translations-id=31>Bonacci,</span></a> 1987, <a class="anchor u-display-inline anchor-paragraph" href=#b0190 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0190 data-moz-translations-id=32><span class=anchor-text data-moz-translations-id=33>Ford y Williams, 1989</span></a>). Al principio se reconocieron dos componentes, uno explicando la rápida e intensa respuesta de las tasas de descarga de primavera a las precipitaciones, y el otro explicando la lenta disminución de las tasas de descarga (recesión) durante largos períodos de tiempo sin eventos de recarga.<p id=p0085>Varias ideas conceptuales se desarrollaron a través de varios tipos de modelos matemáticos, que pueden agruparse en tres familias numerosas (<a class="anchor u-display-inline anchor-paragraph" href=#b0525 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0525 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Teutsch y Sauter,</span></a> 1991, <a class="anchor u-display-inline anchor-paragraph" href=#b0340 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0340 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Kovács y Sauter,</span></a> 2007, <a class="anchor u-display-inline anchor-paragraph" href=#b0200 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0200 data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Ghasemizadeh,</span></a> 2012, <a class="anchor u-display-inline anchor-paragraph" href=#b0185 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0185 data-moz-translations-id=6><span class=anchor-text data-moz-translations-id=7>Fiorillo, 2014</span></a>):<ul class=list data-moz-translations-id=8><li class=react-xocs-list-item data-moz-translations-id=9><span class=list-label data-moz-translations-id=10>1)</span><span data-moz-translations-id=11><p id=p0090 data-moz-translations-id=12>Modelos basados en datos o de caja negra, incluidas funciones de transferencia de reservorio y no paramétricas</p></span><li class=react-xocs-list-item data-moz-translations-id=13><span class=list-label data-moz-translations-id=14>2)</span><span data-moz-translations-id=15><p id=p0095 data-moz-translations-id=16>Modelos hidrogeológicos (flujo de matriz de cierre con o sin conductos)</p></span><li class=react-xocs-list-item data-moz-translations-id=17><span class=list-label data-moz-translations-id=18>3)</span><span data-moz-translations-id=19><p id=p0100 data-moz-translations-id=20>Modelos de flujo de tuberías (basado en la hidráulica de flujo en conductos)</p></span></ul><p><section id=s0030><h4 id=st050 class="u-margin-m-top u-margin-xs-bottom">1.4.1. Modelos de caja de datos o de caja negra</h4><p id=p0105>En estos modelos, el sistema sólo se considera como una caja negra o una combinación de cajas negras, cada caja transformando una señal de entrada (influjo) en una salida (salida).<p id=p0110>La transformación se basa en una función puramente matemática, sin considerar los procesos físicos que controlan el flujo en la clandestinidad o simplificándolos fuertemente (<a class="anchor u-display-inline anchor-paragraph" href=#b0130 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0130 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Dewandel et al.,</span></a> 2003, <a class="anchor u-display-inline anchor-paragraph" href=#b0185 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0185 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Fiorillo, 2014</span></a>). El modelo más sencillo de este tipo sería un modelo de una sola caja, tomando la precipitación total como entrada y transformándolo en una serie de tiempo de descarga. El modelo clásico de hidrografía .unirtario, suponiendo una sola función de transferencia lineal (núcleo), introducido por primera vez por <a class="anchor u-display-inline anchor-paragraph" href=#b0500 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0500 data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Sherman (1932</span></a><span data-moz-translations-id=6>), fue probado en sistemas kárstico. Rápidamente resultó que KHS no es lineal ni estable y que un modelo tan simple no puede describir <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/karst-aquifer title="Learn more about karst aquifers from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=7>los acuíferos kárstico de</a> una manera significativa. Así, los esquemas más sofisticados, acoplando varios núcleos, eran necesarios para adaptarse a los datos observados de alguna manera (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0150 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0150 data-moz-translations-id=8><span class=anchor-text data-moz-translations-id=9>Dreiss, 1982</span></a><span data-moz-translations-id=10>). <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/neural-network title="Learn more about Neural networks from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=11>Las redes neuronales</a> son los desarrollos más recientes de esta categoría de modelos.</span><p id=p0115><span data-moz-translations-id=0>Otra categoría de <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/lumped-model title="Learn more about lumped models from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=1>modelos agrupados</a> fue intentado por varios autores, utilizando depósitos paralelos en cascada y paralelos (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0195 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0195 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Forkasiewicz y Paloc,</span></a> 1967, <a class="anchor u-display-inline anchor-paragraph" href=#b0040 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0040 data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Bezes, 1976</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0230 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0230 data-moz-translations-id=6><span class=anchor-text data-moz-translations-id=7>Halihan y Wicks,</span></a> 1998). Por lo general, un reservorio simula el componente de flujo rápido, y los demás el componente más lento de KHS. A continuación se produjo un nuevo parámetro, es decir, la distribución de la recarga en los componentes respectivos. Los mejores resultados se obtuvieron con una combinación de tres a cinco embalses, algunos de ellos parcialmente vaciados por evapotranspiración. Este enfoque condujo a resultados de simulación bastante precisos (por ejemplo, <a class="anchor u-display-inline anchor-paragraph" href=#b0380 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0380 data-moz-translations-id=8><span class=anchor-text data-moz-translations-id=9>Largo,</span></a> <span data-moz-translations-id=10><span data-moz-translations-id=11>2009). En la mayoría de los casos, los parámetros de entrada no se distribuyen, es decir, cada embalse se supone que se expande a través de todo el tamaño de la <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/catchment-area title="Learn more about catchment area from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=12>cuenca</a>. De hecho, este enfoque también puede distribuirse en el espacio, y puede tener en cuenta algunas diferencias espaciales de</span> <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/aquifer-characteristics title="Learn more about aquifer characteristics from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=13>las características</a> en las partes respectivas de la zona de captación (por ejemplo. </span><a class="anchor u-display-inline anchor-paragraph" href=#b0050 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0050 data-moz-translations-id=14><span class=anchor-text data-moz-translations-id=15>Bittner et al., 2018</span></a>). Con tres a cinco embalses distribuidos en decenas o cientos de zonas el número de parámetros se vuelve muy alto, lo que dificulta la calibración de los modelos. Gracias a los procedimientos de calibración automática, esta tarea se puede lograr ahora con un esfuerzo razonable (<a class="anchor u-display-inline anchor-paragraph" href=#b0470 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0470 data-moz-translations-id=16><span class=anchor-text data-moz-translations-id=17>Pianosi et al., 2016</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0415 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0415 data-moz-translations-id=18><span class=anchor-text data-moz-translations-id=19>Mazzilli et al., 2017</span></a>).</p></section><section id=s0035><h4 id=st055 class="u-margin-m-top u-margin-xs-bottom">1.4.2. Modelos hidrogeológicos (modelos distribuidos)</h4><p id=p0120><a class="anchor u-display-inline anchor-paragraph" href=#b0095 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0095 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Darcy (1856)</span></a> <span data-moz-translations-id=2>describió empíricamente la relación matemática entre los gradientes de agua subterránea (diferencias de cabeza) y la cantidad de <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/groundwater-flow title="Learn more about groundwater flow from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=3>flujo de aguas subterráneas</a>. Este fue de hecho el primer modelo de flujo de agua subterránea, que era unidimensional y de estado estacionario. Se desarrollaron soluciones analíticas para algunas geometrías y condiciones de límites típicos. En la década de 1970 el desarrollo de computadoras permitió resolver las ecuaciones numéricamente, haciendo posible calcular el flujo a través de una gama mucho más amplia de situaciones (por ejemplo. </span><a class="anchor u-display-inline anchor-paragraph" href=#b0070 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0070 data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Bredehoeft y Pinder, 1970</span></a>). La aplicación de este enfoque del kárstico se intentó por primera vez a mediados de la década de 1970 (<a class="anchor u-display-inline anchor-paragraph" href=#b0310 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0310 data-moz-translations-id=6><span class=anchor-text data-moz-translations-id=7>Király y Morel, 1976</span></a><span data-moz-translations-id=8>), pero rápidamente se hizo evidente que KHS no puede ser fácilmente modelado usando este enfoque, ya que la simulación de eventos rápidos e intensos de inundación, así como un flujo de base sostenido, como se observó en la mayoría de los resortes karst resultó difícil. Los modelistas decidieron introducir una red de conductos dentro de una matriz con una <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/hydraulic-conductivity title="Learn more about hydraulic conductivity from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=9>conductividad hidráulica</a> baja. Con contrastes de 10 </span><sup data-moz-translations-id=10>-4</sup> a 10 <sup data-moz-translations-id=11>-5</sup> m/s entre matriz y conductos, los hidrografías kársticos podrían ser simulados. Sin embargo, el proceso de recarga también tuvo que mejorarse, lo que se logró añadiendo una capa con una alta conductividad hidráulica en la parte superior del modelo (epikarst), capaz de absorber la precipitación y distribuirla a los conductos y la matriz (<a class="anchor u-display-inline anchor-paragraph" href=#b0315 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0315 data-moz-translations-id=12><span class=anchor-text data-moz-translations-id=13>Klyirá et al.,</span></a> 1995). Dado que el alto contraste de las conductividades hidráulicas en los modelos espaciales puede ser bastante desafiante desde el punto de vista numérico, se han desarrollado varias soluciones, como doble continuidad o modelos medianos dobles (<a class="anchor u-display-inline anchor-paragraph" href=#b0525 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0525 data-moz-translations-id=14><span class=anchor-text data-moz-translations-id=15>Teutsch y Sauter, 1991</span></a>).<p id=p0125>Dos mejoras más tarde ocurrieron: flujo turbulento en conduits y flujo parcialmente saturado (<a class="anchor u-display-inline anchor-paragraph" href=#b0530 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0530 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Thierrien y Sudicky,</span></a> 1996, <a class="anchor u-display-inline anchor-paragraph" href=#b0010 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0010 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Annable y Sudicky,</span></a> 1998).<p id=p0130>En estos modelos completamente distribuidos el número de parámetros es muy alto (con varios valores para cada célula) haciendo manual de calibración muy lento y prácticamente imposible. La zona de parámetros y los procedimientos de calibración automática mejoraron este aspecto (<a class="anchor u-display-inline anchor-paragraph" href=#b0145 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0145 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Doherty et al.,</span></a> 1994, <a class="anchor u-display-inline anchor-paragraph" href=#b0065 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0065 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Borghi et al., 2016</span></a>). Sin embargo, dado que primero hay que definir la estructura básica de la red, su geometría y topología no suele ajustarse durante el proceso de calibración, aunque puede desempeñar un papel significativo en algunas situaciones (<a class="anchor u-display-inline anchor-paragraph" href=#b0325 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0325 data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Kovács,</span></a> 2003) especialmente en la zona epifreática (<a class="anchor u-display-inline anchor-paragraph" href=#b0285 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0285 data-moz-translations-id=6><span class=anchor-text data-moz-translations-id=7>Jeannin,</span></a> 2001). Suponiendo que la física considerada en el modelo sea correcta, los modelos pueden revelar sin embargo estructuras poco realistas y, por lo tanto, pueden actuar como una herramienta muy avanzada que proporciona más información (<a class="anchor u-display-inline anchor-paragraph" href=#b0180 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0180 data-moz-translations-id=8><span class=anchor-text data-moz-translations-id=9>Enemark et al., 2019</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0210 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0210 data-moz-translations-id=10><span class=anchor-text data-moz-translations-id=11>Gill et al., 2020</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0170 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0170 data-moz-translations-id=12><span class=anchor-text data-moz-translations-id=13>Duran y Gill,</span></a> 2021).<p id=p0135>Estos modelos pertenecen a <em data-moz-translations-id=0>modelos de flujo de agua subterráneos basados en procesos</em> de acuerdo con la clasificación propuesta por <a class="anchor u-display-inline anchor-paragraph" href=#b0005 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0005 data-moz-translations-id=1><span class=anchor-text data-moz-translations-id=2>Anderson et al. (2015)</span></a>.</p></section><section id=s0040><h4 id=st060 class="u-margin-m-top u-margin-xs-bottom">1.4.3. Modelos de flujo de tuberías (modelos seemidistribuidos)</h4><p id=p0140>Los modelos de flujo de tuberías sólo consideran una red de conductos y flujo de negligencia a través de la matriz, o agregarlo como una entidad no espacializada. La idea no es nueva, ya que muchos autores originales (<a class="anchor u-display-inline anchor-paragraph" href=#b0410 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0410 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Martel,</span></a> 1921, <a class="anchor u-display-inline anchor-paragraph" href=#b0355 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0355 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Livre, 1915</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0360 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0360 data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Livre, 1940</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0550 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0550 data-moz-translations-id=6><span class=anchor-text data-moz-translations-id=7>Trombe, 1948</span></a>), explorando el kárstico con una perspectiva hidrogeológica y espeleológica, sugirió que el flujo en kárstico fuera bien descrito por las ecuaciones de la tubería hidráulica. Estos modelos también pertenecen a modelos basados en procesos según <a class="anchor u-display-inline anchor-paragraph" href=#b0005 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0005 data-moz-translations-id=8><span class=anchor-text data-moz-translations-id=9>Anderson et al., (2015)</span></a>, pero el proceso principal considerado es la hidráulica de flujo a través de conductos en las zonas fréticas y epihreáticas (turbulenta y parcialmente saturada). El flujo de Darcys no es o sólo marginalmente considerado.<p id=p0145>Unos pocos autores utilizaron entonces modelos de tuberías para analizar sus datos (<a class="anchor u-display-inline anchor-paragraph" href=#b0565 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0565 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>blanco y blanco, 1970</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0015 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0015 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Atkinson, 1977</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0055 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0055 data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Boegli,</span></a> 1980, <a class="anchor u-display-inline anchor-paragraph" href=#b0345 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0345 data-moz-translations-id=6><span class=anchor-text data-moz-translations-id=7>Lauritzen et al.,</span></a> 1985, <a class="anchor u-display-inline anchor-paragraph" href=#b0510 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0510 data-moz-translations-id=8><span class=anchor-text data-moz-translations-id=9>Smart, 1988</span></a>). Todos ellos demostraron que el flujo es turbulento en la mayoría de los conductos naturales (cuevas), al menos durante las condiciones medias y altas del agua. El flujo puede ser transitorio o incluso laminar durante condiciones de caudal muy bajas. <a class="anchor u-display-inline anchor-paragraph" href=#b0190 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0190 data-moz-translations-id=10><span class=anchor-text data-moz-translations-id=11>Ford y Williams (1989)</span></a> y <a class="anchor u-display-inline anchor-paragraph" href=#b0575 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0575 data-moz-translations-id=12><span class=anchor-text data-moz-translations-id=13>Worthington (1991)</span></a> <p id=p0150><a class="anchor u-display-inline anchor-paragraph" href=#b0290 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0290 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Jeannin y Maréchal (1995)</span></a> y <a class="anchor u-display-inline anchor-paragraph" href=#b0285 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0285 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Jeannin (2001)</span></a> <span data-moz-translations-id=4>evaluaron las cabezas de cabeza debido a las curvas y los cambios en las secciones transversales en comparación con los relacionados con la fricción a lo largo de las paredes. Resulta que estos últimos suelen estar dominando, y los valores típicos podrían ser asignados para conductos naturales. También calcularon la hidráulica de una red de conductos naturales para el flujo de modelado en la parte aguas abajo de la cueva de Hálloch (Suiza). El papel de los conductos epifráticos en la hidráulica y la velocidad del flujo se encontró que era significativo para muchos KHS. Este comportamiento específico no se puede simular con los paquetes de software hidrogeológicos habituales. Por esta razón, los modelos de flujo de tuberías (por ejemplo, el software SWMM desarrollado por US EPA y otros programas de drenaje urbano) se han utilizado cada vez más en el modelado <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/karst-hydrology title="Learn more about karst hydrology from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=5>de hidrología kárstica:</a></span> <a class="anchor u-display-inline anchor-paragraph" href=#b0125 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0125 data-moz-translations-id=6><span class=anchor-text data-moz-translations-id=7>Campbell y Sullivan,</span></a> 2002, <a class="anchor u-display-inline anchor-paragraph" href=#b0465 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0465 data-moz-translations-id=8><span class=anchor-text data-moz-translations-id=9>Peterson y Wicks,</span></a> 2006, <a class="anchor u-display-inline anchor-paragraph" href=#b0580 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0580 data-moz-translations-id=10><span class=anchor-text data-moz-translations-id=11>Wu et al.,</span></a> 2008, <a class="anchor u-display-inline anchor-paragraph" href=#b0205 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0205 data-moz-translations-id=12><span class=anchor-text data-moz-translations-id=13>Gill et al.,</span></a> 2013, <a class="anchor u-display-inline anchor-paragraph" href=#b0105 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0105 data-moz-translations-id=14><span class=anchor-text data-moz-translations-id=15>Chen y Goldscheider,</span></a> 2014, <a class="anchor u-display-inline anchor-paragraph" href=#b0275 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0275 data-moz-translations-id=16><span class=anchor-text data-moz-translations-id=17>Jeannin et al.,</span></a> 2015, <a class="anchor u-display-inline anchor-paragraph" href=#b0295 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0295 data-moz-translations-id=18><span class=anchor-text data-moz-translations-id=19>Kaufmann et al., 2016</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0395 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0395 data-moz-translations-id=20><span class=anchor-text data-moz-translations-id=21>Malard, 2018</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0560 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0560 data-moz-translations-id=22><span class=anchor-text data-moz-translations-id=23>Vuilleumier et al., 2019</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0435 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0435 data-moz-translations-id=24><span class=anchor-text data-moz-translations-id=25>Morrissey et al., 2020</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0435 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0435 data-moz-translations-id=24><span class=anchor-text data-moz-translations-id=25>,</span></a><a class="anchor u-display-inline anchor-paragraph" href=#b0495 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0495 data-moz-translations-id=26><span class=anchor-text data-moz-translations-id=27>2020</span></a>. Estos modelos permiten simular el flujo en una compleja red de tuberías con diversos diámetros, rudicidad y formas transversales.<p id=p0155><span data-moz-translations-id=0>Si este enfoque es eficiente para vincular las cabezas observadas y las tasas de descarga, no es adecuado vincular la precipitación para descargar hidrografía. De hecho, siempre es necesario hacer un modelo de recarga para hacerlo, con el fin de transformar <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/meteorological-data title="Learn more about meteorological data from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=1>los datos meteorológicos</a> en</span> <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/groundwater-recharge title="Learn more about groundwater recharge from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=2>recarga de aguas subterráneas</a>.</p></section><section id=s0045><h4 id=st065 class="u-margin-m-top u-margin-xs-bottom">1.4.4 Recarga de modelos de sistemas hidrogeológicos kárstico (KHS)</h4><p id=p0160>La recarga se define aquí como la cantidad de agua infiltrando y fluyendo a través del sistema kárstico. Corresponde aproximadamente a la precipitación total menos evapotranspiración y la escorridad terrestre eludiendo el kárstico en algunas circunstancias. También se considerará el almacenamiento de nieve y hielo en los procesos de recarga.<p id=p0165><span data-moz-translations-id=0>La recarga depende en gran medida de la evapotranspiración. Se han desarrollado varias fórmulas para evaluar la <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/potential-evapotranspiration title="Learn more about Potential Evapotranspiration from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=1>Evapotranspiración Potencial</a> </span><a class="anchor u-display-inline anchor-paragraph" href=#b0545 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0545 data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Thornthwaite, 1948</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0450 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0450 data-moz-translations-id=6><span class=anchor-text data-moz-translations-id=7>Penman, 1948</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0555 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0555 data-moz-translations-id=8><span class=anchor-text data-moz-translations-id=9>Turc, 1961</span></a>). Estos modelos proporcionan una evaluación de la cantidad de agua que se evaporaría o transpiraría por vegetación si hubiera agua suficiente disponible en los suelos.<p id=p0170>En realidad, durante períodos de bajo agua, los suelos se vuelven fuertemente insaturados y las plantas pueden sufrir un déficit hídrico, reduciendo su capacidad de traer agua del suelo a la atmósfera. La Evapotranspiración Real (RET) es por lo tanto menor que la PET. En los climas húmedos la diferencia puede ser insignificante, pero se vuelve significativa o incluso extrema en los climas más secos.<p id=p0175>Muchos modelos se pueden utilizar para la evaluación de PET y RET, desde muy simples a muy sofisticados. Las mediciones reales de RET son difíciles, especialmente a la escala de captación.</p></section></section><section id=s0050><h3 id=st070 class="u-h4 u-margin-m-top u-margin-xs-bottom">1.5. Objetivos del desafío de la modelización</h3><p id=p0180>El objetivo general del desafío de modelización, del que presenta este trabajo, es explorar modelos para su capacidad de reproducir los siguientes aspectos: recarga subterránea, velocidad de flujo de aguas subterráneas en el espacio y el tiempo, cabezales de agua subterránea en el espacio y el tiempo, hidrógrafos de descarga de primavera.<p id=p0185>El primer paso del desafío se limita a la relación entre los parámetros meteorológicos (parámetros de recarga de las aguas subterráneas) y los hidrógrafos de primavera (tasa de descarga a la producción del sistema), es decir, se centra en aspectos de la recarga de las aguas subterráneas y los hidrógrafos de primavera.<p id=p0190>Cualquier grupo o persona interesada en comparar los resultados de su modelo con los demás fue invitada abiertamente a participar <a class="anchor u-display-inline anchor-paragraph" href=#txtfn1 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=txtfn1 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1><sup data-moz-translations-id=2>1</sup></span></a>1.</p></section></section><section id=s0055><h2 id=st075 class="u-h4 u-margin-l-top u-margin-xs-bottom">2. Enfoques de modelado en comparación en KMC</h2><p id=p0195>Todos los tipos de modelos descritos anteriormente se aplicaron para el desafío: Las dos categorías de modelos de parámetros agrupados (redes neuronales y depósitos), así como modelos semidistribuidos y completamente distribuidos.<section id=s0060><h3 id=st080 class="u-h4 u-margin-m-top u-margin-xs-bottom">2.1. 2.1. Modelos a base de datos 1: Redes neuronales</h3><p id=p0200><span data-moz-translations-id=0>Dos equipos de investigación aplicaron <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/neural-network title="Learn more about neural networks from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=1>redes neuronales</a> para la simulación de hidrográficas kársticas (KIT-Karlsruhe con tres modelos y IMT Mines Als con uno). Este último equipo utiliza</span> <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/self-organizing-systems title="Learn more about Perceptron from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=2>Perceptor</a> MLP) el antiguo equipo aplica el modelo NARX, Convolutional Neural Networks (CNN), así como Long Short-Termry Memory Networks (LSTM).<p id=p0205>Las redes neuronales artificiales (ANN) forman una rama de inteligencia artificial. Se basan en operaciones de una sola neurona, que se arreglan y conectan en una arquitectura específica utilizando parámetros (también llamados pesos). El comportamiento del modelo está programado por los valores asignados a los parámetros establecidos. Durante la etapa de entrenamiento, los datos se presentan a la red y los algoritmos de entrenamiento especiales se ajustan a los datos objetivo modificando los parámetros entre las neuronas. Una neurona calcula dos valores: (i) la suma ponderada de su vector de entrada con sus parámetros, y (ii) su salida, que transforma la suma ponderada en un valor escalar utilizando funciones predefinidas (por ejemplo, lineales o sigmoid), conocidas como funciones de activación. La respuesta no lineal de las funciones de activación sigmoide de la calificación permite a la ANN capturar relaciones no lineales dentro de los datos de entrenamiento.<p id=p0210>En esta etapa, es importante notar que los modelos ANN no tienen función predefinida antes de entrenar. El objetivo del entrenamiento es construir tanto la función como para calcular sus parámetros. Para ello, es necesaria una base de datos que represente todo el tipo de conductas. Por lo general, para representar el comportamiento con buena calidad, la longitud necesaria de la base de datos depende de los comportamientos a considerar. Cuando la base de datos es continua con un pequeño paso de tiempo (1 h), como en este estudio, se llevan a cabo varios comportamientos en diferentes escalas de tiempo; por lo tanto, la base de datos debe ser lo suficientemente larga para permitir que el modelo capte los fenómenos subyacentes: 15 años deben ser necesarios (<a class="anchor u-display-inline anchor-paragraph" href=#b0020 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0020 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Artigue et al.,</span></a> 2012, <a class="anchor u-display-inline anchor-paragraph" href=#b0100 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0100 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Coutouis et al., 2016</span></a>). En este estudio sólo se disponía de 4,5 años en dos períodos diferentes de tiempo. Por lo tanto, es importante notar que la base de datos utilizada en esta labor no es suficiente para los enfoques de ANN. Los resultados mostrados a lo largo de la siguiente A continuación pueden considerarse como una prueba de viabilidad.<p id=p0215>A continuación se resumen los diversos enfoques (modelos) utilizados en este estudio. Como estas técnicas son diversas y no se utilizan comúnmente en la modelización de kárstico se dan más detalles como <a class="anchor u-display-inline anchor-paragraph" href=#s0255 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=s0255 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>material suplementario</span></a>.<p id=p0220>Todos los modelos de red neuronal diseñados en este trabajo utilizaron precipitación y temperatura por hora, datos diarios de <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/evapotranspiration title="Learn more about evapotranspiration from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=0>evapotranspiración</a> remodelados a paso hora por hora, y descarga de datos a un paso de hora. Los rangos de datos utilizados para la formación o validación difieren ligeramente dependiendo del modelo.<section id=s0065><h4 id=st085 class="u-margin-m-top u-margin-xs-bottom">2.1.1. Perceptor multicapa profunda recurrente (ANN/rec-MLP)</h4><p id=p0225>El perceptor multicapa elegido es una red neuronal recurrente (RNN) con una capa oculta de <sub data-moz-translations-id=1>h</sub>neuronas <em data-moz-translations-id=0>ocultas</em> y una neurona de salida. Las neuronas de capa oculta no son lineales y aplican una función sigmoide. La neurona de salida es lineal; su salida es igual a la suma ponderada de sus entradas. Esta arquitectura específica muestra las dos propiedades de aproximación universal y parsimonia, esta última debido a la nolinealidad relativa a los insumos y parámetros del modelo (<a class="anchor u-display-inline anchor-paragraph" href=#b0030 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0030 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Barrón,</span></a> 1993). La aproximación Universal (<a class="anchor u-display-inline anchor-paragraph" href=#b0265 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0265 data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Hornik et al., 1989</span></a>) significa que el modelo es capaz de identificar cualquier función diferente, siempre que la existencia de una base de datos que represente el comportamiento para identificar. Para más detalles sobre esta arquitectura, el lector interesado se refiere a <a class="anchor u-display-inline anchor-paragraph" href=#b0160 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0160 data-moz-translations-id=6><span class=anchor-text data-moz-translations-id=7>Dreyfus (2005)</span></a>. En este estudio se utiliza un perceptor multicapa profundo añadiendo una capa profunda, conectada para implementar un preesqueque de datos. La neurona de salida es sigmoide. El modelo contiene 381 parámetros.</p></section><section id=s0070><h4 id=st090 class="u-margin-m-top u-margin-xs-bottom">2.1.2. Modelo NARX</h4><p id=p0230>El modelo NARX (red autorista lineal con entradas exógenas) utilizada por KIT (Karlsruhe) es bastante similar al modelo anterior utilizado por la escuela minera en Als (Francia). Las diferencias son, por ejemplo, el número de capas ocultas, los datos de entrada que se utilizan y la arquitectura.<p id=p0235>Los modelos NARX también pueden pertenecer al grupo de redes neuronales recurrentes (RNN). NARX en particular tiene una conexión de retroalimentación global entre su salida y capa de entrada, permitiendo el flujo de información en diferentes direcciones de la red, y haciéndolos especialmente adecuados para modelar sistemas dinámicos no lineales. Esto también significa que cada valor de salida no sólo se retrocede en las señales de entrada independientes, sino también en las señales de salida anteriores. Dependiendo del modelo elegido, los RNN pueden tener dificultades para capturar dependencias a largo plazo mayores a 10 pasos de tiempo debido al problema de la desaparición de los gradientes (<a class="anchor u-display-inline anchor-paragraph" href=#b0595 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0595 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Bengio et al.,</span></a> 1994); sin embargo, NARX puede mantener la información hasta tres veces más tiempo que los RNN simples (<a class="anchor u-display-inline anchor-paragraph" href=#b0365 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0365 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Lin et al.,</span></a> 1996, <a class="anchor u-display-inline anchor-paragraph" href=#b0370 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0370 data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Lin et al., 1996b</span></a>). Para construir y aplicar los modelos NARX, utilizamos Matlab 2019a (Mathworks Inc.) y su Deep Learning Toolbox. El modelo contiene 371 parámetros.</p></section><section id=s0075><h4 id=st095 class="u-margin-m-top u-margin-xs-bottom">2.1.3. Red de memoria a corto plazo Largo</h4><p id=p0240>Enfoques de aprendizaje profundo como Long Short-Term Memory Networks (LSTM) recibieron mucha atención en los últimos años, debido a éxitos significativos en varias disciplinas. Los LSTM también pertenecen al grupo de RNNs y se aplican ampliamente al modelo de datos secuenciales como series de tiempo o lenguaje natural. A diferencia de otros RNN, los LSTM han sido diseñados explícitamente para superar el problema de los gradientes desaparecidos. Además del estado oculto inherente a todo tipo de RNNs, los LSTM tienen una memoria celular (o estado celular) para almacenar información y tres puertas para controlar el flujo de información. Esto permite que la información permanezca en la memoria celular, razón por la cual los LSTM pueden manejar dependencias a largo plazo (<a class="anchor u-display-inline anchor-paragraph" href=#b0255 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0255 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Hochreiter y Schmidhuber,</span></a> 1997). El modelo LSTM que simula la salida de la cueva de Milandre tiene 11.111 parámetros entrenables.</p></section><section id=s0080><h4 id=st100 class="u-margin-m-top u-margin-xs-bottom">2.1. 2.1.4. Redes neuronales convolucionales</h4><p id=p0245>Convolutional Neural Networks (CNN) son un tipo de red neuronal de aprendizaje profundo multicapa que fue diseñada originalmente para manejar eficientemente los datos de imagen, pero que también se ha aplicado con éxito a la predicción de series temporales, tratando una secuencia de observaciones como una imagen unidimensional. La CNN que simula la salida de la cueva de Milandre tiene 950.921 parámetros entrenables.</p></section></section><section id=s0085><h3 id=st105 class="u-h4 u-margin-m-top u-margin-xs-bottom">2.2. Modelos basados en datos 2: embalse</h3><p id=p0250><span data-moz-translations-id=0><a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/gardenia title="Learn more about Gardenia from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=1>Los</a> modelos <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/gardenia title="Learn more about Gardenia from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=1>Gardenia</a> (BRGM), RCD-Seasonal (TU-Freiberg), CHLEM (Uni-Zuerich) y KarstMod (SNO KARST) son herramientas muy similares diseñadas para simular los principales procesos del equilibrio hídrico a escala de cuenca utilizando leyes físicas simplificadas que representan flujos a través de una sucesión de embalses. Utilizando series temporales climáticas (precipitación,</span> <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/potential-evapotranspiration title="Learn more about potential evapotranspiration from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=2>potencial evapotranspiración</a>, temperatura del aire) en una zona de recarga, estos modelos son capaces de calcular el flujo en la salida de un KHS (primación).<section id=s0090><h4 id=st110 class="u-margin-m-top u-margin-xs-bottom">2.2.1. CCD-seasonal</h4><p id=p0255>El sistema <span data-moz-translations-id=3>kárstico</span> está representado por tres almacenes, que están conectados por enlaces: un almacenamiento para representar el sistema <u data-moz-translations-id=0>de</u> carga <u data-moz-translations-id=0>de carga (</u>suelo y epikarst), y otros dos para el cachorro <u data-moz-translations-id=1>c</u> y el .</p></section><section id=s0095><h4 id=st115 class="u-margin-m-top u-margin-xs-bottom">2.2.2. KarstMod</h4><p id=p0260>KarstMod es una plataforma modular para modelar la relación a nivel de lluvia en las cuencas kársticas desarrolladas por el francés SNO Karst. En su forma más completa (<a class="anchor u-display-inline anchor-paragraph" href=#b0420 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0420 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Mazzilli et al., 2019</span></a>) ofrece 4 compartimentos organizados como una estructura de dos niveles. En este estudio, se utilizó para probar el rendimiento de una función de transferencia de dos parámetros con un tiempo característico infinito (<a class="anchor u-display-inline anchor-paragraph" href=#b0220 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0220 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Guinot et al., 2015</span></a><span data-moz-translations-id=4><span data-moz-translations-id=5>). El modelo propuesto tiene una estructura de dos niveles. Un compartimento superior está destinado a explicar el flujo dentro del suelo y <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/epikarst title="Learn more about epikarst from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=6>epikarst</a><span data-moz-translations-id=7>. Sobre un cierto nivel de agua se desborda a un compartimento inferior, lo que significa la <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/infiltration title="Learn more about infiltration from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=8>infiltración</a> y</span></span> <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/phreatic-zone title="Learn more about saturated zones from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=9>las zonas saturadas</a>. La respuesta de unidad del compartimiento inferior es una</span> <em data-moz-translations-id=10>función</em> tiempo, que permite tiempos característicos infinitos. Tal respuesta puede ser muy adecuada para reproducir los efectos de memoria a largo plazo de KHS.</p></section><section id=s0100><h4 id=st120 class="u-margin-m-top u-margin-xs-bottom">2.2.3 Gardenia</h4><p id=p0265>El código numérico GARDENIA (<a class="anchor u-display-inline anchor-paragraph" href=#b0535 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0535 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Thiéry,</span></a> 2014, <a class="anchor u-display-inline anchor-paragraph" href=#b0540 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0540 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Thiéry,</span></a> 2015, <a class="anchor u-display-inline anchor-paragraph" href=http://gardenia.brgm.fr/ target=_blank rel="noreferrer noopener" data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>http://gardenia.brgm.fr/</span><svg focusable=false viewBox="0 0 8 8" aria-label="Opens in new window" width=8px height=8px class="icon icon-arrow-up-right-tiny arrow-external-link" data-moz-translations-id=6><path d="M1.12949 2.1072V1H7V6.85795H5.89111V2.90281L0.784057 8L0 7.21635L5.11902 2.1072H1.12949Z" data-moz-translations-id=7></path></svg></a>) tiene 4 embalses: i) un reservorio superficial S que representa la capacidad de retención de agua del suelo (ley cuadrática), ii) un embalse lineal intermedio que representa aproximadamente la zona insaturada, y dos depósitos lineales subterráneos G1 y G2 que representan compartimentos de la zona saturada. La descarga total es la suma de los flujos rápidos, lentos y más profundos, que se enruta a la salida.</p></section><section id=s0105><h4 id=st125 class="u-margin-m-top u-margin-xs-bottom">2.2.4. CHLEM (Uni-Zuerich)</h4><p id=p0270>Modelo de Elemento Lumped de la Cueva (CHLEM) proporciona diferentes tipos de elementos hidráulicos que se pueden unir libremente. Para KMC, el <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/actuator title="Learn more about hydraulic system from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=0>sistema hidráulico</a> se simuló con los conductos paralelos que producen la descarga del modelo, que se compara con las mediciones. Cada una de las cuatro tuberías consiste en un elemento de almacenamiento y un elemento de retardo y recibe agua de un elemento de entrada con o sin evapotranspiración. En los elementos de almacenamiento, la descarga depende del volumen de almacenamiento después de una ley de alimentación, y está limitada por un parámetro ajustable. Cada uno de los elementos tiene de uno a tres parámetros ajustables que fueron ajustados con un algoritmo de optimización (COBYLA de la biblioteca de optimización de nlopt).</p></section></section><section id=s0110><h3 id=st130 class="u-h4 u-margin-m-top u-margin-xs-bottom">2.3. Modelos semidistribuidos</h3><p id=p0275>KRM_1 (SISKA), Varkarst (Uni-Freiburg), and InfoWorks (TCD Dublin) models are all semi-distributed models but with significant differences. The number of parameters for these models is moderate.<p id=p0280>KRM_1 is very similar to lumped parameter models with reservoirs but input parameters can be semi or fully distributed. It has been developed as an alternative to models (daily rainfall-runoff models based on two reservoirs and three parameters (<a class="anchor u-display-inline anchor-paragraph" href=#b0175 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0175 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Edijatno and Michel, 1989</span></a><span data-moz-translations-id=2>), in order to further refine interception and soil infiltration processes which are assumed to be the main recharge controls for <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/lowlands title="Learn more about lowland from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=3>lowland</a><span data-moz-translations-id=4> and vegetated karst aquifers. Storage capacities of interception reservoirs vary according to the type and respective proportion of land-uses (forests, cultures, urban areas, denudated areas, etc.) and according to seasons in order to reproduce the dynamic of the vegetation. Interception and ETP parameters are not uniform over the whole catchment. Two slow reservoirs are introduced to mimic processes in the soil/epikarst and in the deep <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/vadose-zone title="Learn more about vadose zone from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=5>vadose zone</a>. They allow underflow and overflow depending on recharge intensity. Parameters of soil/epikarst reservoirs vary according to a drought index while those of the deeper reservoirs are unique. In addition, an “exchanger” makes the recharged water pass from the low permeability volumes (LPV) to the karst conduits and vice-et-versa depending on pseudo hydraulic relations. The complete description of KRM_1 is available in Malard [2018]. The spatial distribution of rainfall over the catchment area can be taken into account, but this was not applied in the present application (rain is spatially uniform, but vegetation cover is semi-distributed). The number of parameters for the calibration is 18.</span></span><p id=p0285>Varkarst (<a class="anchor u-display-inline anchor-paragraph" href=#b0240 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0240 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Hartmann et al., 2013</span></a><span data-moz-translations-id=2>) is a semi-distributed model that considers the spatial heterogeneity using a distribution function. The meteorological forcing is assumed homogeneous over the catchment area. With shape parameters describing the distributions of soil and epikarst storages, vertical <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/hydraulic-conductivity title="Learn more about hydraulic conductivities from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=3>hydraulic conductivities</a>, the separation into diffuse & concentrated recharge, and the groundwater hydraulic conductivities, the model consists of 15 compartments reproducing the spatial variability of recharge and storage dynamics. This value was tested in </span><a class="anchor u-display-inline anchor-paragraph" href=#b0235 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0235 data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Hartmann et al., (2012)</span></a>, and shown to be enough to obtain stable integrated discharge and its variability. It has also been confirmed by several following applications (<a class="anchor u-display-inline anchor-paragraph" href=#b0240 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0240 data-moz-translations-id=6><span class=anchor-text data-moz-translations-id=7>Hartmann et al., 2013</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0250 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0250 data-moz-translations-id=8><span class=anchor-text data-moz-translations-id=9>Hartmann et al., 2021</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0375 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0375 data-moz-translations-id=10><span class=anchor-text data-moz-translations-id=11>Liu et al., 2021</span></a>). Each compartment includes three superposed reservoirs (soil, epikarst and groundwater). The first 14 compartments represent the dynamics of diffuse recharge and matrix flow, while the last compartment represents the dynamics of concentrated recharge and conduit flow (<a class="anchor u-display-inline anchor-paragraph" href=#b0245 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0245 data-moz-translations-id=12><span class=anchor-text data-moz-translations-id=13>Hartmann et al., 2014</span></a><span data-moz-translations-id=14>). The discharge of the main spring is determined by the addition of flow from the groundwater reservoir of all model compartments, which are controlled by the variable <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/groundwater-storage title="Learn more about groundwater storage from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=15>groundwater storage</a> coefficients and conduit storage coefficient, respectively. The number of parameters for the calibration is 8.</span><p id=p0290>TCD-Dublin developed a semi-distributed 1D model of the catchment based on the drainage software InfoWorks ICM (Innovyse), which models the hydraulics of the karst conduit network in both open channel and pressurized pipe flow (<a class="anchor u-display-inline anchor-paragraph" href=#b0205 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0205 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Gill et al., 2013</span></a>). The governing model equations are the Saint-Venant equations of conservation of mass and momentum. Diffuse recharge from rainfall is modelled per sub-catchment via a series of reservoirs: rainfall runoff, soil and groundwater stores in series, all yielding different delayed discharges in parallel into the pipe network. Flows can also discharge into permeable pipes, one connected for each sub-catchment to represent the primary and secondary permeability expressed by Darcy’s Law (<a class="anchor u-display-inline anchor-paragraph" href=#b0435 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0435 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Morrissey et al., 2020</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0495 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0495 data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Schuler et al., 2020</span></a>). The higher the density of these pipes included in this type of model moves towards becoming a fully distributed.<p id=p0295>In the InfoWorks model, the Milandre catchment area is explicitly subdivided into 30 sub-catchments of 0.45 to 0.475 km<sup data-moz-translations-id=0>2</sup>. The sizes of the sub-catchments are the result of modelling experience with respect to balancing the numerical stability of the model along with reasonable diameters of the draining pipes and heads. Every sub-catchment generates recharge and storage through a hierarchical sequence of processes related to a) runoff (quick recharge); b) soil store contribution (intermediate recharge); and c) groundwater store contribution (slow recharge). 19 parameters control these dynamics, and in this model, all sub-catchments have the same parameter settings.</p></section><section id=s0115><h3 id=st135 class="u-h4 u-margin-m-top u-margin-xs-bottom">2.4. Fully distributed models</h3><div><p id=p0300>In KarstFLOW (<a class="anchor u-display-inline anchor-paragraph" href=#b0445 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0445 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Pardo-Igúzquiza et al., 2018</span></a>) recharge is split between fast recharge and slow recharge. Fast recharge is a percentage of rainfall that reaches the water level through the conduit network instantaneously (for practical purposes). The slow (diffuse) recharge takes place over the whole recharge domain, while fast (concentrated) recharge is limited to specified regions (<a class="anchor u-display-inline anchor-paragraph" href=#f0010 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0010 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Fig. 2</span></a><span data-moz-translations-id=4><span data-moz-translations-id=5>). Diffuse recharge is estimated by the method of Thornthwaite applied to the percentage of rainfall that does not undergo quick recharge. The model domain is discretized into voxels with absolute altitudes using a <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/digital-elevation-model title="Learn more about digital elevation model from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=6>digital elevation model</a><span data-moz-translations-id=7>. The diffuse flow along each column of voxels is described by the <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/richards-equation title="Learn more about Richards equation from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=8>Richards equation</a> (assuming a porous medium). When the non-saturated flow reaches the phreatic zone, the one-dimensional vertical flow couples, as recharge, with a two-dimensional domain where flow through conduits and fractures is simulated by the equations of a two-dimensional Darcian flow in a </span></span><a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/porous-medium title="Learn more about porous medium from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=9>porous medium</a>. The karst spring discharge is simulated by using a drain. The parameters needed by the model are described in </span><a class="anchor u-display-inline anchor-paragraph" href=#f0010 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0010 data-moz-translations-id=10><span class=anchor-text data-moz-translations-id=11>Fig. 2</span></a>.<figure class="figure text-xs" id=f0010><span><img src="data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAcQBxAAD/2wBDAAMCAgICAgMCAgIDAwMDBAYEBAQEBAgGBgUGCQgKCgkICQkKDA8MCgsOCwkJDRENDg8QEBEQCgwSExIQEw8QEBD/2wBDAQMDAwQDBAgEBAgQCwkLEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBD/wAARCADcAXoDASIAAhEBAxEB/8QAHQAAAgIDAQEBAAAAAAAAAAAAAAYFBwMECAIBCf/EAFcQAAEDAwMCAwQFAwwPCAIDAQECAwQFBhEABxITIQgiMRRBUWEVFiMycUJSVgkkM1NVYnKBkpOU0hc0NTZjc3R1gpGVsbLR0zdDRIOhs7TUJeNFosFk/8QAGwEAAgMBAQEAAAAAAAAAAAAAAAQBAgMFBgf/xAA7EQABAgQDBQYEBgEEAwEAAAABAAIDBBEhEjFBE1FxgfAFIjJhkbEGFKHBI0JS0eHxcjOCosIVQ2Ky/9oADAMBAAIRAxEAPwDthd+VOl2/RKlWLsuKROrq0MRYkFphbkh8trdUhCemAAENuKJUQAEnJ+OR7cuiRFPM1PdeVAkRWurKjypUVt2OMIylY6fZQ6jeRntzT8RqMlU+1nttKbXLsD7cW3oQq6ZUaQ6w/FU2wsKcbW0pKwrpqcTgHzBRScg60axa2z1FS1UrqcEBU0e2paqNVeJS4osBx7iXCA4VIYStwd1KOCSVnkviK6uybTIJjG5NAU31Ru+7w6C5RV7XGwhpClpUtR6flAU24O+O6FD1B1jRujbTjjLLe8bi3JCC40hMyMVLHNxHYBvOebLqcevJtQ9QRpJu2LsZbtZqVsXFBlsu12EuTOYD8gR56X5L7gSpPMJW51nn1JyPJy9U4TiflWzs7a8iDTqguDCfkmE5HbemrCnCxKcdjq+96deS5gnspTgT37DU4ijZM3BSMDdu16lT2qnF3elll2AzVPM6ylaIr37G4tJaygK74yB6H4HXxG7tqKM3lvE+0mBJTEecdkR0I6imEPjiotYWnpOIVyGUgHudKVKp+x1wWJTnH2JEWh1qSxSqemfOfQ7LVEDqWEI+0KyA2l0hJOeCSVDy9sciheHZwMJfdgrYjLHBxVRcKI7rTUSH0+JXySpTa4aCAnz/AGZPcpJMRUbNm4J5h7lUuo3FMtmBuVU5EqnQnZ8txt2Oplhtt3pLCl9PAUlYII93E59NeWt0rXfDSm958pfjKmNqM6KApkIUsuAlv7vBtas+9KVH0GdJ1KrGxlNrlaNPlFpk0guTJy5rqoKIs1511TbfJZSjktLriuCQE98keg+tULZm6qxXA1aT8yZT6S5Fl4Kh7RGZ60MJQQvzL4tuJSrscFJz2GCpU7Jm4Jti7qUOXXHLeRuhUUS0x2pTYcdjoS8hYeI4Et+YgR3SRj0SfnjIN0bZPDG8i8uNOvJHtkbPBtfBaiOnkAKHE59/b1OtRixNvr0osWsrjy6pFq0aPLblP1CQp15vovJaUVlfLPTlvD1z5/3qcaUzYmwV21ULZpMWXSmqk0GXn40pZd4+0CQcFZOMuJySMHBOCO2IxI2I/SFOUncig16XEgUbd92ZJnJK4zTMuMpboHLPEdPucNrOPXCFH0B0x869+mFa/lMf9LSVZuzVlWXKh1WFEek1aI10fb5Dn2jiQFpTyCcJJS2sthWOXEDJJGdPOguOis2Cyl2hJe7173Vt1ttXb2p9yVOXIpLCXm2X1tBCyXEpwcN5/KOpGyrjua6bGol1SLqqzD9WpcectttbJQ2pxoLIGWs4BPv1zl4uqvuZZUa4BImPVix7zhNwm2lAD6ImIUhQAIH3VhCj39cn3pGZ3wuVfcy+4UO6KjMepVlUKis0Om00JGKg+20lDklRIzgKScEfwR6KzxW9pvPaBl6HLLme9wpT2pVfTY3wPLw/hBvbOKH4ycdTUgsaBCpnjD8VqUoC6uGhXY1jz5dVsugVSoPF6VMpcWQ+4QBzcW0lSlYHYZJJ7am9Lm2//Z5a/wDmWD/7CNMDjrTKebzqEJzjKiAM69EF8cK96NQV9XG5aFl1262YqZS6PTpE5LKl8Q4W21L4lWDjOMZxqJ2r3B/sg7ZUDcOoxGaZ9MwUTHGQ9zQzy/J5kDP44Gs9q3abL81K8sk2JCYMmZ/D+EHBlajxEFwFM8gb0onPVB7heJep2T4sNuPDexakaVDvqlSqi7VFylJcilkPEJS2EkKB6I7kj72rMva8pdu2hWrvYpFSkQKLBfnuIhxurNlIaQVFEdlRHJZAwORHf3H11+dl2+IjabcbxobAb8Wvdhh2wxZ9Zl1CRVV5dp62UTC83IRyPFY/NSe+U8cgjW2GmaRDq5L9Q9GqT2z8SVO3j2PqG8trWzXqZSzHnqgP1CGEKeDCVYfDYUo9NSknB7jsc476Wv1Prd7cHfLwz0Tcbc+ttVSvVCdPbW+3HbYHSafU2gcGwEjAT6476gghSCCukdGjXhx5pkBTrqEAnAKjjJ1Cle9GjRoQjSpumAdv60k9wphKSPiCtIIPyI016VN0/wC8Csf4pH/uJ0KDkqO3Ou2hbfzVtN2fbDzbFFl1paJQbYdkhhxtPs8cdM8nV9Tyj3q4j35C3UtzlLrC6db209JksomNQFPPx46MOuVlynd0lYOE9Fa/3xIHlGrWuK4qNSa7S6fUKK/Lkyl4jvoYbWGCVBOSpRBT3wfKPdpKrO7Uqh3pWKNNtZlNMpcxbXtI4KceQzS11F5acK+/nilKVAdypXL1A6JFNUkFrN7v7SPLjJYtRpxLqYCn3BBicIYlrkIb6x5eQhUVQUPcVt+ue2zQ9zdtq/aNw3rBsCUin29EVNWJFFZZclNBK1fYheAVfZqHFRTgkZxnWvVN8bNp1S+hXrUkx6wYqakWnWI7gaWQ2sJcU24oBzg+lYwT949/j4nb5U5EmLBolmPse3y2nZjs0RkNmCv23nJwh3zHNPeGFEEApJB9NRUb1NCvr+7W28Wq1Oiy9tprEqkuNRpJcpEZLKZLjkZAZ6xV0x5pbXnKuGAs8sDJxndOx6dUq1FuGwIcOLAeU3ElNsQ3W5HGDDkqbJSs/aZl8RjKFcRhRJAOKJv3Q7mq1OTQbabXHqUlNNmOT2kpdGZUJrA4khaOnNCxk+oAx66Zr63Qtqxl09yXQhUaNOo9QqxmQiy4npRkMng23nLvNDoxxyMJ/NyQc0USs7vhtS1RHbhVYDqKewzGU4+9BgspQ8/IdYQyeo4khXJh0knygAebJA1O2PuBt9flfdt2nbeS4ciPHDzzk2isttNr6bKy0VjI5gPo9Mg4VgkDJh6nupYd0QYVMm7Y1qaw8/DLbKmIzPRfdmPRIxBU8kjLra8KT2CV5JGSNeIPiD2tt+lx10u1ahEgxaWyppMWLHLjTAp6pbbIZQ4XeKWm1J5BPTCwBywc6Ab5oorZ+rVt/o5Sv6E1/V0fVq2/0cpX9Ca/q6Qmd/LfdhvVBVtV1iPGiIlvuSEMNhAclORmkAF3kpTjjR48QRgjJB7awUHxDWxWVNy5FKl06mSkwFRZkp9hGRKYedBcR1MthJYU3nuCogD11erVGEqxPq1bf6OUr+hNf1dH1atv9HKV/Qmv6uq1e8SdrNUhquItW5XYkl6NGjKajsrLzr1ORP4AJdJTxZdQCVYBWeKSr10z35uKm2LUjXHRo8aeuTUodOLbr4bDKnlhKuffspOe6SR37HGgFpuFFCmP6tW3+jlK/oTX9XR9Wrb/AEcpX9Ca/q6ry490ryt+XVgmhUWZFp8KkymizIeU66Z0pUdKeKUEHiUKV2J5AgD46dLZuhypW6xVbljxaLN63s0qKuWhYYeK+LaFK7cVrSppQQfMOolJGdAIJogghb/1atv9HKV/Qmv6uj6tW3+jlK/oTX9XUS5fEFy6qFRKZIhVCHWmZn64jSA4WnmW2XkA8SRwU04Tn17t+oVpo1NAhRv1atv9HKV/Qmv6uj6tW3+jlK/oTX9XUlqIu6m1ysWrV6TbNeNEq8yE8xAqQZS97HIUght3grsriog4PrjRQKFTN97wUqzvE7t74fmdtaDJi3tS5VQdqam0JcjFkPEJS2EYVnojuSPvfLV1/Vq2/wBHKV/Qmv6uvyNv3xObx2b4lrFqG6tlvVHc3bGm1C21ttIAbrEp8PpiSEpQBlKuugniPMBlOOWB1HTNlP1S6r02JVZ/igpFKlTWG5D8BVNjkxHFpClMkpZIJQSU9jjtrBsSpNlqWUAuus/qrU6nYblk3BZ1cLMymrpssMobzxWgoUUqC/XBJB/DXyq2DDrz0SZXLHuKZMiMMMCSFFlawy8h9BUGnUpJDzSF+nYj4EjTZStxrvq9NjVNmiUZtuU2HUIXMdKkg9wDhv11mG4F0qlKgiFbxkpbDqmROd6gbJICinhkJJBAOMZB+GuZ3N67QdMEeEJVuOzYl28PrDYFcllpPFonKC0eaV8kcXRwXyQnzjCsDGcEg6kzbiiznKe5I25rmKZGYhMIbUpttUdhxLjLTiEuhLqELSlSUuBQBz8Tl6+ut6fuRRP6W9/09H11vT9yKJ/S3v8Ap6jub1OKYP5QlBNmw0QaXTUbfVsRqK467BRg/YrcadZWc9XKstvup7k/eyO4BEHRdmLOt5LSKTtbXGehITKQS64sh1Koykqyp45wYUX17fZfNWbCi7jXJOckMwo1uSFxHehIS1PcUWnMA8FAI8qsEdj31sfXa8/3Iof9Le/6ep7u9FY5/KFWdS2Usur0x2jz9rK45EeVzcbDzqOR+396Xgf/ABT/AG/f/JOJeFYcSkCe5Q7Gr9Ok1GM5FckNJClpSta3MpC3FJBC3FqHb1OPTtp1+u15/uRQ/wClvf8AT0fXa8/3Iof9Le/6ejub0Vj/AKQl6hUeXblEp9vUuza43CpkVqHHSWUEpabQEJBPPucJGt7lWv0Rrf8AMI/r6ZLRvCsVuv1ChVamwmFQ4UaYhyM+tYUHXHkFJCkjGOjn3/e+WnDVhDabrIzUVhwkBVXyrX6I1v8AmEf19HKtfojW/wCYR/X1amjRswo+cieSpW6rYTetvTrWuSw6zLptRaLT7SmEDt7iDz7KBwQR3BA1sUykSKDRY9Coti1eLBhRxHjMNx0BLaEjAA8+m69t5NtturptSy7yuZum1m95aoNBiqYdWZr6SgFAUhJSnu4juogd9Sq78tNF5p28VWGxcK4P0kmDwVzMblx6mcccchj1z8tUMOEx2N1ATav2TcOen5iCZaHVzG1eWipAtQuIyFqAncvdjQZdLsq36bUGCzKiUuIw+2SCUOIaSlScjt2II7a5G/VbS+14WYkth1bao93UtfJCiDg9Qe78ddZXdftuWYhhurSXHJ00lEKnRWy9Llr/ADWmk9z81HCR6qIHfX5KfqhW/wDv3MpFf2f33oaKTRarU49fstcOCgJkxW3ylUeS6FrHVZQe/A/ezklJRloQ3YcRsFxzEBdhGa/RfePeezEbYXJbcGRLqcp+gy4jr0KI6/GjPGKs8HXkJKUqwDkdynty4g51IeFGJT5uwdizFyhMWzSmkJSVhSGFDOQEjsFD4nvriO0PEhubdu3dw343t/8ARWyc9cq3LfDKVIebdEFTAklIX9xaz5gQUj0GSDysXw0yqvelCjqtCjyrfplv0WBTZldgreZlTKip3BCUhXScQlKlJPUQr7qcY1znR5c9qNgMJDsNMtSQa/4015Zr6JC7Cnz8CxJ84TC2wfXELAMewt348RFG50Idldd+nAST8tfk0rwBWz4nN9ou9NoQkWntdU6vNN10wykhyLJjqSXER8AANySr4fZ4c93Eat39Tg3e3PZ2HmpqNLqN8oauSrJddXPWqewlsMk5U+em4nzdkhaSO+AdWz4E67aM/bubR5tYimri4qhJbpr76UrSlRQQtLZ+8fn3x8tMRRFhzEJv5XBx35YfTNeckZSVmOw52ccPxYT4LWmpFA/a4vI1wjhRbm9PiF2n29pNxeGuhpVb1VY28qNSorLsFTMN+IzEeCWYqjgKWEtKOOwwk45emqm/U9N1tnLF2B2a2irlYUxfd9JqcqnRYTDq5BaTMkEOOqbB6bfkUAV+U8Ve4E6t7x5eFemeKTaiTCt5xhm/7WQ5NoEhDgS4pRT9pEWR3CHU4HfsFcT6Zyo/qdngiT4b7Mbv3ciMJO5NdiJaeDqg59Cwz3TDbPcBXvcKTjOEjsnJYxUysvP4a5rrrhW4X3HG6g0PcvDb2PxHlUf4k/jriX9VXrHU2q23bZVJivjcOm821goVx6b3vHZQ/Aka7dkVZPXMKnsmXJScLSg4Q1/DV6D8O5+WvyG8eviG3IdmjY/xGUOPT7gta/INwUadS2HE0+o0ApeHUQlRJLicpBJJJPJPYpOZOXeUDOy/YdP3Rr6SEjJIA+euZPB/u/vF4g4N1bqVOixLb28n1Do2NEfaWuXKitlSXJDxUrslZCePHABCsAgAnR/VIqtWIXgw3FUWX4khDMDhJivHiP1/HB8wwpJx8sfPUU3Ka711SCCMjUNeVDkXJbFQokSQ2y9Ka4tuOAlIUCCMgd8dtKfhxlidsBtvJ9p66l2pSitZXyJV7I3nJ+OfXVjarkrZpA+ql8d8O0MZ/wAI9/V1qS9vrknuR3psS3H1xXvaGVL6pLbvBSOY8vY8FqTn4KI9+rK0a12796z2TVXRsm7VOdVTVulZATyPVzj4Z4+mvQs28QQoJt8Eehy7kev735n/AFnVh6NG2fvRsmqt1WFc62FRVRbb6K0lCkYc4kH1GOOvTdjXUywzGaj24hmMgNstp6gS2kDiAkccJGO2B7u2rG0aNs/ejZNVdN2TdrSUoaat1CU44hJdAGDkY8vx761423NwRJkmoRYFstSZnH2h1IdCnOKAgcjx74SlI/ADVm6NG2fvRsmquRY91paLCWLcDRTxKB1OJT64xxxjue2g2NdShxVHtwjiE4IcxgHIH3PQHvqxtGjbP3o2TVXS7Iu1xssuM26pskEoV1SnI7A44+7A/wBWsTW31yMR1xGYVsoYddW+toBzipxaipSyOGCoqJJPvJ1ZWjRtn70bJqrwWZeCQEhNvgAAABTuMD0H3fd7tYmLCueKl1MaNbjYfeVIdCeqObpOStXl7qJA7nv2GrI0aNs/ejZM3KtBt5caZ7NUTDtsS2GXI7Tw6oWhtZQVpB49gS23n48E/DW39U73/bKH/OPf1dWBo0bZ+9Gyaq/+qd7/ALZQ/wCce/q6Pqne/wC2UP8AnHv6urA0aNu/ejZM3Kgbj8L1GuvdO3d5q7bltybqtdh2PAmKW7gBX3VLTxwtTeVFBP3StRHuw/fVO9/2yh/zj39XVgaNG2eNUbNqpC14bku1KEW50qN0WGXSGCgdUBP3F8knyn34wfnpMuPYyVXrirF2MX7UYNTrTEunurZjpCW6e7HS2hhBSUugtrbbeC+p9/qEJT1DiXXaUq7LNtZuNV0w/o8tS1tOsreYlJ6C2+m6hDjaiAXAtPnGFoScHGNRUPZCQmeiRV73mz4olJfej8HW/bEhT6v1wrrELX9ukckhCeLQHD0wiu7SoFl5k7JVf6Urk+lbh1GMzVG0sxIzrS3kQmuUcloAuhK0cWFJTkBSess8ic8s9W2krUuyqDbbu40xtyiU+RCkT3m3P1wXEJSJWEvJKHmuJLalLWlPJWUnsRFRdkKxLttEBO7Eqep9qnqFUXGW4/IDDLKFIWoSOK2HCyXCgAHk4SVq7hWWreHtFXpU2mSL2qC/b4TsB5byXXgtlVPbipSpK3jyCXWhI7nJUSCc5URRS2SfLIsuLZFPnwYj6pBqFVm1R11SSFFch5ThB7nJHIDPbOM4HppgQpLiebakrTkjKTkZBwf/AFBGqqr2x1SqsOQ1Tb/kUmTMqCpz77ENauIDSENNNBT2WktlClJ4q/LIIPqYer7SwbIVDuKTf9RgUqFU3XG4UCC9yW9Lnl1sIDbpJeK3Q0VcSlaAkFAHLlLWlxoMypL9mKkUAV0tzIj0l+G1JaW/G49ZpKwVt8hlPIeoyO4z66y6p/a+BC26VVFyBdNVfqggIccFqz2iVNM9JK1cy4VLc+8o5AKiSAM6tGg12m3LSY9bpDq3IkkK4KW0ptWUqKVApUAoEKSQQR7tWfBiQxVzSAqwpiHFs1wJ4qVsT/tCrX+ZKf8A/Il6sjVYWe5Mb3BrBhxm3iaJA5BbpRj9cS/kdP8A7fU0D7SiOqP+CebUP/7FOtYbSWhc2ZcBFKkNYpUqNCjOzZkhtiOwhTrrriglCEJGSpRPYAAEk6iKjdApqmA9R6govL4cG2uawPzglJPID349BpbvTc6iRpD1nUSjrumsSMxX4DCCqMxyQSRLdCVJaBSFHjhSyPRB1qIT3ZBLGKxuZXIfjNvizbl8RXhDvW3rqpNSt9y7JjyKpFmNuxVIQ7FClB1JKcAgg9+xB0wK3dpm6Xi5kVvZa6YEtmn2i/SpVRAbc6a25JU70UrWlJcBUkDqYHcqwoYzwp4mfC7vltLuJaMbbFyoyrVqtQk1+3otMiSVxrcnO4XIbQHElSUpQylaV/lJazjKTro7w50/aDw+3lAn0C4xJoTNhPzKjWHC8h6fOXJ4urUkJ5BZWOAR7glIySMlDtB8KXdDEUioe03yoMz5he4+C5Ccnmzr5aG5zXQIjAWgnvuAwttqaGgzNDTJdAbsXJH2l2evnc230JqN1UmioqqZVfRHkvSH0NqWA4SsKLeR9xvCQBgAaUF7c2b4zvC7ZU/dylR2J9VojVeTJpLUWOuJKLiSvolSypKFDKCk5yD8QCGTxTVFmueETcWqNRpaWZVmiQ0mQmUlSQY6yOSVowD+J1q+FD6YX4W9tUU12Q0gWY0FqfElTZ7pJ4BKM57+vLgO2Ae416IOEZ4p4SP2Xzt8N8uHNeKOBIO8HVa3h0pNuTNva7YdagolUKDdlToUOAtEdfThNxihtlHJXLCRjBHoe+c99K2/Nw7h+HWy7QvDba16XK21tyPTTeNJRGjuVJcNbqlCS26FEhQVy5fNQKjxzh48LUhuHDul19h/l9fqxzkAP4wGT2UptBx3Oe4H4aSfHxuReO3nh+pdYsavSKa/Pn0CnSXEBx1L8ZaZRW0oOtlBSopTkfLHx1yeywDIsjuHfpTFrTEbVXtPjeK+D27NdnwnYYOMOwCzcWACuEWrQkVouPvCd4jroodmSdutsqfJjiBcM+5q/VXKKxNQzR3lxm+lxWfKThWTkY7YPqR0/wCFe7KfcTcbayLZMSsx3rglVaqz5jLQRBiKQlLXSWvv1VKSe2RjHb3lN17M+HrafYK2q/BsegmHEuFdWl1FclySrLQUEoYKi3+wtoyEg59VFRJOdKVV2ivpG0ldoHg0XNoM321FejXG7J4sTnGklQgt9Zvk8hX3AoAIGBlasrByiyM581DmGRKMaDXKouLA+dL650zTfZfxJ2HLfDs12XMypdMxHMLXAnCaB4Dnitaw8ZLaUDjhxWaa1Rslc9U2+8cu/M6i3A3iLSaYhuNV3FTFzOQYAZQoOlwr9yeHUOABxOu/bVuiv3VRY1SvO3ptodcZMJxzmpQ9xU8nHTB9ySEr+OPTX41bM72b83j4kL1pO2Fry4e7W48aFQXKjPbQp23nWAhNQk/cHTA6SwO2UJOPMoDP7b2dRqrQLRo9AuC4H6/UoEBmNMqchCUOTXkICVvKSkYBUQTgfHTsSKw1o29TdeJZDcKVOgsoTdW4JVhbPXjdlrojNy6DbtRqcHLYU11mYy3EEpH3hySM/HXKtmbO2z+qQeFza7cjf1ppFyx5r01c6ksCOpyOiY4h2LjJw24htIPfIUAofA9D+JGFS4Gwm5LrLyobjtpVgBtlWA6fY3cjp9wfmQM/PVNfqb4rDngx26Q80v6NDM7tDX9sv9ev55ZwQPkgk/7tYhuI5rTFQUouoKemk25Tolq2nSWUR6aw3EjQ4qQ2xFaQkJQgkdkAADAGTj0GvzP/AFTwb/7U0+8X6vXp927WbqRosRth5xXTteqsPNOpS0O4DbiWVgZxyKlZwUjl+n1LkUos+zU1TaEterITwUj8UnBH8Y1F7h7e2durZtUsC/qGxV6DWGSxLivZAUnIIIIwUqBAIUCCCAQdQTSwUi9yuLP1PCLvlu+zE3vvauVS2bCptuxLVtO2orvBqcmM2htyovDHnJU2riog5KlAeVI5dvyZdRosdyVMejyYjCStx1a0sLQkepJPkP45Tqsb/wB99qth6LGtGjssSZVMjIiQqJTOITHbQkJQhRHlaSAAMdzj0B1VMayt+vE4+3U78nuWlZy1BxqC2gpLqPcUtnus/v3O3vCfdrpy/Z0R7NtMnBD3nM/4jMpCPOsY/ZQBifuGnE5BdNWbf1o3/Cfn2jW49RZivFh8tHu24Pcf/wDCOxHcE6YdV/tPtlZ+2DdUo1qU7pJ6rIdkOK5vPkNJOVq/Ek4GAMnAGrA0hMNhNiEQa4dK5pyAYhhgxaYtaZI0aNGsVqjRo0aEI0aNGhCNapqlNBINQjAj1+1T/wA9ZZPaM7j8xX+7XL9Vrse1aFYMSHQ6Ko15Dcd+RLjNlLQTF6nLzONgklOPvZ+AOqudhW0KFta3pRdN/StM/dGN/Op/56PpWmfujG/nU/8APXKUreW2HnZsOj2TGMiJMkRAtxlh5slmTIjK6gbOWiXI5UkK7lBzjIIGCmb82LKjUIS7HKJdXQeqwmK0lyMsJc++2RlAUpleATyCSgkd+1dp5LX5YfqXWn0rTP3Rjfzqf+ej6Vpn7oxv51P/AD1ytUd3aRCRHaO2cRiY/HpswMTJEdCRHlSGGlLLiELSnpiQhSgrHfIGU+fWzuBunbG21aqsWt2jEkw4cSDIY9mjIDzhdEtT3qOJ4pip4jtkqIJ9MRtPJT8qP1fRdQfStM/dGN/Op/56PpWmfujG/nU/89cp1PfCyqct9hvbabIkQ5zlLktpisIDMxtLq3GCtWE8+m22tPfCg+0QQCopcrcr1FuGSlv6lRYLLs+oQGFyDGDjxiOKbccS0PMUlSDgDJAwTgEaNp5IEoDk76LoBmQxITzjvIdSDjKFAjP8Wsmq92qjx4tRuxmKw2y2KjHIQ2gJSP1mx7h21YWtAaiqVe3A4t3I0aNGpVVzg1TqbcNtWmw9dQp6KW63LkMszSyp/iw4gNL4rScBa0LwrIJbAIOly19talb4phn7zOVww3lOSUT5jnTkkoaSHcNvJKXAW1HBKkEuryknB11Oqk0tRKlU2KSTkkspyT/q18+h6T+5kT+ZT/y1ns0380NW/VckL2dnwbbjW1bW9hpUdiNDayl1Syh5llbanWyX/s+ZU2rgO32YHz14ufb67HK9Qnrd3DYdp7VSlVGql6vPsuOKdnNPNpSOagEtsoU0E4KVDKeKQokddfQ9J/cyJ/Mp/wCWj6HpP7mRP5lP/LRs/NHzI/T9Vz/thQE2FRn6dWtyVXHIecQsypksFRKW0oKgCo8Sop5KAOMk6N2JMep2ow3Sn2Zr0asUuYpmO+hThaZmsuOFI5DJCEqOMgnGugPoek/uZE/mU/8ALR9D0n9zIn8yn/lq8IGFEEQaGqrFmNrDMMixFFQQ3IoodCjSa3jqQ1f2ofRv73/f697VSY1JsOmwarKjw5SVyXFsPSGwtAXIcWnkAo4PFQOMn11fX0PSf3LifzKf+Wj6IpP7mRP5lP8Ay1vHjGMzAetErLASz8YvanVlX23kqLLv+tuRJLT6U0WnpKm1hQB68vtkas3WFiJFigiLGaZ5evTQE5/1azawAoKLSI/aOLlz34l7o3kYmN27tvR5cmlNwUyqsun8hLUFrWlKEqQeolPkJJbHL5gaQ9vd+tsY7kehzKOqz3EVIOqilhYjt/rfgolYcTglfclYBOck+urwv69ht/WrrulVKlTRTLUTOAabUpvk0ZS0oWUgqSFcccuJA750jzrYpnis2YsK/wCJR6HAqddbpFZqCgvktmM4lDkmKl5KeZPFRSM47/DXbhzsvAgCHGhEimbTc13g2P0XLhyMWbme5GDamnf8I5gVA4Aqbgy4c6isyoclp9ldMYKHG3kqSftT6EP41RU/wsWg5vw/erbUeRQy49JVQkdMNompSFJCvtOIZJJc4lWSQQAR201Xf4Wq9ZFahK2ZvubCdrDjrQiTHSlJ4NqdwXEDCh5MAKQe5znUQvdfdjaN9qBvBYclMQvOludBaYbS44tBT2VwU2o+/GQexOrzXY0n2qGua4RKHEAe66vA55XvdPdifFva/wALujCTe6DtW4HltwWn2IrZ2bb0Oa5//VBLX3ksWj1vdy07hn1mzrjttq3brt96YtcSAXWuDExhrqr44XjJx2UTnss4WPCRZO/2+G29Pvudd1StO3LHtpFIsemxZXBipSoywXZL6VrHlUvKOQHfOB2QeXb9D3K2v3QpDlvv1iG/HqTcSDKplTVHaU+ggpcbKFs4WCCQeJIxqVMy1Nvraptq0oNxWGaY9T6PR4AZcedQhwJQ0wylkqVgAfh6kgZOlprs90QmFGBAOlws+y+13dnxWTkqRiYagkAjgQbEEWIOYsudPDDZm5dzXcuqXsqRRqFRrpnz3IDLxZ9rrC0ErCh1ElTbfAD1weRGTk6T/wBUlr9tTtiqTS6HJal1Bit0BMoxMuNMOJblZQ+4hxaEOeZOErwrHoMa7cpW3963tIVUbsmyrZozr5kopMVxr6Qd5NhBD8lpIDQIz5GTnv8Af92vzk/VHvCreuxntNzbIw5Tm2d81uDMqtAYbU8inVxsqSw4hPchL3UUkY/KPE+qBrlSUrD7Kl/l2OLycz5/xlQWXpPiv4lmPjDtL/yEaE2E0WaxosBr5kk1JJvemQC/Q21tt2Z81pW4NZgXXPgtplLoUOTwiwS6eYUuMtRL5J7hbpx5QUoBHdf8T/ierWxNa2oo1q0Cmz035d0a3JyJ6XW3IjDikDm2lJT5hyOM5T21oeCXw4V/aC2qxuxvDPVUt19xVJqVyzHlD9Zt/ebho9yUtg+bHbIwPKhOqb/VAb4srcKseG2rWJc1JuOD/ZRis+0U2ah9nmlbYUguNkhJB7Eeut4kV0Y9/rkvOshiF4V2Jb2yG1Vm7mXLvPQ7VhQbqumOyzVqikYLiG/eB6IKsJKyMcihJOcaafpCXUsoo7YSyfWW6nyEfvE+q/x7J+Z9NK1v1W37rqVVp1BvykV+fbskR6lSmpaZCKc/3IbdCSVpX2P7JnGDhIxjTWmsJjHhVYrkM/th87J/0x6f6QGq4QMrqak52XEP6oftRu3aFMm+JTZyvVapuRbbm21eNBkSFux5dIkNuIVJQ0DxSpouc1BIH3Uq/JVmp/1MDbfd7dyJZO4161ipUXbfalmVBtWlxH3GEVioPOuLfkvAEdRCOqUd+xKUpH3V5/Tqq1CiRaRIm1qXDbpgaV7Q7JWkM9MjvyKu2CPjrmKr+ImDTY0PaHwtWO3ITCaESGuLD4RYzY7fYsjA4jP314T7+/rpiVkY864iELDMmwHErGYm4Mq0YzwGZPAK/Nxr32/sOlCr3zVI0RKc9BOcyHFD3NJT5yfw/j1zXUd296d85DtA2nhzqFbC30RXKrKVh3zrCAFPgeXuoeVHJfxVpktHwvrmS1bieI27PpSaSHHIrsviw137JddJAIyQOCMJ92Tq6KvUaNApkS3LHpbU56I5FfagU1KEtNMocSsFSh5G0lIyM91fkhWulDfKSJDYQ2sTefA3gNeJskHtmJsYon4bNw8R4nTgLpN2q8MVg7ZBNfrhTXq439s5PmpHSZV6lTaDkJx68lEq9+Rpa338SNgPU+9NlLcuSM3d0ix6rV2BJDrKHYyYr3mjLwA+vyKI4qAASTk4I1cMCkouV0SbpqDc1bZChS0JKIzB93NCsKdI/OWMehCU6pDx1eEprxKbaifZ7n0ZuJarTz9u1BlfSW6FJIchLWO4bdTkD3JVg+hUDzZmYiR3l8d2J30HDoDin5eAyEzDCGFv1PXrwVbeAfePb22fDbszZl37iPxb2vxypilxXnXZL8xSJj6crBCglACAkFXEZBAPrrtMz6jB/ulDLrY/7+Kkq/1t91D+Llrhn9TL8FVZ2it5jefeenyRe8uKqDRKdOyV0GnFSiUhJ/Y3XVKUogfdSrHYqWNdlbi7q2RtdSzUrtrDbClgliK355Eg/BCB3P4nAHvI1hDa+M4Q2DETpqtojmQml7jQJpjTIs1oPRJDbyD25IVkZ+Gs2ub9vNwt4t4rzTctCtKJb1mJSpv2qUFJffHuWlQ/ZVD83HT7kEk99dBUOQ/Lo0KTKWFPOMIU4oJ45Vjuce7W03KOlHYHkV1ANaeRpqspaZbMjE0GmhpSvBb2jRo0omUaNGjQhYpX9rPf4tX+7VKW3IYRa9txX4L0gSIMdIUhguIbIaT3WfRI+Z1dMlaCw+gLBUGySM9wMHVM2tOZi2xbcd1uQpUmBHQgtsLWlJDST51JBCB81EDWcROyWZVfQtzavb86pNvWJDj28mtzKYxLaUIjYmGYUhTy1qKShzLzineKQleE4WXBjaZ31p8qK1U27MmMRnnosVqTOkMx2w89D9qwtZyEIDY48z2UsoSnPLIzVTce86XX6jSXbcpM+NFrsCjttxnVpeeTKZDoWeoQhJSk4Pc9wcfDXih770K5XW48CkyFfSCEyIvtCmmE9H2ODI85WvCnP/yDQDaMkhKsfdyc6JvFS1Vrub6RJp6VJtKQmW8zGebjTAA4+09DckJCuGQ1hSOmeRUoHPkxgmZszdqFeVZgW05brzFUk0tmtPcHUPx24i2kFDyXQByBdWtkDAVyacOABkwdt+Iii1igUmtvWvUkrnxhJnlsNJ9lKXIrK1KbWsOY6ktBQCOSmxzxhSeXukb62TOJuONaVUiIly4lKXPfjtNfZKLxaLh5cglKgsFGPKXU5xlRSIB81aUamU6GHxFgsNCU+qU/xQB1HjjLivirsO/yHw1s8U+vEds+7S9Y960++qXIqcCK/FMWW5DfjvlPVacSAcK4kgHCknsSMEYJ0w6rktRQ3CkNsf7q3b/nGN/8JjT9pB2x/urdv+cY3/wmNP2mW5BceN/qO4o0aNGpWSNGjRoQjRo0aEI0aNGhCNGjRoQjRo0aEKs71uymWdXLvrU5EWSuFaKZrcF91KPawyZS1NjIPYgYPY4z6aT1UJ7ffZrbm/7Ko8O1p1Y+gbhebjyC0Y8PCHnGAtCAHOKTxSFJCTj0GnG+pVHp8u+KvXqHGrMGm2m3NegyEJUiQhv2tZbPIEeYAjuD66RadQa9uZsntxdm0Tr1sRKn9A19NEcmkxYcLDbqmEKCQrilBCeCSlBAxxGrTgBhECtcIsNbBN9hxHQp+E/E1tHeJ1wPMjUc0/XCi66Pc9rFbzddaTLklpJSmPJP61dyCf2NZxn3I7/jkau4FNo25j1q0yTUk0eVRLhiVhcGpxylcpLPLk0gE8V55eqSoa2rguGfAue1lXNRnIaGZckmTFUZLCsxXR+SA4n4nKMAe/RuZaMDdSm2s/TqtTH4NGuKHWXuZDrcltkL5NJxkcjy9+rzrawW4mVtpb8x5eyp2JFEKeDhF2edyMVO5TI3vlzqkrebw07V1O36jclLov0HU47fVQ7Tj0m1KyPvNfc9/uAPz0gs7QeIrY2rP3Ft9Uafd8dbaWnEvtBUlTKc8UcXDzCRk+Vtz192nazLvVuv4eUboW37bQYtWalD6HlyfbmUBmWtjstQC056WQEkJGccTjOrhFz1Cl+S5aC/GQP/ABcPMqP+J4gOI/jRgfHXWhdpTDIIYSIjamzr7ufoVwIkjAfFLqYHWu22/l6hUpavjIobU0ULdW06latSQeLq+ktxkH4lJAcQP4lfjq7KdeVj3ZSU1Kk1qm1iEpaFJ6K0vfaAhSRwGTyBAIGMgjOsdbtjb/cylBNZpdJr8JYwhxSUu8f4Kx3SfwIOueHPCehyr1us7UXpPtioUqprjRmy6tTfAMtLA6iT1B3Wr15dvdqAzs6bqbwnD/c39x9VYvnZegtEHof2K6XLVSqwIf5QYih3bBHWcHwUR2QPkMn5jX5OeN7wZbv7UbsWzUvDRGqD1pXbdjFUplJiJ5N0O4yMBYBBDbSkp5BR8o4EK7JTrs4breJrZg9HcyzE3TR2exqUQZUE/EuNjA/8xAPz070zxj7NT6C9Vpc2fBlMI5GnPRSp5xXwQU5Qrv7+Q+eNZReyZoDFCAe3e24/f1WkPtKXJpEOF251v49FteE/w2ULwy7XM2qxJ+k7jqjn0lc1acyp2pVBfdayo+bgkkpSD7u57qJP3drxRWJt0XaNSSLiuDPTTCiLBbaX6YdcGQDn8kZV8hqrZN679eJ2Q5TbDguWlZylFDs5ayguo94U6O6zj8hvt7lE6t/a3w67c7Qx01hxtupVdlHN2qzwn7L4ltJ8rQ+fr8TrUSUvId6dOJ/6Gn/9HTgLrIzUectKijf1H7DXnZUl/Ym3n3tiy7y3TqCraoDLTkyPSGGy0VlKSoEM+iM/nuZX8sY10nbVp7cbNUVunUGmR6eh4hI4ILsqY58MjK3VfIZx8ANU5XPE3U7q8UCfCpaluxTDqdpO1xy4pDjgLaSVoIbZ4gOJOE4XyA7k9wMG8KPCp1CkLmVdmR9IvDg5UZaw71B+alwAJbTn0QAgZ92s5qfizTcD+60ZMbbd1U1K0l5Nku7G3vOP5jfr6LlP9URvNF3eEbdinwa5SmF0JNNRPpLMpqRPYWubHKBJShREfI7hPdR+I7jV6+Guv2VTtottbHi1mjR669ZlMqX0SiQ2mUtkxmwp/og8ynl2K8Yz79cbfqoXhTuVqiXH4itkuu2a3AbgbgUmGnkmow0ONuNTOAzlTa2m+ZHfCQr3LzbX6nl4Wbj25txG++9jj07cu6aZGhMIljz0WkNNoQxESPyFlCGysDGAEp9QrPLc6uVl0Gtp5rsaXToc4J9oYBUj7ix5Vo/gqHcfxaj5j0ygRXZz9QZegsJK3FS3A0ptA9T1PukD98B81aSt2vEDYe0zKotRl/SFZUnLNLiqCnST6FZ9Gx8z3+AOqgh2BvX4lZTVZ3Onv2pZ3MOx6QwCh15HqDwPfOPy3P8ARTjXRlpB74e2mDgh7zr/AIjMpGPONa/ZQRifuGnE6KavPxRVe6an9R9gLekVurvApXUFtZZj+4qSM4OPz1EI+HLW9t14WW1VQXzvXWF3XcLxDhjvLK4rJ9wOf2THuGAge5J9dONor2f2aqMywqbU6PSZz3Sfj01UlKqhMR0gOolrJdeyUq7gHuDj002lNzXOfMXqBTFfkggznk/j3SyD8sr7+qDreJOiC0wpEYGnNx8R56cBzWTJUxXCJNHG7QDwjl9yoLcfcezrbg1Cw4lYhquiTRZcmDRIshtMvooaVl0IKgUNp/OOB27ZPbXNH6mVupS4nhRtj+yPe6WahWLiqUCnP1moDnMdDvlYaW4rK1AeiB3+A00eNXwkStyrMg7ibIZom6dhIdlUSZHOHagyoEvQ3VKz1CsFRSV58yiD2WrXMH6mD4TLwu9uibzb3xpYt2yJMtuyKBNa4ITNW8VSJqmyB91wcUk9ypGfRtOeOS3RdMB2q/VEEKAUkgg9wRr7qLcpLcMKfpswwMZUpH3mD8coPYfikjVcxPEPaFYuqRt3bspup3CGHPZXIp5w3nkgnph04AIwSfVPbHIntrWFLxI4JhCoFz5DzWb47IVBENCcvPgrXddaYbU884ltCBlSlHAA+JOov2yfVjxpmY0U+spxHmWP8Gk/8Su3wB15hU2ZP4S7hAU6g5RGBBZbI9DgfeV8z/EBqYAxrOzfM/RaXdwWpGpcWHHcZYSQp4HqOKJUtZxjKlHuTpBpW2130qlw6W3dFEWiGw2wlaqS9lQQkJBOJPr21ZWjWbhizWkN7oXgsq+Ng3cTk3BQCchWfoh71Hof7Z1jO3VzqCQqtW4QhQWnNFd8qgMAj9cdjjtqxdGq4GrT5iJvVeDb26xkiu293AB//DPd8Y//AOn5D/UNeP7G9y8uf0vbXLqdXP0I7nn+d/bHr8/XVjaNGBqPmIm9V4zt/dkdHSj1632kAk8UUd5IyfXsJOvf1FvL9JKF/sl//wCzqwNGjZtR8xE3pYsq1KjbS6tIqdTizH6pKRI/W8ZTKGwllDQThS1knyZzn3+mmfRo1cWWRJcalGjRo0KFzXU6zNtywqVcrcGoVh6QuFHfDtwSY55yXm2UrzlQPndTkYHbOPhqOO9O08d2RCqNdrEabBbcM1j2ucvoLaD5kIyFeYtCM4VgDISps4+0TmTnXZTrVsq0xVaA9Vo1ScbYW002lxTXSjOyQ6EK7KKTHBHcYOCDkAGLc3O2V6jlcZoKZJlF8tTWaKlRmuuiMiQhBxyUtQejJXyACgAMq4EBddajRTJTC9wNv2IE+ZNqVchvU18RZEJ+XMEoPENFKAgOHkVB9nGCR5x3HfEBSd5LSmokGoUu5YjrUyVDRFbnzHpClMy57HdIWAnIpzyz5jjOM+hO6rcXbOg0ydbU61PYaWhc5EqGxAbcYSlgpS51mk+hUtbaPuqSVKSnkfd8h35tFBmNUiHaJiPsSUw3WkUZpAhSHJa4yW3CPKFKffcSCkqB66lZwpR0IoPL0WNvenal0xEJqdy9WaQppjnP6vSLUd0PFHPPAty46vTl9pjjkKA2k7rbclDzi5NytJZcZSpTr8xtHTddfaS8VqdCEtc4r6eaykZSnGeaOS/K3c2jbt6mVhyxmmqjLo6q5T6dIhxwtBahOOttqW3zDSixEVxVgji2B7gnVquWlac5lAlWvSXUlKMJXDbUAElakjuPQKdcI+a1H8o6FIaDlT0SMjdnbttl56bLrzSYrrrEl1qXNcaZcT1yltSuYPNaY7igAMegJHJOfY3Y2zD8WJIqVfjSZVQbpgjvypaHWn3Ex1N80dXISoTIp5DOOqOWMKw7rs60XJImLtakKkJbcaDphNFYQvlzTnjnCuovI9/NXxOh+z7Sky258i16S7JadTIbeXCbK0uhLaQsKIyFAMtAH1w2j80Yiqts/Iei0LNkQLvtGiXWhupxU1mnRqgGDV5Sy11W0r4cuY5Y5YzgZxrLc8BEC3582HNqjT7DJcbWmqSspUPQ93NTkSJFgRWYMGM1HjR20tMstICENoSMJSlI7AAAAAajLy/vUqv+Sr0AmqCxuE1CkdwWaQxULzuCrsSnmKPaaJTrUd3iX2ke1qW0pKstrCgkjitKk9+40nU9d+39sltxcO2MFmDSJv0DW2YMNxNPkRaZht1UTi2UsrHTPDinppOMccejhuA8un1C9K648lyDTLUbkToD0VEhmbHT7WpbSkqKTkpBA8wHm750kt3Zcl27Iba13bmzJts0ma5b9UZapbyehFo56bi2FJRhSUpZPEoCSntgE6dnA50EtAxd0WGeQy/hc/sNwh9oQ34ms73if4M/zeXFWbUbmpVUvG04iFOxpbcuSVxZbSmXUj2V0ZCVfeGcDknI+etLdbahu85FuVOiORqXLpFfiVaZJby26+w1y5IBSCFK8wxzBHrqSuQ0C6q1aSQqFVILsyVnBS62T7I7+IyNQe5W1tw1YWy7Z9xOR2aJcESrPxJ763WVMtcuSUnBcyeQ8pVx/DRNgGEAAa0y/wBx1/hHYsR0KeDy8Nz7xFQO5u88s9Uj7c3ku8PDWxdlnWrAgW1PbliPTW2moj8XpzHG1ni0AwsqWhS+wb7q75OdXnT7uos6QmnPPOQZyvSJNbLLp+PEK7LHzQVD56o/b7c21bv8N0evWxty9aFvzmZYiRYzTa4rCkTHEO56X7GFOIWvKkgebOc50/bybp2tY9nUivT6MxctPrdw0ugIbadbUhK5shLCXckEEIK8kDuce7W7x+EC8am45cvZcxh/EOE6Cx58/dOU+0aLNkqqDDTkCcr1lwlll1X8Lj2WPksKHy0nUB68aKbtXBZZriWqq93ylmUViMyQQMdNZ9O3k9PnjTT9WKjS/NbFefjIT6RJmZUf8ByIcR/EvA+Gla37oqFE+trlZor7Tiao8v2mIhUqOHBGZwDxAWPQHJQB3xnUwyXMcAcWVtcx1YqH0D2kin9dZrS8Ou4tw3ntHaMvdQuU+9p9NQ7VIU6CqA8XiTnDK0p92PujGvV27RbZVbcChPz7JpLipzUtcrEcJDym+kpBWE4CiCT3Prkg9te/D7dFV3h2FtC7NyKdDerNYpyH6nGMXpobf5EEdJWSgjA7HuNQG9O2t21qTQaJtvek6hzFJmPNh2U4UAJS3lCXBlxAOR2BKe2Ma0kiBGwteWVBqb7ju681WbBMKpaH5e/mrQfuCDTli3rXpqZ8uOkNiLFwhmKMduqv7rYx+T3Vj0SdV/vtt1utem09xM2Neopt8+ze0UNSGEKhsSG1BYb6bgUFFYSUdRYJTy5JCcaq+k70707DtM0LdjbgTaMyeCalT20t9vzuaB01k+uFcFH399Xlt9vxthuUhDdvXKyiasd4Ev7GQD8AlX3vxSSNazPZ8xLN2jBiZ+pt/qMllAnIMwcDzR36Tb+1+RNs+MTeK5PFjLva39uZMjeGqWczt/Cpi2QG49ZS42l+Sts/dQnpur4nsCfMeIJ1+xm1tKv2k7c0Gl7qXFFr91tQkJrFQjRksMvyDkq4oSAAkZ45AGcZwM40t274cNoLV3ouLf8Ao9rMNXjcsZuNMmHBSgJGFrbTjCFuAI5qHdXAfE5X91vFDbNmSza1lRTdVzuK6LcSHlbTTh9AtSclSv3icn4lOkZaVizb9nBbU+3E6JyPMQ5ZmOKaDrJWRdEy2rMpEiu1OuR6HBaBLqnlAx1Z/J6Z9SfgjBPz1z5VN9t1N76i7ZexVGVBiI8kyvO5RxQTjknkPsgfUDzL+GMa27a8Pu4G7lVZvbxD11/og841Ajr4BtJ/JVx7Nj4hOVH3qB1JVPf2zNsvEpbnhctOw3XZ9YtxU+IuC8w1FjcFPKKHATyBIaJJ7nzA4OuqDKyNhSLF/wCAP/bnZc4/MTd7w4f/ACP7e6ZdqvDTZe26/rPckgXBcfd96ozu6GV+qlISonB/fqJV8x6an9xd15Vs2NXr1ti3KhWoNBgPTnn4rHULyG0lSkxmypJkKwCexSj98cY0yNWvLrDiZl5S0TSCFN09nIhtH3ZSe7yh+cvt2yEp0x8EceHEccYxjtjXPmJl8d+0jOxO+g4dAcU7AgNhNwQxhH1PXqvyxm+ITaS5fHHtT4iKffLyLZ/sZ1Go1KoVDh14rrft6XGHGh5UrSoBAbR6kp4k8gT3/sVvlG3w2ypW6dNtKt0ykVguqie3Rg2+tpCykOlpKlHgvGUlJVkHPbXGO436kzb11eLODfVHejwtqaqpyr12lNr4ONS0rBMRlI9GnlK5ZH3AHAMeTX6IMs0W2KK1FYREplKpkdLTaBxaZjsoThIHoEpSAB8ABpXESb3TGENFrLYizIk1vqxZCHUZwSk5wfgfgflpR3H3ZsXaale3XRU22XFpJjwWQFSHz+8QPdn8o4A951TO4PiKlXbXlWbsBb7tbrivIusoQUtMj0Kk+gUkfnOYR8la3dn/AA40motxN1d2as5dVaqjLc9KJSiqOwFJCk8gf2QgH34SPcO2ddZnZzJdgjTxLRo0eI/sPM+i5rp58d2ylACdXflH7ngkD677keKm+1bcIq31KoSqYK0YPBfXl08vFkO8sAOgrSU4yED4K1eVrbTWRtPW7TplqU1LbzzsoSZjpCpEnEdXdSvhn8kYSPhqMSqxqx4iHKzY9YDl6izUU5xDyx7C1SBNWoPtNhIU8vrckZSsI7YJz6uMq3Y1JvC1prsh+dUX5MpL0yQQXFD2Zw8UgYShGfyUgD39z31MWdfEBgtAYyho0cDd288fREOVYw7UnG6o7x4jLdy9U+68OqKG1rHqlJOvescj9gc/gH/drjLqKnIG5VyOU+kP1W8aTClVanmoIZVTsAoQ2hbpSS56ICwTnvjv7jrci39XJsFFSjX5RlxltNvBwwUpAQ4nkgnLoKeQIIzjSVVLOta9dt7Ypt1VByCwzHhutPNSUsLUVR+m4zyUDlLrS3WlpHcpWrBBwRHx9mtrYtaqVZVUes4KmmrSWZElhxuO4JJkFCuSeSWy734qPbGBjHbDGV1Ngz9IVgQ9yKpUErcg7gUV9pvq83m4ALSS270nAXOpxCkuApKSc592skq/q7DWw2/fFJC5EpqE2lNOCyX3M8EEJcOCcHGcDtquartDt7S4X1gnXFKgxokiRUo8gqi9Jkuyly1AJU0UOp5rXxCws8SMZKUkYLd2+2zTXDfEa86jJfmy405v22S00gqaUt9CUILaDw/XB7D0GAMYOpxFGwZ+kKzpd+1+FMjU+Ve9KRIlPKjtN/RwJ6gaW6UnDnl+zbWrvjsnWwzdt0yEIcYvOkuJcxxUiCkhWSQMEO98kEfiDqs61tBt3XpFVeqFyy0orjjlTcYZmMNNJ5x32VvISEdwUvqKlnPIpTyJGQYuvbJbWToiKrNuuosvNmXUPbYMqO2468MLD6G2muBXHUgqbCEYSorJCio5MR3qdgyvhCudu4L2dQlxq56etChlKk0wEEfEHqa+/Tt8/pJB/wBl/wD7NQ1Nctm3qWzRIE2DEh0dhmIGi+keztpSEtpVk+XsBjPrr3Ubnt+kxHZs6rxUNMtJfWUuBag2ogBfFOSUkqHcDHfVcZVthC3KW+nb5/SSD/sv/wDZo+nb5/SSD/sv/wDZrz6dtfNGNyPl4W5e/p2+f0kg/wCy/wD9mq+f37v2K85FU3SXCyotlfsqxyIOM459s6ftc91P+6Uv/Huf8R1dji7NLzEJjAMIVmtV62bfsu2ZNzshTUgsRI61Ruqlt5xpSQVKxhsFPNPJRAPLj3KgCvs7ibMCsGHSbdYkyC+YMt+JSEFMdLEZchKnFYGWwiIeOM4LQ7DiNbdbrdv0axbRZuC1TX01GRHixmDF6yG3Qy44XF+VQSAhtfcj1x6DJGhRLt2EMSnzmqDSqaqc5GlJP0KQ2iWpCBw6yW+mXW0zAFYV5ErWThIURmma2AW/LvLZiTWnTIo0SVUFxX3ZL5opWUxvZ2ZDqnXCjAQW5jRPI4JcI7nOo+Hu3sswzmJR0MUyNHaeTKTSUpYS6KiuO2wMDPUEtBI7Y5HmFe/U5YSNlLrpsn6g0SgSYLkYokezUtLbamXx01NqygA8hGSFIPfi23kY4aYE7e2GlhMZNmUQNJQ42lAgNYCVudVY+773BzPxV5vXvospAcbiiRYd4bPVKu29S7fsqHNXWpSqZ7SKMGW4fUhSZRQtS2wApTaVgt5Bw8T6EgzCt5KPAlAVykyadTnZdUhRppdQtLjkBxSHgpA8yAeCik9x6A8SQC0s2VZ8epxq0xa1JRUISQiNKTDbDrICFIASvGU4Qtaex9FEeh1hVt9Yan58pdmURT1U5+2uGA0VSOSwtXM8fNlYCjn1IB9RoqFNHKChb3WDUg0abJqUvrJY6fRpryubjzzjLbIPHBcK2XfLnsG1KOB316d3mseFNbp1VnOxJL8x6E02phZVzbUU+YccpJUCPT1GM6nhYtlJp7lJTaVHTCe6fUjiE2G1cFqWjKcYPFalKHwKiR3J15XYNjOSWpjlnUVT7B5NumC0VJOc5B459ST+JOiyO+vln3xb99Q35tAdklEZTSXUSYrkdxPVZQ82eCwDhTbqFA/PHYggZ7y/vUqv+Sr1u0+j0mkBwUqmRYYdDYcDDKW+YbQltGcDvxQlKR8EpAHYa0ry/vUqv+Sr1AzUmuE1Rujflu2HW7gk3ezTZNJqNCZiOQZMtDL08hE1xUdhK8JdWppt3yFSScHGlah7p2xuPtfZNL8O09FJXBlW66/QEPtRpNOpLiGnugtClYx7MpPlSVZSRjOp7erb+w92U3fttuRT5blHrNEgpE1hhSzAfS5JLb6VpBLa0KwoKI49iCcEgrlubC0vZrYa0NstkG01eNHrtMdq84Bl5yosl1IlyHSrIOUDGAcpSlKU9kjTk8BsqvBphHhzyHWi53YTnfPw8BZXF/7PBmfF5K1rxtulSrvteUy0qFMflyUrlxFdJ44iuEZUn7wyPRWR8tQ25VnbkVUWwql1ePUolEuGHVpXNXssp2M1y5tkp+zcKuQ7EIGpC4bdmUm57W+rlYeaCpcnpxpqlSWEfrV09skOJ7dsBfEZ9NRW6K73qH1USabIpzdLuSHUKlJhyVuRnYTfPqoJQAvvkeVaAn56vNtL4DWt7wplr4jl/Cr2LEECfD8Qhmp7xu3wa8dK6pY283F2pu7w7MP7aUNVrUeY1L+j6PJaDC0lMxxLvAAlK8uJcV5FK+93xqe8Qa9tLSoFGuG7bPVUDULoo9OjqiIbC2pz8pDceSpK/IstOKCwVBRGMjvqMsSq7U3j4X2qntRbpp1oPty/YYEhsFTRTMcDpKSpeMuhxQ8x7KHp6a3N+PqdYNsUKo1+FKqdNk3TRoEOmyGkzY7M96WhEZ8JdWlTYbcKVeRfYDsk9hrUECGMJoan2HWS5xBMQ1FbD7qyed50YYcaj1+OPym8R5QHzSfs1n8C3+Gl+0bqpxfu1pL3s1RVVHnmoUtPTeJEZntwPdXcfk593fuNT/1nqVKHC56A+wgesuDmVH/EgDqI/jTgfnag6Aqh1+nXhKDUGqxPpZ55sKCXm1FMVnGP4xjt31Zo7ji8brjiOXsoJ7zQ08jw9VA7Ey6juxs3aO5d4UsUK667TUSp66Y25CUh0kjBQokkdh5XOWpmsC66RetuF1bVdQlmdwCUpjyePFrOTnprPp6cBpd8PFdvTcLZOz9yHaimnVKvU1EyXTH0OPRG1kkFKOoovIHb3uKA+GmGs3JLp96W45c9IcgJaZnAyGFGTHVlLXfKRzSO3cqQAPjog4i6gOIUPHI8/RRFoBUilxwzHV00x7mt6rrNInAxpLoKVQag10nFj3gJV2cHzSVD56q/cDwlbXXmpyfRorls1JR5pfp2EtFXxUyfL/J4n56c9x9xNvbXtSFXrqDdTo9Uq0CjsGO0iUlUmXIQwycZxx5uJyr3DJ1L/Vmo0vzWzXn46B6RJuZMfHwHIhxH8S8D83RLzMSVdigPLD9OuIRGgMjtpFaHDrrNc0xtoPEnNnVDapO7QXbsQse0zFvKLvRcCiEJyOp6A5Rz4+gzjVyWDtPtdsRTm3YjIeqsgdMzn0dWZJX70NISCQP3qB+OfXSRf2/R2ov2dSbkpRiSauiIXJ0b9dNR2koWOYQeClqJP3TjHc+bGDaG2lw7b3Wyut2jc8evTnEASJTzwVLA9eKkEAtJ/ehKU/LXUnY046CHRBSG6h7oADjTMkWz/pISsOWbFLWGrxUd41IvoD15qP3Kr9/0+wK/d9KtafK+iYD0yNb8CSluo1QoST0i95ks5APZHJZ9ygfLr825ni22JrfjC2z8RCelSLYpO2tRFVpTvFUmJPQZoVDUn8t5S1JCSfv80q7Z7frhrhq/f1LrbW9PFnA3v5RWLMkldVrtuBGEyqolSSjiAOIYdJK3E/FJA7L8vCLyRQWC64YBc3K6O8P24lzbwbT0PdF+0J9oGutrkx6LVZAkqEbkek6HAApIcRxWAQcBQ7e82KKyhghFVYXCV6c1Hk0fwWOw/wBLB+WvVVq1DtakOVKrzolMp0JvzuvLS200gDsO/YfADXOF0+IO/N2qs9Yvh5ob6289OVXpDfFLST25J5DDY+ClZUfcnOm5STizhJAo0ZuNgOJ6KWmZqHK0BNzkBcnkrW3X37sLaaEfpeeJlUcRyj0yKoKeXn0KvchPzV/ED6apmJZW9XidktVfcOY9aNlFQcj0tkFLshPqDxPdR/fuDH5qdMuyWw9k2fBk3/f0pmr12NOltPTZ7mY7C2H1tc0BfqSUZ5Kye4xjTHv5cW787Zq7qnslS5kWtx6a4ukO+yhyZKkZHEMR1DsCM+ZwZ+CfQ6eMaD2dVsoKvGbzp/iPvmlBCiztHTJo05MH/Y/bJNNCoNgbR0pFqWdQwmU6jkIsVIclSD6dR1ZPYfv1kJHoPhrW27tqXXLGtx66JaXoSaZF6NNZyGCkNpwXj6un5HCB+acZ1NsO2/ZVJZZW04ajPbC1NJBemTHuI5E5ypZHvJOEj1IGoPb2h1e4bEt5Nel+zUsUuKEU+I4QX09JPd9wYJB/a04HuJUNJbRzobohJuRc5nPLrmmgwNeGAZA2GQy6+yXHUWPJ8Rr1es2uInXwizUUl2ml5CYUWmpmrWJKsJ5LV1ipHFBI8uDx9dNcmieyXfatUnVV2p1B2ZKbW+o4bbHsrpKG2x5UJyPmo4HInGl2sWptZRd737un3azFrjtkN0BNtx1pQ4af7YtxL7Tbf2pJcKmxxGO3bvqQFInG4rT9ohyIFNM2QiPHeluKlH9aPkKWUq4t9gRxHIn3qByNVg0IJFhQ87Hqg5q0SoIBuajlcK0tY5H7A5/AP+7Wl9BQPzpn9Ne/ra8P0KAGHCFS/uH/AMa98P4Wk+7vTVTuXP8AO2+t2+7KthFeqEmJ0aOIqVMrZ+0akxkNutkOoWnKkgYUkBafyVDJzqVbYbbeoodQ5NnRFtOPPOOMSm0LSt2a1MClkoPLi4wgJ55HEqB5E5GK6Lc27r9g2u3uFW2Kax9CORIzj7jaQkvwghxxJcBAU2jKgrsE4ye2vEnZ/bdlybdFWrrr0KHKenyDJDBaTwmtTng8rp8nkhyOnHMq4I5JTjOdLLsEeSlqtszYs+3aZRPa5sKNTmwxHeafbUFIVHcjlPTdQtgcm3VjyNpOSCMHUavZbamK9FjTaitx599liMiTJYUpbrDrEkNpBR3J9jQVAdykL9OxEirZm1XLYo9uRavU226U82/DkiVzWgt8+PTQrLTeErUnKEDKexzqDd2e28tGpi665d8sPP1ZuUh6oLYUjrdOS3005RgEolOdxggoQRjHcqpI1osjnhx2vSqT1J1SCJ8J+AptcplSQl5p5sqRybPBQbkLSAnCcYyk5JOxVtnttKzUZkf6wyYj1Rj1B96LDkxm+UaW2WXsfZlXRB7hOeAWMkE6x0LYiy1Rm6mzclSqSl00xocxxMbnHaXBYjNutYaAStLLDZSognKlE5BwMCfDZY6aeqB9N1UiQ04hToW11FJVLRKHFfDkEhxH3QcKSpQVnOdRVGHyW7J2p2yp1cp8mp1eQ4/UKmX6bFecYUhby5f0itAw3zdSp1kqy4pZSlJShSQcag6XtVs/LuFykQr0rMiXSIT0Aw1S0dKO20lhDvEloDKeTHIBWAVemc6YqPsNaFCr9Gr0GozUuUh5p9hlSWVBTqGHmhhakFxKCmQslCVhJUAceucMraPbu4J0iowK4Q9Ikv1RSopjuNKW5LjPKKwUFDiQ7CQkpVnIKwe+CCqC2mis/wBoZUOYfbIPfPIYxjP+7v8Ahr3qoJPhmsSVJjL+ma02YzIbUy0+0hDnJauuooCMDqsrMdQSAA1xSkJwDq3wABhIAA9APQag0WgJOaNc91P+6Uv/AB7n/EddCa57qf8AdKX/AI9z/iOtIWaWmsgrbobFBftO3U11MJRShr2QSlJH26m1IARy9VlK1jA74UdKD8zZSJW65bVVsuHCVHabpL6X4KD7Y2+lpkIZYTl1xtYLTXNKOKuHEnygadrZl0yJatBFSlxGC+yy1HEhxCC46U+VKOR7rPuA76ra9q7sW7c0uFdEaYzVIdZQxJkIU9yZUxGRU1OJIUS2yUsoKumEqUpAyOwUKhbGzQmOibk7FW4qREt+uUOmmcDU5DcZgtdRfR5FSwE/svSbB4Hz4T6az3FvXbNvSmYa6dVpDji1hfGItvoIRFdkrW6HAktgIaV2UASSPdkhcqVW2BpEhNKhUKPNVMkR6BKahRniwhLji4wU8rs0cFp1IWSVnpLCSSk63VXNsC7GYdmPM8KlTXqg0udGmByRDEZxLjmXU815jl31ypSCSM9jqEYvMJ2a3AtN2lVKuJqhFPpAWZsox3ei301FLmHOPFfBSVBfEniUnOMaiajvTttSHpSapc8aO1FWGi8oKKXHOchK0oABUriYj+SBjCCc9tQsi69qbeqNYtmOymJGnCQ3VHwopitOF1xt8FDh4pPIrWspTxVkk8jnUrbm3G10+2qQzS7ZKoVOixosJ2QzIjSEtsIcS2crCHM8Xncq7cw6rOQdFlNSbBeHt7LMaLbSFS35DtVapSWWGS4R1Zvsjb6lJyhDSnAopKlAqCFYBIxp/wBLT221iPzm6m7bMUyWn0yg4CtOXUvF9K1AHCyl0qWnkDxUpRGMnLLqLKwrqjUPeX96lV/yVepjUPeX96lV/wAlXoGaHeEp3gToTW5lYhPS2UPvUmAW2lOALWA5JyQD3IGR6fHS3vHb9wx4VIqm2sSVDq71wU9ue/TUALVBU8BIU4nHFYCMnKgceo0v35f0G3d351uVaxnbgp8mgwZshbCm1vR+L8hAKGl45+pPlUFDHYHSTuf4iKPaJpUTbq9KihL0yJIkJns9dluKh79dsq6gMhlxDXnKVjug+TuOz3aI2EuYzyWggXHAa6c0n8LysbtHtSHLS0NsR+InC7I0qTUZkAXNKmmllcVwquukXPaypIarrSJckt9FKY8k/rVzIIJ6ajjJyCj8O/aK3SuVyqG0GqPcH0G9Fumnu1KNOl/R7rsTkpK2glZT1uSlIAQnkFEjGe2iNuI7XXLFua4rWqduNS3H5KRLAWEoVFd48ijKkHBCsLSnAPf341vEbtsnfSx6FZ8JH0lQ6hXoblYVEmJbIgJ5FbiFgjulXBQ4nOUgj01E5R8u1xaTUZt/yPL2WfZDXy3aRhlzWuaXA47gUbQ1vyF86JVshexFwbEiTstG+hoCYciTFhJfU1NbaM15C1utlalKQp9D3HqcknHb0wGTfWt0izLbocjdKgt3nQ3roo8WC20gMy2ai5KQmI8RyS2vg6UqJyjGPQ+mq/2l8I9O8LWzVbpVv3jJrlXnpUuqVCoxwoy2w6ostJ782koCzgBRTyUtXHKu1jb13fYVHtujDfazZa6b9ZaSiE7EWqRHTUjKQIjvJCkLSEu8VHmkDt6K9Nb94wRrc+w5+i5PdEU6WH36urMp920adJTT3Hlwpx/8JMQWXT8eIV2WPmkkfPSnTqBS5Ei8awhtyNOj1V5SJMV1TLh4xWCASn7wz7lAj5adHG6Dc9PHNMKpwne4PldbV8x6jVN3TedJ2Pt66b2m12ZAosS4kQ3g9FXNiRw6ywlLrxB6jTQUoAucuKQRlOB2mHQNfhOE/wAhS+pc2oqP4Uh4fdxtxtxNl7Q3Lq9Np8524KaiY/EYzGeZUSRxSVZQ56e/p6Z5lzU2oX7bTDyX4ElLE9Ko81osryUtdgT5V+h7oJHb11XHhj8W9v79WO3XTbsqBIgxoxqr1PZXJpkaQ8kr9nS8kZK0I4lY44RzSCe+rOrZoVzXTbODCqcF6PPzji82rytfiNELOr26G44Hl7IiWFGnUWPEc0sb+W5s03blOqW5USTEjPXJR0Rn6cXEOGpqmNCEpYb7LAfLZ84Uke/tp66l5UYnqMx6/GH5TeI8oD+Cfs1n+Nv8NV3vpaW3sK1KV9crjn0+jJuWjKhRXS9JYNTE1swk4R9qhJf6YwFBAHqMasb6y1Kl+W5qC9HQPWXCzKj/AIkABxH8aMD87WYqcu9xz64FWNBnb264pXgOWfeF2XNRbip8d0S2YWadVY4StRCHAcNuDzYz95OR8Dqv7z8HNtPTPp7a24p1p1VslbSUOrWwFevlIIcR/Eoj5atGjN2/dd03ItQg1WC4xAKT5XUZCXQSPXBH+salvq3VaUQq2q+822P/AAc/MlnHwSokOI/lED83TrJyNKP/AAXlthUG4yGf9JV0rDmGfitDrm+uZ6zXOqdz/EvsaoMblWqLsoTJwalHOVBPxLqB2/8AMQD89MtT8a23H1bROt6mVSfXJCukzSVslCg4fQqcGUlOe3l5KPw007N78L3MtOdcVyWm9RmoddqtEU7HUZcVRhy3I5WVBIUjl0+XmQAM+ussen7W0HcaRckCk0NJk0yO+w9DituOPSC68nLQQCVLKe3l749dMtjy0z348DvC/dNAfIi9OSwMKPA7sKNb/wChUjgbfVVlRtmd1t/Kmxdm+lWkUihpV1YlBjHpq4+7Kf8Au+35SsuH97q8aQm3LNhpsvbS3I7jkXyrYjfZx46sfefewfN7yPM4fXGO+t32O4rp81UU9RaWr0iMuYlvj/CuJP2Q/eoPL9+O6dZXqxSLdCLbtumCVNbTlECGAlLQPop1X3W0n1yrue+Ao6VmZuJNUYaYRk0WaOO/j9UxAlmS/fGZzcbuPXQS5t7QIaaZMr9zTESpEWsVVxJdPGLFUJr3JbaCcJOQTyUSoZ9QO2oHfq+t0UbO3bW9iaNJkVuDTXHqZL9k66pD4IwmNHIJeJGfMRx9456ntvbZ+lqfKqFzv+1lutVNTcBJzEZWJr2TxwC6eWSFL9O2Ak9zoeJTcC6LC2fuqo7aIdl3tHpjkiiwYsIzXlugjB6CQSpPr3IxrCYNYrhmb8B16LaAKQ2nIW4lPFBt+FSIBmrS47UJLAVLlyVcn3FcckKUfQA5wkYSPcBpV2/uCpVOxLeplpw0uqapkZt6oSUkRWVBtIISBgvKHwThPuKge2plihVm5ITcy9H0paU0lYpUdRDPLGftldi6c/k4CB8FYzrQsu5YVKsO16ZGZdn1JdHiKbgxQC5x6ScKVnCW0fvlED4ZPbQ0Vhn8xqOGR63cVBs8aCh46db1BUrbK1qT4gntzZ9/Cdc6rOTSnaXKca6iIgmKd9rSkEFtvmS3hKQnt3JOdT1SuOPXLttMUuK89BRUnx7fjiy4v2KR5Wye7gxnzAcfcCT6KzO00Ct+IBW594XPTXqqq0W6OLXYOQ3GTNU8mQslYU8nmSnzNhHIfEac7lqtLN52jRWJTapbVRecWw33LaPYZOCrHZOfcDjPfHodVhULjqaHgLHrcrRK4RoKjjmE56xyP2Bz+Af92smscj9gc/gH/dpVMhc3VPbOibmWJbMKtzJrDcWkcEeyulBJfhhok4+8AFZ4qylXooEEjUSra2ybjqdfpsa/GZ02bDmwalFAjvuxEvLd5qaR6x1BTykqIHcJQk44jT7asISbXtqQZMlv2enRlcGnSlDmWUjCx+UPlpKf2psipLVLgXKy5VZ1YmzWX33TKZcHtSnZMP2fqBKmg4lPUSgoJWyhS+6caWC7JG4L3L2AortYiVKJcdRgxoVUFUagsNNpaaXzZUUIIAKUks4UO4PUX2BOdYVbD2RR5ltynKsI0eiCmsMsPR2OL7kTrFJClDKC71nC6E/f4pPuOfiti6JToUdld2LXLMmIlD1UZS+iSGoZjJjuNlaeognLqUBQw4lB83Hv6p2yVMfDUlq9Zk2mvMQwpKkBxUxLEZUfm66pSivmk5ISEoyCSkklWi6jD5KYO01Kn0K7KU3cU1MG8nva1KhlKBHKlFZU2vJKuZV5sq4kDCUpycr8vw8wEro7lLuB1sU+TDcfQ6zzCmmpTz6wwpRUtkr9oWCAog8W89k4O9YO2Ee2LuTJjXOzLp9v0qNTTTY6S0hqpBlKFyltZIbUqII6QkKIPJasAqybNfkxoqA5KkNMoUtDYU4sJBWshKU5PvJIAHqSQBoqQrBoIuFUznh3iyBEEu/aw8YsiLIH2DKM+ztRmmscRlJ4xE5OcHqOdvu8dpGwFCjsRY8OsutNwXVOMMGGyqOoF553i8zji9j2hQ747obV6p1aeRrGuTHbeajuSGkuvlQabUsBThSMq4j1OB649NRUqcDUhWpsvb1oVanVmn1Se9Kp6h9q+UqcfbEBuIGnFgAlGGkuY9Ofu7DVg6159Rp9KjCZUprMVguNtBx1YSnm4sIQnJ96lqSkfEkDWz6aK1UgAWC+a57qf90pf+Pc/wCI66E1z3U/7pS/8e5/xHWkLNLTWQVs0ev0S37PoDtZmJZ9qaaYjI6anFuu9Mr4oQkFSiEoWo4HYJJPYHUTKurZt59x16fT3ZCqh7WOMdxxT0tTLkXqtpCT1SW23GuSQpPlI9dS7Fmz51Ao1MuLb+vOSKRwcZcYebaWy8G1NlSHG30qGUrWk4PdKiCMHXyn7bUWlzWqjB2qrLciO6HmFFxCwwrLhw0lUghtOXnTwSAnKicZxquEq+1bQXCr+nyPDnQlWrbVPjQprEKA23BnICnGWWYrIktLddGErXxkdRP3lAvk4SFnMrXVbAQkVGdUKXTpa6LTZEeY1HiOyFMxY7b0VxC0IBAwhuQyOXdQQtIJx2m2dl7OjsNxWNnau2w1FMNtlLwDaGiwiOeKfaMBRZbbQVgcyEJ75AOtmNtTb0RElEfauvoTNZfYmD2rPtaHluuOB/Mn7bK5D6gV8iC4ojGdTQqBEbvCX4atiW3pVOVS6aw8y8gLZltKLj65BYXkpXlSlKXNZCgvzcnRyGCCd61dz9qoVIci0ypmlwoDaXCJiXQ2EmGiYQh1eUucY6grCVHAScdhqZ/saUVU6HU3Nqaw7MgSTMjyHloccS+Y7ccrKlPkqPSaaT5s920q+8ArUTVtrbWeZj0lzbG4mosOfT54bZbCmVuQkJQx95wpACEIQojBWlISokdtAYTkFBjMbqFYCVBaQpOcEZGRjX3UcaxKGSu26qn+GqMn/e9rIzOqMgco9rVZ0fFCoyv9z2o2bxotNvCP5h6rd1D3l/epVf8AJV6kga6fSy66f9GP/wBXUfcMC5qrRJtNi2VWg9JaLSCsRwkE+8nq9hqA01Q6NDINwkzdGEmd4gpwMiQypq04JQtl0oKSZUn3eivT0UCNLN02LBuVDP1josOuGK4l2PJSkR5rCknKVIWPKSD7spB+B0z7nInub/1MwJDLS2rVpuQ62VJWDJl9jggj09R/qOtb6ZdidqzT3YoH/fNZeY/lAck/6SQPnr18JgiQGscA4UFivGCM+WmDFguLHAmhBoRzGS8wt2Lvo9wW+zXlOXMxBkPKQ0phEOqAFhaTnJSy+AD6p4ZwB3znUFvB4hraplz0Fmy49To8l2pRJcx6CRHly2QpYkQ3YjgBLuS0UZSvqBR4nsdStXRT6vOoYUmPMjOPvEfdcQr7Bf4jWvWbFp1VDCuXJUNwPRfaOTio7iTlKmXQQ60oH0KVjHw0r2j2eJqCGwH7N30zOY/pdb4d7XgdlTwjz8AR4YrVtaG7aAg3uPMG1aAGhFtXDUtwV7XPTrlosZS5UBt2Qw28EyYizxJQoY6bpHoSko7+idaG8d97Fz6TRqNue684tu5KKuLSyHGpjVQVMSmE6poFK+mHwPP3QcH1Gq4qu425ttWxOpFeBuClONcC6+QZLKcjul9AAc/B1CPm4fXUbv5svs744W7dco98S7QvGiS2FOOoHQnvU9LyXHY/HI6gBTzbcQVpQ4Mg4KgVZiUiQYIxitzccBn0Euyahx47jD7tfymtrmwJJNvMk7102m37Yrqfp635aWHXyrE+lSAnqKBIPLjlDmCCMKCsEHSjTaXVZkS8rfrlFh3VR59SfiT2HODbr7a4rKVpU2r7NYKTgjKPf2741NWrto1tpbdOtTbSamn0Wkx0RolLloLzCG0j0SvIcSSckkqV3JOO+tG0boNKnXKm5aY/B5VpZXIaCn4qVezsdi4kAp9xytKR3xnWMPE5jgO9lbXMc/RWfha8fl/r09V82ZtXanaazqdtZtxSU2/ApiV9KmSUqbkFSlFS1q6nmdUSe6wVA9u+Ma2bmtmlSL9t6RFQuny32ZylyoSuk6ohLWORHZfr6KBHy7DTa9GoNz09IfZhVKG7hSSQl1B+BSe4z8xpKrNv1GkXpbibdrDiQWJxRHnlUhlICWshKiQ4nPb8ogY7J1EAgPOE0ND7HrJWjVLBiFRUe4S7vrAorNp0pjdmqCdQWrloz0N1gqjTFVFE1pUNshCVIc5PhtJ/Y04Pcj11Z1Pu2iz5Ap7rzkKcf/BzGyy6fjxCuyx80Ej56rTe16y6xa1Kpe9ypltU2PclHmRqhEeDsZyezMbcitFzgVJC3UoSQtCfXAUOx1ajjVBuengOIhVOG73Bwl1tXzB7jWTqfnHMdU9ldtfyHkeq+6U49tUypX/cb4S9DkojwFIlQ3Sy5kh3OSnssdh2UCOw7amwLzpHoqPXow/OxGlAfxfZrP8AN6XaVQKjT72r7Vs1lcZLUWCfZpgVJZUD1u2VHqJAx2wrAz906YRdE6mnhdFCfiJ9Pa4uZMY/MlIC0fipAA+OtotSRTvCgtrkOfoVlDoASbGp4Z9ZpH2D3ltPcy2psuDb8W1X2bgq9M+jCtALzkaY6yt4FKUpUpakFagnlgk5J9dTsg21bm49QrUtmFC5UWPzf6SUrWtT7owMDKlKwBgZJwPXURsZeFk7v7f1GrUOyqfS6Ui4axTlw0IacafejzXWnJPlSE5dWguZxnKu5J76mKXbNHp25z62WFudCisKjiQ4p72fk86FBorJKAQB2Bx8MarBLAHZi2Xp1qrxMVW5G6lOVy3UMN9eg0pX5RAE2Qn5D0YB+eV/JB1KxolAtKlrLaY9Phs5cedWvAJ961rUcqUfepRJOtSpXUhEtdHt+GarU0dnG21cWY+fe87ghH8EZWfck68Q7WckymqrdMxNTmNK5st8eMWMr4ttknKh+erKvgQO2qHLvWG7XrzPJSM+7c7+vtzSvYsu4a/SZjNCP0bTl1mqLNRdQFPOJVNeOGWlfd9ccnB2Pok+uozfqs1fZnYi9b024hpeuan05UmGt9pUt2XKyAnmM8nSc4Cf4gPdqXs+5H/YajR7dp5qE9FbqvVUolEaNmc8QXXMHvgg8EgqPbsAc6gd96hdW2mzt27qUhpu5LwoFNcmUxl+Kt2M28MAJajoVn0J75Kz+djtraYs9wNhXLU8f55BZwLtbS5pyHX9lOrE66LqgNrjNu0SAtpKnJLzWJb3bv02lD7Ifvlgq/ejsdae3P1ctPbGgznvZ4LT1OiuPOq+886ppPqfVaj6Adz6AakDd0mqx0wrZhJny+mBJkKJREjKx5gpfqtQ7+RGT7lFPrqN2rtmG1aFu1qoOuT5wpkfpOv4KY6S2PK0geVAx2yPMfeTqMoJxWFRbXI5/wA8gjOKC25ob+i5epfh78S9b8Wr14Pbg1mBtk9TB06s64hutLhicqWmklQJWhHWWfOQFdABBwcjXVVXpFModYs2HS4iGG1Vp5SsElS1GBKypSj3Uo+8kkn46kJd0vTn3KbaURFQkNqKHpK1FMSOr3hSx99Q/MRk/Ep9dVxULutZvc2z6R9NTLhrhrTsaVMZaKocNZgyvsAQem2r38AVLwnzn00QYbqnQUNuRz/nkpiPbQamovzCuvWOR+wOfwD/ALtZNY5H7A5/AP8Au0mmlR9BEJizrcqlQqTkNmFToqlK9o6TSuTSEgOe5QyRgH3nStcWwlLrtRrc8XHUYn0m+1JjR0IBZgudZp6UEpBBWiQ4w0pxJI7hWCOR1tvotK8Nqolo1C66ZFRNpcJp4mW1yRwDaikpKwc+XHqMZ0oTtsKXIqT0aHvG1DokiJUeTLVTSFtSnXXjEKE88BthqW+gjkOXFg4T0wdLBdgkEC1UwUzZK0nGkKhVZqdLpdVpz4kPMCUWHILKGvZzzWVAEAqIKspKwe+O+F/ZKLSaFUWHb4W1GXFi9ebPgtvOsNRkMhaeqtXkYUljkpAwQpxxYWCdRFP2ntOnoqjrG59GalT0ySyWQ0mPEW+8lxwoZU+oEFKS33PLio4UNY3Nq6a7RWrfd3yjOQvZPZpAW42pbpDMxlOCZGEo4zQSghWSw33Humqi25NFE2jpTdgVOz4d2u1NuoextOz3R1FJdiR48fKum4kqUfZUqUCrHJRBBGQY+R4emp1Weq1TvKRUFr9hW23JpjJaDkWRGfQtxCClDneMU9gkhLqxn01uW/bFv0G6EXErcyl1BtKniI0x9C+hzeec+wUHgGzh4IUSlXJLSAOONQ9U23odSjyI/wDZbpSetKnSC64pK3ViT1OKnFCSnm6x1MMrASEBPdKuxEVU2pkpyPsbGVWotRq1zzKlEhSQ/GgvRk9PiHpTyUrPI9Qhcs+bA7NN9vUmBpPh8o0+CuezuBJqKpTsxTdQjxmkqaD0X2R0sKQri07lIXzRgc0ny+4ZJO21sOPuyou7cdLkh556SH5iHUTCqczJbQ+kPJ5oSlpTPEFOUur9PQx0HaSkU2jRrfp++TMOCw2rl7K6226HTGlMBTauvhtI9qC+OFHm0k8vgVUW3fVST+wFvxm6bHm3mG/YpcV4tvMApLomsvtdFLjqugpxbIbPHPMEgAHVwPToTMhMZ+Ww2+6OSGluALUOQTkA9yOSkp/FQHqRqoxt1Zyqomqqvy3Y7jkenRpSYIQwHG4k1UngkmQooS4FqbWCVZ7EY7pOh/Yoth2nsRanu/Gqz8d9h9LtQfaeQsx5UV2OFI6wzxbhoaUQQVFbi8gqI0WKkGmQV44Pw1z1U/7pS/8AHuf8R0/bd06k2fJr0mqbg02oCfOWKa2uqIWIVOSpS2WPMruoLdeJPrxKE5VwB0gTlofnSHmVpcbcdWpC0KBSpJJIII9Rq8OxKxmHVAXaejRo1suajRo0aEI18190aEL4UgjBAI1rv02nyTmRBjun9+0lX+8a2dGpqRkoIBUf9BUkHKISGz8W8o/3EaPoVhOejMnNH4iUtWP4lEj/ANNSGjU4nb1GEbly1unFqzO+tTFNkyXi3blLU64FILoSZEzASlQCVDsexI/j927ErECW57O28W5AGSw8ktu/yVYJHzGRph3TtS66fuRNv2Dbcyr0idR4UBw0/DsiOth2QtSlM9lLSQ+nHDkex8ulWLUbbuppxhl6NNLCsOsOJw6wr4LbUAttX4gHXpJeI2JBaNwGX36C4kVjmRHeZ1S49HnybmpjFQoCqVJU8+sT6fISpp4BleM5APL07KQff30yIfrUUqThiqttnistENPoPwKSeCj/ABp/DUbU6dNhVejfRlQcUOu9xZlqLqE/YL9FffHw7kgfDXiRT6S1U01ip0mTSJoWFuTITqg09g+jikY5JPv6iR+OnXEPpw+586+hSrasJ4/YeVFnuKsQJVBnxg6WZHRP2D6S256j0Sr1HzGRrYr1mW5cPmqFOSl5KuaH2SW3ULHooKT+UPj66ir8lTnqHIcj0eJVaStgKLrb4Lras/eSkjioen3Vcs6mRDkwy39G1VbJdGURJp6oPbJSkk8wR8lED4arTCwFppn9lauJxDhXL7rct6793rBUlmJWGb0o6O3sdWc6M9pPwblAEOfg4nJ/OGn/AGo3StW4KlXYEl5dHq06qreRTKkA0+R0GU4SclDhyk/cUrtg+/VcfTLsTy1inuxQPV5v7Zn+UkZT/pJA+etGkxaVXGKw3JZjTojtTWpPIJcQfsmu4Ppn5jSkWUhxWuJFCdR+2XsmGTD4bgAajceqro+XZ1JefXOpynqVMWeSpEBfSK1fFafuOf6aTpWrDt1Ue9bcMxpqttoZncFRkpYkFPFrPJKlcFEdu4Kc+5I1WdPru5FngGz7lTUIiP8A+Krqlvt4+Dcgfat/6XUA+A1K0/f+j1G9bdZvmhTbRmx25bSjLIdiPLcDYT0n0dl54nsQCPeNICTjMNR3xQ8cjz9LJozEJwv3TUcMxy9Uw7wr2qv62aVRNwLudttqNclGnMIfUIr65zM1pyMzxdSeSXHkoR5R35dlD11YE20KPIkLnQg7TJqzlUmCvorUfisDyuf6aVa5y8Y3hrvDxG023bl2Y3HbpFXhT4LcsOP9anyYbUpD6Xg33SH2HE80qSAVDmgnBGLpsbbOpbY2lSrVtS76jUWaZHS0r6edVMXJX6rdU6SHErWoqUe6kjOAkDAHNBAPdNOKeNx3hVYqU7dNFvevco7deQIkHqLbKY8jj9tx8h+zWfXJyj5DTZTrrotRkCD11RZuP7UloLL3zwlWOQ+acj56U6Xcy6XfNdXc1Oep/KFACnW8vxk4L/cuJGUA/FaU+n4ZdH41BuaAgvNwqlDdwtCjxdQfgpJ7jPzGtpgXGMaC44Dl6UWUE2IadTY8fVVfsq/t7uPatSuWx7Tcs8MXFWac77ApLDjsqPNdaefUG/IvqOJUvCwr73fvqZTb9cmbhyafUrpeXGFGYK1xmAxIeR13cIW4k+X35KAknIxxwcr2xEi265Z9Rq+zMN23aSxcVYhPUqoMJcZcmMzXW5LyShXNPUdSteeZ+9kp92mESrzmbhSYrFLh0+UqjMJdlOP9dlCOu75m0gJUsnv2VwAx6n32gk0dhcKU10y3/aqiKBUVBz016802vzLcsynsxG2m4zZJRHix2ypx5fqQhCfMtR9Sf4z7zrS+j7huk8q2tykUw9xAYc/XDw/wzqT5R+8bP4rI7a3abQKTbweq0uUp+YUEyajNWC4UjuRy7BCB68UgJHw1pGu1i5fsrUZEaCeyqtJaPFQ/wDZwXPktWEehHMdtZDe31PX87lodzvQdfwoezKvRLWtx6CG+KlVmrNxIMVvk65ic8OLbY9w7ZPYAdyQNU/4vvEDWdi9t5F23FSn4wnRpKaQwG1O0/wBubQFtR6i+0tK0l1PMIQjCCUFKlqyAbl2ypdJolEqlTdUj2g1aqCVPkFIccSia8MrX2AHbOBgDv21B7nmx92rVn7e3VGorlr1hkiW/W1pQ2+0k8iqO2pSVLKSkKDuQlJAUCrGtYgJiuwDU3PX8rOGQIbce7Lr+ln2x3qsu/tvIV223S58SlzXXotHZMMNrqTTR4e0RWUkqLCiCUrUE+XzHAIOoamXBCpO31tLvqrqjxZFOjogUKm8nZlRIbHZQR51596EAIT35qUPTRh3LDg0owtp4ManUhlhEd666q0A0WUDilMRo8eqkDsj7jKe3ALHl1DbaW66bep1Rpkd6G9MgsplVmaevOkJ4jCGeeQ00PyQcJHuR+VpqDLUhkneL66+n1PBYRIxdEAbuP29foOKlqzV7huRLVIq7TtApKkARrWorgE2Q16D2p9BCWW/ihspT7i4rPHWhNo0qk3PtklSIkCKzdKWo9LgNpTHjpMCZ+VgFavmAkdz295cUN0K04K3lrRGQ4sdR1xRW7IcPplRytxZ9w7n3DSpck2rTru26lv09MKCLsZDSH/7YcUYcoBSgDhsYz5TlRyM8cY1s2gaQwUFD7dbz5qjhcYje3v15eS6D0aNGuEuqsfs7H7Q3/JGj2dj9pb/kjSVc9z3LFuxdBo0mBHYZp8eWpT8Nb61rcdeTjs4gAAND3H1OoGlbiVuuFaaNedtzi2226v2eAXOKHAS2TiR2CgCR8R31UuAWzYD3jEFafs7H7S3/ACRo9nY/aW/5I1XL91XlGZckybhorTTSStxa6WtKUJAySSZHYAd86+t3RerzaHmq/RltuJCkKTSlkKBGQQfaO4xoxhW+WiblYvs7H7S3/JGj2dj9pb/kjVcv3VekZhyS/X6MhppJWtRpTmEpAyT/AGx8NfUXPezraXUV2jlKkhQP0SvuD6H+2NGMI+WiblYvs7H7S3/JGj2dj9pb/kjVefWO+f3bpH+yV/8A2NH1jvn926R/slf/ANjRjCPlYm5WH7Ox+0t/yRo9nY/aW/5I1Xn1jvn926R/slf/ANjR9Y75/dukf7JX/wDY1GMI+ViblYfs7H7Q3/JGvoabAwG0/wCrSRatz3LMuv6DrEuBIjrp7ktKmIamVpWlxtOCS4sEELPuHpp61YGqxewsOErDMlxafEenzpDbEeM2p151xQSltCRlSiT6AAE50r25utYlz2xBu6HXWolPqDz0Zk1AGI4HmVLS60pDvFSFpLa8pIBGNSV62nEvi2ZtrT6hNhR54Ql12GtCXeKVpUU+dKklKuPFQKSCkke/SKnw72suLNi1O6rmqDU6stXA8mRIYSj6QbLBbeCG2Up8pjIPHHFRUsqCiciVVWhCmxKjDYqECS3IjSW0vMvNKCkOIUMpUkjsQQQQdfVyY7byY6320urBKUFQCiB7wNRonSJwEWiFPST5VTFpyhOPcgfln/8AqPn6a2GqLTkNKbejpkqdILrj4C1OH4kn/wBAOw9wGrYaeJVrXJb2Rqobg35ZpG7MKyIyaTIpTkk0OS6ZiUyk1lyOZDDIRnAbKEhClEH7R9odsK1Z30Kw2cwpEmIfg06Sn+SrKf8A01EzrPYkqfdep1KmOvghT5j+zygSMFQfb8wV8FAAj1znRQHIoqRmFobX7nxd0aa9VqbRJcKNHS008ZCk8m5nE+0RFAf94wrDaz6c8pBJScO2luk/RVl0ZFNRRXqdAi8lFSFddJUpRUpZVkrWpSiSVKHJRJJyTqfiSmJsVqZFXzZfQlxtWCMpIyDg9/TQWkCqkOBssujRo1VSjSzdu21k3vwduKgMPSmhhma0SzKZ/gPIIcT/ABKxpm0as1zmHE00KhzQ4UcKqh7h2LvSly4lQtG5W67HhLW4mBWSGn/MhScJktpIV2V2C0Zz6r0ry699CSkU+8KVOtuWtXBCak2EMuq+DchJLK/wC8/Ia6g1rzoEGpxXINRhsSozySlxl9sLQsH3FJ7Eafh9pRBQRBUehSb5Jlyw0XK920an/QU+bFSqK8pvkpyOrgF9x3UkeVf8YOslXo709kR65SYtbYbUVNuNgMyWz+cnJxn5pUk/AatG5/DnbM+K+3ZlWm2up8EKjRz1oKvl7Ovsgf4ot6TaxQtw7RKlXLaiqhDR61KhBUlAHxXHI6yPwSHAPjrqQZ+HEaA11/Ox06sVz4ko9hJcLeWSgo832amJplvPB+ewBxj1V5xDqk57glQKj27BWCPT10vx5iIVOrlxTqJOp8mPUj7SIkhHNDfTa5L7eV1KRlWCD2z2zpsjTLduuIpUV+HUWUK4qAwotL+BHqhQ+BwRpYjWTTahNqslUWHMUzPU2luos9dPHpNHAWfOPX3lQ+WnoTmXxJWI11sKYo0yrJjtS2FR6xEeQHG3WCGnVIIyCATwX2+BT+GtCoSaPXa3S6ZMjocC2pQdhzGMEgpR6oWMKHzGR8DrdhTItDjIgSKN9Fx2uyCynnGSM+5SR5R/CSnWOqNU+rVeih1DEuM43KUnOFoPlR3B/wD9GqNoHZb78vRXNcOe63P1RAodatOQZ1iVx6F3yqFIdWplXySsHmj5A80D8zT1b+/cyE83Tb1gJhPrUEIMpaGUun4NyR+t3CfcFlhR/N+Kh9FzYfelVNYSP+4lZeb/AACieaf9ZA+GvD9SSllca4KSpplwFK18faIyx8yBkD+GkDS0WAyP4hX3W7Ir4Xht7K5Lbuek1DcCtNKdVEffp8Dpx5aSy6shT+eKVffAyO6cj5637pgWVbECTdlUrTNqtMqSZFTTKTFaClqCEl3l9mrKlJA5g9yNc50WHJpdZqDdlVKGiAYsVf0dMYE2nOjk7gBBPJoDBx0lJAJJwdN8i86BWaDOsrcaIqiU2rR1wpcWrE1Khy2ljipAkHDjGQe3UKADjAONITMk9jqwzoPYdZFNQJlr20iDU+/W5b+w25Vmy6ZPoe3dLp5bRV6089SmltRKm44zUHGZcz2cuKS42uQFnmFIBKvug+XToLvad3Ck/RVIqMucujR2hEVHWyptQfdyXVLAShPcebvn8nlqrvCt4eLB8KNFrCIUao1CRcE12U5cTjntyDELilsR0uI86G0BZJUpKQpalrJ79rfNyUFi95VbXVopgmiRgl5DgUlajIeASnGeSiewAySe2lIYPexNrbTXLd9qJh5BpR2uvXvVSrFryqq8ifeUluatCgtqC0CIbBHoeJ7uqH5y/f3SlOtmq3RFgSfoimRnKnVMAiHHI+zB9FOrPlaT81dz34hR7aXLnu5cemqq1fqptSglQQhbg/8AyE1R9ENNgEoKvckBTp9wQdI86vVidATEhNyLFtt9ZCG2xzrdVUfX05Fkq9/33j6ktnUw4Dop73pp15DmQh8VrPD/AD1xWOmXTTIol0uaw9dNxorFSdbt6EseyQlGa8Q5IWQEpGe4U75sfcQT68T33VKxdt4XBWL1CV1V2oyWpKVKKxG6bikhpBIBCEAAJ7DsM4yddm7f268uhyYTKV0ilCqVAGMys+1v4kuD7d7JIIACTxUVHHdfu1guvaXZiRV2KrWbLiyak4AGosXmFSuPYc2UKCHAO2VLGPzjjXoZGZhyMd7nNxE1y0+3Wa401LRJuC0A4QPr10Fo7MT6vcFmW7Wrwp9Tn1P6OadaelBPDGMIcbR2Tko4krOV5J9BjUzZlzT5dn0eDbdFelvMwWG3ZLhSmM0QgZ83LLih+an8CU6YW6NPq6EprnCJBSAlulxV4RxHoHVjHP8AgJwj3HnosFKU2PQEpSEgU6OAB6DyDST4jS1zqai2gzTkOG4OAB0N9TkvlPpzkWQKlNp86oVEjHtLymRwB9Uto54bT+Hc+8nULfMyUK9YC1Ut9JRd0UpClt+YmPIGBhfr39+Bp50l7gSWBcG38IPo9qXdsN1DAUC4pCUO8lBPqUgHufQawa7E6pGh9ls5mFtju91cv0lUf0emfzrP9fXz6TqX6OzPT9tZ/r6k9GuNUbvf910KHequrcl97cSY4/BdjKTRoOEuKQc/by+/lJ1R1y7Fbe2/ZrNp1XcGoUajSpZW0h1bIK5pjrC3G1lPILKEdTAOElskDGRq9Lzc9iv1+VJZkpYfo8Rpt1EdxaVLS9JKk5Sk9wFpP+kNJdboVOqTNEFMuCuUyTQZ66hFk+zPS3CtbDzKkrMhCypPGQvGT2ITjsMaXf4sl1IBbsgClx3Y/b2vRo1Rh1FlUeW5VJrsqO3HWZzdSWtxzkspKVow4lKVYJ4oRhQ75k6hthQq3TrcgRK0ll+0G3IMZ1mO0jKuCEqQUAcUghCeQSnPrxKTghbl7DbdvW/KoEWo1qMmYI6HpQgcpC22o6mumVlnPFRUXSBgc8Ywnynfb2esVMxqpO1Otvy2HI7rL7sJSnGVNSmpGUK6OUlRZCVEHJSpQ+eq0K0Bb5eq16B4cbTpNKZiVape1Smqc9GUpEVhpllx2GxFVIabCcIcCWArqfeKlKKiewGvd/hzptVguptiv+wVKS6tcpx5lpQltrmRpJDo4KC+PsoQgrStIS64Ck8u03XdtbWrtXqVYeqlRS5Up0aorSukB4JdZS0lLZLjJK2CGUksqJRyJUMHGIdzYnbxyEIgqlfbd6CY3tTcQpfDQhPxS2FhnsjjIUvgPKFIRgcRjRdBwZUHqnPa2h0e26CaJTalTJTtLbi0uYIEZLLbT0eM2jhnupXkKCOalEBQAwO2mtdRp7ctqA5OjpkvpWppkupC1hGOZCc5ITyTn4chn11UTOyVoxlPKi3JXIwmTRNltxaUhlp0h+M+EJQlgBtPOIj7uCQpffJBGYbNWciDDiM1mpNPQIc6AxMRRW/aejKQhKytwxyVujpIPUPmPflnOoorCJQaeqt0ONq4hLiTy7pwod/w+OgONnGHEHkSBhQ7keuqcp+xW38GG1H+lq8t5gt9GUIq0vspTLkyShtwNcmwoy1oODnglAzkEnYg7N2VErFv1typ1R163W2Wo7aKcphpXScC0LKGm0pDhI868ZWAArIA0YVO04equG0/+0Rv/Msj/wB9nVlarKyXRNvwSo7MgstUh5tbi47jaQpTzRCcqSBkhJOPkdWbrdlgubMkGIaL4TgE/DSyzVIVd4u1OcmNDX3aiOZb6qfcpZUByB/NHl+JVpmUMpI+I1B019VNpcenVSmSU9BtLalpa6yFY7ZHDJx+IB1sylCdUo/MblNNhsISGwkJAwAPQDXvULGiW9KJ+i5DbK/UiI8WyPxSk49fiNbPslWj949TQ+PzZLQyf9JGMfjg6qWhWDjuUjrRmVRDDvscZoyZahkMoP3R+cs+iU/M/wAQOtB6bWJNQboymm4anGVvqkNL6nlSpKcJBAwolXqQQMaypdi0wqplHje0Sj5nByPYn8t1Zycn55J9w+EhtM1GKuS8vRW47ZqddlIceSD004PTaUewCE+pV7s91H3Y9NbdBbdaokBp5pTbiIzaVIUO6SEjIOlHcK74u2Vvru6qUybcNTBUmLT4SU9Z3ihTjgYQo4HFptxZJOSEEZJwNSLW59oSa3RqBDlyJMmvxkzaetiK4tp5go59ULAwEBOMr+6CtCSQpaQRzrUQ1t6ps0aNGqK6NGjRoQjRo0aEI0aNGhCUbt2psa83/b6tRUNVJIwiow1qjS0fg82Qoj96cp+I1WdQ2Xvu01SZFq1WPc8R90vuRqiUxZvLiE+V5A6S+yR2UhHzVq+tGmYU3GgijTbcVhElocW5F1y+q4o0KemkXDEmUCorPFEWqNdAuH4NryW3f/LUrWhVKNE+slLdic4T7qJSlOxsIKiEo7qGClX8YOuoqvRqRX4LlLrlMi1CG8MOMSWUutrHzSoEHVW1zw70pt9uo2DX5dCfjBfShSOUyAOWMgNKUFtg4AwhaQPhrpy/abK9+31GXW9IxpF9O7f369EgdSuQf2ZlqpND8tnDTw/FCjxV/EpP4azw6vAmO+ztPFEgDJYdSW3R/oqwSPmO2stYg3tZ4UbxtGR7Kj1qdH5TYuPitCR1m/40FI/O1rMv0G54KXo70KpxCfKtCkupCvkR6KH+saea9kQYhlvHX7JUtcw091Foo8R26qk4wXYb3skVXVjK6ZKip7JUPur9B94HUTfV/Ttt4jEmrWzXLmgyA51XKLS1vuR20AFSn0jyBOD6kpBwe3bUlGp86Jc1QTSp54phxT0pZU6kjk92C88k/wCtXr6arbxJWrujuPa9Osy3HaZSaPKlhVyCXLfaTPhpKSIyJDDa1NIWchwqQk4AAOCdXjF1O7ew45KkINr3rXKbdnNzKDeVvxr32RuaoUGJPccLUGZEKIkriohSvY3CBxJB87JQTg9z6aZ4O8D8veNVosWtasW/pFviYh4ygqO3GQ8pLksowHirzpAb7EgHz8RnSbIrVXsbZSfWrg29o85y3oSzT6DapdmsutNpSlhtrk2hQOexwnypGcnX52xdrvEvXPEnTrjVUuhuhU7edviFS5AUrDbSl8KapKz+Uw3w6au3cIV7zrnzERsMCrauOfX2KcgsLzY0Ay6+4X620umvT6p9OxZblw1ogtruOpJyxHB+8iI0nCQn5N4Bx5nFEaZ4dJp1F6tWmSi9K6ZL8+WscggdyAeyW0fvUgD+PvpJ2m3aqe4e3VDr9QsOrUO5psYe3W/MYUw5AeSShXUKx9m0SkqQVDkpBBCSdOLNAdmPInXJIRNeQoLajJSRFYUPQpQfvqH568n3gJ9NRiqK5D6/16BWAoaC5+nX1UFaU6rVinzGqGExYa6tUlmoupCitKpbpHRb95wfvLwB8FaaaZRoNJDi46VuPvYL8l5XN54/FSj3PyAwB7gNaFm96dO/zzVP/mO6xzb2piKguh0KLLuGsIOFwKU2HVNH/DOEhpgf4xSfkDqYziXuDcus+qKYQDWNLjU9ZJhHrpGty8qHRbUt6lOPPTaq7TY6mqZAaVJluDgO/TRkpT++VxSPeRpohbe31c+HbxryaBBV3NMojpMhSfg7MUAR8w0lBH5508WnZFp2NA+jLToMOmsHuvot4U4fzlrPmWr5qJOlXTMOG0tzPllrr+y2EJ73B2QSFCtXcq7SF1GS3ZlNV36TJbl1NxPzWcssH8A6fmDp0tTbu0rMU5Jo1LBnPjEioSVqkS3/AOG84Ssj5ZwPcBpl0aSiR3xLZDcEwyE1t8yjRo0axWq+aMD4a+6Rbhm1qTer1GiV6bAix6ZGkpRFQzlbjjr6VFRcbWfRtOAMe/VXODBUoTzgfDRgfDVbUeeu4YCKrQdyqjUYTi3G0SIrsJ1tSkLKFgKSyQSlaVJI9xSR7tbnslb/AE2rn8mJ/wBDWXzDFFU+4Hw0YHw0heyVv9Nq5/Jif9DWF1U9ht95/cKqttxUlT61rhJS0AMkrJYwkY79/do+YYiqsPA+GjA+GqqgXRSaq08/S94zMajFCXlx59OcS2VnigKKWjxKj2GfU9hqRnuzKVEcn1TcSqQ4zQy4/IchNtoHzUpkAaPmGIqrEwPhowPhqtI1URMhM1KHulMfiSXEssyGpEFbTrhOAhKwzhSiewAOSdbAVPMw04bg1Uy0th4scofUDZOAsp6GeOQRnGMjR8wxFVYmjVW1ebV2bcqlYpO4FUkKhRZK0LQYbiA60lWUnDPqFJwR6jBGrIp77kiBGkOnK3WULUR27lIJ1oyIImSlaBvK0QSk3RScg4P68b9f9ej652h+lNJ/pjf/AD1VVrrRGtSg8KS9K67LLaiyhv7IFP318lDyj34yfkdJdy7k3XbdyVGhGykzW6Y+ma5KbjOJZXS3um0wrmAoBaX3XC4QFFLUR1fDzI06ZcAVJWG2NaUXQMm47DmDEuvUR7Hcc5LRx+HfWsa3Z7XeDfESN8hUW3E/6lk4/ixrnidvzPgpynaapyk+zuvB+OVKYUUSHWQkLLQ7LS2JCCQMsclnBCUrmaTuTXJVHuO5JtjuJYpdHjVCJAZAdcfcUuUlwJdQCFpIZaUOKchK+4JONAhDIOVTEJvRXE7WKQ/Um5bt/wBFS0lhbCi082h0hSknsSsgfd9QPw+OpeJc9jwWRHi3HSG0Ak9pjeST6knOST8T31zdbu6151KY7GdsqJLVIqT8OGpnqtRukJRabdLymipSeCeXLgOQIIwNYxvxWhTqbKXs5UUv1NmPKQ2ZCUNtoejIeQ0txaEhL/nKOGMckK83u1Jhh1ifogPpcD6q+rqh7T3o/ClXDWoDz1ODoius1lcdbYcCQsZacSSCEp9fhqPte09jLMmRJ1svUeC9BjMw45TVSoNsNNltpsBThHFCFKCR6DkojuSdVNUd46nS2pz8vbV1LUda1MLEgnqMomvRlKKelkuEMhxLSAtSkuDGT6/Ru7UY9SNMesCU+r2p1tJCFNuPN+1yWQtpHTIWltDCVuEqSQlxJAOQDXYN3q21duXRP1ztD9KaT/TG/wDnrap9eodWcWzS6xCmONp5KQxIQ4Uj4kA9hrnGj7t1GZUKTTqtt2unrqUtiO4sPl0NB6Mw+gpT0gteC+ULISEoLSipQHcWpazTbe4jBQ2lOaLJzgY/79jVXwQ1pcCpbFLnUIVk6NGjWC2Ro0aNCEaNGjQhGjRo0IRo0aNCF89fXSTdWzth3VJXVHqWqm1VY71KluGLJJ93JSOzg+TgUPlp31zH4rd+N2fDXcVHv2n0Zu6rEr8ZygJpDLCUy4lwLCjBc6mQVMvKHSWD937w9casx7oZqw0Kq5jXijhVVn4uXN/fDBaEndmwqVRdwKFHS2xVjOjOszaeykqKH1BhYQ4jKyFKCU8fKcYyRy1t549d79w6jDizHNpLIE1QVFNyCpRm5bXLBU08OTRGQR3WO41+rG2tEvh7a6l0feydTa7cc2CU13oRENxFrdBK2Et9wptIV08n7wTk+uqs218DOym3lBuux5lPXddmXFU1VKFblfZalRKIpQPNEQlPNAUSO/LPlT3zklkzsZxq5yxErCAoAoaPTaZOYaqtLkpZXIQHBKpzo6bpI+92yhY+ZB1zTUIM5X6pXYUSZVXEGZY0tHWip6LikD2olJPfBPHupODj0we+rpuH9TveseQ7W/ChvTcW3Mkq6n0DPcVVKG8fzSy6SpGfzsrx7hqhav4af1RS6PEdbm4Ioli25WLdpD1FTdceWl+nLZc6vJ9MZZU71MPKASUYyAcAaaiT4isA1BHVUuyU2bq6Ls3cuuU7bfber1CmXVa9nvtRnPYahX18YLckjsp4ckqcyfXBKifjr84L2/VGvFPabqZFtT7HvWiqkiCmtwLVnsQXpRzhlpbq0FxeATgJ/DOu5LB/U7dvfphi+fEXeNd3muxBC+rcD6vo2Or4MwwSgJ+SiofIatG5vCftJdu61mbqVmnyVqsGIqPQqC2pCKPFd5ckyExgnAdT2wQceRBxlI0lFjueahNshBookrw8WLuPeO2kSveIy4olPqktTtTqFs0NZhMQQ+ovdOYvmp0r8xUpHNKE54kKwSeh7dpFvUWkx4VrQIMSmhIUw3CbSlopPfI49jn1z79fn74sdsdz713jv+69mrBuhm04NKpkTcmHGDkRd9NokIWqPABRlxxqOlaVvIPnB6Yznzd9WS9RX7Ooj1uUl2l0pdPjmFBeiKirisdMcGlMqAU2UpwkoIBGMazfFfE8RqrNhtZ4QpvRo0azV0aNGjQhGjRo0IRpDqRA3LmE/uLB/wDkStPmkKqAncmaB76JB/8Aflayj/6ZUFcl7Mw97LCoEuJdFpXfJhoptxPWpS6NyiNmWZkt1bU/KSpEh1JQqO8v7HirASHMctOJUvEV9Wd0mKGL6DbtiMTKEXI1TW+xXOs4HG4zk4ddboQUZCQEEgFKfXXSm2W9G3u7y68zY9Xdku23UHKbUWn4y4623ErWjmkLA5tlTbgCxlJKFD1B1FULxJbPXDa1x3rT7nWKLa9SFJmynIbyQ5IUEFtLCePJ8OdVvp9MHnyHHORpPEdyhV5u3TL+sKVbxs6t7jXb7NHddfoyXagV1KS462ep9Ix0lplaeKgGH0iPxWeyB30r3Tt1XIcvxNwGKFe8qoXfRn5VBb5TZUGchykNtqQggqYU8H0qbQg+dKQAkccavy3t6bOr1yRbQkRK/QqxUI7sqnxK7RpEBU5poAuFhTqQlZSCCpAPMA5Kcah7d8S22dxwqNWEJuCnUa4ZCIlKq9RosiPAlPLWUNoD5SUIK1jinmU8jgDJIGgOI0UKs9wtr7uoXhpfgCmQ6vUVO2zJMGgWr7LLQ3HnRXHgttpbi31JSlRPYEcVdu+pXcapRr63DsTcKo7eXVcNgUBNVi1GmSrYl9aNU3EsGLOVT3mg4+hKA+2FoQvgpzPxItCm7yW7WrtqloUShXNUX6JVRRqjMjUlxUOLK6bbhSt7sMBDrZKvQA6nrRva374j1OVbslx5ukVaXRZRW0pvjKjOcHUjPqAr0UOx92oxEZhC5ylbPy9ype6tTsSzptl0Cs0WlroDMynGmqkXLBddkN1FERQSpoBQjNla0oU5xPYgZK6sbyx56fGHA2xuQ3dVH37detNUVftbdHMNLUfkz6gJqTXXKsdmnlKPYa7N0aMalVxaNht7ZbDt2R1uvIp1Ak+3SffJmuNLckvE+8reW4v/AEtXTSP7kwv8nb/4RpGvD+9Gu/5slf8Asq080j+5ML/J2/8AhGmJY1qpCpqnVgW/tmzXTEelfR9K9p6LKFrW5wbzxSlCVKJOPcCflpEhb43TJMaou2M/Dgy2+iBJU4lqK+iTObU84+htZLS0xGeHFByZDffCs6lqfddYo8NmlxXWyzEHQbK2wVFKTgZIx37a2fr9cOSeqxn49PXax1AolMN1B1fxA1mhUxderO2tTgwilpCOu+sPtyFxorwQ62GjxHOUWspK1FbRASScDNXd3rye28t69bSt2NMnVZyQmTT2Q7N9mYQHAuRzQlJyyUJUtopC1klpIDmNSEq8qtOjrhzWYchh0YW07HC0KHrgpPY6It5VaFHbiQ2ocdhscUNNMBCEj4ADsNGJRhUted81eDZTt3WEiNcTlOlhiTFCVj2rDnRdQ2R3SpDigonChhtaffkYbo3GrdkVO2LcmW+/W5tW6TU6VBQ42y0srQ2txIKVApCllRTz5JQMnt31ppvuvIGEKjp757NAevrr0L9uAZAcYH/l6MSMKWLg33vuHakqVC22fhVdykmfELrj78dLi4QfaZPBjkt/koo6eACUK849NN1VvSuN7m0G0INXix4r8NM2ptSYK/MlQWltth0KALqlpyRghCG1FXdaAcAv64c/sjHf/B6Pr9cITgOMYPu6ejEiisjkrBHI4Pr30Wx/2iMf5lk/++xqtvr5X+/nj/zWnHaWpS67d0moT3AXItNUygISAnit1BJPz8g/9dUjPBYQrwmkOCt/Ro0aSTSNGjRoQjRo0aEI0aNGhCNGjRoQjS1fu29k7n0uHRb7oLNWhQKhHqsZp1akhuUwrk04OJByk+70+OmXRoQjRo0aEI0aNGhCNGjRoQjRo0aEI0aNGhCNGjRoQjRo0aEI0h1MhO5U0n3USD/78rT5qBq9oQKrVPpkzZ8SUqOiMtcZ7iFtoUpSQQQR2K19/n+GqRGl7SAhcjRvCtuRSKDT49rXnTaJVqjLrdJuiUwp0+029UKk/LT0DxBEtkOkNqIwkvO9z2yw1Dwz1dEC7Ytq1Cj0lRvKhXZajKkLXFZFMhxGG40lAAIQox1p8hJAKVDJHHXRn1IY/SGt/wBJT/U0fUhj9Ia3/SU/1NL7KJvCrRU2qxdzb+3BtG8NxYdtUGBZLsybDhUioPT3Zkx+MuMFuOuMMhppDbrh4BKipRTkgJwUG2Nhd4pOy1ueHu7UWlTbdpUiKalV4NSkS5kyOxMEoNssKjtpZUtSUJK1OK4jOATjHUX1IY/SGt/0lP8AU0fUhj9Ia3/SU/1NGxf5IoufbR2auy0t4btvh6z6DVWLiuo1uLUzckuM/DjLix2VIVESyWnFpLThGVd+QGRjtObR2funt3W7jos+g23Kt2uXZV6+iptVp4S2mZbqnUIMUxuJUDgH7XHcn3YNzfUhj9Ia3/SU/wBTR9SGP0hrf9JT/U1BgvOdEUWHRrN9SGP0hrf9JT/U0fUhj9Ia3/SU/wBTVfl3b0UUFeH96Nd/zZK/9lWnmkf3Jhf5O3/wjS/K29gT4j8GXXa04xJbUy6j2pI5IUCFDITnuCdNTLDcdlthoYQ2kISPXAAwNbwYZh1qpAov/9k=" height=220 alt aria-describedby=cn0010><ol class=u-margin-s-bottom><li data-moz-translations-id=0><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr2_lrg.jpg target=_blank download title="Download high-res image (228KB)" data-moz-translations-id=1><span class=anchor-text data-moz-translations-id=2>Download : <span class=download-link-title data-moz-translations-id=3>Download high-res image (228KB)</span></span></a><li data-moz-translations-id=4><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr2.jpg target=_blank download title="Download full-size image" data-moz-translations-id=5><span class=anchor-text data-moz-translations-id=6>Download : <span class=download-link-title data-moz-translations-id=7>Download full-size image</span></span></a></ol></span><span class="captions text-s"><span id=cn0010><p id=sp0025><span class=label data-moz-translations-id=0>Fig. 2</span>. Parameterization used in KARSTFLOW methodology.</p></span></span></figure></div><div><p id=p0305>MODFLOW-CFPv2, in short CFPv2 (<a class="anchor u-display-inline anchor-paragraph" href=#f0015 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0015 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Fig. 3</span></a><span data-moz-translations-id=2>), is a flow and transport discrete-continuum model which combines CAVE heat and <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/solute-transport title="Learn more about solute transport from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=3>solute transport</a> routines (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0350 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0350 data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Liedl et al., 2003</span></a>) with MODFLOW2005-CFP Mode1 flow code (<a class="anchor u-display-inline anchor-paragraph" href=#b0505 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0505 data-moz-translations-id=6><span class=anchor-text data-moz-translations-id=7>Shoemaker et al., 2008</span></a>), considering some additional improvements (<a class="anchor u-display-inline anchor-paragraph" href=#b0485 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0485 data-moz-translations-id=8><span class=anchor-text data-moz-translations-id=9>Reimann et al., 2018</span></a>). In CFPv2 the aquifer encloses a network of conduits with turbulent/laminar flow embedded within a matrix of lower permeability (porous medium). Recharge can be split between diffuse recharge through the low permeability porous medium, infiltration through the epikarst reservoir (e.g., <a class="anchor u-display-inline anchor-paragraph" href=#b0115 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0115 data-moz-translations-id=10><span class=anchor-text data-moz-translations-id=11>Chang et al., 2019</span></a>), and direct infiltration into conduits and their associated fast-response reservoirs, namely CADS (see <a class="anchor u-display-inline anchor-paragraph" href=#b0300 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0300 data-moz-translations-id=12><span class=anchor-text data-moz-translations-id=13>Kavousi et al., 2020</span></a>). Accordingly, several recharge conditions were conceptualized and initiated as numerical model variants of the catchment. In the presented simulations, recharge is calculated by subtracting potential evapotranspiration (ETP) to total precipitation (P), considering a simple degree day snow-cover-snowmelt approach for estimation of hourly snowmelt, as well (for the methodology of degree-day approach, see <a class="anchor u-display-inline anchor-paragraph" href=#b0475 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0475 data-moz-translations-id=14><span class=anchor-text data-moz-translations-id=15>Rango and Martinec (1995)</span></a><span data-moz-translations-id=16> among many others). It should be pointed out that, the distributed recharge was given here as a single value all across the modeling domain. Moreover, only six input parameters were homogenously assigned to the model, keeping in mind that the overall aim of karst modeling challenge as a proof of concept, not calibration statistics. It should be noted that spatial variation of recharge and model input parameters (which were not also provided with current state catchment data), could improve the performance of CFPv2 process-based model. The <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/inverse-problem title="Learn more about inverse problem from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=17>inverse problem</a> was solved using the PEST parameter estimation code (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0140 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0140 data-moz-translations-id=18><span class=anchor-text data-moz-translations-id=19>Doherty, 2015</span></a>).<figure class="figure text-xs" id=f0015><span><img src="data:image/jpeg;base64,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" height=444 alt aria-describedby=cn0015><ol class=u-margin-s-bottom><li data-moz-translations-id=0><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr3_lrg.jpg target=_blank download title="Download high-res image (259KB)" data-moz-translations-id=1><span class=anchor-text data-moz-translations-id=2>Download : <span class=download-link-title data-moz-translations-id=3>Download high-res image (259KB)</span></span></a><li data-moz-translations-id=4><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr3.jpg target=_blank download title="Download full-size image" data-moz-translations-id=5><span class=anchor-text data-moz-translations-id=6>Download : <span class=download-link-title data-moz-translations-id=7>Download full-size image</span></span></a></ol></span><span class="captions text-s"><span id=cn0015><p id=sp0030><span class=label data-moz-translations-id=0>Fig. 3</span>. <span data-moz-translations-id=1>Conceptualization on <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/karst-aquifer title="Learn more about karst aquifers from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=2>karst aquifers</a> by MODFLOW CFPv2 (See </span><a class="anchor u-display-inline anchor-paragraph" href=#b0480 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0480 data-moz-translations-id=3><span class=anchor-text data-moz-translations-id=4>Reimann et al., 2014</span></a> for detailed explanation); note that sinkholes and losing streams are not present within the Milandre catchment.</p></span></span></figure></div></section><section id=s0120><h3 id=st140 class="u-h4 u-margin-m-top u-margin-xs-bottom">2.5. Comment on the various approaches</h3><div><p id=p0310>The challenge is focused on the recharge-discharge relationship only (mainly the uncertainty of forecasted hydrographs at the system output). <a class="anchor u-display-inline anchor-paragraph" href=#t0005 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0005 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Table 1</span></a> provides an outlook of the main characteristics of the respective models. Lumped parameter models are thus basically sufficient to address the question. Although semi- and fully-distributed models have been applied, they neither considered the spatial variability of infiltration nor of the aquifer parameters (parameters were assumed to be the same all over the catchment area). Only the spatial variability of the interception of precipitation and evapotranspiration was considered in some of the models.<div class="tables colsep-0 frame-topbot rowsep-0" id=t0005><span class="captions text-s"><span id=ce.caption_lm3_xrq_zpb><p id=ce.simple-para_vyk_xrq_zpb><span class=label data-moz-translations-id=0>Table 1</span>. Overview of the main characteristics of the 13 models applied for the Karst Modelling Challenge step 1.</p></span></span><div class=groups><table><thead><tr class="rowsep-1 valign-top"><th scope=col class=align-left colspan=2>Model summary<th scope=col class=align-left>Approach<th scope=col class=align-left>Main characteristics<th scope=col class=align-left>Parameter #<th scope=col class=align-left>Calibration type<th scope=col class=align-left>Objective function<th scope=col class=align-left>Computational cost (for 1 run)<th scope=col class=align-left>Remark<tbody><tr class=valign-top><td class=align-left>BRGM, France<td class=align-left>Gardenia<td class=align-left>Lumped, reservoir<td class=align-left>4 reservoirs<td class=align-left>9<td class=align-left>Autom<td class=align-left>Nash<td class=align-left>Seconds<td class=align-left><tr class=valign-top><td class=align-left>Uni-Freiburg, Germany<td class=align-left>Varkarst<td class=align-left>Semi-distributed<td class=align-left>15 compartments<td class=align-left>8<td class=align-left>Autom<td class=align-left>min. residuals<td class=align-left>Seconds<td class=align-left>Distribution functions are used to reflect spatial variability<tr class=valign-top><td class=align-left>IMT Mines Alès, France<td class=align-left>ANN/rec_MLP<td class=align-left>Lumped, neural<td class=align-left>Recurrent Deep Multilayer Perceptron<td class=align-left>381<td class=align-left>Autom.<td class=align-left>mean quadratic error<td class=align-left>Seconds<td class=align-left>Not enough hydrologic situations in the database (too short) to design a robust neural-network model.<tr class=valign-top><td class=align-left>SISKA-Switzerland<td class=align-left>KRM_1<td class=align-left>Semi-distributed<td class=align-left>3 reservoirs + 1 exchanger<td class=align-left>22<td class=align-left>Manual<td class=align-left>Volume conservation, similar shape of peaks and base flow<td class=align-left>Seconds<td class=align-left>The upper reservoir (soil) is semi-spatially distributed<tr class=valign-top><td class=align-left>IGME Madrid, Spain<td class=align-left>KarstFLOW<td class=align-left>Fully distributed<td class=align-left>2 submodels (recharge + aquifer)<td class=align-left>12<td class=align-left>Autom.<td class=align-left>minimization of residuals<td class=align-left>15 min<td class=align-left>Aquifer simulated with Darcy's law<tr class=valign-top><td class=align-left>TCD Dublin, Ireland<td class=align-left>InfoWorks<td class=align-left>Semi-distributed<td class=align-left>Double recharge reservoirs (diffuse/concentrated) + pipe flow<td class=align-left>min. 19<td class=align-left>Manual<td class=align-left>Volume conservation, similar shape of peaks and base flow<td class=align-left>5-10 min.s<td class=align-left>Subcatchments linked by a pipe network --> spatial distribution of recharge<tr class=valign-top><td class=align-left>KIT-Karlsruhe, Germany<td class=align-left>CNN<td class=align-left>Lumped, neural<td class=align-left>Convolutional Neural Networks<td class=align-left>950921<td class=align-left>Autom.<td class=align-left>MSE<td class=align-left>Seconds<td class=align-left><tr class=valign-top><td class=align-left>KIT-Karlsruhe, Germany<td class=align-left>NARX<td class=align-left>Lumped, neural<td class=align-left>Nonlinear AutoRegressive networks with eXogenous inputs<td class=align-left>371<td class=align-left>Autom.<td class=align-left>MSE<td class=align-left>Seconds<td class=align-left><tr class=valign-top><td class=align-left>SNO KARST, France<td class=align-left>KarstMod<td class=align-left>Lumped, reservoir<td class=align-left>2 reservoirs<td class=align-left>6 for KMC <br data-moz-translations-id=0>(up to 12)<td class=align-left>Autom.<td class=align-left>MSE<td class=align-left>Seconds<td class=align-left>Infinite characteristic time configuration with fixed ra, emin, kec and calibrated hmax, tau0, alpha<tr class=valign-top><td class=align-left>TU-Dresden, Germany<td class=align-left>CFP-modified<td class=align-left>Fully distributed<td class=align-left>Discrete-continuum model<td class=align-left>min. 6<td class=align-left>Autom.<td class=align-left>sum of squared errors, SSE<td class=align-left>Minutes<td class=align-left><tr class=valign-top><td class=align-left>TU BA-Freiberg, Germany<td class=align-left>RCD-Seasonal<td class=align-left>Lumped, reservoir<td class=align-left>3 reservoirs<td class=align-left>9<td class=align-left>Autom.<td class=align-left>peak max and flow min<td class=align-left>Seconds<td class=align-left>Calibration needs 40 seconds (10 runs)<tr class=valign-top><td class=align-left>Uni-Zürich, Switzerland<td class=align-left>CHLEM<td class=align-left>Lumped, reservoir<td class=align-left>9 Elements (flexible)<td class=align-left>min. 30<td class=align-left>Autom.<td class=align-left>RMS difference<td class=align-left>0.01 sec<td class=align-left>Tracks flux, temperature and dye, flexible chaining of elements<tr class=valign-top><td class=align-left>KIT-Karlsruhe, Germany<td class=align-left>LSTM<td class=align-left>Lumped, neural<td class=align-left>Long Short-Term Memory networks<td class=align-left>11311<td class=align-left>Autom.<td class=align-left>MSE<td class=align-left>Seconds<td class=align-left></table></div></div></div><p id=p0315>Most models include some parameters which are more or less physically-based, and others which aren’t. InfoWorks, KarstFLOW and MODFLOW CFPv2 are mostly based on physical descriptions of flow with most of their parameters having a physical meaning. However, the physical concepts used in these models are considerably different and simplified compared to processes really taking place in nature.</p></section></section><section id=s0125><h2 id=st145 class="u-h4 u-margin-l-top u-margin-xs-bottom">3. Study site and data</h2><div><p id=p0320><span data-moz-translations-id=0>The Milandre karst hydrogeological system (MKHS) is located in the Tabular Jura, in the front part of the Jura Mountains in Northern Switzerland. The site is known as being the karst laboratory used by SISKA for conducting surveys and experiments since early 1990s. The system outputs are formed by two <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/perennials title="Learn more about perennial from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=1>perennial</a> springs: Saivu (20–200 L·s</span><sup data-moz-translations-id=2>−1</sup>, 373 m.a.s.L) and Font (12–600 L·s<sup data-moz-translations-id=3>−1</sup>, 369 m.a.s.L). During medium to high water periods water overflows at Bâme spring (0–3000 L s<sup data-moz-translations-id=4>−1</sup>, 375 m.a.s.L.). All springs are located on the left (West) side of the Allaine river (<a class="anchor u-display-inline anchor-paragraph" href=#f0020 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0020 data-moz-translations-id=5><span class=anchor-text data-moz-translations-id=6>Fig. 4</span></a>). MKHS is fed by a recharge area of ~ 13 km2 (<a class="anchor u-display-inline anchor-paragraph" href=#b0215 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0215 data-moz-translations-id=7><span class=anchor-text data-moz-translations-id=8>Grasso and Jeannin, 1994</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0280 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0280 data-moz-translations-id=9><span class=anchor-text data-moz-translations-id=10>Jeannin, 1996</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0460 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0460 data-moz-translations-id=11><span class=anchor-text data-moz-translations-id=12>Perrin et al., 2003</span></a>) which consists in a limestone plateau at an altitude of ~ 550 m.a.s.L. This area is occupied by forests (~30% of the catchment area), pastures (~30%), cultivated lands (~30%) and urban regions (~5%). The limestone is almost completely covered (95%) by soil with a thickness in the order of 0.3 to 0.5 m in forested parts and up to ~ 2 m in cultivated land. GW recharge is purely autogenic and diffuse (no surface stream and swallow-holes).<figure class="figure text-xs" id=f0020><span><img src=data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAcQBxAAD/2wBDAAMCAgICAgMCAgIDAwMDBAYEBAQEBAgGBgUGCQgKCgkICQkKDA8MCgsOCwkJDRENDg8QEBEQCgwSExIQEw8QEBD/2wBDAQMDAwQDBAgEBAgQCwkLEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBD/wAARCAIiAm4DASIAAhEBAxEB/8QAHgAAAAcBAQEBAAAAAAAAAAAAAAIDBAUGBwEICQr/xABzEAABAwIEAwQFBgYJDAsLCAsBAgMEBREABhIhBzFBEyJRYQgUMnGBFSNCUpGhJDNiscHRFhc0VHKTlbTSGCU4Q1NXgoaSteHwCTU2VmN3lJay09Q3RHN2g4Wio8Li8SYnRkdVZGVmdHWkGVizKGeEw8T/xAAbAQEAAgMBAQAAAAAAAAAAAAAAAwQBAgUGB//EAD4RAQACAQIEAwYEAwYEBwAAAAABAgMEEQUSITFBUWEGEyJxgZEUMqHRscHwFRYjM0LxJDWS0iU0Q1JTYnL/2gAMAwEAAhEDEQA/APRjcTMudeJnFJU/idnOAxRs1KpsCLTqoGGI7CYcVekJ0n6Tij8TiTVkCrHnxf4jfy5/7mEcj2/bI4y2vvnpz+YQ8WyoyQxEcUy+2lwJum6wMXqVpXHE2h8t4lreIW4pl02myTEc0R6dVYVkOrJ1JRxg4jFxKSQn5c5/+hisPZWzshwoc4ucRGlFJXY1rcAf4GJhT0hoqW08svLOpxYN/v8ADE1S3ZNXpMinhwB4EJLpHtJ364h3pmnaI6vTf8dwSkZ82X3lLbRMz4bzEdP5+ihGkZsBBHGLiGPEGsj+hidoWXKvU9TEji3xGS8kXBTXQAof5GFZ0CXAdDcpspvsk8wfjg9JnCn1BuUQdIGlXu8cRUiKW5bOvxPfV6HJbTXneI3rNZ7/ALn/AOwCq2uni/xH3/8Axv8A9zA/YBVv78HEj+XP/cxbWXWn2kvMrCm1gFJ8sHx0YxY56vlE8W4pj+HJlvvHf4lP/YBVv78PEj+XP/cwP2AVb+/DxI/lz/3MXDAxn3OMjjPEf/lt/wBSj1DIlcZhPOscXuI5WlJKT8tj+hiqt0fNJcQpzjLxDCdQ1f156f5GNdlpdVFdDQuooIAPXFDYTELraJHaNpUSFkAEDfnipqqRW1Yh7f2U12o1emzxntNuX62jp4Iyo0yqpu3TuLXEUk+04qt2B9w0YZLoecUEJc4wcQ0ki4BrHMf5GLo9R25KTHpLrDrKAC66faB53J5YlI9Up4ksU8lMhwo0dsE7BQ5DGvuueeq7k41+FwxbT0tknx3nrEecz/p+Uq7S8h15+C2/K4xcRVLcFxat2sP8jDocPqqLf/O7xG8v69/+5i3KUhCS4spAT1OwAxD1DNUaOS1Eb7dY2KibJGLN64sVerxmHWcZ4rmtOltba079/hj5Siv2A1fn+3BxG3//ABz/ANzEdXsn12n08yY3GHiNr1AC9bB/9jFqp2Yo0yOtcgpYW3zBVsr3Yr9RqlRqLvgyknSgC4+OI8s4Yr8Hd2eD6bjeXXTTNe0Up33npP7qmaTm0HT+3FxCv51oH/2MD5JzZ14xcQ/5a/8AcxYFtOLbW52GlQtyHXCGk8uuKPi+hztMzsh5dKzc3AkyG+MvENtcdlblzWAbkJJH0MaNwqyBnzOPAnL+dV8b+IztcqdGblKSmoRtBcPMhJYO9vM4p9SUpimzGUd1RjOFR537hxsPo719NJ9Hfh3CiMrlVKXRWRGjJFio795X1UjqcZYL0LgxUajTo85j0heKMlp5sELVUYqST5gR9jzuMSX7RtV/v8cT/wCUov8A2fF+yzR3aLS2okhYW+Sp19afZU4tRUq3gLk2xL4DKv2jar/f44n/AMpRf+z4ZVTgRmlxgGkekBxIafSsK+fnxlIWB9E2jgi/ljYsDAY1TOA2bG9TtW9IbiS+6o6g21NjIbQPAfMEn4nEh+0bVf7+/E8eXylF/wCz41XHNQF7m1sBlY4G1W4vx44nn/znG/7PiFqPCWuU2v09mTx24mpp01DjJX8oxdpJUkoBPq+wICgPM42p99hlhb7zqUNoSVKUpVgAOt8VRKns7SmXG21NUSK+l5C1JsuY4k3BA6Ng9eZwFa/aPqp/+vjidfrapxv+z4H7RtV/v8cT/wCUov8A2fGpIChzwfAZK/wMq/ZqKePvE5sgX1fKUXb/APZ8Fg8Faw9FQ85x34m6li5tUooB934PjUalGflRlMMLCSoi+rkQDuPjhqme9CWGKi2y0gpPZqbNxt9GxwGeq4H1NKSpXHricABck1OLt/8As+G8LgrWpWt88duJ6WlGzY+UovIdf3P1xpiI8moBL0tam2FWIZTbcdCo/oxIp0pACRYAbWG1sBlg4G1Ub/t78Tz/AOcov/Z8NZvBesNOsR08eOJyO2VYrNTi2Ft7fufmca6VoSLqUAPHCUllqU0WXBcHYEcwfEYDMf2jqqf/AK+OJ/8AKUX/ALPhq9wfnsuFpPHPik+tG5SioRjY9AT6vtjUaY+t2KUvKu40pTZ8TY7HBAfV6rpuCiUnVbqFJwGZI4L1dmJ6xO488TGzcqI+U43d8v3PvgjXBWvSXEuI458T2Y6RzXUY2pz3D1fbGlvusO1LU44lTUVsle9wFE7YWFSLo1MQX1JI2WoBA+84DOhwOqoItx44nfylF/7PhvM4LVhhcfRx24mkOPBCh8pReRB/+740sSakLrXBZKBuQl3vW+IAxX8y8Q8mZeehu5jzLS6V+EJZS3OlttKW4rZITc7ncbc98BUm+ClY9ecjK48cTtIbStJ+U4t/C37nw4VwOqn9/jiaev8AtlF/7Ph7RuOPBnOy23cn8U8rz5DMv1IBFQb7zxFyyBe5VboN8UbPHpncIeHmcHsm1yfJky4LiWZ71PjreZiLIvpWq1rgHcC9uuJsWkz6y04cNJtPjERMzsizZ8WniLZbRWJ852HTwxzI1ntWXpPHXiT6pIjB+MRUYuo6b6gfwfxxNVDg5Wox0tcduJ1i1r/2yi9CL/8Ae/hiezJVIEp3Kuf6JKbkwHXkASGjdDkeQnuquOm4OLxMgifH2JSsC6FDp/oPXG2aK2pTJSNo22n5x0Rae1ua1bzvPf6MIqmQ63TptVhDjvxHXKbejpp0ZVSjBUgOITy/B7qSFXuRyscPFcAOKKYSXv6pfPgndoFFtUiN6vo+oPmNV/yvuxrURiS8lp1UaI4+wdCX3EXca8Ryv9hGD1Cqihtes1WoNGMhBW6SixSPL3nYDriutMcqXBrPeX4bdaqfpHcQ5KUPtoejImRkIKVqCTpV2Gq++JeTwoqTT/Zt8duJy22ge0IqUUlR8B+D9OpxYWDUs3ThmCqtmPAjHXCiKTfs08u0WOrh6J5AYsrUcs6TITdw2U0xzUogbarbJ33sNr4DNEcIcw2Jc468SmA57Gqpx1G3kn1fUTjq+EtZWAw1xy4pFQ3UtVRi6rDyEfb42xqaISnFeqpfUV21Sn07KUfqg9P1YOww1JUpCEhuJGJQEJ9lahuSTzsOWAydPCWvuXcRx04mobV3U3qMUknrpIj94/cMOUcF8wuOJjft78Tg64NR/rlF+ab8/wAH3UcalGS0UKq8hOyQeyTb2EeQ8TjjanI8My1p1SpZFgehPIfDngM2HBaquS/VmeO3E3SganCKnF2PRP7n+OHX7RlV/v8AHE/+Uov/AGfGmQohit6VHUtR1LV9ZRw5wGVftG1X+/xxP/lKL/2fA/aNqv8Af44n/wApRf8As+NVw0qMhxlkIZ/GuqCEG3InrgMqf4M1jtREicdOJy3TbWflKLZseJ/B+flh0OBtVA3488Tr/wD6yijf/k+NPiR0Rmg2m5NzqUeaj44by5DhdEOKbOrBVqPJCb2JwGXy+DVTaIjM8duJzj6/ZSKnF28z+D8sVbibScr8GcqPZx4j+k1xGpNLjqQ07LeqDCkIWs2RcJjEi55bY3BxaKcvsWmdTi063H3l6Qr3q5/DHkD/AGUZ1iR6IlbS66hS5Vdpers7qASXbab+W+A1GjZbpFeyYzxDpXpNcQJuWVwFVRdVaqccserJSSVJ/Br9De4vscOOHWVaHxRydFz/AJL9JTiNVaFP1qjTWakwlC0pUUmwVGBFiDzGPI/o/V+scB8rcXPQ7zrNdXGkZPmZryRJfVcyIL8NS3WU9LoN1WHgvHoP/Y1iGfQrycuSPmVIlgI5knt1jb3nAaijhFPcdbbb448VCl1VkuGoRdJ2v1j3wpA4J1iRFS6vjxxOuSrlUovK5/8Au+NG0usppjMlSe1Sok222CD+bbDuj7U5q553I9xJwGYv8Cswkj1f0gOJjYHPVPin/wD58BHAmvA3d9ILievyE+KB/N8a0SBhq9UobKi12utwfQbGpX2DAZoOBtUtb9vjid/KcX/s+B+0bVeX7fPE/wDlKL/2fGjmbLUNTdMdIHK6gCfhhSPUGHXCwu7Tw5oc2Pw8cBmv7RtV/v8AHE/+Uov/AGfA/aNqv9/jif8AylF/7PjVL72x3AZV+0bVf7/HE/8AlKL/ANnwP2jar/f54n/ylF/7PjUHpUeOnU+8hA8zhr8p9ofwWG8/fkrTpH2m2Azg8DarYj9vnife3/2lF/7PivZZi5jyTxwk5NXxIzPmOnP5VRUgzWH2X+xfEstlSShtFrpsOvLGzdlVZCT2i24yT/c+8v7TsMZVNjMxvSPDDSSlP7CAo+Z9fO5OAynJ508Q+NCtBXbO7vdHM/1vh7YYvtPNr+dZcbSpSiNV/Hlh/k5xuPxD4zPPE6U55cJtz/cEPCdYqbtUkh5Qs2kEIHLbFnJ0xV6vIcJ95/bOprFImsz1nyk1S+6AG0ukAX26HbF3pCWWKUwtOlALetZtbfr78UVLTjqSW0LVsfZTfF6pjYYpEduSQmzQ1X22+OM6aOvNMIfbG8W02HHjt0m3WI7/AGMqtVqRJp7yUvtvrAshP5WKiQRa5ubYc1REZuc6IiwtsHunDXEOS/PMzMO7wLhuLhun5dPMzF/i2nrt6LVk5/UzIjqdJKVAhJ5AYsPiDsRvbxxnLUmRHQtDLykdoRqI5kYuWW35MmmJVIUVELUEk8yPfi3pr80crxXtRwWdLe/EZn4ZmI27dfqlNzyxGTMw0uGbF/tFi/dQLnDmpviNAfdVsOzIBPicZ+VK3CQN7dN98M2bknaFf2c4Bj4tW9887RHh6+X2WZzN4S+NEQdmRfc78tsVpbinFqWQElZJPXrgz4SFhKSSEpA38cJ4p3yTk62fRdDwvS8N/wDKV5d++/Xcoh95tsstuFCFbqCdgffjjbi2lpW2dBSoEEYJgYxvPmu+7ptMRWPi7nU2ZLkPKEiStY2Okm4w25giwHhbCj+6gv66b28MJYxMzPed2MeLHi/JWI+XRwpvYlW/UW2vg4IwXAxhvERXtA2opUlaVG6Te3Q4WcdDL6lMsISSBvzsD4YRQntHEtjmoEb4O6QpQ/JTo36+eDJrU1EwZaibn1d0E+PcONv9EyjwYPALI89tClyZFFYLjritSyLkhIJ5JF9gMYfUEk0+X/8Ao7v/AEDjdPRgnxo3o8ZAQVlThojADaBqUefTAa44622krcISlO5J6YZpqjjqS4zT5C2+igB3h4gXvgvqr9QWlU5AbYSdSWb3Kj0Kv1YflITYiwttgEY0+NJUUNKPaD2kKFlD4HDgG4uMRyiJNVaLPKOFdoscrkW04kUgjmcBwrAJFjtiuSs2LkyFQMsU9yqPoUUrdCtEVo+C3eRPkm5wbPMhyNl6Spp5TIWtlp1xF9Tba3EpWr4JJxLQYMSBFaiQG0tMNpCUIQNvfgIVOVJtVWmRmqopl2UFJhMgoipIN9xzWf4W3lixttBsBKQAALbC2Dk2F8cKrYA2BgpJHMYAXte1sB0m3PrhJxLToutAUAdiRex8cVmuSa25miPSWqsqmQ5UZSmHW2ELLr6T3kErBt3bEW57+GFxSM6K7pzdG7M8z8mjX9uq33YCekTIsOOqTLkNssti63HFBKUjzJxX/wBl71Q1jL2Xp1RbGyZB0ssK/gqWQVDzAIwqxkyG5IRNrk2TV30boElQ7JB8mk2T9oOJ9LaUgBIAA2AA2AwFWeXxFUkyW2aA0lHe9XUXlqPlrFgD56TiaoNTRWqRFqzTSm0ymkuBB5puNx5+/DHO8h9igOsRXS29NdbhoXe2ntFhJN+ndJsfG2JFDLNJpZZhthtuM1pbT4ACw/MMA2lR0S5vZwtTamyC86hVrfk26k4QQ45LW+4lZQpN0OvEWDKAfZT59ScSUJCY0ZtNiorGtSuqlHGLcavSEiZSqCsh5Goa8zZtciPyjFaSTBp5bTcKnPpBDKSoju7rUSNrG+AvWds/ZL4d0E5jzrV41FocYizslxKO2V0JJPL34yap8e+KvEhx2LwOyKmjQY0xoPZkza0puNLjc1rgstkqfuNIC1aUb3ubWxGZb4TSK5Uhn3jLWk5zzLNiMtqjupvR4ASpLgTEiLulFlpSe0N1EjmBtjQDOCwG4bJeKe6T7KBba1z4dMBlY4L5/rCUHPPHbN1aW1UDUWgJioiEL5pb0MaAWxsNBuNvM4VoPBvhJT6f61A4dUVNQZmuPSfWWzKcTJsoFzU8VG/Mg8wD4gHGodlUVJJ9abbuPYS3cD44r7zi2s1OsOtoD0mO24rSO64Urtf32V92G0T0t2Ox3U8iZGzQ1ENeydRaimO6JLHbQm1FpwW76Da6VWAFx+ffHjTiF6K3EKkcRo2XMmU5idR83Vd1unTe1WRBU6FvOetAgq0psqyxqvsNiTj0xnzinmrKOZsk8OcjcOGM2V7NcKfOSmTWkU1phmIU6zrUhQJPaDbbliMzFnFzLdd4a594/JpvDQ0muVF0xWsyNzGZLXqZSgq0JCnAFLPcCTpsCSMdLh3FtTwnJbNpJ2mY26qGv4dg4ljjDn/LEtrpfDj9hPB2lcP2ZKpIplJbhl47Avtp1JWB0BWDt0Fhi+5Pqhq+WabUFm63oyC5/DAsofaDjP8AN3pF8BMqQoac18WcvU9FTgN1OGXJIV28VZIQ83pvqRcHcbC29sVvhnx74bR+Kw9HqDXW5lbepX7IorzTgVHcjvErQ2hQ9pXZWd8NJ59MVvexkxWi35t9/v3/AFSRitjyVmvbbb7dv0bR2wirqciw0IUlVvE6RimyohqmanoVXfLnyc0iWxGUnaQop3dP1gDsE9LYukJsSW5inTs88tBA6BPd/Ris5sYT8kUutJXpqcSQ23EKPbe1q0lseN07+8Yqwtp9puIqRDZQodgEdslR27RfLc+IudsOIJSVzJC0fPBZSq/gBtiMaWlTnYuFK0KeIcSOWw3cT9Wx59L8sLRnz2iXluXKmF9qo7amx7C7YyHUd71ekiSEEuSPnAOpWrkPvx2Q2mHTUwWT846OyTbqo8z+fCbQJbpMY+0kBah5JRsftthaOlUmS9MV3uxKmmR7uZ998Bx1PbvM01r2GkpU8Ry25J+ODSO5UohdBKCFpQOgX0PvtfApWlUYu3BeWsqe8dfUYcyIiJLXZrUoWIUFJ2KSORGAcX3tgE2F8R8Sb2a3I851AdZNtaiE60nkcGdqHbfM07S84eavoAe/r7sAtKlpYCUJQVur9lscz/owi1BeW8iXNe1uIuUoT7KL/nweNDMcqefd7V9z2lnYAeA8BjsqaIxDaEdq6v2UJ5nAKSJbMNvXIWbchYXJPkBhoysds9U5fzDZSENhZAOkb3PvwxcmJZcLq1CRMJIB5oZ8hbmfdhuiS7KdS6El5w8jp1hPkB7KfeTgHM8+tt+tLJbQgEx0q9p1e1tvDYY83f7IRw9z5xZ9HWsZPyBQJtarKqnS5LcCLYnQl0lZ8DbmRj0chMjtilCFF8jvFKgt1I81Hup+F8KSozEGnyZtWebjwora5DoSrYBIJK1qPOwF/hgPE/pqcCq9xa4dUI8KZ8Z7jDwypMVSqVCcSJ7lOls9i4w4m+yTZwpubEBYxsPoM5Ezfw19F7IGS8/0KTRaxBMj1uFKSEraJdcUgKsSNwQRjK+HEyrUjiNTPS0myJCYmfKyKRVkOOHQzQpauypKyDy0LbaUSf30q/LHuFcduUz2b6UrSd+XXxGAjqlKa7UJJWhyPZXa6LpGocleF8IR+xShKXZkqMnkNCwWh5BXT44UlMSITqXivVpFkvHmR0Qvxv0OFUtQ3mHJLS1RFIJDtrWSRzBSdjgE1ppadpFVdfBNggu3ufhhZmWzFbPqlKe0JFypKAkfeb4TKHoiWpLb8d1KnEpslkJ1XPjhdeuovOMBRRGbOlakndauo92AeMvtyW0OsqulwXFxgPRWJKQl9pKrcr8x7jhRDaW0hKEhIAsAMGwDEUrTcInSgn6vacscNHjq3cdkLP5TqsP8DANGaZDZIKWEEjqq5/PhyEm1tgMGwMAjIlMRWy4+4Ejl5k+AHXGPzXzI9JLtOxcbT+wcAawBf8PPTGuTYin0JUyoJdaVrRcXF/PGQzXnXvSRCJDJacTkgXHQ/h55HAZbkxKXeI/GVtxAUk55cFj1/AIeJkZRh+tKdU6sscw2ByPmcQ+SP+6Txl/8eXP5hDxdjub2t7sdLHFb467+D5LxPiOq0HEtTXT35d5JsRmIzYaZZSlA6Ac8N6lTo9QYDclxaUoOq4PTww7JsMMKoipSAmLEKG2l7OOE963uxJb4Y6Q5GmzZLaml7ZeXad+afDz26T3jorT8agfOdjMdR2XJJF9Y8AcRSigkkXSL7BR3ti3wcrxokgrecEhAGlIKbEXwsmh0Vj5hTaSpStaQtdjfyxTjT2nrL3mP2n0elmaVtfNHh5fylTmAx2g7YOKT9VGxOLwifDhwGXFgsICNSGiO9YeXjhSQqmwUmU8hlq3IqQLk+WKbWJ/r1TXKZcWUHZFxyHuwifw/bur15fa7NWlqzXFXed+8T26fOP5nVYrYqmlCXC2wCbJtcq9/hiObQlIW4HErSBcEcwcF1NvXF9Ln3HCiWChC1OlNinkj34r2tN53l7PR6TDpMUUwxtH9fc2Oo7rN1HmccwL6u90OBjVZiNukBjoA6nASAVJSTYE7nwwstSWToQkCx3VzJwZdcac7BDq0FASSgkjnhA26G+Dh56+zh+O4wdTkZ0BSiUqVtYDlgEMDB1tqRvcEdCMEwAN7e48/DDh55aF/NoKSQCFeOEm0BZ0k2B9o+GOvOpWUpbUdKBpBPhgEKjIcVTpZWQr8Hd5j8g43j0Vmmh6PWQVpbSlRojFyBY9euMDqFvk+X4eru/8AQON69FWSw56PmQG23UlQojF03364DWgAOWE5Ic7BZa9oJOkeJtthXBVXttgGlIDYhNqaTbWNSj1Kjzv53w8wxpdg5NSnZIkEAeBsL/fh/gEZEViU0tiQ2lxtxJStChcKB53xW3snvQWi5lqtz4TyLrQh15T7Kj9VSVknT7iLYtGoA7m3vxwWVuPffmMBV0Z1kCOIsjL8/wCVz3PVEskoUr6wd9kI63JvbphRqjZmqXzlXzG5CK9xGp6EpDY8C4oFSj57YsuhPn9uBpSNwOXLAV8ZJgKAD9Xrbq+qjUnUk/BJA+7CblBr1MWlyhV558DnGqJ7VsjwCwNYPmScWUXO+BYHAVlijV2sVCJPzI5EZbp7vbx4sRSljtNJSFLWoAnZR2AHxxZkjSkJuTYW3wAAOQ5Y7gBjijYE+GAeWK3xBz3lvhvk+pZ2zdOVEo9MRrlPBsr0JJCb6U7ncjlvgEs7Sm5kFnLrIDk6ovN9i2ncoShaVKcPgE25+O2H+YqrBpVMccqM6PFjJbK5MqQsNtstpF1LWokBI8/PGNZu4kM5Bit8QM1ZpYpYzQphikxi2FyZJWkluKygJUor3BUfZG5IFr4pS8v8SOM8BqocWUCm0eQAHcpKdQ+281rCkqlOJGlZNr9nYpFgb3xbrpYmN5yVj6z+y9TRReN/eVj6z+yz13i1nTi7JfoXBqW/lzLcJ2MpzOL0RuQirsKGpbVOQo2sRpHbrGjdQAuL4XypkPKnDWA5S8pUdESnTHVvSl9op1bz61EqddcWStxSrm6lEnoNgMc+Tq6lb7NFltx2o6AxDKQEtRkJUNkICbctuWDOUTOIaLKcwJWXkkOBaQQFX6d3ljaNHWYmfe16es/s3/s+s1mYy16es/sVAco0x+iskmG9HVKYH9wCVJCkX8Dq2xOPPIjIShKCvWdKGxzV54pFeiZwgqaqTk5LqGNaFuIbBS0wuwJXtc2ITbyBxZaYt5qNTpU1/wBYWtkMOPBIFlX9rw3xpn09cFOf3lZ+W/7Qi1OkjTY4yTkrb5TP7QkOxmPWMh4soJ9hv2reZxWKuFxczQbOLJLbyNRNyBpSsH4FJ+GLNNlOR1di2UoJbUsrXyt5eeKrNfXUMzRu0CD2EF11YA6myb/eRiqpqTxK4GQ+LvFjh1WM1ZVRV8n5dpdebqGuWprspbimSxcIWlZuQrkcR0jI0Tg7xoyzWuGHCpebaZGylUqcrLEKUlUmEt6UlapSPWl6VtuFIbX39Q2sm18bjTUAMFaQArtVm5HW5A/Rit8OH4dX9KHNJbqVR9Yy/lCnx3YpAEX8JkOOaxvfX831HI88Y337Mb+bLOA3oxcQ8mZgZn52ytBeSvh7VITYCkOtU+bNqD0gQWieiG3EouLDYjlh/wCjRwp4hcOuKWRXcy5MIZjcMI2XKlLZfZX8lzmZK3g26L6rLQpISU3B5chj2M68hptTiilKEglRvsAOuKhkVBqD9Zzi4kpRU5BDJOxLDQKUn47n44lx05qWvbtH8UOTLNb1pXv/ACWamnTCdcvYF11dz4ajiqVArlVzLsFxXzLMN2WtI56rBIP2FVvfiyJX6tQFvFCyOyKylIupVzcgDqT0GM4yjWczT6uxKn0d6RqC2HZT4QwuOhtw2QEXsseYscRR06LE92hxYbRUyyu1nGe3eI+lyASPyR4Y6ltDsFBWPnKg6lKz+TfkPAWGCx1FhxhBX3Y92iT1aV7B+3bCjdkxYKlHSIz5Cz0uAofnIxlg5TczpK0gfgzKUIHgTc/qxykSoohtRxJQXQm6gTvqO554KUy40t9baI6+3KVd53SRYWta2CyCkkKq1PaSjkHUHUEe88x78A4kRHUveuQylLp/GIPsuDw8j54UjT231FlYLTw5tr2Pw8cIJTOh2LSjJj/VUbrSPEH6X58KqTDqLQVzIHtDuqSfzjALORY75+fYQsjYakg7YUS2hpNm0hIHQDDAOzYKtL4Mlno4kd9PvHh54C5T1R+bgqKGT7T9v+hfn78ArLmKCxGiALfUN/BA8T+rEPJkpYWtEZfaOL2ee6rPgPBPu92Dz5jMRtUWGbDftXL7q8r9B4npfbnhtCiuur1WDZRupZFg2PG3j5dOfM4BRiOy2AZoK1EbMNi6z/C8B5fbh2t9SUhDriYbfIMsjU6r9WOx2g4k+qK7CMN1SD7bvjv0Hn9mDx7rAVS4aA2b/hD17nzHU4AzDctbaWIsf1JkblarFZ+H6TjFfSonvpydTOFdBmvM1jiVUEUIlKzqRCA7Sc95AMJUm/i4nG0TI8tiK5JXUXCtAuAAEov7rY87/KauJXpAZlzelaXaRkRhOVKWojb19el2e4D4i7DVx4KGAuKOHVCz3kjNXDN9hDVOn0dqCw0nupZICuyWnwKFJQRbkUi2J3gHxAr2euF9JqNZiJcrtN7Sj19GvSpupRVFp8abbalJ1jyWMP8Ah0AapVNr/MRwPhqxT6K3I4cekfXMssupi0fibCFfgqUNQTVooS1MbAJsCtrsHPMhZ6YDXn3pVQSYqIjjCVbOLdFrD8kdT+bDR7Q6t1tgfNvvtMpuLhZT7R8xYWw5kt9m3eoVZamzsUAJRq8tt8Hhx1OOJkKZLTTSdDDR5gdVHAN2NJiQ2wLIMxW3uUq2HdKASh1lWy23lg/E3Bw0QNNLafG3Yyiq/gNZB+44dyUOxXTPiJKwQO2R9YDqPO2AkccJwmw8iQhLrS7pVv8A6MFmyExY6n1XOnkBzJ6AYBOTO7JYYYZU88RfQOg8SemE0uVc97soxH1Qo3+3B6bHU0yVvj59063D5np8MO8Ax+UXWtpcJ1H5SBrSfs3wPlmJfuh5XuaUf0YfFIJvbACUi9ha+AYipuOCzMCSsnqpGkffjKJzkhfpJjt2Etn9g42C9R/d+NlIBG+Mfqv9kqB/+Rx/P8Bk+SP+6Txm/wDHpz+YQ8XbFJyR/wB0njN/49OfzCHi7Y6WD/Lh8X4//wA1z/OAxxTiGklxxSUpHNSjYfE47hN9lEllbDgulYsb9PPEs7+DlVmnNHPO0bxv5oyRmimMvBttS3LmxUkd0fHEfmSdClIjyIUpKnm1fR5gYbzcrTIyCuO4JCATZOmxt7sQiklBKVCxHMHpilmvkjv2fSuFcG4ZkvXUaG+9o7xPXf5wWkTpM2yZD63QNxq6YQtvfrjp22OOYrTMz3ewrjx0jlw1iK+nn4hg6HFoPdJAPMeOCYhc6ZkbyhlWpZlcjqf9QZLiW0m2pR2Av4XIxpe9cdZvbtC1pNNl1uemmwRva0xWI9bTtER9fPZYtDLiCpKSkjp44QOMM4S8fq/nTNzOWMwU6I21USpTTsdJSplSU3tbqCBa+N3bYLgu0pBH69xirotdh4hjnLgneIdz2m9luI+yOujQcUrFbzWLRtMT0mdo7dO8TBLDhCPWBa/zoFgrpbBVNFvZa0gDwN/uwVxSOzS206CCbqsmxOLjzkDdiQAFrCV32SeoxzsFj50rSEHbmDhMDffC4LSWAHtR1nUmw9k4ApfI+bbbSW+diNycDU6sEoYAHK+i9vfjmttG6QVK/KAsBgq1qWQSRYeFxbAKu6W2CVI0Kc3AtbYYb3vueuHBcKRpkHtVj2Qsnu/HBNcc82VJ8bKwDOof7XS//wBHd/6BxuvoywkSPRzyCUgJfRQ2C24NiDvYX8MYfUHkpp0vsUBN47vPf6B8cbz6K+3o75A//UjH6cBpsGSZTKXDssd1ab+yoc8OThlKg3dEqI4Wnr72HdX5KGCifKQAJNNe1dezsoHAHdpxLqnY8p1jtDqWEWsT47jBfUZF9K6pJI9yR99sAzZq/wAXSndJ5FS0p+0Y4PlZ06i4xHHIJtrP24AwpMMi7wcdPXtHCq+E1wXoiu2phFvpsqJ0keXgcH9WqPP5ROodC0NJwQyp8axlsIW3excZube8YDvyg+fmzT5IX0Fhb/KvgJiyZR1T3VoBPdaaWQE+8jc4cJqMFZARMZJPTWMKl9kc3Ub8u8MAxbSIc9DDbi+yebUQlSirvDqCfLEgk8/fhKTFjzEAOp1AG4UDYpPiDhqIEtB7NupOhvkNSQVe7VzwDmTLjxbds6Ek8k8yfcMNhLnyN40Ts0E2C3zY/wCTzwsxBjxTrTdbh9pxw3UfjjlWrNIoNNk1mt1OLT4ENsuyJMl1LTTSBzUpSiAAPE4BF2LPbQX0TluOoF9FgEq8rYwzjvxqp71On8MMgUtjMOc1wU1JDMhCzTYAQ4ChUt9KVBKiUq0t+0spI7vPEZmrifn3i1Jdy/wuXMyrlticqLUK9UIRTIrDBRe1NBV3Wjc/PkciC3fngmS8jZXy3R4eS8lUr1PL9MdLkl5S1LcnSb6lFbiiVOqKrlSybnlgO0DhyIU2fnnNNRlZgzVWA284/IcK40DawYgtHaO0m6gPpm/eJxcSiXM+aWEx2jdJ37xHUD34d3upW25N/cPDFYzvPU3EYjRZpDnbBbzEd0pfU3pO6dO4sopJ8r4bQSb1zNpjhVOoSexfQ8GUPuhIY7TUApC/qjmL2tfmcJ5XTWK1ClplV2qxpkGStshxKRpWoA2UkpsUg35HEfRKJIqrlWiQK+h6G8G0vPFkLDwUCTa/srB2232BOL4p9iGUx09otekA6RqWQNgT9mG0eTGxnSZxq0Z2HUG0tTGCpiU1bZNxYKA6oUN7+OI6hn1BlyjSW/3EEtvtj6SLnQ8jxFtjh/LiJqUgVCmyTDqMdOkKUjmn6q0n2k+fMdMJw4dYfrLVQqseIylqOpi7DillwKUknmBtthtEdmY6dIOipSW0tLZ9caVcsKSnVY+Cr8sVx+8KuyEr3cMBB8wkuC/34s9109ZJTqirN1W/tR8h4eOIDODao06LWdAU0gKQ9p6IVbe/lgJ7tRT6U5IKC4WkKdKQbFVt7X88Z7wDqcyo8SuLGdMtZfcnzVVKBSKmt6UltoGPGCkBgW9lKXe9qN73xfIMyHOpTnrD7fYJbJcWpekdnbmVeFr4yDhdmvgrV+BvGeLQOIDlFTUFVeXWpjbpdlwB2ao6pSG098pAaCk25i2OjpqRGnveKb7bfrKlqLTOeuObbbtopfENjifPnZOoWfMl9uxdudFpVXblzEIGygpKTdB5g40lxmNSKCzT45S0w22GgbhKUtgd5RPQBIJJx4MyvS2eC1c4PVDiHwy4eKpDL7cHLmeMizFRpb61RFlLs+KUa3mXGklSwFKsrc9MdyV6QnGKfnPK71SzNW63l3PlCzJPQqtUmDGp76Y8Nx6OuAyhSn0tAAJUXvbB5b4pZcs5a8kREV8oTUw+7vzTMzPq93yZkOs0OO/RKk05FmhAiyo6w4ghQ7i0qGxT1xkiPVo1QRDlU9htMZaEyazGS/JutK7rVr0jSohJCrqIFzjzHln0i+K2TOG1DyeioxVVriLkrLcrhw3Hp7TTUWpSFpjTGkNpSElLWtL+m1kpuMXeu8Z875e4WcRlSM2QVVLK3EuHlGmoVFZTqiPOwkODRa+taXX9/BR8MReKd6/bSy5FjvlWtpKjHWQdlIJ7puOnIg4MA/FkLhymDIaeSVd23fI6jz/+OIdVcn0tb0WXlCW9EWkAfJ6230i23s3CgLAdMN15ydLQjuZXr73Z7tuCCtKhbqdrX6YyLI0ilBaUPwnWlO7JU+Lm56aiTY4cRXEBK6bLPeSSlGs+030IPU4pys8SygoOW6/ICv7W5TVqA/wrDfDX9nbCXUxahAlU4KNktz2lISo/kldvuOAvqVzoKQ2WjJZTyUk98Dpt1wUCFPWXYrpZkpHeIFlD+EOuIOn1h9KkthTjBJuAtWtB999wPdiXefgSBaVeJJRyURZQ8LEcx5YBwZFQjAiTF7dA+m1zP+DiOn1Z11CmmEqbSNlWI1HyP1fz4QmVZensnJva2H9rSUJPv6k/dhGJaUUnQSkGwSpQSAPAk9PIfacAeFBfmL7QaW22yPnLbIA6Dpf3cvG+JSJDblN95OiGg9xsba7c1K+PTHFJjgBNQltlKB3Y7J7tvdzODPurkNhbqjDhgWN9luDwtzH5zgA+4zOUoO6UQY5Gq/JxQ/8AZH58KJZfn/OvLdjscm20K0k+ZI5YgKtVX5EtqiUWMlUqwKEKB0Mp6uuDoAN0pO5OF00jNdMIfptcVUtSQH2aidIKvrIUgdz3WIwENxdz2nhVw2zHm2cn1pFPhLVDQtVlPS1HSwxfxW6pCR78ZbwlyhJyTkCkUGovdtVFNmbVn1G5fnyFdrIWT49qpfPmLb7XwXjRU6rnribkvhJUWYiItIV+zGutRnVLBbZPZw0KJAtqkEqseYaxY67XqTlqkS8wV6osQadAbU9JlPr0NtJHNRPv6cydgL7ELpw3sarVE727Fk89vpf6/wCtzXvSdolRGRofEjLkYu17h3UWsyQ0o9t1hu6ZbIt/dIy3k28beGM4yfxpzxlWWOJeeMiKpHCrMC2okKprKhPp6AbMzZrOn5qO6VADqgWK7BW3plPqdThFK+ykxJjdgQQtDjaknlzBBB58jfAM8sTaBX6RAzJRnky41RjNS4z6lalKbcQFJO/K4I2xLOqS02pxRASN1X8MYr6NSjlFnNfBObZDuQqsqPTitXfdoz47aEv3JStTV/Fo+GNbWU1VaY7C9UZJCnFjcLt9HAHhMJcpqm306Q8FrKT0CiSML01xx2nx1r3UptN7+7CdTcPq3qrJ+cfPZptzA6n4YdtNpZbQ0hGlKRpFugwDBVqW+VpQr1Z43VpFw2v4dDgMqFSldtpV6ux+LB2C1+PwxJ45y5YDiQkXthCZKEYJGhS1uHShKeZOHOGVRS6lTUtpJX2BJUi3NJ528xgE/WKq4QG4CEeKluggfAYOBWBzXFX8FDDqO6h5lDrZulQuDhTAMCKyR7UUfBWMonpkf1SSfWVIKv2DjdAIH7vPjjZTjH6r/ZLD/wARx/P8Bk+SP+6Txm/8enP5hDxdsUnJH/dJ4zf+PTn8wh4u2Olg/wAuHxfj/wDzXP8AMMDAwMTOP8gGxCgbEYrOaqWlFqiyAkHZ0D7jizYZVhIXTJKDa2i++Is1IvXq6nBdbfh+uplx95mI28OsqDgYA5DAxzH2zbabbeYYJIpsCrw5FOqbDT8V9socad9lYO1j1+zB8Dcb2B9+MTEWiaz2lvjyXxWi+OZiYmJ3jvvHb5becbSp+UeDGSMkVddVolNeEkpKW3X3SsNJJ3CQeW22L0hssL+deSQVXIKPo9MJSVKLxupRFhYX2wmoKVa6jsLbnEWHT4tPTkw1isei/wAS4txDjOeNTxDLbLeIiN7zvO0doie/Qqt4JWrsUISL7HSDjoQ082pSipCkb35387YQA5DxxaEZbZRSVvMILkhxsLF1eI5DFmtJvPR5zX8TwcNnHGb/ANSdq+k+qupQ3cpA7TxUdgMdfCXFa2rWAAKR0x24BVGda7NYslQvyPgcIglJ1J7unbbGu0x3dGsxbbad+gJbWs2Sk4V9XLNnHrAX2AN74MypTqHEuuGyU3HmcEaIUCy4LdQR9E4wElLUtRWsWKjc45g60rT3lgje1/HBMAhUNqfLP/3d3/oHG6ei7EkK9HjIS2p62z8iMkJ0pKRz6Wxhc/enTLD/AL3d/wCgcb/6LAt6PGQN9/kRj9OA0ZEyRGNp8dQ/4Roakq+HMYXTUoCk6hLat5qthYi25v8AbhPsI6jqUy2T4lIwCK6vTk8pKVnwQCo/dgglz3yTGg6Enkp5Vr/Dnhy4qPHSXHFIaSOpsMNvlRtZIiR3nz+Smw+07YAyTWAO83Et5KVgpmTGf3TAKkHmtpWu3w54763UTuKWofwnUgY569LaF5UBSU/WaVrt8OeAKJlFeTZRYF9rOI0/nwZMSkKT3GopB5kWwPXqa8NLrqAeodFj9+CqFCWbn1Qn4DAd+TktEKp8pbJ6ICtSPsOAag9F2qDRAAuHGgVJt4kcxji6fTFt9oygC/ItLIN/IjGOcRuOs6kZgmcL+EbDGZ87xmmXpDM5TjUKlMuKt20iQE6VEAhQYB7RfS3PAXbiDxhyNw9ZjfLtXSanUStuk0htQEypvpST2TDZtqJ5XNkg8yMYtOoGbOMrzWYeNzAp+XXmGvV+H6lokRmH0rC0vS3kW9ZculJ7O2lG47974fZXyA1Ra3JzHmCvTMz5lmzHJsmqVEhfqIUCnsIqbWYaSnuBKLarXVqOLjFbEtapbqSAR2bSbckAWuB4nxwECtTuZJiaXSgqNTIIDTrjQCOn4psDltttyGJ2VUaVRWG2XXGmEISENtNi69uQSkbnEc1lpqFGEZNeqDMMFR7JLiUDvG5GoAHrz54cQIVLhLUaRTSp0ixeUO8oea1b/nwCQTWq4dbhfpUH6KQoesuDxJ37Me7fD6n0in0pGmFEShRN1O+04o+KlHvE4N6q+8e0lyFgnkhpWkJ95+ljvyc2BvIkfF7AdfdbiAIbaSVr9lCRbUf0eODRYvq4JeOt5W7ivrHy8sBuPEhguJ0NHqtatz9uCmeXNobLrxHM+ykfE4Dr8cvFLiFdm62e6sfpxxuWtpQZmNdms8lJ9hf6sc7Soq+cMVkgfRDpv+bALiJyHIryFNrAJ0q5pt1GANPsYL3m2cdVHYkRwxKYDqSkXBFwduWGbz6nI5guoX26lBGyTZQB2VfDmYtYDcZC9BdXo1dQMD5sR9IbKGYI/DHMsHJsntYzsbtH4qXNDjbKVpU82g39lSQRY48o+j/XYEPi/kuoOpREp051bTypscsx5sVxpQcijWAHC4lezZvcchj3Pxznx8tcEs81hCEBcLLs91sqAKisMqCdz1KiMUSpcF8v1Tg/lXK1eoQqVMj0GBDlMo7r7a0MI0vtLHeS6hYKgob747XD+Nzw7TZNL7uLRk6Tvvv9Ov8AJytdwuuu1FNTN5rNO222316Nu4e+jb6OmVKsjOWQeHdGZlhpxuPISpbyYzbgIWhpC1KQ0CFEEJA2NuWFKJ6J3o9ZfqMeq0rhXSY0qO646w+krKme0QpK20XV3GylagUCyd+WPJjnF3iXwApK8sT3DmVtV26DmVE31WQEAbMVCNb5xaBydRbVyVvjW/Ry9LDOvElmflavUKK/WadDXM+UGT2bRYTbUpbf1hcWAO/ljb+wNTl0c6/Tx/hxO3XaJj1+SOeNYcOqjR55/wASevTs9BN8G+FrKsqFrItKScjtlrLiuxF6Wgo0EMnmnugD4DriArvoy8CcxZtdz1XuGlKnVt91qQ9MeSolx5rSW3Si+kuApTZZGrbnjFcrel2xmFqg1qpROIVDy5mec1T6dmKoZdaTTFvuuFtkFaHFFKVuDSkkWuRjdjVMxQ6sqiMcScvzqkkBZpzyEIkBPjoQort52xzPcUnpGSu/1/ZfjUX8aStlRai02OZc6ZAbjpJUpyQ0EEH3gjFccz9lFkHs6ql+42EVx7f3C2/wwxju5ZmVByoZ5zhRqnKioL6YKZTYaiNjYrKCrcflKFhi2jMOS4lNRV3KvR41NVpDUtb7SGFavZ0uEhJB6WONbTpsPS+9p9OkfeYki2oyda7Vj17/AMVbj8QaSmQ23PhVyDHcUEtyXisNknlzHXFjq+qbSJUTtGpjMqO622p5A7jmnupVfbn425YrrbZz5mSbDqNQQmmUp1tbURm34WkgKS4pW90+AHhi6yIKgsuwlNoUruuIWnuOe/z88b6qmLFtFd4nx67/AC8INNky5Jmbx08Nmbws3Uul09mLVqfVI0tlkIcZdhLOohBSSFpBSd99sOTnioVJo+rs1ZTVtISzTniLeN9OLww2haiy08uG+jm2o6kK9wPMYdpeqjBAdiNPJ+s2vSfsOKq0oUJmqSFB5OXau+o7hbjaWx/6xQIxKhjMj6D2eUCg9BImsp+9KV4tJqLiT3qbJufBIP6cdNQkL2bpsjV01WSPtwFajw83pTZFChRlHwqiiB/ktA/fhVOWcyTFpVPrTEZv6QiNqU78HHCdJ8wAcTrj1VCC4tqI0EgqOpZNvuwvAkOvxW3n2wlaxchPI+eAaUui06iMqjQI+kLOpxaiVrdV9Zajuo+/DPMGZ2qOoRIrAkzVILpQV6UMtD2nXVfRQN/fbbHK/X5DMsUOgtpkVV5Orf8AFxmz/bHD08k8zjFPSkzLB4a8IpWXRmFqJWM9y26K7UpLgStDLoJlvkn2Uojpd0gcja2+AqHDbMMKpU/OfpDZtlsRImapzkuPJfWQ0xRYoU1EtffStKVO25kvWAOJzh7w+rXHurQ+I3ECHKp+RYLqX8tZcmN6Xak4PZqM5H1fpNMKG2y1C9gI3hdw5/bVdomZs50KTTOHOXEs/sTyy62rXPS0kBidObAuEgAFphV7e2rewHp5tc6SQILQjNW/GOp3t0CU/r5YAkmhU5+M9GqEdEuO+0pl1qRZbS2yLFJSe7YjYi3LGERJ830W6mmC/Ifn8HJrwRHknU45lB5avxS1c1QVKOyv7STY921t/TS0FWuW85IPMhRsn7MI1hqlSILtKqEdl9iS2ppyKtrtEutqFlJUjqCCRbAY3xRLOS+MeQ+LUZSXKJmYDJtdcbOptTb6i9AfKgbaUv6kX5WkHG3OvswmUgAfVQ2kcz7seT+J2SpnCzJNa4cVV6Y7whzGgtUmoPKK38kzyoKjBSt1epB4IKFk3ZIse5YjcuAud0cSOGNDzpM0rqr0f1appSsKDU1k9m+kW2A1pURboRgL3Eivl0zZhBeULJSOTY8Pfh2480yguOuJQkbkk2GCuPtstlx06UpFyT0wyZjuznRLlN2bT+KaV/0j5+WAWNYpoUE+tJN+oBI+3Dlp5t9AdZWFpPIjHC2EoISlPutthnSvalabBsPqCAOQ2HL43wEhhN3UEKWBcpFwPPCmOb4CPoi0GEE6jrClFaSN0km5H34flaUi6lAAdThtIpseQ5211tufWbVpJ9/jhNNIZJ1SXnpBHLtFbfYMA8DratkuJJPgb4yGq/2SwP8A+Rx/P8aXUIkWNGL7LCW3klPZ6diVX5YzOqKP9UikqFj+wdNz/wD5xwGUZJ24kcZd/wD6cufzCHi5PPssJK3nkNpB5qPMYpuSP+6Rxlv/AL+XL/8AIIeJjNkR15ll9Cr9mdBBNhbpjoUnlwRaHybWaSmu49fBkttFrTHz8jhWaKMFlIfWbddBthROYqMq34aB70nFJLbySQG1Ep2JHLBN+oGIfxU+T1U+xOi5YmL2j7L+mr0xQ/dzV/M2wxr1QjKpTojvocUshPdPIYp3wGDJUUbDrz88Ytqd42k0/sdp8GauWuSZ2nfaRbgdRbHQLm17e/BytknWWe8OgO2OkM8wvSB0O+K227195mN71j6OsxJUhHaMxnVpBtqSm4wR1h1o6HUltXgrY/ZjQIOgw2eyASktgnRtc47Jgw5hCpMdDhBuCRvi5Gk3iJ3eCn20nDntTNi+GJmOnff9lVgUOVVEB11JZQbd47E28BiVlZXp5hkMBQeQkkKvfURiaNkJASNhtbyxFUaqSqlJmIeQlKGbJQE9d+ZxtGGtY5Z7uTl41xLiNrauk8tMW07R81MIUm6FDvA74tmXq6mUG6c8kJdQmyFfW8sR1coDsRSpcZJWyokqA3KCcQ8d9yO+3IaVZTZ1DwOIazfBfaYew1OLS+0ui3pO9v8AT6W8d/Tdaq9QlzV+uQmx2wNlJ+t54rUuM+yAt1hbZtulQtv5eOLrT6rDqTaVodSly3fQpVinBqlAaqTPZuXCk95CgOX+jE2TFXJG9Xl+Fe0Gq4TeNDrqTy16esfXtMeajxELUpWwKFCx35jnhAEj9R54lahR5VOLfRjclxPIH3YYutpcs60pJSRbUra5HPFOazXvD6Lg1GLU0jLhtvE/o6lxKo60u3Vc7WHs2wkG2yL9qB/gnB7dkwpSVhWs2Vp6DCIO3sj7MapidR7EU6XfUv8AB3eRt9A4270WpE570echCK20lCaM0jWtRvcXHK36cYhOQtdOllCLgR3d/DuHG5eihdHo+5HYO5TR2V7eCrkYDUUwZLh1P1F4k9G+6kYBpTV+/Mln/wAscKy5S2EoQy2Fuuq0oSTYHxPwGEtFXUbmXHT5Bkn9OA6mlw21alNlxXTtFFdvtx1ypQY6+zU8LjbShJVb7MEMGW/dMyetSD9FpGi/v5nCwTCgoCQpthPmQL4BIVVhR7jMlfgQyq3344aqwkd+PJR5lk/owt8oU8DSZrHxcGDImRVnS3IbUfALGASTMp0kA9syq/1rfpwo5HihBJjNkDn3Byx16JFe/GxmlHzSMZHxn4oScgv03JmSYUmo5szSVxobLSQ61TRpP4fJCj3GG+fMaiNI3OAg+M/EqoOZki8H+E78VzME8pTmKU1L7M5fprqFfP2F/wAIVuGhY2NlEW5tMlZMo2RqLGyhlpMhqHECmC484VyHUpupa3HDutayskqJO5NrYZ5AybGyNR22FVN6tVZay/Vq5JSPWqtNtbW4sDvab6Ui50o0pubYsSCtouupUjUE6Sq1h2izuPgMA5Q03I/BYw0RWwNZAsXFAcsKdu++sswSEITYF1Q2FugwiXE9mILDlkoF33U7hvxt54UbS7JQOyWqNGTskJHfV+VfpgDLZhRT2spztHD/AHQ6j8E/6Md7aY6B6vEDTXRTvX3J/XggVBhrs2C6/wDk95f24OTUXe980wDyBGtXxsRgO+rzVe3PUL9EJCcc9QUd1TJSvGy7D7sD1SS6q7k14eTadOOGBGG77jp/hvH818AVTNLYUFvKStQ5Bxev7r4P8oxj7KXlpGw0tKIHuwmHKZHVZAZ1eCU6icKevFezcGSoeJSAMAZufGcV2ai62rkO1RpvgkqzcqNIcFkDU2V+/qcBctlQ7KbEcbQeRcTdH29McLKmUFyOC8wobtXuPeDgHCn2GwQX27dbLBOEJ1gy2pBBIWkt+Z8Mcj/Ja7JaZQkkW0KTY3w3bGqQmAFX7J8ui+1kgbfffAZh6WMnXwjVlNsq7bONcpeXWrC5JfkJK/gEIWT7sap3zGTEbpji2AgITdQAKRsOt8ZFn9xzOPpF8MsnFKlwqDGqWbpCOepTYEaOT0uVOuFP8E42V6awlfZgl5480oRdRt+bnjO8z0hi3aWQekPl/KVQ4ZVqo5sWuDHosZdQZmlNnYqkA2tbdaSdrczy574wrJeUq9wvq3CrM2WJU0ZmzrLXTahRe2K2ptPeirU982E9wtJSh0G/dOx54Y+nPnriJX87QfRzgeqPUnOsaA5EiRwfXG5BfUhXaqBIKbDtEiwtoxv/AAuy4iq+lZV4zq3XmeGGSKbT4IWgrWxKnlfaujqFLZjoBUOQV54lxcQ1NcFtHS8xj8ax0iZWNTw/BSuPVXxxz361nxrEbxtt2+LpPXr0eZuH2WKlI4bZBynlWPxrqHEWj16DJGXa3TpSsrMLbmlS1rQ+gMJbQ1qWkhVwoAjfEw5Fr54y0HNauH1Qo9ah8VTLrkeBlCa9NjxHJDjZefq7iiHmXApFm2khsIPIWufo+h6QooVJWF6xYOJcLRNvyup8jjqpLgPdmrPkl9SiN78wmxxDHw9kE9XgTJvo15elscGKpmvhOtcqoZ8r72bn5cJwKXA0y1RxLUr+0FSWSlKjo9nbfFTrvD7NVAGVo+aKAuJw8oVfzfGjQZuUZNfiQlrnfgajAaWlYbUyFBp3vIT0tfH0lMiYQUqmuaVcwpCiD/6P+tsKNqeKwtUq5QNwZKUkjpe6dx5HD0k79GDeijkPMdL4J5UalVep0urxRLMRNRp6WHUwVvrUwytjtFlsBKu6hSypKbA2ItjXoueKhl55+h5riPypzSwYzkWMpQktnkqyQQk3uCCcRVeZqGWamMw0pZejuuJXMjAhdyP7cjTsVfWG1+eKRxOz/mLPXEfh9wnyPnGXlhnM0KpVWoVamttrkpRFQns2m1OJUlIUtYKrpJsnpfFiNTzV5MsbxHbz+/dVvpp5+fFblme/+3ZrdPzZBr8w0ibTJdLnaS9GTLRpUsDqk9fdixMy5CXkxpiEBax3FJOyvH44w/g1Xs38XuBVEzXWnmn82UeZMimYhHZplPRJDjBcAHIOJbuQNu9jV6TmCBmikokwXEJmtEFTCjZxl0c0kcx1xm+Ot8cZcUekx5T+0sY8tsd5xZp327T5/wCy0aRaw2wUjSCdX24SiSkzGESGiNCxcfpH24E15LEZ15dgEJJG/listmjSDVz2zxIig2Qgf2y3U+XlhvmifKpdMHyclBlSHWoscKT3UrWoJCj5Dn8MSVObLUJlBtcIHLEHnfWxFg1QtqWxTZrUp8IF1BAOkm3W2q/wwC9PpEfLdMdcaKnpDqu1kPrF1vuctSj4eXQcsZrxK9HnKHE3PuU+IueZMmrRsopdci0dVjBXJdUgmU6j+2FAQAlJ2HPfGxtlt9lKgQttabjwUD1wyjNoiT1QWlK7JbfaJRzCDff4HwwCsVyKsfgjqFJuD3SLj39cOdZA2A92PONXzpnnK/pO5goVVzWahl45GfzDApbEBDSorrLwQEpWCVuqVZV77bjbGKVT0iONXC7ImTeLFUzx+yV7iflap1M0h+O36rRpiGkvRlMBACg2kOBLgWpRJTzGA98lZ8LYY01gNvym12W4hY+cPMgi4F8eKs28VOOfC3MzHCBziVLrMrOsCgvw63PjMmRSH5kwx5RRoSlGjSCWkqBINrlWNy9HLMea284cTeFOa8zzMzOZJq8VESsTW0Jkvx5MZLoQ6W0pQpSDqFwkbEbYDaqhS4NWiPU+pxWZUSS0ph9h5sLbdbULKSpJ2IIO4OMy4N8Dctej3DzFSMm1OcqhVurqqkOlPnWmnOuJAcaZWe92aikEA30741VxxLaStSgABckmwGI9Li6lLadbQpMeOoq1nbWbW28sB1mnvPrS/UXArTullIshJ/TiR0+JwALAY7gE5CloYcW2nUpKSQPE4aUns0wGlIUFXBKiPrE74fEXFsRdTpbrjD6aZUXKfIkpUlLqEJWELINl6VbEjngJBTwRuuyUjqT08b4RVU4Sb6pTItz74OPFucuJHE/hdWeIFJy9xOqGe4VJp1KhrqdYjsqbo9emTEshtBaShCwlpztC2dRR3ATvg9c4g8Xsn8T5HozMcSJ0xyrVmhNRM1SIsdU+DElsSHJTY0oDZcKox7IlBsFm4VbAe1G5cd7Zl9twnolQOFNV9rdPtx4goGfeLGe+Jjfo4OcRptNdo1YzAJWaobDCKjNjQkR1RWrlBb13k2dUEWIRsBe+NCyh6S2bqX6J0HjHXaKjMVbhz26M+y2v1f190TxD7RJsQkqJCrcr36YD0fNSfXISOaS4pX2J2xl1WJ/qlQD/ALxwf/2/GXVL0u+I1El1lea+CkeExkStwKbmWQzXUuhpE5TaWFxk6Ap0jtklYVpAsbE40udLak+kgHGgoqGRwCkixT+HHxwGW5K/7pHGW3+/lz+YQ8WXMYHyO+TzsD94xWskkDiTxlJ5fs5cv/yCHiVzbPKW26eg7ud9e/Tpi/F4jTxEvls6e+o9pOWvhfm+kdVbZK1qF1qATud8JrUFuKcta+1sKN91C1W2HcwiOVvDFB9RiIjsGBgYGDMzv3LxIr02QiOyLrXsNth78Wan5VjMfOTne2X9RPsjCeT4rfYvylW1lQSfIYnJbriIy3IjKXnEbJQF4vYcVYrz2fO/aLjmqtqp0emnljpG/j1KpQlCUoQkJSkaQB4Y7hhT3p01liVIQqORqDjRHteGH+LNdtvheL1GK+nzTS9t7R4iPutR2VvvqCW0DvKPQe7EFQptOTVZLMQANyN21X54m5TZdjOtA21oIviq5UjByolxbR+aSooV0JviHLe0XrtDucKw6e3DNXmy22msRG3nvK3kA3BAIO3vGK3WcsthD02ErSod5TZ5W62xYxewubnHbDEmSnvXL4dxHPwzN7zBbp4wzYBbarDUlYOobEf/ABxZIGbj3G57ASk2T2iNvtGJOq0SPU0lyxbebBCFDkrEblsUtyO5AkMoVICiVJUL3HlirTHbHfaJe21PE9BxnQ++zYptNOkxH+mPOfkka+60uiPOpUFIUBoI9+KahHaNhAWEqbJJv4YuzNKYRT105wlbSie79XfYXxUKgwYTzkRKL2UbqI5jDURaNplL7HajBFMulx2328fCfSPkRbdYbbUTdzXtc7AYVjxZMpaUog6tRNtNx95wrRXqcmQWqiyFNuAIBI2B6YuYZ7OKlmMoNhIshQF9I+OI8OCL9ZXOO+0V+FZPc1x/XwUCrNrjUmbGdZW252DvdJ/JONk9F2S3G9HzJUzST/WOK2lI5qIBFhjK896S08NaXFtw3EuEbG+g740z0XNDHATh2mQsJS3REyV35WJITiK8RW0xDucO1VtZpaZreLXosd5bwlzVpU6BZKE+ygH9OHD8piMnW85pB5DqfcMNTNlSe5BjLF/7a4NKR52O5wpEp6GVdu8ovPkWU4r9A6Y1XSZlT5ZtDZDKP7o6Nz7k4VapsfdyT+EOHmpwA/YOQw7sPAY7gERDigWEZr/IGEJMSnIZcddjNAJSVE6QLfHD3FG4r8QaBw5yu7WK5JQVvLDEOGHUpenSDfRHaCiLrWRpHmcBE8UOMFI4U5ap6ak+l/MNecNOoFOKVKXNmlBKUq0AlLabArXySnc4yXh/lSp0JqoZyzxJi1PiFmpto16oRG1pbGkfNxmQpSuzbQk7AGxPeIucEylTsx16sucU+IzclvM1ajtpFHXJD0ShtJuUxmLbE73dctcqunli6xoy3XGlLXZb4K9Z56OpHvPLwGAMw05McFkqbQ2AjVe6WwBySPHxxILjQ/VfV3U6Wiffc87jrfBnVx4TaEIv3e42hI7xv0A8cU/P8ybHyJmypxpSoz8Wh1Jxh5CyktLRFdUNBHJQIvq6HAWFRTZCCwsNEgtR2/adI6qPT44c9g+8n8McDTfVts2PuJx5x4Z5szTkzIGSW6VkCoU3MnEIUmnxqpmXMS6lEkuLil9ySppKypHdBIbBSVlaRcWxKtcZ+KFZzPTuGUKkZVRmh3MdVy9MnSTIcpiBFiIlIkNNoUFlSkKKVNqUbKvY2GA3lD7aPm4EUuW21AWA96sH7OpOE6nWmr89CNRPxxgmTuK/FXP3Efhx8lSqHRqJV6LWzXaWph18OyYMtth5bK7g+bVwAAperVti25n4k5ty5xXYyhWWYVIy7LgF6mT5MR9z5alBh1xcZl9s9mw4goHdWCVp1EHlgNN9SKjZ+Y+fEAhOCOx6ax3nShYG11Er3x51p3HniVT8nZWz1nPLWVpMPPOXKlVabHpsiUXIcmLEVJQl7Uohba0oIUUgFCiBvfB6txjzzlSkwc21zL1Bery8gVLMrcanyJCIaCHI3Zxlo1EOKAcN3bA35WF8B6IRLZSPmYr2n8lq2Dmao7epyT/gYwidx/4iZaqdZybmag5YezIhFAcpDkJT7cBBqrzjSBIKyVKDRQblBAX3Rti8cM8+5wrlfzplLO1OpK52UZ8WGmVRkOhuQl6Ol3UttxRU2q5sRc2wF+M5kdyQ080k9VN93444mOtsl+nvJUlW5aKu6fd4YUZksP8AzHfS4E3KFi17c8ILZRHfZMY6A84UrQNwdugwBZT7MiG48Wi26yoAggakrvtY+eDTEnaOhKUyJCOzK/BI3JP+vXDd9aEvLU6bIVLJVvzCU7YTqdZh5ep0vMlYIDcdhySW080MoSVE+WwxibRWOaW1KTltFKxvM9No8ft1YhR820mJxo4rcQKu+8mDlxFLydF9XR2jrikNmQ6hkD2llx1KfgcVT0i+LPHZjh3Ll5Z4eysl5flvNMv1+ROCJkeOtYC3yhCVLbbSD31gXSm/vw+9HvLtfzdSIGaK1ShDpjmZajmt+Q7ZSqlPfeWGG2hzCGm9IufpA+/GrekRSpE70fOJLS5iGJUrLM/tHloKkoR2KlKSQN7aRbbqcVKb6i3NEzFI/LPn57vQ6mlOBVrgyY6W1M7zeJnf3fXpWKx0327777dp6vOPo8eiNxFa4mUPiXxIi0CHGoaUzIEinVV+a7WZax83JdcfKldmlCzZNxckG2PTfoiR6pmqu8U+NaEw/V805k+Sqcux1OQqY36qlV/AuJct5e/FWzRxKn5O9FePnumxG40pWUoDdOYWSouTJEZpthpNud1rQL+WN+4J8PX+E/B/KeRDUGm/kelMtSl9iAVyCnU6s+ZWpRxZpXk7ODmz31F+e+32iNo8to6bLY/GmKWtZhFtKzqWkONlBV47jb34Q7NmwS/KipHg66XbfAWGHSW4z1loYkz19FuqIR99h92HKWakkgNR4LPwJ/NbG6JGlqEPYqVPB6WQU/eFY72D6bLaeQ+PBt0LH+Sv9eJNSKunmYKvLQofpwg9GeX+6qLGd821gH7wMAwfS04hTMhEVwqTp7NbRZX8Fcr+GMN4o8HKHVc2ZfrlOrddoDkWUsRptHlJYkw3X29CxulSShYslQ08ygjcE43eUtphlQQ281pTdTDxulSbi5BudwMZLxuzzQ+GvC6scQ8xmQYNDQ1MlmOjU5dLotpG2+4wGocNMnZbyJkWjZSyow41TKZGDTIdWVuEndSlqO6llRJJPU4h8/Uin016m5ghsJiy/lFhL8lslJLZVZWu2x+OPJlL/wBl19FKCXGyznEoXdX+1Sed+X4zwwvUf9ly9D+qRHIM6Dm15h4WUldISQf/AFmJtNlnDki89vGPOFfVYffY5pHeOz27AcQl2SyhXdDgWggixChfby544wg1N8SH92GiQ2noog21HHz8o3+yxejdl2q+qxV5wnUlVggu05IdYT9W5c7wHTri0Qf9l+9FCG12DjWcVJQTpV8kp7wJ5n5znjXLT3dtqzvCSlpvXe1dnu4ADkMVjOS3pr9Oy0HCwxVVrTIeGx7NA1KbSeilDb3X8MeQf/3xHoln+05y/klP/WYiK7/suPoqVeXTEoZzemNElCU858lJ1ApB0pT851J38r4jSPeT0hEKJrSiwQAlCB1PQDCKG0U+KuXKVqdUNTir7k9EjHiCT/svvomOBkBrONm3Qog0pPIf+Uwad/svvomyENJS1nCyXUqUPkpPIf8AlMB6lkcM6LUOKDXE6Y9J+XH6GuiLbS5eO3BU4HD3dO6yrbVfl0xS6P6IHCbLyZTdTmVqsUkUubSKdTKnMC4tIhylEvtRQEhSdWwClFSgEgAjGGj/AGXz0S/XlyCxnDStsJv8lJ6f+UwUf7L16JrspT8hjOC0oUAyn5KTYDxt2nPAbXC9EvIcmjVim1Ot5mrkqrR4MVut1eeFTYjUJzXFTGUlCdHZr7wUUkqPMnGhcMeE0fhTDqiaXWJtXqFdmfKFVqlWdL0qa/oSgKWpISkAJSAEpSkADljFYH+yH8OKnEj1OmcHeMUuLKaS8y+xlB5bbravZWkgkEHoRhc/7IFka+3BDjP/AMzH/wBeA9LiE/KUF1F5K0Dk0gEJPmfHD2yG02uEpHwAx5Vkf7IFlJR7ONwP4yJvuVrya+QPhfCI9O7IK1AzOC/GySb/AE8nPhP2DAeqjNiJJSZjQI/KGGxnTHlq9RYS620bKUVW1nwT5+ePM39Xtw7T3U8BuMVhsP8A5FP4On0/shoQEI4G8ZkpHIDJj4AwHp1ioMPK7NSi26OaFixGEq7TTWKRLpYnyofrbK2fWYiwh5nUCNbajcBQvcG2xx5kkenxw+lJ0yeBXGVQ8Tkt8ke7fDYenbkJOzXBzjmhP1RlF+3wuScBc8p+h3kfKuUKrkN/PWda3lysMPNvU6pT2FNpfccDhlpWhpK+3C06g4VEgn3YA9GDhymjVOLOzPmqrZjn1CHVV5oXLSqqsyIaSmMptxKA2ns0FQ06SDqVcEk4pp9Ovh0r8dwS42Pg7WdyhJI+y9sCo+n9kJuA4j9pXjJGYSm7rhyc8lKGhupRudgBffAX1XonZAVRaW1Scy5pptcgzZtS/ZPFmoTVZD00ASu0WpspUHEpSCNAACU6bWGJxzgTkaXwri8EUNTotBpD0R1ksv8Az6ixIS+hanFA6ipae+SLm55XxbuFXEXK/Fvh7ROIuTHnnaLXIokxFPN9m5ouRZSTyIIIOJmqpQyBOb7jzZAH5YJ9n44DMs0ejfkDN7OdY9TkVUN58n0yoVTspCU6XYRbLPZd3YXaRq53ueWE6ii3pIIB0kJyOAL9fw88vuxsQta+kDbGQVYD+qWG3/0HH8/wGT5J/wC6Txl/8eXP5hDxMZrpyHYgnNps6z3VHxBxD5IIPEnjLb/fy5/MIeLhOhtT4yorxIQu1yOexvjo46TkwbPk2p1ltD7Q2zz2rfaflLP037FwhWm9tx18sJ+OwAvi6fsZpwiORkIcWoi4cVuoH9WKs1TZzkpcNpsqcaNj0FvE4p2096TtL3+i49o9dF5pM15e+/QzwZIUuyRzJsMThydOKQoyGkqPMb7Yl6JRE0pLi3uzdeWQAq2wT8cZpp7WnaYVtb7T6HTYptivFreHWClEpLdMjBRWtxx0ArN9reFsP2mGmEpbZbCBcr288EkzI0NvtZLobHnz+zEW7mqmJBLYccUPZ7tsXd6Y45ZfOZwcS4tktnpS083lvEfVNYHS+Ks7nGTf5iI0PNRJwH84SFsBDEdKHVW1rJuD7hh+Jou/3U4ntE8kR9d1pISfaFxyI8sEYZQw0lppAShGyQPtxVo+a6gp4hxtlaVdCDhsvMlRMxMkOnQg7NA2SRjSdRSVivsfxG1bUtMRHddcRK80U5mQqO828kpURewsbYc0qRLkxg/NaS0paroSjoMHm02FUGQ3KjpWeiuRxLPNaImjjaaml02pth4hWZjzrbxNf2QMSg41TUuOyNBWgFNh7sdotJTAZ7WQgGW53lq8L9MN6bQn6ZUVSGnErYWDsom6cTeMUiZ63WeI59PponT8OtvjtFd5nvPpIXIxGZhgMy6et1ailbA1pUBc+7EngdD7iPI43tXmiYczR6i+kz0zUmY2nw77eP3joz2JE9dOhp9KHPoIULA388XGhOzlxFNVFGhxhehPmMRKqC7CqLDik+sR1ODUT0JPTwxaNrlOwAvt1vitpsXu95et9p+K4tZjrXH8VLRvHp6T6q3m+FV5NOnGN2K2RDdsjSAsHQb74v8A6MMVE7gNkdt1PdRl+MkEfWJJ/QMVupAimzNQteM7/wBA4tHozK9T9HDJK0glblIaAtzKlKIH58R6qm1ubzdX2N11s2G2n22im367/s1emvrfjJU4QVJJQVD6Vtr47Okrb7NmOAXnjZF+SfM+WES81T47cRKVOuBGzaRcqt18r+OFIUV5CjLlEF9wbjohP1Riq9qKinvKBL1RkqV4pVpHwAxxVLb3V63ILlrJUp0m2H4FtrYTecQ2kqUoDSCd8BC1TM9Oy7luo5kr0lLESjsvPy3QkmzbaSpRAG5Nhyx5npSKnxnzQzxlz3TlN01tTD2TKHNiBuRSWVoOuS7ufwh0E3+qjQAAbkWDjZVHc95ny/waZTU00aMlOZswz4EoNJV2bgVFgOWBUS6rvlIKTpQOhxYFl91ZUAAt57e3IrItt5AYBRLAWhhOkapPeWfBA+iMOdaGZjrzlwhmPe/5N8dQlCZzTKAVJZjn7yLH7sGlQlSnAS7o12S4Le0AdhgGMl5eoqcJD7wvsN2UHYBP5RwVcGlS4Eul1CnJqLc2OuLKj6btqacSUqaJuNikkE3HPAkE63HyLqLjhHlpTsMP2348aO0yBdZQNKU3KlXHM2+7zwFfrWQ8vZmytFyRWMswl0OEiO3EiFxYMbsEgMqZUkhTa0ACygrYdTgUPh5w8yl8jCi5VhRn6JIlzKapsLW61IkIKJLtySVKcTzKtV+lsZ/SeJdQqGbOIGYc1cQ28uUfh/KlR1ZbYixlypEZmKHfWne0BdcUe8pHZ2Fkkb3OMMzHxZz7m/IHEKlIzzPgsU9vK1egVVYgJqCYs2XpU08Yt2m27BCwnZaQClRIwHqpvhlkxEmhTKfk6HTnctSpUyluJfdbVHcfX2kg91V1JWrdSVkpOkbC2BN4dZIzfVW89SUMVCouxiIE5ElbkdBLSmkvNtay2VBCiAoC/PFHpea82Zm4h5vy5N4pMQomTlNwWaW1CiJfrrbkTtVS3VL+g6VWQGAkDQcZdwyz7xD4W8O8kNnNMerUyucOa/WqfBfpzSUU16nMpcaCFDvLSoOWWFk3ttpwG0cMvR14a8OMo02hSqFBqlQZoZos6e+p7TIQ43pkdk2twhlLpuVBNiSBfFrquSeG9ajNQqtlymyGmaSuhthZVZNOXoKmBvfT82k+O3PHnqsZ242U01tyTxMp7yqPw9icRQVZfjguOrVocgkb2jnRe4+cF/a2xNUfjDxZzrxHcYypS4sWDTKtS4ppMl+nMx5MOQw068+52y/WVuWdWWuyGj5sCyjfAbBnHh1lbN9KzAun5dy4/WK9Ah0196qR3H2HI8Z3Wy04htaVWTdelSVJUk2N8MeDnB9HCiNX3H5ERdTzNUET5yYDLjUVhSG0tJbbDrjjqhpFypaySrGT5Y4i8VKg9kjMNbzbCkx8/VXMeXkw49KYb+TfVWpLkWQ0sDUt1Jj2VqJSq42GNA9E+bUpno4ZHqVazW/XpjtNDr8qQpCnY6tarsuKR3ipJFu/3uhOA1KZYyIgHeJduLeAGCuOBM0r5iOxr958MCOvtFKqLt0oI0the1gOp8CcJqbelIkvoSEF9sNNBXh1J9+APGbYRES9KCSCovEq6HpbGO+kVW5rmWaJkOiO/wBdM/15qlKUndSKe0e2lrF9rdkgpH8LGuEoBT6yrtnG0hCGGRcN+Xhf34xqI8/nz0ksx1t+GpyBw0oQoEVLRC0/KM5AefVc7XSwlpG3VfnjW8c1ZhPpclsOemSneJif1W7gOUnhRl+bIcSpDLDrTFz7DKHVJQLdFWAGEvSCqlPhcDM+1StvNx6e3l6c0kPKsXXVsqShFupUohIHO5GMjyJ6QtGyrlen5EyvkrMudazDMidJg5bjIkJgodkuertyXCoJaUo7WubAbgYm26JxF488QKRUuNHDd7JmQMmFU9FHfqrEw1SqpUOyceDe3ZtJBISbgq9wxFpd4w1rPhH817jkf+JZ+u/x2nf7f9wlEiSOIdT9H7gRGCnk0Wk0rOOaEOoOhmNCitiO2sHmVSSjY8+zPhj2m2kvOJVFR648Du+7cNpPXTjzr6H9PTmejZz4+VoKXJ4g5gkogEp7/wAjw3FR4jSfqo7i12A+mcekmoT0hAMt5bSfostHSEjwJGJ3KGVHmc3qqUE9G0pA+/BFsxUC0qtOqHUF0Jv9mOTW8v0tgyKk5HZbSN1SF/r54i05vymCBT478kHkY1PcWPtCcBIhFDJ/dgV73zgzbFNUv5mcsHwTJOGC840Jka5sCfFbHN1+nrShPmTp2xLoTS6nFbeZEd9h5OpC0WIUnxBGAia0260W2HH1OpXdTa3D7NtlJv4EHHnj0x8rZgzl6MfEPKeV6PKqlamU8JjwoyCt11fapJSlI3Jt+bHo99tz1OQ2+rtFQSClZHtotex+G2IGQw03V3Q2T2VyjwsLcsB+e+P6JHpNTnXGYnAzODrjK9DiUUtwlKrA2O3OxB+OF2/Q69Kh1IW1wDzooXtcUpw7/Zj76o7WiZthymlWZqiC0tG9u2bSXEK9+krR57Yvq4q1n1yA/wBgXAFlJTdKzba4wH50B6G3pWE2/aAztf8A/VLn6sNnPRH9JtqW3TnOBucEynU60MmmOa1J8QLXtj9HLNQursJbamXjsL+yv3H9GKdktDUzNVXqsi653rj8Ylz2kNJCdKUjoNycTYscZKWt5IcubktWkeL8/rfoc+lS4nW3wBzqocrikufqwR/0QPSkjFsSOA2c2i8sNN6qW4NSzySNue2P0bw1dhLfirPtq7VsfknDHOcR2VQJTkYHt4gTLZ089bZ1j7bW+OIUz87S/Q49KptJWvgDnVKUi5JpTlh92OH0OfSpSguHgFnUJA1E/JTlrfZj9HER9qqU5mW3u1KZSse5QvgtNKnIKW3RqKLtLv4jbAfnKPodelQAkngFnSyrWPyU5vfl0wE+h16VClKSOAOdCUGygKU5t18MforZbXJpTkVK1B2MooB66km4OOtPhL0aYFWRNSErvySu1x7vDAeRuEHpV5lyDwsyjkSq+ifxsfqGXKHBpkpcegNltTjTKUKUm7oOklJtcYt/9WvWOX9SNx1/5vtf9bj0hLCoclE4fi1WbeFvsV/r44fjfbbAeYP6tesf/wAI3HX/AJvtf9bhJ7036kx+O9Evjij+FQWR/wD7cem6k8uPHK2/aUpLafIk2vgR4Edka9CVudXFi6iffgPMKfTplKVoHonccCf/ANQNf9bhx/Vr1j/+Ebjpv/8Al9r/AK3Hpxxht1stONpUk8wRcYZpi1CISmMtt1rohwkFPkD4YDzefTWrI3Poi8df+b7P/W4H9WtWbX/qReOv/N9n/rcekVS5zf4ylrV5tuJP57YMKowkgPsvMX6uIskfHlgPNavTYq6QVK9EbjoABck0Bnb/ANbiMzP6YVdr2VavSYfolccA7UKe/GaUugNadTjakpJs7yuR0x6wcCXW1IuClabbeB64aU+QpI9RkjS81cC49pPQj4YDH/QmyjmTInot8Psq5vo0mlVeDTNMqHJTpdZUXFq0qHQ2I2xrigHqwpt9QCWUpWyi/Mm9z78SYO2G8mG1KTZwWIN0qTsUnxGAcHljH6sP/wCpYf8AiOP5/jU/U5yRZNTUf4bYOMnnsut+knZ2Sp5RyQDYgAD8PPK2Awam5+hZY4s8Y6bIyxmuorOdFu9rSqG/MZAMCJsXGwQFeXPE/wDtxUv/AHh8Q/8AmpL/AKONL4JVam0OrccqzWahHgU+n5+lPyJMhwNtMtpp0IqWpR2SBuSTi3/1QfBE05msfttZT9QkPqjMyRVmS048kAqQFarXAIv4XxNGe1K7Q8vrfZPRa7PbPkmd7d2C/txUv/eFxEPuypL/AKOIL9tx9msuz08OeIKm3AUhH7FpQ2/ycexHM15cRVINEVXYHyhU2FyoUX1hHayWUAFTjab3WkBQuRcbjDaLnjJ9RixplPzRTH2Jc1VOYdalIUl2UkkKZSQd3AUqBTzBB8MZtntbaU2i9mdDo4vtEzzRtPV5XTxsjgW/aw4jj/FeSf0YQmcalLZKIXDPiE2s/SXleT+hOPSLnHPg5HkVKHJ4pZXQ/RUFyooNUavEGoJu53u73iBv1IxY8s5uyznSkM1/J9eg1qmSCoMzIMhLzLhSbEJWkkGxBGE57TGzXH7K8Nx2i/JM7eG7xXL4lSnwX5uROIStIJucsyrAf5OIyi8WKRmSlR65l7KGeKlTpaSpiVGy1KW06ASCUqCd9wR8Me0+KfEChcMOHdfz5mZ0op9GhOPuhCSpThtZDaR1UpRCR5kYp/ol5KruQfR9yblzMkdcWpCI5Nkxlc4y5Ly3+xPgUB3SR4jEMzM93fxY6Yq8uONo8nmr9nbn97viD/zXlf0cD9nbn97viD/zXlf0ce9beeCq7ovfrbc4wleD2c+qQ6lSuHXEKw8Mryv6OCnPaySP2u+INtx/uXlf0ce7nHkto7RR0gc7q5fHlgJeS4AW1BSTyIVgxt3eN6PxfYYioZqWROIKXAdKL5Vl+z4k6ff9mHyeMdJsm2QuIgvYC+VJfu+ribf435om1mq53r2eJWW8qUjOTmVGKZT6GJ2osvIaK5zx7zPbLWAmwASFJNzfFDp3H3jU9wuqGdplczUxIk12PTGHzluL6klldVXGPqStRXIcDaQCFgb3xNXPasbPMaj2T0WpyzltvEzMz91i/bjpV7fsD4iXva37E5d/+jgftxUva2QuIfW//wAlJe3/AKOK5R/S14iUTMWQpua6q0/k2RIrKK/PkwUxZSYbcpMWI9JaH7ncDxspI6e/CFQ9LnizC4NVirVGXT6dm+o5zXTaMFwVutxaaWUy0lxtsKUo+r2Gq3tOJxv+Ksg/udofO33halcYaWmwOQuIdz0/YpLv/wBHHDxjpQJH7A+IZI52ypL/AKPhhvmL0us102uQs3QvV3OH9SyPCnvyUs3cpVUlh8MPOHqz2rQaVfkSDidynxX46VnOWUeHjz0dw5zp8HNrdbEZKUxaW20gT4pQD+NLym0oP1Xifo4TqbeR/c3Qz0mbfeFfqPHbLFMMVupZSz3ENRkohxC/lmSgPSVglttN07qISo28jjqOKVbWqzPDTPMwWvcZbkJ287jGs+ky5p/aqdSoG/EqjpA/hNyB+nGlUh1ceQpCj3e82D46dx9wOMfibLWm9lOH6fvWZ+bzJ+2c/Ngyoq+GHEFp5xh1KAMuSCFHSRYfHbG1+j7SKpR+D2Qsv1qnSIEyn0dlcuLIRocZX9FKh0Ve9x5YvVPdQwC88vSGY6L+ZVdR+04UUh1inPynNpD9jYfRubJH34jvkm/d0eH8J0/Db3thjbm2/T/c4p+p156aoAhaylvxCBtYeV98SGoWvhgtTkdLVPigFwoskkbJA5nHRT5QALlVfJHglIB+FsRuofhV77HbFQ4r5zo3D7I1VzbXZUdmLCaFu3eDSXXFKCW2go7ArUUoHmrFigyHit2NIsp1ki6h9JJ5HHn3iZUkcYuKKsnRJtPk5V4dONSq6wuOVrdrirORG0rIsA0gFwlJuFKSDtgIXhRk+oRYcvNGa4MWFm3OMk1nMiYzy3G2pBAQGW1KJVoQhKEWvYWVbnjQoqELeVISgJQ0kssJA9kX3PvvhjGeTJqDb4RpKyVOJHK2jf77YctuvREGCiOt14EltQ9lQJvcnAGDU1t59xttqzirhxZ9kW5acKNyXEyPV3nELS57Cm+QPnhs6EFVpK1ynxYhpBslP+vnhB0NOaS00GVK/ua7oUfD34BZ5sI7dnmttwvN+aTscEjO+qO6lJUUlAFwL2QPZNvpAbgjAVNUNLjybusXOoi2tHJQ8tsLmNZ31S9kq70dYPeQfq4BnUcv5Sk1FGaZmWaVNqaEmM1Ochtre0rBGjWRq0kEjnyJGE6bkDJlJpsqksZPojTFSb7Ka03T2kokIvfQpITYpBJ2N+Zwu4VNKWFBGtK0trQD3HAeZt9Gww7gBOt4MFz1WySgKP0r9PLANV5Lya7Og1R7KVGXNpLaWoEhcJsuRW0iyW21EEoSBe2+19sOE5fy+piPGFDpqmYkZ2HHR6sgpZYdTpdaSLd1CxYEdbDC0wF2QzGWTpeUpbgHUDl7r46adEJ7rfZHxbJBGAK7R6M6XA7RoB7aH8nu3joJXFvfsSbexffTy3w0fyllOTV4WYZGV6S5VaalCIc0w2y/HSm9ghZFwADYC9h0w6cQ/DQXUOuPIT7SFHe3iDh0haHEhbagpJAIPjgI1ilUpufDaYpMFpumFcqKER0gMPO6gtbY+gopUoEg3Oo4TgUaiU5TlJo1LgUuKp1UqQzEZS0l11RuVWTYXJ3J5nDtogT5Kr7BKEfYMKu/J75Be7BVvFYB/PgC1BpuU0Gly+xTqClWI3ty54R+bAKU1ohKtiCpO3u2wqI1NZAWpuOEn6RIN/twUyqcCEpSF+JQ1e33YMxEzG/ghs25wpOQsn1rOMlF4NCgOz5Lqj3VJQkm1+pUQEjzIxnPCKjV/InAaVmHM6o0ev5hjzcz1iQt0oKJcu7gSokbaEqbQB+QAMNuN1Wa4mZoy3wBy8lcz1ioR61mxptJCIdJYV2iUOnknt3A2lKTuoBRtbF94jmlVLJFbo9arMKLEnwXY9ylJaQSk6dRvvYhPLwGIs9uXHafFc4dhjNq8VLxO02rv57bx2Vr0ccgZf4acHsu0rLdMTFmVaKiZOkrZSiRIdXdSnHVAd4jUee4FsK+kJmp7JvBPNldphdMlumOQKSlHNcx6zLbm/Oy1g/DE3wtrbuauH2W6zJUoPTYaGXbi3daugkeAJTfzuMU/j42MxT+F+RgshvM+faclxrTdBgwiqW8D4X7FAv54zin4Ik4lX3Wsy0iNoi1o28fzdYj/wCvSOr0bwhyLS+FXC3K2TgpATQKNGhuOrIA1IbHaK35XVqJ9+JE1SrZqcDWXlrg0xP4yoLR33vJkHp+UfhfCTaBnmWmS6lYoMVz5lqxT664k+2odWwRt48+WLK/JZhtpQT3js22gbq8ABiRSRsHJuX4L4lmH6zJG5flKLzl/wCErEi7Uojaw0lZcWDbQ0NRH2csJmLJmjVOWW27fiUH85wsowoDX9rYHTaxP68AiqpMLs3JbejBRt86kgEeF8V6XGVlOfGl0l5KqdUZjbD0P6KHF3Aca8N9ynliyNzYUtSmgtLlkayFJ+j48sVCbJYqGY47EQBMWmL9dkgbhJ0KS0PeoqKh5DATzjyVRpIKhqlyCgeBSmwJ+wHETJWh1519BtqNxfzKj+a2EHprjyUNlWlttoNk3tq+sR7z1xCVWpznJ8ahUaP6zVZKSUNk2Q0i4u64eiRy8zsMA5rDrc2v0OkxVJckNTBOWEm/Zx2wvU4fAHUEjxIti9onxafREzp8pqPHYY1uvOKCUISBzJPIYrtEy7EoDbzTk31mVJsqoz1gDUOjSPqp8APPqcdr2b6KUv0FqA5Uglk+sx2Y/bBDNrHUk7AW8T8MOnixPZQM0elBw4Zh1NGVWJub3qUwqRMVSkpMeKhN7rcfUQhIBB5Ek22viX4d192TU6PmaTSpNOazpThK9WlEF2O+ACEkjnqTvjM+NmWMvnMWT65Ny443QY8ftJC6Y2UqrHeSYtMDCPa1LAUdQsADvzw3YrXEPiJntAaWhdWhrQn1RiRalZZZuDoecT+6JigLFKdkgkbdfS49Ho7aetsEbRaJ5ptO8xt0iOnSevxdo27d52eavq9TjzTGaetZjbbpE7956+nw7d/Hs9PVEKSlE5lN1Rjc26o+kMO+46gEDUhY+BBxVsuZpfkyVZczJCMOqIbJAJu3JQNittXI+7mMT1NUplK4Dqu8xukW5tn2f9fLHnMmK+K81s9BhyxmrFoR2RFqby8mCo3MCS/E3/IcUB91sScO7c6YynYa0rAPgRv9+IinufIuaZtKdFmqsTOikcisAB1Hv5K+J8MSrhLVUZdUQEvtlo/wgbj7r41TOoT2FUWABpkIC7flJ2J+zCTEdClSKU4O5cuNnqEqN9vMKv8AdheUCmowiDuS4PhpwnJUPlFpSTvHbW4sj6p5DABlxT1OcTIUFdnraUT1Cb7+/Dmma/UI5dvrLab392GAZddoqw2NS3/nrDzN7fZiQhyWpLPaMqBR08vIjoRgEquCYgUPoOIWfcFC+HiVJKQQQQdwccWhLyChYBSrYg7gjDFLE2AdMVQfYHJtZspI8AeuAkcDDWLPRJK0FBbdb9tpXtJw6wAwRxtLqShxKVJPMEXGD4GAjC2/SlamQp2IfabvdTfmPEeWF1txKk0HNlI5pWDYjDsgG1+mGL0FxlxUiAtLbivaQodxZ93Q+eAKlc6D3VoVKavspFtYHmOvwwcVmngWU6pB8FNquPuwQ1Itd2bEdaP1gnUm/lbHU1emkd+WgH8oEfnGAN8s087B4/5Cv1YyedJZk+koFNEkfsGHMW/7/ONa+UqZ+/4/+WnGTVGQy96SgLDqFj9g4Ox/+/4DMqhQazmvhX6U+WcuwHZ1UqOY6pGiRmQCt11VMh6UpvzJviM4lej/AMXKgzk+m5ZzM4/NhZfrqZNTdoMPQhT0dhLcNbQslJWUqSHBdQ3xrno8gfsr40/8Ycj/ADfCxs/ZovfSL+OA8KU/hhxml5qyvxlpGRH6VH4bihUam0WYFKqj9PbZ0VANqCtJQsyF+1ursEnbYYi4no58cKHFy7EyhS5MOBmTMVRqdWbdKQuiVFPrKI85IB7rbrbiNdrnUhBtuTj3+W0HmOt8JvoSQLDcG4wHjDNNMrNX9G2Nwlo3BHNtEzHQE0oT1MUdl1CzGlsqecZd1FMgq0qdSDcH6Q6Y9K8IZDpyLBalO1lchKnQv5ZpzUGWrvndbLICEjwsNxbFgzPmCmZUos/MleqcaBT6ZFXKkSpTgbZZQkElSlHkMfJCRxu4y1viOnONKzbX361LmJegsNOuBLqVH5pKWTYaSCLAi1jvjm8R4lXhvLzRM807dHtvYr2Iz+21tRXBmrjnDWLzzTPXffttE9tuv07vob6cyXP6mfNCmj/b6Ze/QCcwT8MbDGzbldqtxsorzHTU1t2MJSaaZSBJU1bdYavqKfO2PKPFbjDmL0leFMnghw+4U5wYznmBpiNVnazS1woOXVodQpb0h9fccAKCUIa1FYsdgcS0P/Y9coKzPS+J9T4r56RxJYZcTUc0wpyG3pq3EkKCUKQpLCEpJQhKALI288dKOsRPm8TMbWmJ7vWrjqGklbi0pSN7qNhiuP8AEfITVYZy87nfL6Km8rSiGqpMh8qPIBGq5PwxkaPQs4XVNRGec3cQ85JOy2azmyWplXvbZU2k+4g4kMwehn6OVRyTUsm0nhbQKMZ7BbaqUOGn12K7buPtvG69aFWUDfmMB5p4+cdcn1jjzVMjekrQuIrGTKVJMPLOVaPR5RZzG4jT2s95xopL7YUoJQ0kkfSVe4GHdNzHxR9FHi5Ua5lPhFXG/R8rZp8t8BTjyKIw40A7KQ0tZeZcDqh2jOgpCU3Fjj0Twz9HCrZZztB4n8TuLdcz9maj01dJpjkiK1DiQoywkOaGGgdTitCSpalEkjpjbwwy4goW2lSTsUqFxy5WwHjzM/Fb0EM0ZgezhJXV6rPlyo8ySKVTqwlibIYUlTTrzDSUtOrSpKTdaSe6L3tiY4SPehrmzMy8q5STWqXWJMhNUj0SuyKpCDriXw+Ho0eSsNmzve+bHO+1seq24cRoBLcZpIHggYo3Frg3kXjLl9NAzjTVKVHdEmn1GIsszadIT7D8d5PebWk73Gx6gjAR9R9HHgvVncwPVPJLEk5oaeZqiFyHih9LrqXnO7qsi7iErukDcYlaXwY4cUrOas/wMtNNVtZUoyO3cUApTLbKiGyooBLTLabgDYeZx5+y5mXjec+S/RP4qZxZjesN9vRc49muPPzHSEgFxpko+aRMR3kuKBBCe+lPUaRT5XGzhLl+FMzK7Rsy5eod489uCh9VQTBBNpetwntVoTp1tgbpBIUTtgLVD9Hjg5TaRWaFGyVG+TswwXKZUYzjrrjb0VbrjqmrKUdKdbziha1tW1rDE9R+GeSaLUKNVabREMS8vUhVBpy0uuHsICi2SzYmyhdps3Nz3efPE3TKnFrNOjVWnzGpUKYyh9h5v2XG1i6VA+BBGMYy5H4u1ziJxKGXuKMeLBhV6PEahVClCSiGj1CM4AwQtJsouqKtV97WwCnpRt6UcKQm/wD3TaJb/JfvjTYkV14SEtX7XZ9u+wJClbfEY8/8f8vZ/okvhlU81cTl16MniJRwmGmjsRUJcKJFlhSCVbeBO+PRMF5MUOyFEq0pbYQE/SIFzYe84BDsHInZodRYpbZdWOd9JOoedr4k6mUrhtrSQQt1o6h1GoYa9olUltdSltIWgG7TYJA1CxCjgGzkOZBQ6HEMJBbUOg5gX8RgHqzaqt76StlQAPIm+Htr+1bEbNUHGIctB73aot7lbHD1xSkp1g233JwFC4259a4WZJl5yYiSJdQK2oECHHZLjkqW+sNsICR+WoEnoAcZ5lnLjmTciR8rzqs7V6uzqkVyouNhC59QeUC84oJ5jfQnwSkC5tfCNUq8jiTxufq7kasxqJw1DsGmuKXph1SpPtDt30pAuv1dFm0qJ061q2uMT60uS2pr7Rupw2bNtjpPP7cAnGbcbSxOQyXSsLDiE7KBUq5IHhgz0txSQm6o6LW0Dd1XwHs+/CJ7dIWiOqQhAOtQKQgJ8e+enuwVhLJBK3XUoWLqLLSrH3rO+A620FKEdDfaG+rsUm6R5uK6nywSSXfWlIWltK2hrJbFgqwvtiRYlQEpKGV6QE3AsQLDmcR5cLqnpSidS0lKU+KiLAD4c/PAOFxETJqwHC3qQ27dPUkbj445LmFS0Ot+w0tRbKjbUocyfAJ+/CkZXzz+gX0MpQbncqA/MMMgLJC1gFNm1f4FzrPu5XwBokRyW4CsnskHU4Ttvz+/EkZ7I+ZjtqeKdgltOw+PIYZMFLqY8Rw6U9/tR4rB2HxxKpSlsaW0hI8BgG8Zp0uLkSLdotISlI30AePnhxjmoJGrUlKR1OG6p3aEpiRlPW5r9lOAdDDJtaKe6pl5QQyq5aUTsL8x5YP6zNTZS6fcDl2a9R+zANRaAs7Fk8rFPZEgfZgEnHaO652jikLUNr6SofaMAOUe39oH+DbEPmriNkzI8T13N+YKfRY6RcuVCS3HTp6EBSgd/djI6/6avC6nSoVPy5QsxZlkVV4xqeqDBU1GkvDkhDzulKyegRqPkcYtaKxvKTFhyZrxjpWZme3r/Xq3IKgayYcFT6780Ium/wAf0DGN8dfSAqGSZsfhvkRmPJzvVkXSltlUlFJYI3kPJRzWAO41sVGxNhjNuMHpEekLQcoxc3Tcl0bhzlyRUG4kt6fKL9T7Fa9Lqmm7DdtF3CAm9kG+JfMmRpnCuOxxN4JZmpmY6nNjNKnisNqlrntOFJEhJa76bhVyARsOWKmXPk5d8cS9HoeDaX3s49fk/wASv5ccT+a09q2v2pMz0iI39dk3wxyZxIoWXZTWVKTChO1Z8y6jVMxOOPVOoyFjeU+Ed1O47reohIAFhjQ6FwVorTrVWzc8rMlYACly6iO2SFfkNGzbafDSn3nEdwE4zVPidwwpeZ6/QHWK8XpMOfEhR1hhp9p1bdgp22nUlKVabki9saEZeanrLZpMCMgchJfUtVv8Ebffjammx32teeZSz8W1OGZwYaRhjyr0nt1+L807eO8z36DhDTAVGTGQ1JXZtKm27J0EgEi2w26DGY02iq4pemDT6M1JX8hcLcsLkT0J9lU6oq0oQD9bsG1BXks+OL9OzlGptOlKqeiBUYoP4M4u6dXRSSfaRYkk9MV70IaTJXkrM/F6W065UOKGYZFVi9sTqNPZ+YjKV9UFKFLA5WWMWYjbpDi72nrad52iOvf1nf5vSrz0anR0NtoGkANtNIFiegSB4YEOGpClSpCgqQ5z8EJ+qMGiQg2r1iQrtX1CxVbYeQHQYd6RYC3LGQzly3W1CLEb1vrFxf2UjxOEFMxac2ZUwqeePJRGpSleCU9MEXIcZqMkJjrddUEpbQNk6bcyeQ3xC5qrgoUF55ybGXVlsKMZlSx3B1KEndRAubdbYBGt1eakmBT0BdSqSuzbCdwhI2JP5KRuo+Owwg3T2KVBRCiuqWVrUtx9XtPvfSdPl0A8MRmVqjLTU2lPuQp7lahluFNYtdvQq62loFwmwOq/jseWLFUGUKjdo2bJJLbYPMoQki/xVvgK7Uqgik05+oPIUpqKguqQOagOSR+bFiyNllykwnqtWFJXVqpZ6Sv+5Jt3Wk+CUj77nFSzaEvU52GsXS4pCFj8kuJxbs7zir1TLzDhQZmpySUmyhHRzAP5SilPuJ8MBDVKsPvSF0vKry5T+spdnFN0M3O/Z9FL8+QxL03LDNDiMtkAqecJdXa6w4rkpSzub8j78K0WnLbbT6o2hpLY0hRFkNfkpSOfvxJSmJDYQ07MW8y/3CpQHcXzBFvO2AqWXI0as5ZboVRDiEtetUt0trKHGtzoCVDdJ0EWIx5zzxl2tZQq7nDkUFVRZmTlIy3l71ZKIL0YJA9aTLT863IbJKnFrJ58jj0ZTFLhZknwynu1Btqegf8ACtKCXAPOxB+GJ3OeTaDnrL7tPqUZa0PtlKVtOqZc0q5oDie8lKrWUAdxcY6PDtbOkyf4nWs/1/X8Yc7iGhjVY96/mZDl3MVTzfQKLQqLVo9YqmVk9jMrj5U2iZKbbupERVvwixGlZFgOfljYqLVUZgocPMUOwe0XcQOdxstB63BvjzJnXKdPyShhiZWDK4jR45fpJgLVEpWV4STu9b2EMhI75XqU4bjrjTeG/FfKXypKp9OzA3MgzezkreSw4yGX1gBS9K0j5pxW6VC6ee+L+o0kanBFtPEzEbzHT7/Se8dusTEebn6bV20+fbP05uk+Xp9u3y6z16NLzS3IeiQ8yUtJdfpa/WUNW/GtFJS6n36SbeYGJRbrVZpTVQpziXEuJTIYUPpdR9o2wk2pEFxLiHS5FfVe9wQhR6+44jaCfkOuzctAgR3U+vQAfqrUe1QOlkqsQPBXljz3XfaXoek9p3Sz8pKlwaiSOxsdR+qVCwv+bAkWiTDJd/EPNBpa+ek9PhvzwEoRFlLhvJCo8olaL7gLPtJ/VjvZv07u2L8S3I7rbH6RjI4w69AbQ2tsyIwFkOtbkD8ofqx0NQZajIhSw08fpIPM+aTzx1EVl0es02UWtXPTuk/4J5YQlMS0NuSJMaG5o3JSClRHjfAOi3VmhduWy95Lb03+IOFYswvrU080pl5G6kncEeIOG8P8GlmMlSgy42HG0qVfc8wDz8/jjs0mLNanaFKa0FpzT9G5vfALSYSXyl1Cy0+37LifvB8RgjdRLS/V5+hpzorfQv3HCzEtiSm7LyVW+qd/swstpp1OhxCVjwIuMBxDyHLaHEK9xvhTDNykU1e5ioSfFHdP3YKKaps/g02Q2PDXqH34B9jhFze9sMVGqx9x2UlPUeyoD8xwtGmNSdkKIUn2kKFlDAOLDBFIR9JIPnbBtQF98R6ELqqi66tSYoJShANtfmf0YBR6XTmRodcYSoj2Ta/2Yyaa6w56SQMdNk/sHH0dI/d5xsDUOJHHzMZtHuSBjJatY+kqAQLfsHH8/wAAh6PP+6zjT/xhyP8AN8LG0Yxf0ef91nGn/jDkf5vhY2jADBFDvA+GDFQSLqIA88U3iDxc4YcMYap3EDPtDoLaElWmdMbbcV4aWydaj4AC5vgTG7MPTVlZYl8GpeRK1EqNRquc5MekUCl018NSp1Q7QONJCiDZtJRrcUUkBKSTijeir6Juf+FOeKzxE4x5lpmZatUqdHjNuJW9KWl9KipTxU9s2oJ0tgNgAhN8WzgvSa1xp4ky/SbzlR5cGlRY7lJ4fUuayW340FR/CKg42r2XZBSNIO6WkgfSOPRbZShsAm1treGNbVi0RvETMebfHly4d/d2mN+k7TtvHlO3gRKCpOyLdeVsOQkDkMc1ovp1C+O6k2vfGyOI2Cw8MCwwNSTyUMcC0nYKGDLtgNgMdwXtEfWHjjpUkczzwHcc2wAQQCDzxWeI+a2MjZBzJnGRsii0qVUDbr2bSlfoGAwTh/CkekT6Qde4oVPNcg5Y4SZkeoeWaRF7PsXZyIvZy5by7aydTy0JTcCyb2x6NzRQ4uZcuVPLsxx9tipxHYjqmFaXAhaSk6T0Njjw/wCj+moZb4QVrLPDevUqoZmzflKk1SWpiot/7c1B15MmStQJKShC0FW1/m+WJfLuSPSAjRJ2Q69BzN63JVRKZBrEWW49GSzT5ai9IU6ogjWyUG5SC4bg3wGi5GzLxNybmbNWS25NdzZNYXFpeWqdKpIhw4TDTVjLeebQEIZVcAbqWrsjYXO23cOclHJNEdjTKm7U6rUZK6hVJ7osqTKXbUQOSUJSlKEJ6IQkdMeX6RwNr+ZX6M7X8uZlfZpeb3lLqcqoTYMio09TCnC9JY7eySJBCe6AlWkEJANsVzKPDj0k8ltgs0muyZcmmupZInuqjtypUVbQcdC3FqDiHCFuEd3upKUg3wG8+lcgKhcMQf741J//AJcjGsqhiOEutuFT8ghLQVybuNyPgOePLlXo2fMvcMOEOS+JLboqlA4p02mpluyjIEyMlMgsuh1VlL7igklQBJSb49RyXwtfrCUlKGkrYYH0nFnbby2wCjJulUWnR21oSbOOueypXX3m+DKiTVNlhwRo7BPzgZBurxHl78KLK6fTmmW0gOHS0j+Eev23ODfJiV2XMlvPgc0qVZP2DAJMITLljTtFiWS2kclLHXztyxQvSC4jVbImSEQ8oNxJecMyyUUnL8F+QGhIkL9tdyQbNt63DpubJ2GNJeXGixlKUtDTTQ1KN7AAb48uUGrucbuJdQ4vNyac9lahF2i5QcbjqLwsSmdK1qA/GLT2aLAjSi9+9gLfw+yPSOHeT6Zk2gtq9Vp7Rutaytb7qlFbjq1HcqUtS1G+9yeuJb1FpJJS/IbQolRQF2F/swPk5ggHtHgepDp7297/AH4HybEO6wtw/lqJH58AEQoSl6tJdWN7rUVm+OypSI40qOpxXsJJtbzPhhFbbESa2WkIbQWVlekEcsIx2VzJJceuRp7Vzbr9BPuA3wDVXrk58JUbqc5BI+j4jwT+fphwyl2GpSuwbuhRShbjoCUp8beJwNIDjgTIekXVdWgBI8rr8vDHAWW16SYTZ5m93FD9GA486txXbNvxlOjclk2WB157K92BGZckNuLbcbcCB82AeaCO8g+/lbphRZYkNkLqbNh9ZlIt+nDdtLjTgS0AFpIKFDe6hva/Mg+fXAcTZQtcpsBqUTvz7p+B2OJiNJEhtS1Cy0K0uJ8DiNb7NaX3m1j5m7neFwtKvaSfAg3wYByPICmHUo1M9oA4Nij6p/QcAoFh59SaiFtWc+aaKTpt4k9cSKbJASkbDkBtiAzdmrL2VMqSMy5srESkU1pILsmY8EIR4bnr5DfGXJ4j8WeMaPU+C9Fcyvl1dkrzlmCGsOOjkTT4agFOA8w65pTe1kqwGh8QeKvD/hbBbmZ2zIxBdk3EWGj5yXLX9ViOm7jh9wxlsjiBxh4rLLWXVo4WZZc2M+oMiZmB9Hi3EF24oPi4pSx4YsPD7gXkqhVyZXHjNr9WCw1Or1We9ZnTn/aWApX4poE/i2wlN7i2NVC4FPSmPHabb08mmUWuPh/pwHnGvQPR54FqgzJeQKznrPdadKaYmow11Os1aTYm6S9fQkdVpSlKB9mM5heivxr438WIPF70lpDDUGmht+kZWpEtLLMIpUFJYUvci2xUUgqURa9selskcHsnZIzNVc7xKbVq1mWrLUX6zWJBkyUNKNwwyV/iWx0SkJ88XxcmWDo7Jhj/AMI4LgeQG2G0T3ZrM035JmN++237S8++llS5VSpfD2s5uhQF0mn53prMuntFS1yY8oOR3G1KUBtpd5AYkPRRRSsrDiVw4oVVM7LmTc3O0+iPrk9uluKthtz1YOk98srUpPM25dMY76c2ZZfEbiPww9GjLWc0U+ZV60zUqtMSChuGmygxdabK1kB1QSDc923LHq/IeR+H3DPLkXKuSqFEp0GMNkMtqVrcUBrcUTcqUo7lR3w8OViYi07zv8/4T5bx8nmr0jPSTzT6IValDL+X6JmCn5ymP1OD61JfbcjPWQlbehAsQo7jvDr7sel+Gubcy52yDl/NtaykuhVCqwWpciC+6bx1LF9A2v4EXF7HHn3iTkiRxg9MPLMl/h2qt5VyNAAq7k9KQwlx5C3GlIQSCtQUpNrcuuPU4XNVZDMJLaQAAXLAWAtbSOW3LEODaa/D23mPs6nFrWyZoyZJ3vNMcz8+Xb+ERv5z1ZP6TlYzExww/YrRWEt1bPdTh5NgyG3e9HVOWULXcgEANB03HI2x6eyNlKnZKyzTcs0xpLcSlQ2IEZtPJDLSAhCR47JuT1JOPK/pPtSWMs5IrEia2luj8RctTl6AAUpEsNk79LOHHq1TsZRIXVZbw1HZlJt7u6MTOWmFuttIK3FpSBzJOGfyvFUsJHa6SqyXOzIQT4Xw2j/IZcTqWSvVYB4quT/hYfy2g7EdbVYgoNtvsOAa12qfIlHl1MRHZJjtFfYtjde3IYyaTNdzHU4spVM+Um6s2mPISkBDMhxrUbR3lX5XIIITfRcG4xc60tdbrFBy+t1wxlfhkjSba+ySCEk/wtJt54X/AGP0/wBcRWUF5KYz62kstrs0wkkgrQmwsTfc+eAYZZokei09pPqUZNSW32b7zYTqAue5rHtECwUrxF+uJN5xs2AcSdtIUBcD+COo/OT5Y46EtpS2u6CQoJUkXTz2APVFvvO+DPIaaZkymVJFkJuVg620nqOih5jzwFYzZG1Q5a20XCUhZ0m+kAg8/Lqrl4YdzZrdYzC/XI1nIjbaIUd290OFBKlrHiAVWv1tiy5ZQiehyW7HRbR2KtrpJ62B6dMMMwBv1uMxFabQzHJaS2lIAvYcgOgvbATMWIJrSA5+40JCWm77LI5qV+jChZIL1JW5pQ4nXHUeY8R8DbHW5rsZKgmMn1WMQ0pQN1bczbwwtVFJ9VTLbsSwpLiT0tff7icBTMytSWDFzHEZu9BcL6kDrbZ5HxTc/DFpo85h6MhMd0LYfSH4qz9Js7294JxybFDry2AsJEpIdZNrhLo6/EWxRY9QqFBly8uUpEXsWpCZSXJi1IZgk94t3G51WUR0+3CY36B3xm4ezeIGVPUKRHguzGZkaYI8zusTgy4FervKAJ7NQuORtztip8M+D1SoVVmZqzy4xHhtQXYECjl8Pop8Va+0cSp8pT2iAQNFwNCdsa7latGvUWNVXWQyt9F1IvsNyARfex5i/Q4r/E3h41xHagUmqVybGozTinJ8CMvQmoJ20tuLHeCAdyBz64vaXW5ceONPa/LSe87bzHnt6/1vs5+o0OO1/fRXe0do/r+vTdk9D460OkZsXlelR6pVcqSFOMxKlIa/B3HkXLjUd3+22F7DyNjjWpj78hil12ntqmrpazIQlB+cfjOIIOjxIB5dSnHl/MDUVdIzBkGi5igQ+F9IrEb1GpPx3HZTMvWVOwoFie2KViwIG2op3tjecn1/ObmXYcN/Jfb+pEsRpcGeghbKdkqNwClRA7yCLg7HHX4nocNsdcmGOW3rPeNoned9oi3XrEdnN4drcsZb48k71jyjtP7eUr0rNuUagxoNehpCkggKdCVoPuO4IwaJmuihQjP1uCux7jqX02V5HfY4rT9WzWgdpUslSFMD21IDDy7dTptg0eqZIrDallVGUpGzrE6IhlxB8wQD+jHCtpM0RvERPynf+G7tV1WO3fp81oiT8rVqe/FgVCHJksC7zbD4Kh5qCTv78OGVae0pEoqBIIaUeSkeR6kYrBouXKyhpuE3T1Lj3UwqmyBHeYP5Kkcx78F05qpaiy0+MwREC6ostIZmpH5DmyXD77HzxBMTXpaNk0Wi/Ws7rK4UBKY1VQsFr2JCAQPtHI4VaXLQm8OS3MaH0VKGq3vHPEPQs1Qqk4qLT5JXIbHzsCX81Ka8iDzH+t8SS10lxf4S0qG6NwSOzP2jY4w2GcXAdIVPp7jChzWpH/tJwdEWmvgLiyVg/wDByD+vBkNTUgKi1FD6LbJdAP3jCb0WQ4bu0uKv8pLmk/mwC/qk9o3YqC1Dol1IVb4ixwO1qrRHaRW5A+s2rSfsOGK4ZQReC02o7AeuqT+jBXIy2S2JMJQQ4sIBRMUo3PlYYCQVUnGiDJgPtJJtq2IHvscdqLGpPrrFkvMjWFeI6g+OI9d9Ehlh1xxp0hhsKVquq/eI92JlxP4Mpkc9BT92ADKxIYQ4AR2iNVvC4w1prgShUNQCXY/cUny+ifswtTFBVPYIN7IA+zAlRA+oOIWW3U+ytPT3jrgHJ5Yx+q/2Sw/8Rx/P8aiZU9juSYZdT/dGd/8A0cZRPfEj0kwpKHEH9g4uHEFJ/d5wHPR5/wB1nGn/AIw5H+b4WNnJtzxi/o9f7q+NP/GHI/zfCxrlYq9OodKl1mrzGYkGCyuRIkPOBDbTSRdSlKOwAAJJwDh1SVDSDvjyt6GXC7h7mrIC+K2bMpwa3nydXqu1VaxV2RLliQxOea0oU7fs0pShICUWAAxLjjFxu4/xVR/R5yk1lfKku7bWe8zNlKnm72LsGB7botulxwpSdjY42Lg9wuovBzINNyDQpEiUzBDjr8uSq70uU64px99z8pbi1KIGwvbATOa8x07JeWatmiqqcEKiwXp8kNJ1L7FpBUrSnxsk2GMspfpMUd+JQqlmbJ1Wy9T8xQ50+nS35Ed9h2PGjCQolTThspTdyEne6VX5Y1vMEGozqVNZpKoYmOxnG2PW2y4z2hBCe0SDujfcdQceX6t6GGZcx5MqVFmZqotBlVHM8KttwqHFcbgQI6ElqaywFkqSqS0pwKsAm5G3M4C2R/TL4fTqNlqtUfL9cm/smYjPoZbQ2FRVPz0wUIeJVZKu1J+CDiVy/wCkVmbNM7MdOpfArM/b5VlepT0uVGCgJkaUL0Al2x7jgVfltipxPQ4fpU3NkilZnittV3ONHr1PZLBCafT4ctEpcRNvru9qq/Lv4nc68AeJM/LnEqkZHzvS6ZJ4h5gaqDrz7LvzUHsG2nmLoIUFrDZTqTawUbb4BHLvpcxM0/IDNH4WZgMrMXrq4rb82GwlTEZ5DK3kuLdCXEKWvu6SdQSSLjHJfpjZSpk6soquTa7Hh056sRYk0qZW3OfpoUZTaEpWVN2CTYrASq2x3GISu+jVxMzDV8nV6qU7hVKVlGnO0iNS3qbJXBajFbSm1MgnUhaQ0B4YYVD0Kau5HrNSpeZqTFzBmGTmMVOV6u6W5ESpJu02QSTqZWlBBHMAjrgNk4s8dspcHsh0/iDmGLNkxqnIix2I0RAW+S8ASqxPsoRdaiOQScRNc9Jrh9QM81vIc5Ez1+kZYGaWnNCQ1Pj6FrU2wondwIRqKT0N+hxVKr6NWeOLLmXqbx2zLS3KHlqBJYixMrvy4Lj0h1lLAccc1BVg12g0g2OvfbFcm+hRXq9kOr5dzDn2KuvfItOpdCrbMZXbQ3IaZDSXFgnvBxh4NrTex7x8MBdh6WdBQqs1V7IGZEZWy7LYhVevpDCmYS3WWXQpbQX2uhKX0alBJAsTyGNwkQolThrjSo7UqLKbKXG3UhaHUEcik3BBB3vjzQz6LXFFyJmzIcnPtAjZJzrMiyasY8F1VSWy3FjsOR21KUG0JX6ue9YkJWdr49QR2Ux47UdAslpAQB5AWwGOVf0OvRkrKnHZnBPK7brm6nYkT1Zd78wWikj4Yyen5cT6FfFLMeb2Ms16ocJM2xoaO1pr0ierLMhlJCy9GUVKLDlyououUnYi2+PXxBIIBscJOoGhWsagdrWvseluuA87cRfSnoOaKdS8iejRmajZwz5m89lTVQ3PWYtIY/tk+bp/FobTchCrKUqyQOeK9w4quauFfpPs8H838eKzmeFNyWisTBmJ6OgOVIyQ0gRQEpKUlKXCUAnpj0fSMsZSy9Nffy/lykU6XLsqQqHEaYcd81lIBVv1Pjjz7w0p2WvSA48cUeI9VoVPqOWaGyxkCAxLbQ4qQ9GcU7LdKD3m09otKE3tqCSobHAK+kHxKypmrMHDfK9IFRnIicSKUmRUmIi1U9DiW3wWhJt2ZWCR3QTvtzx6JgR2kTHWym4iobQi/Ibc/fjF/SEoVKpFK4R0Kj0+NAgscRqQyyww2G22x2ckd1IFh9nPGwp9biwpYkNlDry0tNqve9wEg/pwDkKVOnNlm/YRyVKWfZWvlYe7DiXObZPYJR2zqh+LH6fAYQdUWQ1ToIAWU+19RP1vtwvHiNRG1dmDqVcqWT3lHxwGMekXmytNUSl8NaDWZFKzNnx5cCmyY0UvJgMoSFyZLhuAkJbukE7alJ2OGEKTl/KNCp2XMtttPNQA3TosSOpIVq8+gJsSSet8Z7UMyVDNfHviHmmPPrcRvLbDGWKXEeTohyexBemOoB9oKWsJKrf2sc74nKLQHqgulsSaU4Q22QtRa0MuM6btrK02UV6gCeoN8BeoVXjPsByW41FfKlIWy4+glKkmxFwd8P0kKAUlQKTyUDtivUTKMaA0/wDKLUaSZLnaloN9xG1tiSVEmwJJ54VixI1JzG3Ep7aWGZUNxxTSPZ1JULEDx3wD2YdchSUg3aYWV7cr8sIhMeQloMlbrqm06kJWdBFvp4cpeEWQ+HWHVF9YUkaSdQtyvgiYzxbKVlEOOBqUhsi596umATS1rX6ulr1lxPMX0tN/Ac8LITJhqStRS4hV9SG2Qkp8OXPDJFcbc1RqDS5FQDWyloPZs3/8IrYnDmFWH35vybUKa9Dkra7ZAK0uIWgG1wpPngFnZjFgt6C4WjYFamwLbjocR1gjtCFaSkKKVX6hz9WJKokCEtKlhIVawva5viJDyG4syoSVJRHjqCllRACWwdSiT4bc/LAPqk+y1pW+UIZaQX3bkJHLYKJ5eO/TGM1PjxOzRUpOX+BmWBnapx3Oxk1Z1ws0OE5yPaSAD25T0baBJ6kYiJ02qelDVXKRSJcmncLI0hSalUWlFt7MziDZUdlXNENIuFuD2yLDbfG85fo1LotOiwaNSo1NpcBvsoMVhtLTTLaRYK0JsOXKwHxwGb5Z4BR5lZjZ7435hcz7miN87HEtoNUmlnnpiw7lsEf3RYKjbnjQ3J0+uO+p0RamYae65NAtqT1SyL77fS5DoMEQh3NLnavakUZBKW2wdKpigeZ8G7jl1O+JJSRI/AIo0RWbBXZiw/gp8B44BKK002winUdHYxme6XAL/AE81eeFUKaZUpinshage+6s2Tfz8Tgzjhd/A4aEpSNlrHJA8E+JOOrdjw2hHZHaLAsG07lR8TgEH76wxIkOvOH+1Nd0fG3TBmoLi3EJ9VjNJJ9pZLqhYje3LnbmcdSfUW9b/wA5IeNikc1HoPLB/VZMnuzFlKOrLatIt4FXM4D53ZmzXkVmqZrqmf8AOsRvOj3GKnLqEF8hE6HSIj4aYLTVvmwAb9wnuqJx9DAmnl4JNSWpS1ApAfIvfccsVPiPwu4Y5uy7VoGbOHNMrxlx3Cpn1VtMp9wINtDxGtCyQAF32NuWKN6JmUeKWXeDjNJ4qPzojvr8o0+mVBSXZlOpoXZll2SPxhAF9dr2sMApl7OmUaJxgzvFzNUXYSZDkOLDky+0aZWG2yC0FqASpWok3vvfyxrFOl5Wq0Vc2k1SLNYQQlTjEkOJSSOR0qNj5HB58ai1aL8nyaSxUmwbht1hLiCbWJuq45deeMuyNTvVuMGa3aDDh0zL0JMeNUYTFiiRO7IKbW02kADShRCjuSd8Uazk0k1i/wCW0zt9Xqs1NJxvFlz44nHfFjrM7zFqztNaREbbTEz4RtPXp26wz9LOAh/gZWalDpL6kUSXT60txKLqS1GltOuLAvcgISo+4Y9ZxswwpdBhVqj6Zkea004wpo7OJWkKSoe8EHGVVhinZgpU2hVSnyH4NSiuxJCCjYsuIKVJt7jiuehlmCU9wyl8Nq1UHmK/w3qsjK7/AGtg49EaOqG6Uq+tHW3v+ScXttu7yu8d46w9B1Fxs01xT6AlS06QDz1Hp78LOuIjQSXl6QlvST52tb34ZkxmH0rlTVy3hfs20gEjzCR18zjryVKvPqI0paF2mL373S/io/dgIWo0GTJVBdgyfVakwzrYctdKVgWIWnqlQJB6jD2jyhXKQ3XEtKgyilxLyRy1pJSoEciLp2J3tjtRqaaLCVNkAv1KWnRGjo3UpZ5ISPAHcnpvhKFDVRqFFobjmt7s9ctwHmSbrPxUSBgCzHYyWQ9oDZW0FOpSLDWfZI87XxESZXyg83BSsobRcrKeRSTfQPLa3xx2ruLcdbiuHQRdThPRZFzf4WHww6oEaJFaVUJYJQgWsR7az089rYCcZc+SacErADr5KwkWsm/6AMQbZ7eoh5aSpuOAoDnqXzH2m3wxyXNk1R4ulXZtA2F9hbw8h4nriWpkOPBbblSVBCRdSNR761nmojxtyAwEtBjGPFS26AVr77t+qjzw1bSYajT3xqjOghoq6A/QOFRLnSbKiR0ob6KeuCr3DmPjhN997s1N1KHraPJxm6gPhzGARbbWpsU9ThEqIvWypX9sHT4b2OKjn6gs1WMuamluCpWabeSgElTYV3loTfS6UpJsCL87YtnekoDYUiahvcEK0PJwg/LUmMsOrLyBtZ0aXG16bj38ueAz3LlZi5azLAQaZMhJlNGGmMXFOPynCrZxxCiSgActRBO4tYY1i4lNpejLS4k736KxQ5nDynvLkymYUsyX3hIfcMp3StQIUlQTq6EbAbc/HD3L2ZFPSBTKwU02saj3hcx5Z8U35K8U8x54xMRPcnqo3GDIPyBT52eckZWnVfMoaTApMRgBbFNLyrPSWmdkhyxKivmTbcYqvD7jBG4WTYvDjMVIjUWl0mEHJDDa3J9RaKzvKmFlJbZSpRUVFSr3x6OamLDgYms9k4o91QN0K9x/QceaOLHC+TlSTmDMcirsU+k1aa/KTVITa3KqFSkht2EGAkpkIctpTqI07Ecsd3QZ8WprOl1P089+3r2+W/WXC12DLprxqcH1jz9fm9ORpMedGalQ5KHmH0Bba0G6VpIuCCOhGGU/K9DqjoeqNFiSFp5LcaCiMZxwWn8TIrbVFzXkRii5fbiNIpBRKS49FZbSEpaki9y6oWPdFhvfGwY5WXHbS5ZpW3bxif2dXDeupxxe1fuo2aMhU9NNVNyvS2IdVjKS7HcZGgqKTcp28QCPjiTodcg5tgEtFyPMjEB5pabOMOeBv0/PiQzVVTRMvzqokp1sMqUjVy1W2+/EVkagLp8RysT33JFSqqUPSXF2FttkADkBfEtre8082yzvMT0/n9EO3u9REY+09/5D1yh06roQjMVMQ72X4uYwpSHGvPUmyk/mxHhrN2WWkuRnxmWkAew4AJTKfEKGzgHnv78XXQlQOoXB6YYPRX4JMmDcoG62OhHUp8DinC8iabWMuVhtSmIigtv8YhCSFIJ6KA3HxGHrTdNUpLUWfIZUvkAtQ1f5WIrMkeG7PodUhN9nLenttiQ2dJLRSSpKvrDpY3xMesBXrFWcupDCVhlO/wBHmfffbAEkxooJhRWEuvOA9o44b9mOpJ8fAYTUtyR2DEZS9Kfm2CrmbbKc+Av9uFVR1IaYiFZ1zF6n3BzUAL/qGF4qR8pyE2A0NoDXgEEb2+OA5DjoE5SUexFbS2jfko7qv58sPZJcS0soF1aFW99sNI60Qp8ll029YWHUKOwOwBF/HbD8kE8t+mAY0+XCYgMIXKaSUoGoFQBB64N8rMLJEZt2R+U2ju+6/LHH3KQw4VPerpXfchIKr4avTQ6Qhx8xWL90D8Y78BuBgHfrs7cmluBNv7om/wBmMonvqd9JIKdjrZP7Bx7dt/w/Gkqcgp2EuoMEG+pZWR9+2MyfcU56SJ/DUSR+wgWISEkfh5+3Ad9Hr/dXxp/4w5H+b4WIb03O0VwKcpz7i0Uyp5gotPrGgkFUB2c0h5G3RSTpI8CcTPo+G2auNR8OIcj/ADfCxR/TNzzT8y5Om+j1lGl1evZ/zCiDKhwqbCU6IDYmIKJclz2GWwW1EFRudPLAbJnnipwr4K0OLJzzmmmZdhWEaDHWsdo8EpsG2GUArWQBySk8sZun02eGJV2jWReKjsG5Pr6Mi1AxyPrA9nqI89OK3wyptNk+mpxIg5zpLOY61Fo1On0GrhaZDNDp5QG1QdBH4O8t1Lju260kHpj1ItJSRp+/pgKXwy41cN+MMKXO4e5lYqfyc4hqcwW3GZERahdKXWXEpW2SASNQF7Yu6Fa0hViLi++PMPo8SouTvSE4x8L5wiVOtVKe3m/5bjupccdiP/Ntw5IG7TjGkpSg7FCtQHO/p5F9O5BwBsDAwMBw7dMJvyG47SnnVJQhAKlrUbJSBzJPQeeIzN+aKJknK9UzdmSamJS6PFcmS3yCezaQm6jYbk2HIbnHnVdM4sel1HYOZKbUuHPCF8Jf+Ti92dczKzzSHtJ/BIyxa6LlxQNjpGA9AZJ4hZJ4jQZdUyLmmmV2HClrgvyKfIS82h9ABU2VJ2uLj7cWLHnD0QaLRKLmHjVFyhSI1Ny3Hz25Ap0aKyGmEGNCjMuhtKRawWgpv4g49H4ApUbmw5Yw2N6YXDKTnxnJqaRmdECVXF5Zi5lXSyKO/VUkhUVL976tQKQSnSVAgHG3rV3iDy5fDHifK/DjMOfOCnFjhhklcZyu5E4ozKrl+UtakxpchqS1Pba18r3WtlR5JPuwHt3EDnyZmKBkmvTsoxESa3Hpsl2msrF0uSUtqLaSOt1AC2PP9a9NVmLl9VBh8Ks5Q+KUns4lOynU6NIQmRMWQm4loSplTCCSpTiVeyk7YY52zh6W/A/LTXFbiLnbImZsu06RFOYKLTqA/EejxXXUtuuMSC8rV2evVdSO8EnlgKdw24Jo4icHqDx64ScQKrP4xofRVZVXrFRf0yKigkSqVLY1aWWL62ezCBosFb88W3hPROLlW9JNPEpXBOocNKVU6M9Fzq3KqkZ+NWZyQkRXmG2iSVoIWC6QkqQQCMSXo2uUmT6Q3HOSyGKBJdqUJLeW2kFvto6GrpqxB7qzIKz3kCwCAFEqx6a7JBJJG53OAxL0nFKS3wpKFBBHEqj2UrcX7ORzxrDs1Mp+O062ptTSlOvAjYaR4/HGQelcxIcp/DGPCdCX1cR6PoK06gVaH7AjwJFsaZTKyqbQ0SChafXY+pls7lDh2W3fyVf4YCdgoK0rnOjvP94X6IHsj9Pxxn/EridVaDkPMmasuUd56NRoMh1MlKe0ckOoSQG47Q7ziivSkHlc9cWrNUuVFpcajU9ZRMqbiILShzQki7i/8FAUcY56SDMWqJyBwVRSpy6VWaomoVGRHdKG4kOnaXwHVAG4cdDSNJtqJOAiOEnDmn5c4Z0OlVyI9NqL7QqNQdqJ7SQqXI+cdC1dSFKKegGkAWti8CQ68VNQW06GzpKlGyUHwH3Y4oyqiStV22VncnZxznz8BvhylLaEpabFkoFgOgwCDUhxLvq8xKW3D7Cge4rDCrpeh1ODW0RnH0R0uR5DbQ1LShVlawOtiOXniUdYaebKHEk+CuoOEA1PbsG5CFhJBSVIUFAjkbjngGQzGZY0UilzJalW+cW2WGRt1Urf7BgoosmesO5hkiQb3TDaJEdH8If2z/CxI/1yTdZeZe8UgFJ+3ng0aQX0qCmdKmzpcTfl5+7AN6jUY9GiIWGdZWoMR2Wu7rcPJCfDxPS2EaVS5Tb7tUqq0uVB0FCgn2GW7g9mnx94wvVKYmpIYdZkLjyYrnbMupsdK9OncHYi2xGEqNU5EuLKNQDTbkB5TDriDZs6bEq71rbG/O2A7OcixpD0qrutojBtSu2cIS20hIuoqJ2FgCScYGxMqXpMSpUCmyZVO4QwHNcuY3qaezU4hRHZNHZSYdx3lc3Om257W5rvpUZik5dpEp5rhNQ5IYqMxkqSMzzUqOqOysc4rZFlq5LIKeWNvpcaBFgNwYDLMSnxkJBaYSEoaYbACGUgeQ+/AEplKg0qKxRKdEaiRWkssNsMICENtJF9KQNgLjp0w5r63JK2KDGIS5PPzun6LCPb917gDzOFIV3ZS5j1kIbBWok2CRbYe5I3JwjRv64TZmYFApYeCWYxVsSyi93PK6jcYB9ICWGmoMFACljsmh/c0DkfcB9+OAarU+IooQ2n51YPLyHiTgQyS25UF2u6Da/RA3HuwpBQER0K5l67p955YDjxTDYQxGQElw6ED6qupwpHitxUnTu6fbc6k+/wwmyPWn3ZJPcbV2bY8du8ftws86hhpTrhISnwHXpgEno7weEqOtOtKSjStNwQfPpgWqZATqZbTb2hdRt7scTJmczTlkEdHBv8MDtZzmzcLs/ynF2A+zngOdnHgoL7yitzcBat1KvzAwktKVIMmpKPZDSUsi2w8DaxJOFkRbuB6Q8HVpuR9VPmBgsZBlvJmu+yk2YT9XoT78GJ7OJVPeSlptoR2zYBarEjbmBy2xiXErJ3DmkMVrP9AzlJi12O+t5KY1S1tfKSALBTSTYuKASko52PLE16Ssmv1bKUXhvlapv0mp5tcMdupMvhvsUtjtChJO5Uv2bbYrXoucBaJwz4U0CsZ1ywkZyaadn1J+WSp1MlS1KK1XJSV6bDUN9sU88fiLTjiOsPS8Lz5OCRj19sk1rk32iP9VazWOW0eUzzfrs2vL8yvVSgUyqT0Nx5UqK28+ypCgUOqSCoH4k4zWTOd4R+k7l/O0yn6cu8VI6Mq1chQLaKuyFLgPG/IrR2rXiTpxosCjrq0dFRrEyUpySA4hlqQtpDTZF0pski5sdySd8VLjHwfb4gcPKtlui1WbEqykpmUp5UlToZqDJ7SM4NRJBS4lJuCOo64t1jlrFZ8Hn8uSMuW96xtEzvEfPr/Cdvo9FyJiKYwuU63DpjCd1uPqAH6vvxEtza3XnA5RIagxc6Z9RQUIB+s0zzV5E2GKP6PWdKZxb4XUDiZmeU3Lri21RahFcTZMCoRz2UlpDW9lpcSq557jpjU5MpxwalKVFZV3Rtdxz+CnpjKMxptJj06ap9LztVq606XJT5HzY8BbZCfIYeCMgyW4qnA6tSg7IUDsNPJOCLkKQlMOO2qMg8m0WU86Pd9H3nAKuwQWFOsw0H2kpV2jy/eRyOArdVVd6VIUDqXZYNuXfI/NbDyYy3TqUwoulbkpsFGrfs06Re3mfHCVfQGwkWLYeFkoPtBKRsVDpywtmgNtNUxCx3QzyHXYbYB1TYa0NMoaaBccTdCVC4Cfrr8fIeOJRt2nxnCCtcqSD3ilJWb+7kMIspdMZGk6ZExKUp29hsDEkhEaAyE6ktto3KlG1z5+eAT9emq3apbhHTU4kYL69LQnW/TFpR1LbgWR9mAmqNFSdDDpYJ09tayQfz4dvqDMdx4C+hBVz8sBHVAxlMNTGNIeWpPZLGxO+9/Ha+G7KG59SddcuGVoUoXPQd0K/PhJxl0Mx4hcsW4w7PcDUo+1Y+NsKvuxHOzUuAlJQnQCt5KU28DY74Dv8AW5PcXNmv6ABrQpekfFO2Iut5dh1RpSpC1vR3QPnUpupJHLXbfboRuMTLVRcQQhpuGpA5NtPDV8BywT1hpt5S4jwirc/GNvtkJJ8b+OAqSXM4Uxn1MzolUij8Sl9tSl283Em9x0JF8Lx87JYKYuYYYh2Wns3JI7RkqH/CD2T/AAgPfi1ORJDwK106C9f6SDZR+NsR66dDnOGE6wCVpWCl4a1NrTYjfmpO/I4T16yHsOsxpbCZUZovtGx1x3UuoPuKTh8irwlLCFOFtRNgHElP58VJ/KtMZcVIbRJpLnNa47qmkKPjdJsf8IHBy7m2itAuer1qCDZKXhZ3T/4QAgn3gYbBxxRZkzMly0Q2VPkLacUlG5KErSpVvgDieotSh1SnMTae8h1h1AKFJO1vD34q4zPCb0uSoM6jIJsFSEBcZX8JSSQn3m2Ieg1lzJDjsWoM9rR5b6pDEyMkrS1rN9Krc0+Chti1SPf4Jxx+as7x67/tsp5JnBl97PaejTwTuBbAI6kffiLpNepNZaLtLqMeUm+5aWDb4c8SaTt78VbVtSdrQtVtW8b1ndUM8QKk1RH3qOWj6u763ZSLra07q7P8oi9vDFGpOYq9SYM1K1SG3XgqZOXUwt3sQUq7KOANhdKSSrlcjnjaQlNiCAb88VTMHD+lVdT0xhLjUh5FloDqksuLAOhTiBsrSeV/DBsSo+b6XXWodPbk6KkEJdQjQrQVpQCpAWRpURqsQCT9mJrty+4mXCAElkaHWFHcjw+3kcUGDkurtuRUuqh0d6DG7CCpo9ql+QNlOuHbmLjTz7x3xLwsxaJYpGamWoNSSAE+skpbfH1mXRsoH6p7w64C1uzoT7QZmxnkA76VsqNj7wMMXXoKUlLXrjg/4V0tp+074LJRHYR2klcSO1a+ovrX9gvviFfzJlqK4G1VJlS+WltaB/lBN1WwEsHABoaPZBW9kp7NJ96t1HBmA+TaPquTfU2Qi/8AhL7ysOKcxFnQUT2p7RiuJ16o/JQ/hHc/dhpJSxJX6vToyEo5qeWolSR4gnkMA8ArbKNCUOqQRyWUuH7bjGXLclL9JEh/s9YyQNhp/f58MaeS7MBQ27IlbW+bPZNfbzOMzkMuR/SPCHG2mx+wgWQ0OX4eeZ64A3o93/ZXxpt/fDkf5vhYr1Z9FDMU7irmribSPSIzrQ/2YJjsz4MFmLduOylQQyw6pBU0kalbje564sXo8/7rONP/ABhyP83wsbPYeGApHCjhFkjg7RHqFkymvI9ceVKnz5chUmbUJB5vSH1krdX0uo7AWFhi5u31JsbDmcKWHO2ASBzIwHiHLuZG/Q/9JWHwrr+cadKydxGVUa05Ok0FTUyJNkSh6u3Jnt3D2pbjjaS4EhKUpF+WPbjV9Avb4YwX0tM/cJW+H1Z4S5vmyKnXs3U92FTKBRWDMqzzyh8242yjvJCF6Fa1aUjTucVThx6U9fyjkSkZX438JOI8LPdNhsxpUanZbfnNT3EpADrTzGprvCxIKhpUSOmA9UY4q9jY2x5zOavSx4vLCcn5Rp/B/LyjvVMxhFRrDqPFuE2rsmT/AOFWSPq4JKj+lVwTliqsVdfG3K60/hsFyNHp1chq6rjlOll9H/BqsroCcBpfHnOfDrJPCmv1LimyxOoEiKqE7Tlo7RdSW6NCIraOa1uEhIA8b9L4gPRTylnfI3A6gZf4gPSRU0l59qFKf7d6mRHHCuPCW6SS4plspQVdSMVbhvwxzvxYz5F468fKOqm/Ja1/sMyY8pLiaKggj1yVpJS5MWPg0Nhvc49EpCeRG+53GA871zh1xd4MZ1r/ABD4BQ4GZaFmeWqqV/JU+T6stU5Vg5KgSVXS2tYA1NLGlShe6ScWLIXpQZTzZmmFw+zVlrM+Q831AL9VpGYqcpkSlITqcSxITdl4gXNkrvYE2tjZ3ADYaeflyx5W9ISk8RMgcYonpKzsqxs8ZGyZRh2NMTUFsSqC4VKEyoMMFJQ+52SgNyFBCVAc8BbeNnFPOtczcj0feBLjSM5z4qZNcrjqdcbK9OWSA+scnJC7ENN+PeOw30nhPwvy3wfyNTsiZXDpiwUlbz7ytT8yQtWp2Q8r6TjiiST54wv0UM7ZaYznxFy7X3lRs7ZpzFNzTHXJaUlNXoqggQn4rpGl5lDGhJCTdBJuBfGlu+lb6PbVQi0pzibTjLm29XbS26rte9p7pCSDZXMjl1tgNcUy0ohSkJJTyNtxiDzvkvLufcoVjJWZ6eJdJrkR2DMZuRracTpVYjkfA9LYiadxe4a1amN1qm51pkqA5V/2PofaeCkmo9p2fq+309fdtjsLitw7qPyZ6jmuFI+WajIo8FCVkl+azq7ZoJtfUjs13vsAPPAVLg36N+WeE+YJGb15uzRm3MDtNbozVTr80Puxqe2rUiM2EpSkJBAJJBUSBc42C2CNjvX57czhTAYX6VlQj0mJwwqUnVoj8RqQshIuVHs5ACQPEk2+ONaylTXoGX4secylD5K3lo5hClrKykHyvb4YyP0rac3VYvDCA4+pkPcR6NZxI3QtKJCkqHjZQG2NQTJzzEtFXSKdNP0ZSZRaHvUjSSPcCcAJKvlHPEdhtN26TEW48r/hHiAgDzASonyIxgoY/Zpx2zfxHeojoay4ljKVKkmQHESGmyXZTiE8kntnAgm9z2eNezPWF8MuH+Z881J5qZUmYz05Y7RLSHXwizLCSogAX0IBO5J88ZLwbyzIyVw8pVFFHEapOpXUqw2JBWoVCUovSAFqJKu+tV7+AwF7ZfakJKmTsNlBXtD34PhjHdS9N7RlpaT2ZS8CLC4O2H2A7cjHNr4HNQF/M+7DJszJyLtvBttxRA7m5HkcA+ty25eWI2syqdSYr9cqFWj0uPGb1SJUlaW2UIH1yogW+OMwzpxwpFHzInIXC+jSs+5zQD2tMgSAI8QeMySfm2E33tcrNrBOI+PwNzDnmowcw+kRmZGZJAe7SLl6ChTNCp/UDsjvKd/4R24HRIwB2OOeeOJAcY4B8PUViChxTDma606uJSNQ2KmEAF2UOoKAEflHEXB4FVPOdQqVT43cSajm1KpCG0UimqXS6I46lPe1MoOp5I9m61G9iSMbJVXWYEVvLlG/B3VthPzaQExo45qNuW3sjBYseK12ccpSwww2LNjk2j6KR1JPU4BTLFApVBpkWFSIEeDT4aCzDjMthDTTY8ANgOdsKPOodKWILaUNAlSRa2pXUnyG+FZL7j4S222UtKsENn21gfmR4nEW207X3Fx4rpRAbNpMls2DtubTR6JHVXXAKtNivlMNhxQo8ddnnE7GWsG9k/kA8/HEs+A8r1BgAIOzihybSBskY4hSUoFPpaUNtNAJ1oFktAcrDx8McctGSIMFILru+rmR4qOAM87rCoMZOohOhSvot+/xw4UpuHH1KICEJAv15dMJhLFPjbqIQkXKuqlX54I20uS4JMlsJQPxbPRP5SvM4A0FtTUNCSDqIKt+abnlghSqTN0Kt2UbmDzUrzw8AFxqHPbDNSnI8p1fqzjiHQkhSBc3t1wDsJAHTck8uWBby5Yb9pOkdxlosJ/ui03PwT19+COQkNp7R2e8lQ3C1rsL+7AOnEhxtSCbBSSCffhvBUoxyyoWXHPZqI5eWONzFGB624kEm4/hq+jbCkRpTMZLah3idSvecBSuNNFl1jJSl0iC5LqtOmR51PabSC4XW3Ao6b7C6bjD2Nm2nZ24cO5mpyX0MVCO80lD6NKkuBSmlIsPBQO4wfPWZlZUyxW81pjCS7SWLMMlWlK3FEJFz0FyL+WGmSMjzsvcLadk56cw9LYZK0PtIIb7QuF0AA8wCdN+p3xWiZrqp5PJ3rbW4HW+XvGTak+dZrM3j78v3lapQW1BQkEpA7NC7c0pA3wV+PToyA4W76lBKFJJJWTyCd9774FJqTdUiKWpkNvoJbksq3La/pDzB5jHewS0+IF9TDqbpSrmgjwxZcFjD1VzB6LGdazxDotGdncKsyzPlDM8FpntZWXZq7JVUWkp7zjCyPnUDdNtQ649TUydEr9PhV+gTvlim1GMiQzNivJKX0K3BSq/skeGKEunRnW+zd7RbagUFtSipOk3v3eoNyCPPGIxlVn0Pa1IzBl1uozeC9TfLtYpMYqdeym+s7y46dyqKpRutsfi+Y22wHr0ritsuRFQXIrj6ClCid1m31h195wkVliCGmYbsd5KLKdLYNj1N+eEqBXKXmqksTINWjVGDUWkSYUtkpLchlaQpK0Ec/0YRzEYcOOYkMLMvUjvElRAPO5wEdT4zVUqxXIUVMkhvUOZNtjc772OF80aHtSkgEMfNITq20gC5+02+GFKRC/BZEovmOlKkKCgLk6b3/P9+I5hCqjU1IIUEN+wjmoknb9ZPTATtPnpQtUlaFupaZQhRT7TQA3BB+2+JKXDTUAxLYdTqb3b1C6CD4jrgsWkNN2VLOtV72GyR7/H3nHVynJaixTBceyXj7KB1A8TgOslVQYfiyEBK0K0KINxe2xTji5b7TRj1CMrSU2LjYKkkefXDhCYlMjAKcCEp3KjzUfHzOEflVlYsyxIdP5DRA+04BiowFIDXyqOzG4S60FWHhuMBKYSTduqw0np+Dow/wDXJv0KS6f4S0W/PjhkTlWvRwfH51OAa9k46NIkU+QnwLek/aDthNxtTAIcQ8wgi257dk+8cxhda4I706j9kPrqaSoD4jCxp6UDtqe6Wja4AJLavIg/owDHWuJocjobbBPdUyr5pw/VI+ifPHdJlPuvNRVOFawoKD2goOmxCuox0tIeU4lphKVnuyY3LWOi04SSEOEFSWpK0C1lHsnx5HocAquPLigu6XEJtutt0uJH8JKufwwSJILLqXI6E61AlbKNku/lJ8D5YWabhFQbaXIgPAdwLWbH4E2OG8tpTTl5MYJWVXUpGzbvmPqq63wD0po9QXZ1tKHVDSQe4v3HxxVsmwihddRGaStiJU3G2mLd0p0J1ADkN9/txMzJQbhqf7ZuS0bNgOJs6hVtrnEDliqMULKtYrEoKUtuqSToHtOOEgJSn3kgYR0neGJiJ6SLmimZPjlNSQh+jzCbNvwyGnFK8NH0/sxDxc85kp7vZN5iplSbH9pnfgkgD3mwP2Yf0OkTahKVVaq4qRPfNnFpsSDb2EE+yhIt78Tk3LkbsiqREdKUblSwhYv7ueLFdVkrHLaIvHr3hWtpqzO9J2+XQxazbxBnJSqDlunNt8y6uUXkq93Z4OuucRJrZYREotMuN5LjynQPcmw+/CcHK1FDikO0hpSybkNKUys26gpIG3hzxJJyjSHwZdIn1CHIb5OCStRB8FIWSCPfjadVG3TFX9f+5j8Lbf8AzLbfT9jPVxEMVTSk0OrNrFi4lamTf4Xw2i1mZLm/sTz1l9tfbNlyOpJ7VLgA30q56gOuxw8Q3VaNUo0asTRIYqCiy3JaSGVNPWJSFJF0nUBa/iPPDOrSHXM/UyLIsVU2DIlAq5OE2SLee++N8V4z82O1K9I33jeP5/yR5Yth5bVtPfbaZiSr9EyJTo7cyoxJkkKWWmITynHyVA2sGhe/vIsPLCiUVWYhUGnU5mgRCbKaYbQZJR5qT3Gr+d1eGDxY6hmOoLUvvxWY0RKlncF0lbix4XuB/g4sMxuOyhuAyNDWlTjunmUJ5i/idsUungvRv4oeNFEOGxS4usRmrpQhOpZWq9zurdSr3JJ2xIojMRw366QSo3THa72o+KvrH37YVb1R47T6Wx6zLIQjbZsHcD3AYcFLNLZLgT2sl0hOo81qP6MGQHyk8NKEMxGxuARrV9nIYyqe0pPpJ/OSlPH9hA522/Dz0GNVTTlP96e+Xj9QGyE/AYymaxEa9JTRGbbSBkcX0/8A6eeeAHo8/wC6zjT/AMYcj/N8LG0Y8xcL+MGSOH+f+MtHzI/U0SnM+uvpEalSZKSlVPhW7zSFC+x2vfGlD0leFp5P1/bn/wDJ6dt/6rAaniBz1Tcw1jJ9bpWU6sKXWplOkR6fOKdQjSFIIbcI66VEH4Yoi/Sg4RtnS5UK0k+HyDNv/wDysGR6TnChwjRKrx/xfnf9VgMl9FFnKfDjMUjhhnnh3IyxxglxDLqFWqUn5QdzO2ggOSo85XecQCblk6S3cd22+PV7e6E3HIY8o8euJOSc/S8iZhyQ7VE5hyZmqHVUyHqHNbLcAktzUX7K51sqI09SBjWW/SZ4UFAUJddIUTYjL87ff/wWA1fAxln9Upwu/u2YP+b07/qsFHpM8Ke07Eyq8FkXAOX5wv8A+qwGq4GMqPpM8KkkhUmvAhJUQcvzRYD/AMlhNj0oeEUlpL0efW3G1i6VpoM0gi5BIPZeIwGs4ZVamxKxT5VKqMZEiJNYXHfZWm6HG1pKVJI63BI+OM2b9JvhQ6opal11ZTzCaBNNvD+1YCvSb4UJ1hUuvDswCv8A+T87u38fmvPAVvg76K1A4RVP5dezVmDNU2m09yj5c+V3UqRQ6Ybn1WOlIAue6lThupQSkchjPqdwn415TyHw4NEykJOYMrZezLGdYExpKWpkoj1UaioAgk3NtgAcbEv0oeEbYuufW0jUEXNAm21E2A/FdThX+qW4W6y322YNSeY/Y9O2/wDVYDzdT/R14y8IISKBS6IM30duvZazWVUttqIv1qK7omo7Nx3vLWlCHSq4CiTffGv8KOF1RXx/zPxdrGRp9Cp9SpkZ+lxZ0htfq1QfBTPWG0KUEOLSzH1KB338Ti4/1THCoEJMivhSr6QcvTrm3P8AtWDf1SvC3+71/wD5vTv+qwGpY7jLP6pXhb/d6/8A83p3/VYH9Urwt/u9f/5vTv8AqsBDek4HdfCrsSnWeJNHtq5fi5GNdL1VbJBiNOjxQ5p/Pjzvxd4m5f4m1fhnR8kwK9UJcHPtMqMhCqNKZS1GbbfC3FLcbCQBqHM9ceiBUVJUA9CfQkm2oAKA8zbpgMJ9Jl5/OD+TuD71FhSY+ZKqmfV235dnGoEEh/tEtoUFKCnkso3unvbg4sbDSWGw2kbX3PUnxxQ6bWIWeuOuc88qVSX4NAaZytRJjTpU8Cn52clR1aU/OlAAtfuY0C+wPQ8j44AE+JwCQNicRuY8yZfyjRpGYs0VqFSqZDT2j0uY8Gm0C1+auvlzvtbGSN8R+K/GRfZcFqQjK2V1Gy84ZhhKLslB+lBhGxVfo49ZPUJIwGn52z5k7hzRHMw55zJBo0BrkuQ5ZbquiW0DvOKPIJSDc2xjbMXi9x/bbaWqq8OeHDlyG0K9XrlcaJ2KlDeG0R9Ed8jmQMXTJno95CyxWG83Vwz855rQdQr+Y3vW5Lav+BGzbIHQNpAHnjSJjzzLCnEIDhH+t7DAQOSOH+S+GVBTQck0CJSIDQ1uJjtgFxX1lqJKlKJ3JJJOH86b2ET5SmNkNNWU2yjdbqybJA95w7DMyUkByS12SkhVm07q8r4YVpJmVGmUljYdqJrpPRDJACfipQwCdJhPITIlVNaEuPPdrKWDdKnLbNj8hAsPM3w8dkIeBlNMttobTqMl9IASB1HX44ZuVHL0N4tKmmS6hX4hHzulfhYbX9+GUv16vPpRUEGHAbIUI19S3D4u25nwQPjgOBx7MC1NsF8wFn5xR7j0z+ER+LR5dcTzVOX2KGXXQ202AEssJ0JSkfRv4fnwGEyUNpbiMIjoA7pdN1nzIH6eXLCa9C1FDkmTKVfvJZ2TfALrfQn8EgNIU54cktjxJ644SxARqUS884bE2upZ6e4DBW0SR3GGmIiOuo6lH7MHT6lCutyRd1Q3Uo3UryAHTAGbjPLdD81QUtOyG0m6Ue/xwo+82y0VrO17AeJ8BhLt5ThtGglKeep46U293PDaQ26VaXnS7JWk6E2shseOAapdnvK7d1wIPakNIRc6rfRA/PiRSiY/333uwSrk21sR7zhq8gpmJhsLKSGkt6kndFt1G/icPDHmtfipCXQfoui5t4XGBM7B6ik85cr4On9WDNwGArU6XHrcu0VqAwVM9tB7OS2Y6vyjdJ9xwd6Yy0kAEOLV7LaTurz8h78O5E7xvBJ0etSw2R81H7ygBzV0w73Ox5nERUa1R8q05dWzNVY8Bhx1KVuvL0pC1GyUe8m2JfUkpC0qBChcEdRa98Y3jeY8u7ecd60jJMTtPafPbuxzj/SsyyoCp7FRlfsWYjOPZgp0R1DL0hg2BIUoFJ025czjUok+mU/L0aclS2oLMRpxu91KS2EDQD4kjw3ucQvEelsVjKVfpUtwtszaS+z2l9gbFXP4YTyjIfzhwuo0p0hmTNprCkqIsA8ACDY9LpH24qUp7vVzMeMO7qdTOo4HixxER7rJMf8AVWLdfO2+8b+URCao0aX6zNrE9pMdyeW9McG5bQhNk6z1Ub3w7c71SbH9yaUo+RJFsJUiqNVSMQW+xksfNyWFHvNueBHh58sKzAqK+meoEtlOhwW5AfSxcef7xv4HS1JTqUo2SnmT0GGjk6nOJWw462424lSXELRqSsEWsb7FJGxBx2foeDEQK1B1aSbdU+N/DDhTgRbUpKAdk32BwOzEabk/irwPnSz6Pk+j1PKU+V67+w2ul1luG6TdaadLTcMJWd+yWkoB5EXOLJSvS7yJU8xxcq8TKTVuGOZJTiWlw8xNhMRxd7XYmoJZWD5qHuxda5VWI7aYzZ1vhaVAJJ7oHniBlZbg5mgzIeYKLEq8R9NnY0plLrS9XWygQR8B78BsLlSjO0OI7CltSGHUKfS6w4FpcBNkqBGxBG9x4YUy0lqM2qUmK5IkuXuUWukJNtr+Jx5CyYqF6NvGaj5Zoy3IPDfiKBTmohdUqNR68m6m1I1Ehpt9HcCAdOpO1r49lUSOilxBLlLVrWLNoCd7c9h5nfAPXRJmD8ISY0VA1KSVAKV7yOQw1nV2JT4XrIUzGhoskyHzobHhpHM/DC6mX5gL01SW0gFSWem3slR6+7ljJXajWp5paYtXemVFc0dtElEFpuU2pSkBFjsghNja4spJvgLdIztDZnvSYr9OfixGm5EmdKkFBLa+jaAkna2J5WapUx0t0CgzKg2nm+Sllm/kpe6vgMU6Dk1yeKjUq7DTSFTJCVsIDKFvJ1J+e7MXVp1He++9zbfF9prEqBT48GFT1hmO0ltBff75A2F+eAYmrZ4K7HJ8PT4/KQv/ANDBzmKtR7Go5OnoSOaozjb4+wEKP2Ylu1qqd1QmVDwQ7v8AeMFVU9AHrkV2OORKhdP2jAI0ivUquBaYEorWjZbLiShxHkpKgFDBmlO0sKaUy45GuVJUkalJHUEeGEKvl2nV9LcxDi48xtPzE2MQl1HlfqnxSbg4jXKrmLLQS5mVEabAulK58ZPZqZF7BTjZPLxKTtvsBgJ9xuJUm0SW3Bcew6g7p8v9GEHETEp0TIDc1Av30WCre4/owp6korU9ElKZDveUlKQQT478jgQpa0okKkSkuMsm3bEADbmMA19XZdaU9ASH2f7bGc5X8r+ycFcQlEFyTEmOhCb3ZdspII+jY/ZtgKqDUZ16epASp5IDTZ2JH1leAxDPSZcpsRGj2baVFazvsoqvc/oGAja28t1x1uPshCTo3vpXova/UAnbEFR5Py7DpdOgpc+T6c2hx1RFlSZpTdSiD0SSfji8/JIhxWXErDbqlXS3p1KULG3P3km+BDgRoretKbIVsVJHedJ+ijy8VHAKUuO5FZQhhXzzgDTazva/eWr3Dl8MSjrdSabU6ZCZKQO82W7FQ6gYbx0yA6pEcJElQANxdEdH1R54cPwpDLa5Lc98upGrvKuk26WwEZFQiQpDTTliQQhy/JaRdB+KTbEgSuS2qaw32c1i6HEdF2+ifHyOI1xhYmpdhqCO0LTiEqHdSpWr/T9uHplaJIk6C2tJSzIaVvcXACh8TzwCWZ4yq1leSITZVIKA+wAbHtUEKTv43TinZ0cRMhQc70wOB6IEPJ0jctJJ7ZtXu/RjQqWCIqk22S64lPu1HFZkQRRqz8nFQTArS1usJV7LEq3fbH5LgubeOrxxLgzTgye88EGfF73Hy+JGoymlus5pgpW9FlxkNTm206lob9pt5IHPTc3A3sfLEzHqUSpxWKkw4252RId0G4W2rYkeXXyxVJ2V5VDhuV3KladgCIFrVCkLK44KeaR1QPd44Tpk1uM3Ss1RYfYU+ufMzI49hp9Q9oDkEqsQfgcZy4YisZcc/DP8Wmnyzafd3j4oXTV2UZKSdS4D6d/+DJ2Pusfuw9UvtasAoXSyyFp8iTb82I1kKQ6uKtWoditkk/SA3QfsJ+zDunKU5KbcVe7sNsi/Wx3xCtHM99xSkQWLpcfuSofQQOZ9/hjKZrDcb0kQ2wAkfsHBJtur8PO5xqUdxJkzJq1XSlXZJP1QkXP34yV5xyR6Snau7LXkZKgj6iTPNhgG/AWnJl5x40PmQ6gjiE9YI2sRToX68bIKa4besVGS6b8grSLfDGP8A3Zic2caERowVfiFI761WSP63wunPGxeoyH95U5wj6rXcA/ScAqPU4Z3caQT4kA/HCK6xBuQ24tzTz7NBVb7sKN0qA2q4joUfFQ1H7ThwQlGyRYcuWAZLMeontYkwodbHtDmPIp64bJYUiYI8wK1PAqStl1SUqsN7pvsfdhaSGl1SOlkDtUkqcUnmE9L/HBqmLOxHU2CxISEHxBvq+7AceiuxG1SYktwaElRQtRWk29++OzndcRqc2SFIKFpt4G1x9mD1F8IjKjoGp14FttPiT192GD8klMeBGR2iWilAtyWpNtgfAW3wDLN1capkKXKDqklEUoSQLkrXsm3nyw4oFFkU2gUuIhfYvxoiW1tqOpJJF1A2PO/XHV01DjklmqU9yW2+koI0BSVX9rb4AfDEarKtLbCVpbzC202mwaFQcCAnw0heAUpkyXEqqmiy6sIKm3EIOrkCR1xJOl4w3FrZdQVTATq2KxcW+HT4Yq2U5TzkFmS+6446i4CnValWQ6QCSetr4tGYpxZU3HaJUq3aaR1PIDAQM5XaOUb1m5bXVWy73vqtKWi/lqB+zFpiuluI5VZCj86nXbwT0HvxUqq2lMyl0llYclMvmdLA3DaUoUlCT5lSxt78W+YnTFZpqAFKes3v9UDc4BSJHfKzLlKJcXsE32QnoPf44eG4F74byZjMNIKySpWyUJ3Ur3DCIbqEoBbsj1YcwhAubeZOAfXO3O5wVbiUAla9IHMkgDDPsKqi6WpjTgPJTqN0/Zzx1FLbcUHJrq5Cx9bZI9wwCvrsRwEJls3/hjFZ4jZ/ovD7I9ezdVKpDjt0eA7JLjzncCwk6QbXNirSPji0GFCPdVHaPloGPOXpuZ0iZQ4ZUvK8WjIq0vN2YYNOTRWYxfk1CKhwOyW2Gxtq0I9okBN7k7YAnB6is5d4d0mnVNqnN1ae25VqsiKClpydKWXpCrKuTdayL7bAcsVPMPG9NRrL+SuAVDcztmOM4Wpb7b2ii0pXImVJPdUoH+1t6lbdMM/2uOKHGzTI40zxlTKqz81kuhSfnX0DkmoTE7r25tNEC9+8caxl/J1CyhSGKBk+AzRaZFSEsw4jaQ0hI6AWvz354DNKF6P82q1uNnfjXmhjPeYWCHYsZ1lSKPS133MSGdrg7dq4VLPiMa36k6r8ZNdCrWBQbBPuHhjo+UmbkpakDqpJ0KO/W+2OGeEbPxX2h1Vp1D7sABKejgJnNrP/CoF0kfow5ZdZeTqQ4hSb76TfbrfHG3W3WyphYWm17X/ANbYZupAnK9XASoMKC9I235X88ArTT+CWHILUE+65wwqsSBUH0ufKsiLIaaUzrjuaboO5SSQRzAw4aW69FRGjIWyiwC1qHTrbCq5EdsmKzFU8GxdaUpFkjxOAjoNPZYjxYsRlJK2yoJULJQkcyQN1KPib4dIabjvBtlIfkhNgLaUNp6H/XfCylNlyLMYPcUpTQ8gRg1OTqhJe21PXWSeZ3I/RgCONvFvVOmIaaPtBtOkHyKjvbAQtMn8Ep6QhhO63E7C3gD1waOhE15yWsFbaFlDY6ADyw72AAP2AW/NgG/ydAI3jJV5q3OFGYsVg3aYQm/OwwqbczdI92OEhKdSu6B1VtgO90WCiN+gF8R7PbKjyJ6U633CoIv9UbADCinnZalNRO42Nlunl7h44ULjFOZQwkEqCdKGxupR92DEztG5nTkMrfUpBKyhsWJ5qJ9on3csSS1pSUpU62kqNhqUBf3eOMb9J7M2bcgcGMwZ1yhUU0yrwy04qSltDhiRFOJQ84lC9lLShSlAWvcY8u58rHo8ZYZdXnniXnbjxX6i7GapMpNReZiUTtk3aBfYWllt0nvdVm3sjGLzFI3mUuHDfPkpjx97TER85e+xWKK7OcovyrCcmtJC1xO3QXkJPIqbvqA94xiPHD0vuEPArtaY9Mh1TMC2FKYp7D4ALnJKXlC/ZpJ58/hiH4behRwTyrCi5xzNS6jV80PtiVMqcirSlLU4sckkLBUN7b3ONQHAjhVIhyIsrhxloNTI/qz6Xaa28443v3VuLBUdj44j58l9prO0Ohy6LTc85onJkieWI7Y/W0zE80zHhERtPm8vZEq/F70uOG1azzUM60+SwzWSy/lWHFKGKW2whRBQ+Rd5w7KBBIIPMHbHrrhhWW6/w+y9VUS25K1wWWnVIVchxKQlSSOdxbe+HWVcn0rJFEYy1lGj0qkUmNq7GHDiBptGo3JsNrnr49cUSXSGeGnE3L8nL8ZESn5sdkRKhCZXojuvhvWl5KSbIcJTbbnvtipfHGnzTqe+/SXYw623GuHV4JeIj3XPfHMR0n4fjrae/wAUR038enlMX7POWlZxyfVsrN1BUFVSjKYTICdRZJt3gOvLl1vik5GzJOyrLVwzzpNgU2o0uMhyDKuEQ6jFGwUgKPzakCwUi+3MYns48UaTlF5qk/J0qp1yYlRh0uJZbrpG3etcIFyASq3XFapfCup5sls5n4xx263UktLSxS1qSmFTkrNyhKRutdrAqJsemM5rXtmicHWY6em3z7M8Px0w8PtTi88mC+9qRt8c3223pHTePC28xG3XfmjZK5p4jcMKNNaazbXYDMvQCh6I6px1KOiiWrqAPQnDHLWdq7UnplYiUapxstR0FTL1Xcu5NQDZRbSRqSLbgq54tWX8l5eyVCeYy3lel02Mu6nUsp7y/C5N726C9gMSOZmzOy/KbQkfONhWnfkRuPzYsY65ebmvLk6nUcNrSaabFa1v/fe0dPlWO31tf02OorDLb6FNzNSOyPZNq5hKt/fgtamCFAUpFitXdTceyfEYjYEntIMeSGgp9xLT4Uk7lJA1DfwO2GcqRKrM0IKbJSohDYN7b/f54nciOkbCxI5UgSFbrcV3lkb28beGFJVUOgQoCFpbB2SPpHqScOapJEVhEFrTpZTZxSfzXwrSKMlxKZboJCxZKQdzfAY36UVPgs+jxnGbNaU5IYjsORHALdhM7dsRltkb60uKTYjHq6m1GNlqjUiPXZL1RrbkJpCw0jW9IcCBrWEjkL3N9hvjzb6Q2Xc28RMhCicM6c3LeolZp9YfZU8GkTkxHw8YjaiCnWpSE+0NNwPHGz8BOKXD/iZl2fXcux58Gs018Q8xU+roKalT5YSCWXwfI3BT3SNxgLgum1nNRBrzS6fSxygJcBcf/wDCqHIbeyk+N8PRT6U3PEmmUqIuc2jse3S2AW09BqA5WsLDDxLb9RBU+VMxyLBu/fWPyvAeWFXX41NQmMyyCTshpsbnACPAbjXfkOhx8jdxe3wHgMGVU6egX9bbuOdjfCLdOMkdtUQHHDulsnuoHhbrhxIVHgx1vdikBA2CUi5PQDAHZlR5YIYcCwDY26YO5sLq3T1xEpfTHlGXMUDJKLJYaBJSD4+JxH12tyELZgiIXpcoH1eAhe67c1OqHsoG1/fbAS0KQw3IlLZcSmIgC6tQ0Be+rf7MQdUmDO5XQaRpcpaiBPm2u2tAO7TZ5KJsLkbAE9cLRsnplIbezNLXUlJIV6sQEREK/JbHO3QqviRddZdZLTK0swWdlqQLa/yU+WA67LDramIag1HbGlx++wt0T4npfDCVJSlCEpSEtN27NtXT8tY6k9B1645KeefWhDaChCFBLbYsNJ6fHrvyHnhM091xkykNKdSlWlJQLlZPNQ8h588Aiyy7NfISCq57wVzP5SvL/UYfvw004sBL7SnBdSgoFSlK6EJHO3nhNElxposwkhlsHvqSoE38VL5JPkL4KGQkdq4UobUbanLgKPkPaUfu8sANaVrKnVKWkbLKnPHopQ2t+Sm98La1IKXnnywFCwcUmyleTaPojzwtGgPPaVpQWtOyXXkjUB+QjkjElGgR46itOpayN3Fm5JwDSK1NCOzisJit7d93vrWfEi/58ccqWimPuSykOBTjKdIsFkXAth3PliKyQkannBpbSOaj/oxX3SBLQhCS8iOi4urZayo/+1cfDAO0pWmOhwoIU8tlDI62TzPkOeFawUPOOKaGox2FlahyvtZP6cDsHTNEftj26mu0ckWuQL+ygdMKiMhcgU5saWWgl1z6yz0wC1H70HST9NYv474h+ILZkUmLAZF5UqcwiMRzStKtZN+lkpViYgENTZjKbaElB8gop3GIfMb4l1+hU1B0rjvrnvKvshpCFJufC5WB8Dh8mJ6QhuJ0UopC5CkhUcPNSX2tVkupbPeSfeN/O2HNYQw9lOOG2ghl6fH7FGmwCC4LADptiMzZV383VF7KOXfVn2zGUZUlSwUMpJ0ki3NVuWJSoNFmlZepfaKdSJ7YQTzWhpKlAn4AYu5qe701KW6TvM7ek+algnn1N717bRH1SGpbZjyCO8pAV5bLCP8Aoqw7pqyZEUjkhl0fALsMISRZqEkkH5hIPxWk/oOOx1dm28tJtphm3kVKVbFJeLNgPQ4ze49ckqcVbqm5O/wAxmL2/pKSFWsf2HabjwE82GNXbQhuVGYJsIkcr+3b9GMouD6Q6XOruSi59tQOAQ4CzUR82caW22lOvHiFIIQgbn+t8LryGNiQirP95x9uODySlGoj3k4yb0ewP2V8aTa5/bDkf5vhY2RT7GrSp5AV4ahfAR8hEqnBEgTXnklaULSsAixNrjww4myn0LbjxwkLeJAWr2Uj9J8Bhw802+2ppxN0rFjiKfDxbcpqu+6wlLzCzzUAfzj9OAcnsKUz3UrcdcPh33FeeCqbdabVUJSVOOIBKWk+yn3eeOks1JpK2VlDzBBTfYpV1B8sGSurAbxoyiDzDqhf4WwDIJ9ZjuONykOS3xZW+khPVAHTCjjZWloN06ax2IIR2SkCw+3Bnuy9mqU5tCTezqTqSPediMKpp7WgLgzHm+oKXNST9uAaqS2lWuW3UUJuAVrdFk+exwlU6j8jxZi+2U82mIt9CVHUokAnY9RYE4f9s/Lp8pCh882Ft6QOfhiJmOxez1rUhQWhtLjarpWgpHT81sBD5YpjjFIprDikdo/HQ44sbguOalWB8LnElKAj1BUqc42ewQt1xY5JCQSPsAHxOIzJKX3MuQ3Y4u6G1utNBWwCHlFKLnwCiMDM6FGGKctd3qpLaiOHrpA7R34WATgHWW2BGojEtTATMeaRKkrtdTkpzcA+Ngdh02xPuIdiSmktvrkyVo0pQsgJQOqsJLbZhLabIu3DaL6z9Zw7C/34WQRCaDhb7WdK30+fh5AYB1HhJjqVKkLDj6ubh2sPAeAwVyqRgopYC5ChzDSb/aeWON05LqQue4p9Z3IKjoB8h4YcqXFiJAUptoe8JvgG3rdRv2hgJ0dEhyyx8OWAKmtWzdOlFfgpOkfbyx35Viq2b7RduqW1EYHymyD+55Bv4MKwHNNVdOpx5pgfVQnUbe848quSZXFb0qcw5kFRTJonCmnpy3T1FsaVVeWA5NWnzQ2Gm7jlcjxxvPGHipT+FXDHM3EKfEk6KJTnZLSC0fnX7WaaHmpwoA9+MU9H7KFcyTwspUGstINcqynq3XZDybLeqExRdeuPySvT7kDAaKYklw9+oyDcWOlITceBwb1FZ29ek/5Y/VgvZT3O4p9lI+uhCir374I7Gdjtl9iS84tIudRuF+VsAfsJrRs1MSs8gHk3+8Y7Fmdowp1xIQWyUr8NuowV91UqMPUnklSrEjVbbqMJuB1cb1Y0pQatY2c39+ATKlJ11NEcgadLaUj2gfpnDuGlKUlYcDpdOpSunu+GGiFyEqDXrElkrBCA82ClRHS+FGmG5jSZSVraLwHaIQbAkeWAULzspwsxFBLafxjvgfBI6+/C7LSGGg02DpBvvzJ8cJKkxowEZsaljYNtjUR+rCjElEgK0ghSFaVpULEHAR6VBKUIHstztKB4i98O4wDT0iMPYBCkjwB3t9uEezixJKnnXVLcWpSm2QNwTzOF4jbpUuW8nSt63c6JSOWA4uKtCi7DeLRKiopPsH4dMAipvJ0qLLQJ7y0EqVb3Yc7A3Iv5YjqxOSwx2CXiHFGx0X7o87YBx8nRz+OLjxPVayfzY6mnQkkKDA28VEjCkULEZoOe2EjVfxwocA1nShESlLKUJUQSPyEjmq3X3YRYiPrs4tzsNZuVabuqHv6DC1RYSpovJALjVyRbYpPtYEN1TK/VnTq0p1trPNSD4+7GY69GJ6fFHg8j8cJ2ROL3G9fDHPTjSckcN46Z1cgOOuJmV2pS2ymNHZbQdakNghWrlqIxp/Cngjlll2DVn+HULLWW6O22qgZddSlwsvAHVNfFiC8QRbVcgDDHgzwZyHSuL3EnOVbpTlQzo7XFyjJnLDwjxHrLjhm97AJFh1Hljd5qyVpit3CnvbI2skfr5YpUj8Vte35XodRenBItp8NNs1o63nrtW0RMRWO3xRO8277dI23lxketv+tq2Zb/ABQI9pQFirDu1wLjpgvzbYG+htAHM2AGEPXS9dMOOtzfZahpR779cXHn+3Q6Cbjb89sQGdskUPPtBdy/XWSEFYdZkNHS9FdBuFoPRQ6HEoYsl+xlSyLckNd1I+OB6u8xdcV9RI3Lbp1A4xNa3ja3ZNgz5dNkrlw2mtomJ6ecT0+m3SUBkjhplfISFu0iIXahJKjJqMg65D+o3OpfO1+mLO8tthBcWuw5J23+GCx3u2ZS6BbXcKA6EcxhKYhztGn0NF1LJOpI5+/4YxjrXHHLXpHozqdVm1mWc2otzWnxnv8A7enh4bC9k/NUFSE9lHG+g7qUelx0GOvESlpgsoBCVJLtj3Ui4sm/nbHXXlyoq3ITl3FctrEHqMRNYkoYoa24jq451tJkqIspLSlpDqr9e7fGyBFsyCimNxGF/NCVI0LT1QHDax6DDtKDAQpTdhZNtfUK6gYUrMKPGZZjxGw22wwkIHgCr9OE6qe3mtxW9ki1xb6R64DlPhSJ2uSSjswNSis2Tbzws7PbcjGi0GSJkx66VONHUllB9pRPIAC4tub2wpKjN1Ge1l1vaHCbD8xCfplR+bQT13BKh4DzxNpbjRGbIabZaRvZCAAPgMASLGYp8NmHGSexYQEhQ22+t8evxxmXDyS1lb0zatDdTop3EjKjU2PdVkLqFOdKFkfWJZdQR/AONICTUAVvam4ySSEWsV36nGV8e3xk+u8L+MCSyw1k3N0Zmc6sWDdPqCTDdvboFONH3gHAes5kv1VkFKNTizpQPFWBBhCOkuOnXId3cX4nwHkMIwmVyX/X306Rv2KPAeJ8z+bEgNhgBYc8NKmlC4ToW4EBI1BXgobjHH6ky04WWwt529tDYvb3+GGL7rmvtJwSpaBqSwlXdQPrrOA7rkMrTIQjSqcje43S4E7H3bYh8miKmks1UNKl1aog+turPfU4glK036ISoEADbCC5szMa3PkuUpqIO7Jqvsp0D2m4w6+BXy9+HjTEaIhMSCwpuO00lptpJsdF9hfxUbm/UDfAP333ZAU286XCAQWY3I+IUs7YbGQVuNJABUgFLSUA6EfwB1PmdvDD5dPDcdbzqFPrQLiOk2QPK3X488cgtTw3dlhLa12K3nhY+4JHIDANZEN2Mwh5xhRGqxQFgaUnmVE81E8yPdhVtMypBICUlpO1gSlkD86iPsw+XEaaHrdSfDykG6biyQfIeOChMmejU5eNFtfTeylJ8/AYBq2ygu9jEAkutmxecFm2vIAbX92F+ziwXA7IUqTLXyNrrPkE9BjqH1uJESkIS2yjYvEd0fwR19+HDUaPBSp11y6z7TzhFz9uAIkVR25Bajp+iCNZ+ODRZjzsRa1hK3mtQUlO1yL/AGXx01Wmg6TLbJ9+GzyqRKcC0y0su8wtC9J/UcAmyyZ0Nb5lJXJfbKdV7aL/AEQOmG8lDjYu7D7MBCW1BfeaUAbg3G4Pww4cpjyvnAIkkK2upOhR/wAJOC9klC9DfrESRY6ELXrQ5bmN+eAbFRdCGVLXfdLZJutte5TZQ9pJth2xM0uPTXBc+qtrI8T4D44jGXQt8utgAIKVpCeQ3TsP8pX24VUoNNrZCu8EdmATz0O2H3YCahMLjRSp5Y7RZK3D5nxPljOswVQP5WzBmJavna6o06nJHtKZTdCdJ/KOtXxxYG/Xc82W4pUXLxspDYJS9NHir6jZ8OZ8sI56or641JlUOM16xSpHaMR7WQoaTdFuQ2H24saSK2z1i3zVtXea4LTB5R6DDo0GJAgwWYjkptCX3G2wCQEC4J8cCuIKsy0iMPYYiTH0jzCEoH/TOFaRmCHmanwqhFQ40RK7N1pwWW04AQpKh5YLmgeoVSj1kpJZC3IL1uiXgNJ92tKftxDl5/e297+bxjySYK1rjrWnaCsgJ1R7XsGWSB8TgMgLiLQdy4qM1/g2B/ScddbUY7SyQVNxASR1KFC/3Xwm0eyCwk6g2UuJ/wDJm3/RIONUp5KUornuA7/NxwfAG2/34zKclSPScWgewnIbaU+H7vONRlBpp5xTqvwWcACsfQXbYk+HLGWyS8PSRSxISStGRh3wdlD182wCHAKImTm7jQp55akDiFIHZhVk/wC18Lwxs3ydA0afVGiOt0/pxjPAKQ81m3jQhmI66TxCkEkEAX+T4XjjYlT1tDXKgvNJHNYsoD32wBFxnqeC7CKnWxzYJuSPyT44MoM1NpEiM4UOtk6VAboPUH9OHTLzL7YdZcS4k9Um+Grsd+O8qVCIUHD840TYE+IPjgG4Q/KbEppLbU2OopWPonxB8QRyws2qpyAVgiIkGwSpOtRwiwZD6VVGD3FLUe1YXuFFO2x6HEhGkiTHRISkgKBNsAhGefLq4kvQtSUhWsC2oE9R0wWnIbRIlltOhPaBASOQsOg6Y5TFiQXpjhHaOkjSD7CRsAfPrga/U6ja4LcxW3ilYH+jABTioMp59bV2HlJutJ9k2tuMM81p1xGAgDUp0aT52OHtSX26mqe2bl1d3LfRQNzf38sMq0pMlaY6L6WVIBt9ZRsB9mAgsmLVEMmKrZuJUH9Pk24gO/cScckp9czVSWV3PZMOTSjpqdcCR/6N8K5RiMTHa2HlKUlFTumx2I7FBt7rKthOmPKnV2oV1CbtkiHDH1kNnTq/y1Lt5DATqEmQ+hKkjS9LWV+Glv2R92HsQF2dKkKt3SllHuG5+84alaI6gEKJagpJUr6zqun34OlLzbLdMZd0vKSXHnLX033PxudsAs7KkTHFxoKg2lB0uPEXsfBPicKNU+Gxdx1KXFHm46b3Px5YKXmKc2iLGbW44R3W07qPmT09+Ct0/wBZV21SQFrP0AToSPDzwDkzIaSEetNDyChhfUkAK1Cx64QMGEE9mIrQSdraBiDzROoOUcvVHMuZKwqFQqVGcmTFuuANtNNgqUSedrDlgMF9KGqR+IvFHh3wCjSgYaZP7NcxhJ7vqUJQ9WZWeXzsgpsk8+zOL9ImMR+84oqKvZQn2t+Vh0xkXA6lzs81rNPpB5qoy4EviA6wijwJCAXKfRI4KYyD9VTt1PKHQrSOmNbbiR45JaYSlfI36e44BP1xSbLfhvIT9ZVj9wwoZkYjWiQ1vz74vhYpIFiCfO3PCYixgSTHR/FjANVJpDx7ymVK6WOkj7MKJp7QIcjOONkclBdx9+FnGmFizjCNtt0DbCC4LbYU6w6phaQTcE25dR0wDZ951L7TM1QPZuXDmmwIIIv8CcLMsT0sNxnFMtJtpK07lYB5+WCrUp9uG8oIUtSwgJI2UDsb+VsLQLJbdSFHQ28pKdR5JFrYAy+ygMlbbZ22APNRPnhu246C41HIckOKCnV/QaFuXmcHmPwpCTEIW+VWKkNpJ5efTAaYlPd1tAhtfVSQXDgOpEaAmzryi6rdRAutZ8h0x1K50hWpCW2G+gcTdSsKJahxT9AL+utY1HA9eiE6RJbJO2ysAn60rsXi6nsnWkkrSORPQjywgtpLNNbAT33VNlZPUk74UlobdkMAjZ1tbJN9txt9+CIEmWw1EcZW2EhJdWsW3Hh48sBIqFlEX5G2OY6SSSb3+FscHl0wDaapagmI3+Me7u3ROGvbKXMaMZkK7MqaaBNivlcqPQDDprvVB8nbShKB5eOGV1x5y0dk442AtSggXIQsdPcRh1jrHgzXraI/qI81G4PtIqVbzznpIZDdVq5YQGye+iKnsi5v9Yg2GNBZdS227UZIuXfYT4o+iMQuVMtxMqUJnK9LQ8lkrW4e2V3koUsqJUepNzidZSiU4HSLx2DoZTbZRHNRxBpsc4scUnwdHi2srr9XbNjnevSI/wDzWIrX9IgPVFOkv1FQUq1+zJslA6X8cDt5D6bREJZYTsXHBdJt9UYdLbDiVtKJKVjSSkjDYU1oJShb7zqEbJQVWT93PE7mkkTpCHgwdErVz7FFlp8z0th/57fHHEIS2nS02hCT9FCbYMAq10pv4DAMXkuwCqQ2UqZVuptWxBPVOHqehG3Ij34a/uqVYkKZjne/0l/qGF5LzbEcvOJ8glPMnoBgEWbeuyNPLSi/hq3viLzU6TH9TbbDj0ptcdtJ5LW4CkA+QGpXuTiXhMraQp18jtn1dou3IHoBiCzYQyqNNadIktLC2UpFypy9gi35QUoYAtYAZZbiIdW4WmW2Sq3Mptf47ffgjQBlxSq/fShxRPPmVYVqi0uSFCOQoKWFKAHdB5KsfhhnOmJaekPRwSdJix0DfUtadKAPznAS+XldtGk114hoVF4yEj6rYTZN/wDBGHzQVLWJLoKWk/i2z1/KV+gYbQ4x9XiUgkFENpCXynYakj2R8d8SlkkAFIA5AJ6DAEccbaRrdVpQP9dsZ7xyyZJ4l8Ic25ZQkhyXS3lwGzsoyWx2jKz7nEIIxemUCY8489coaWW0I6AjmcO1FJ/GWKLbp6W638Ra+2AneAmfm+J/BjJvELtEqcrlGjSnyBYB7QA6LdLLCh8MW5bz9SUURXC1GSbKdHtL8Qny88edvQtkOtZOzlwpefQlGQc5VKnJS0SNUR5YkxwnqE6HvuIxtGfM60DJWWajmavzm6fQqQ2DKkElIJ1BIbFgSBcgEjASFTrdPo0YoYcShJUGwu2tTjnRCBzWs+GItuiyqqDOzPrjQLgpp+v5x89C+oe1/AGw88Z3Q/SF4DMvis1ridS3qktOlKUsvdlER9RsaPtVzOJWN6TnABxz1yZxNp6ngSAksv6UDwA0b+/AaQ4HXVNtts6E7dhFACQm3Ja/C3QYAQqNLCF0+Q8lndJSgELcPNV77YzFn0mOCDa1kcVaQguLKlOpjvlah0G6NsL/ANU5wPHPjDBI8orl/wD+XgNVSmrvXWVMx0nkm2tXxOOKbqrVy3JaeP1Fo0j7RjKl+ktwBdFnuLcdY8NDyf8AotjHB6Sno+pGlPFiOlv6SNL5v8Si+A09ZCHUqnvesPD8XHbT3R8OvvODrZceT21UdS0yNwyFd3/CPX3Yy1PpR8BWh2VN4hUppN7FxxqQD9hbucHT6THo9pu/L4nwZCxv3mXiAfJIRgNQS8/IAbgthlgcnVpsCPyB+vC7dMjag48FPODmpw3+7ljFHfS24UTlaaTnOkRWUnT289MhCj5paDd7e8jBT6RnCedtU+OtOZT/AHOBCda/9NSFH7LYDd0oQgaUoSB0sMcdZZdADjSVjwKQcYQON3ozLIXN4pJnODbVJkS1fdpsPhjuVfSS4D0yfVdPE9pFPU42iIzJTJVp0p7yk6kX0kn7sBs0uGmG2uVBWWFJSVFA9hVvHw+GEatJKGoj1gSFdr8Ak/ptjOah6UnAR5gsMcS6aVOjSSWX7AdfoYjqx6TPAZ5LbcbiXTyGmykfMvm+4/4PwwF9jdwOKUCbEX35gWP/ALKftxH11t52KmKVajNktRF6TzDjneH+SF4orHpI8Cmm9uJFO1X5FiR03/ufiE/ZhNv0iuBS6/T1vcSYPqcRLkgksP2U9slH9r5i61fHAbyyy1GYRGZSlLbaQhCRyAAsMUHMESTnPOX7HfWnYkSkMCStTTxS46tfskW5AW+/FJzj6TfBipNQqVSeJsJpuW+EzJCGnwWWQLqsQ3sTyHvw6y3x79GfLJdNN4jQAt+3aOuiQ4tduV1KRe2LWGaYK+8n80x09PVSz0vntGPb4d+vq0nLGVmaG2ptpp1CC8XnC46XHHnSLa1E4WzrY0ZpF/anxR/61OKb/VUej9/fNp38U9/QxB5u9JrgRUKY01E4kU9xaJcd3SGXzslwEn2PAYq2tbJbnvO9p7z5rdKVx15atVaCe3abV7Bcfj/aScM7KjPFlRShxqwJVuk22sryKQMUA+kxwAWuSwvibTw26vtW1JafBSr36Od8Jv8ApO8B5EZQkcR6YJLQuhxDT5BPj+L+7Bs01l5TQ9XQAW1Cwjvjb3IXyPuOMwV2afSOKUB1BRkkJLLir9n+HnYHrgI9JfgKoho8S6clpcntCnsXwEpTuLfN9SL4gMp8RMk8SfSQnT8k5ij1ZqNkxCHlMpWnQTONr6kjnY8sBZfR5/3Wcaf+MOR/m+FjZyLi2MY9Hn/dZxp/4w5H+b4WNowEZOZRBtUmRo0K+eCeS09dvv8AhhzIUtyI4qLupTZ7Mj3bYcLQFixt8cR7kN2AA9BKi2k99kquCnra/I4AkCVD7BuECptZTpKFgpJJ5/fg0V9yAlqFLSNAGhDw9knwPhhZxpmpRAttdwrvIcHNJ6WwmyUz4yostA7RPdcQTvcclfpwBKg21GeZmpb0qLgC1J2unzxHJkOPOmS9IKFIQXFGwJQL20IH1hyJxJxXEPNLp006nE90hWxWjoR8OeIqXT+ycdSDuylJKj9JvkD7wNvMeeAcoUuMCO0YiLWBcuK1unzJ5YSfU0xF7ZRW1HbUV6nPbfc6beHXCLLzkOU24+kKU0nQRa57PmBf7/hbElT2m5Lj7r57R5CiApRuNChdNh0BBwFRo89+lZZEiOhJq2Y5KpEZk8k6gAFHyQhIJ91sStJhppMCNHQpTvq7eoKUN1ncIPxUVK9xwdGWqDQ3S1ToxaJbu4suqWpLVxZtGonSFHawwv8APKUEgDtlLB8g6RsPclP34BW7TSW2j32YxF/+GfPTz33w8FqezdfzsyUq+m/NXT4DCDRZaQualN22AG44+srx95Nxh/EipYQZD6kl5SbuLJ5eQ8AMB2LFTGSp55YU6vvOuHYG3T3DHFVRpZKYbTkgg2Ogd0fE4RJVU1arKTFQdhb8Yf1YfABtICUgJTytgGjkueBvS9j/AMMkHHlfjdW53pC8UEcBKaWv2FZPcjVPPMlpSimTMuFxaUCNiDYOOjwCRtfGp+klxvXwqySzEyshmbnfNMkUbLEFXeDk1Y3ecA3DLSbuLVysB44pXCbh+jhnkyNlqnyTUZ7jjk+tVeSmzlRqTqip+SfrXVy6WAHTAXIU/tAELfdUEgJDaAEISBtYAchtyGAKbCtYIWR5uqOGq2A+sMNSXnHAbuOJUQls+VvzYlANICdRNtrnmcA2FOYt3S6kDwdVhGSzHYSLKeU4r2Ehwkk9MLzpC2W0hu6VKUElXRPng0VhtFnQ4pbq9y6d7+QwHW1KYjJMpy6m099ZPM+GEm2lzT20rUlo+y14jxV+rCsqIJSEguFBSvUBa9z4HBY7z5dcjyClamwk60i3PofdgG5LkZ9cuTFKkp2aU2bhsDy6YXciQXbyFJv1UrWbeO45YXcbS62pCgbKSUmwvzGGaIKwlKZUkuttAWSBYWH1sAdMpFuzp8UuJ56k91F/G/XAVHecT2kyWEp5lLfdH+VzxwyHXPm4DbZbTsXVbIHuA54K61HaIXOfL7h5J/8AdG2AIl2mJNmGO2P5KSv7zhQvxwLPw1toO34u4+7fB0yJyxZiGhCfF1y1/swNdTNz2Me45XcUP0YBnIDSG9cR0Kj6hcA/iz0Puvth4motXCJXzK7XJUO6fceuGr61dqVPMpaeQtKHLG6XEK2Jw6hpKC9CdVrQwuyde9kHlfAGZmxpDhabc36XFgfdhZTiG0LUTYAFROGy0l2ooAISI6DfbqeWCPEy3lR7lDDRSp5R6q6D3YBaGhXZrkLAu8rWfLwH2YSUma3JW8xHS4FpQlJ1ABNsHFRirUEJJtewIT3ftxyU0H5UeMvvIUlaiAbXI/RgDIh3YebcdUXH76nBzHkPIYKgVCOhLaGGFAJsClZTf4YIWlQHmlRnVaH1aVNKN7eYw+vckDkDgxEbdINu3npGpyG2pA5hDm+AKhFP4xfYn6rhsRhxbHChCvbQFe8A/owZILqUUDS0fWVHkG98FLcuQSHD2DZHeSk3Ur49MOQhCPxbaU+5IH5sdwHGm0NIDbabJH+t8NUgS5KnTuyyAlHgpXXCkx4tMd38Y4dKB1JwqyyGGExmx3Upv/hHAI1Cc1Tob06SQlDSCSfLy88VeIxMqcgVSe2DIeFo7HRhJG1/yldT0GHWYlqqtTj0YbxWCZMi/JSW+Sfirb7cPG9UZLrqDZ0BtAJ6KXz+y+AIZMWOpVPacQV2KXHCm9zbkkeAwyhx0xaxGW4gXjwHpCARtrUtIvbyt8L4YMQY86VPSQ4H4jbPqrwv8ysjUSfInYnwxJxpjlcrUAOQZLTzEZ9uXqYUGwVBPJVrEXTcW8cBPw2i1HClr7yjrWrzPTDge/DNL78FKESWytlIsHUC6kj8oYdoWhxIWhaShXJWAZNLktLfaahlSg6tetRsix636+7FN4ocTKLw3gw11iPOrddqzhj0TLlKa1y6nItslpA5JFwVOKslIuTifz3nnLXDPKc/O2bJyotKpjYcWpKCpxxZNkNtpHtLWqyUp6kjDL0bOFOZJ1YnekLxapyY2csyR0sUqmOC4y7R76moqQeTy763lc9RCeScA+9G7hLxCyjUc6cUOJTtPhV/P7kaS7QqZ3o9LbYaKGm1Ond53SQFr5XFhtif4/MMvcBa2ytCV6ERiQfriQ3v78a0hvTfvXubnGV+kjBLPB/Mr7DuhDgjqcbtcK/CG/swGgVmo5ay3S3q1mCVAp0CMEl6TJ0NtNgkJF1HYbkD3nDwM05VimOyQoXSQ2mx922MR9N55pn0Yc6OOrQlDbUNSyu2kAS2Tc3FgOvwxRvSX45VKNQ8g0jhLm+myItdrKadXKhT65FimO2IyloZ9aWFojKcUkWUoX2sNzgPVKo0BNyYzVh/wY/VgerQLgeqtC/K7af1Y8OQ+IXF52q5E4bcZOMKMlUWoQ67UBmKl1eM87OVHdQIkVyb2fYlxCFrK0pAK+zHicUmBx347Zuy/wAOMtMZ/mPs1yHmCcuuJq0Oiu1N6NUFMxkB95st2Q3ZZZSApYO5sDgPo0YkEf8AerXu7Mfqwzq0zL9Dpkys1ZUaNCgMLkyXltjS00hJUpR25AAnHiqRWvSGzXUavQsycZJ2X6llfhfEzG6nLzsZ2PLqiVvWf7QtnW2oNJKkpsk3I8MVzNnF7M/FHLWbns/caH8jx6VwvpVZpkCMthlFbkTYK1yXlBwXeQVjsg2g92/jgPfNEmZfzHR4Vfoa4syn1FhEqLIbbGl1paQpKhtyIIOHvqMP96s/xaf1Yz/0b7/tAcOtVr/sZp17Cw/EI8MaPgEPUYf71Z/i0/qwPUYf71Z/i0/qwvgYBD1GH+9Wf4tP6sD1GH+9Wf4tP6sL4GAQ9Rh/vVn+LT+rHPUof70ZP/k0/qw4wMA39Sh/vNn+LT+rAMGH+9Wf4tP6sOMDANhAiW3iM/xaf1Y6IEPn6qz/ABaf1YcYGAQ9Rh/vVn+LT+rA9Rh/vVn+LT+rC+BgEPUYf71Z/i0/qwPUYf71Z/i0/qwvgYBuYMS20Vn+LT+rGP1Bltn0mSmO2hsHIqbhKQB+7zbG0Yxup/2TP+Iqf5+cAn6PP+6zjT/xhyP83wsbRjF/R5/3Wcaf+MOR/m+FjaMAMcPgOeBhvMltxUdo5ckmyUDdSj4AdTgG8EdjNlxUD5sFDgHRJVe4H2X+OOVUJZb9ebGl1kpsofSSTyPjhanMuIbW7IA7Z1etduQ8B9mEpV5slMJG7Tagp49PJOANUo6XYynUgJdbQpTaxzSbX5+HlhlKdW6pTradSn4JKRbn4j78PamtfYCOhXfkHshtyvzP2YTUhIqEOO1/aG1En8m1sA1qEZC4bMyMoFKUpQo9bA7H4H7r4QoswtyCF2Sgq7BYP0VAXSfjew9ww7YCfkiYpWyCXbW5AC/LEcw1qfdjHZchGofwgApJ+N1fZgHlRUETnFLSCCWVfYpW35sN2kqeS20lwgu6UBXUFd1LV77bYM8/6809LbF0tstFe30go6h78Jw1lqUjtduwdSD/AJJRf8x+OAlGWkSJSUN2THg9xKbbFyw3+GOvk1J8xGzZhv8AHKH0j9UYNTroMmMoWWh1SifEK3BwKQOziqjKPzjK1JWfE3vf7MAtIksxEpR7SzshtA3J/RhAwn5YLs55YBOzLa7JSPPxOBTEpUyZTm7zi1a1HyJ28uWMv9KXidUuGXB6qystJ7XM1eW3l/LsdPtOVKWeyasBv3dRcNuiDgMiyUuNxd48Z04zvNB+jZYdXkvKYWkFsJZI9ekt9Dre+b1fVaIxr0htT7LjKXNBWnTfr9uK5wzyJB4ZcP6DkKnOqdaosNEdbqiCXnrXddJHMqcKyT54s3lgGcaQ2whEN0di4jYbd1Xnfrh5cnmN8FdZQ+gtugFKiB7vd4YRhrKErjvEqUwvST4jocAvpB2UApJ5gjnhpIhtsNKfjdogp72hCrJPlbDzCciS3Fbu4Cok2Sgc1HwwCjbiHUBxCwoKGxHQfrwzbWuIt1K4zqgtZWFIF7g4UhtutBZdQltS1lXZp3CBhx11DY+WAbJ9ekbgtx0dD7S/s6YSebhsqSZS3JLqvZQSSSfIYflR2PnvhkSYkxTsjuoe2Q8OafLAGCZTpSlLHqrZ+kbaiPAeGDBMOnjtFlKVHkparrPuw4+jp0gA72HXDJxiUmWuSGGpBWQEFZPcHlgDdrMlkqYSY7ZGy1i6j7hghbcYltIjyXXV3+dC1XSE9fccKlNQkCzjjcZKjv2ZKlkeHlgj7YiRwzEBC3lhvXe5PmfHAEkBt2W4DulthQdPS97jHEXQmJL31rs0u/0kHlfzwZ9lLfY09BKUuqutZO5tubnzwtPRpilSduyKXR7gQbYDlw1N73syE2B803sPjgy4EVx3tnGrquCRfYnzHXBJ37lS8gE6VpX/AARh1fUNVwQRe4wBeybW12CkAo+qBZOI5TgZbU6lzV6i5soG4U2eYv4jDiasl1lhxxTbSioKI273RN8LLhsOR1REthDahYpTtgEYiPWFCe8CSoktD6qenxOHY2FsABKEhAFrWtblsLYGAGBgjkhhrZ15CT5qwGpEeQCWHg4U80p3OAPjijpSpVr2BNvHHcd92AZQih+0151BdUCAm/4sdBbxw8SSDv16jDabHiJZdeLSEqCSdQFiVeOAqQY0dpIClvLSAlI+tbf4YCArTK4mYY8htVmZbKozg8Fnvp+9Jw8ddVKYc7FAupZkE32CQkW+/DMRnqhVpEiQ5f1N0RGze6UuqF1n/BBAHvw4muQoDikoZW6Vu6WmE7qfdsLJ/gi2/h1wCMKGmDWn48xLvZ1MmQyUK7rwSBdo+BAOw64nAlyY842ZK0R2VdnZuw1KA3vcHlhrTaS82/8AK1WfD00oO1/mo6DzSkfZc8zh3DOt1+Q0khpwpKdtiepwHPk5lQIW6+tKuYU4d8OfmY7SlAobbZSVEqsAkAXJN+gGEXZYDvYR21PueCCLD3npjGuPdQzRnaqZW9HbKU9iLVuIbrjdXcauXYNCaF5jwPQrFmgfFw2wEjwpy2v0neJaOLVdaUrhlkmW5HylBUdTNcqLZKXKm4nktts3QzfYkFXhj1sEpQSRffFfyvkykZToFNyvl8vQqXSYjUKHHZshDbTadKUgAeAGJX5LaOy5UpfveP6MA8KvAb4zP0kbq4LZk230R9h/+kN40D5Hic0rfSfEPK/XjNfSMpzbHBrMTglSlFKI+ynSR+6G8Bo9ay/RMzUp6iZipUSp06UkJfiTGUvNOpuDZSFAgi4HMYrsPgxwmp1Im0Cn8NcrxqZUihUyG1SWEMyCj2S4gJsq3S/LFjq9apmX6XIrVbqUaBAhtF2RJkuBtppA5qWpWyR5nCEjMlGhop6p1agsirOJZglb6UiU4pJUlLVz3yQCQBe4wGecROA7GaMu0vK2RKzTMm02mOqdENrLUKbGKjuFoaeTpbWk7had7+OJDJ/o+8Msr8NaPwtnZchZhpFG7RxHyzFaklx9xaluPEKSUhSlrWdgOdsWeDn7J1VrESg03NNLkT5jD0liMzIStbzLKw26tIB3CFkJV4HY4sINrDmRgIWNkfJ8NK0xMsUpkOQk01Ybhtp1RE30sGw/Fi5sjludsR9S4UcMqyKcKxw/y7OFHYManiRTGXPVGSAC23dPcTYDYWGLStRFrEc+uI3L2ZqFmymis5bq0aowVOuMpfYXqQVtrKFpv4hSSD5jAPKbTYFHgMUulw2YkOK2GmGGWwhtpAFglKRskAcgNsOcFuRubWwASRzGANgYLcgXJ+7HQb4DuBgYGAGBgYGAGBgYGAGBgYGAGBgYGAGBgYGAGMbqf9kz/iKn+fnGyYxup/2TP+Iqf5+cAl6Pf+6rjT/xhyP83wsasJVRekPtRwyAwsJCVk3ULc8ZT6Pdv2V8ab8v2w5H+b4WNYiqSupyl6gAhKEAHmdr3+/AdIqsghC3WY6Oui6lH3E8sdbYp8NfauOJU4ObjqwVYdvIDjSkBWnULXGK82tLJ9V7CIh1kLLrjySRtbfzuDgJRyqtE9nCSX3DsAkHSPMnljrbsemtkSnwp5w61AbqJ8gPLEeZMp0WbkPBu1yGY/Zj/KVyGONuBn8W60y5/wAGkvuH3qwEgwlc2WmctCktNAhpKhYknmq2CgiRVELj2IaSUvLHIjmEjzvhsXpK099+etJ20iOlF/jbDiO7JabSzEpSkpA/ti0pv5m18AHGn4KHAy0l6Kq5LXJSb7k364iVqQzIuyTpTZxtXP5u4I3+Kh8TiZWuqrSUKjR27jn2pVb4acQc5lDUkIZVrS02hkqHK4C9sBO1FpDNMlBtITqSpRsOZPPENIsHpJuBpJuT0Hzdt8S9TWHIyYiFXckgIA626n7MMExFfKElkKSpa06tLidTa2zbnbcHYYB6VepynHlMqWw8lCdSO9YjxGEvWW233JEB5p3tbKcZUoIVe3MX64RUyY0hpl2zSnlW/BXlD4lOOTEdkspQ+3LXzU240ClI8Sr6OAUakLYcUhlaWw4dXZSAUgE/VUNjjzHnarP8WPSpi02Q2HMucHonaLW05qbXmKagaPizHPvSp7GycYOKWXuDXD6s8QKm2VxoUfsocJJuqoTnO6wy0k8ytdgLeOMj4CZDq+TsjRxmgIczNXJr2YMyvpVqC6jIOtTYV9LQkJR5BAwGpcjosNib25X8sDBH322UBxxWlIJ26k4REiW8NTEOyDyU4q1/hgHOEHo7hX28d3SsixCh3VDwwUCpHbtI46/SweI+6/2oeSk9mrTqTyPuwBPW3WDeXFUhP1kHUn9YwdxMaczusqAVdKkmxScKOvR2R866EHwvucN4mpch2Shrs2lgJSOWq3W2ACnJsVN3WkPNp+mg7geJHXDhCw6lLiT3VDa2OPPtx2y68opT1Nr4Qpza0NrUEdmlbmpDZPsj/TgHJWlKgkqAUeQPXBXW23W1suJGlY5HxxH1BBMpbgS4VpbR2BSm4BvvfEncKAJ8BzwDNMlyKTGfadcUPYUlN9Sff0ODpqMQ7LUtHktJGHXuxxQCvaAPvwDVdQaVZEYlx1w6QmxAHmcGaYcW76xLUC4PZQkWSjxw4CQk30fdhOTJbjkBaipw+yhI72ALNaLsdRHto76T1FsBRTLiFN7B5u1/AnrhLspMlJ9bcLTaubbfM/wj44d6UpSEpFgBYDywDaLIDrYju915CdCkKHtdNvLBHG3oKFLjuktosS04L2HkemFpMZEgC50rG6VD6J8cFYe9ZQtmQLOi7bgHUW54As9QXFQFD5txaNRPRJ3vh1qBGpNik8iDfDXs5rTXYhDchoCwJVpUB4YTaQlWpUBK476PbaXsk/4P6cA+2sbnDVb7stXq8OwSNnHfDyHnjijMlkNuM+rt3su53V+SMOkobbSENI0oGwwCTUOO0O62FE81KFyrHXokd/kChwbhTfdUP1465Lis9x59CT4at8GbcQ6nW24lYHVJvbAIQ1vqW82txLrbR0pcAsVHr77YcqKEp1LXZKeZva+Gh1xpaUqOpmQokfkLwtJZVJZLAcCCog8uuARsqoKTZNo7ZuL83D+r8+OzAtp5qcO8lu+sdEg7FXngzUkBtaXwG1smy/C3jhJ9T0xpelRbYAOguiwcV0uPq3tgIx9ITXp0eEoqRIgplupCb6XQQEH3lIP2YSmOCTLhxmUFElEwSyTt2DItqJPQqFwB1w0pq6/GStlintty3VlcqatYeLh5bITawG1hew88TFLo7dOYXIqb5cdcX2jy12upf5VvDoLYB6NU89q8NEYckE27XzJ6Jxw9pNcHZLU1GTtdIsXPd4DAWVS0lx4qZiJFz0Kx5+WOoMib+JUY8YbJUkd5fuHQYBZLceKzoAQy3uSom32nGb+iymJn7iDnz0j5d5DFUknKuVtLVymlwnCHXE+Adf1qvfcITiP9IesTqdk6PkPKLrhzhxDnpyzRSo6lNKe/dEgjp2LAcX7wMejuH+UcucLcjUHINALTFOy/AZp7CbgFQbTYqI56iQSfMnATvr8pR/B6W8q/VZCRjodqzhNmY7P8JRWfuxz5UbcVaMzIe80t2H2nHTKqaiA1TUgeLjoH5gcADGnui71SKE+DTYT95vjNvSNhrTwXzGr12QqyI/tKBB/CG/LGlFVWUfxEZP8A5VR/RjNfSNcqKeDWYw/GZ0aI/eS4f3w30IwEF6aTTc70ccz5fdQpxFeVBpCkJvdSZEtptQFuXdKt+mPJQRneq1PJVGzK1NDXo95spWWQ68lSUzpcid2bcgH6QTC7PfxcPhj6SPRYstkMyo7TyLpVpcQFC43BseoOEnKdTnQrtYEdQdcDq9TSTqWm1lHbciwseewwHzwo3EGZlbMVPz/WkVBK6dk7PSFLo7CI76LZhQhso7uhBAsSsi1gSb2xKZN4x8aM5Op4ds8UKhDZTxNptDTV4ExmpSBTZNMdkOM+tdilDtnEWCwgWO29t/e3yPRLKSKXCGpC2yOxTuhZutPLko7kdTzwSJl+gU9KGoNEp8ZCFh1CWo6EBKwLBQAGxAJF/PAeKafxe4jGtUThxxB4xVbLeXoNfzNTZGbS021Jmrhqb9TjOOFGhKi24tRUB852YHPFV9H7MfE1ym5Ky/lDPVXRSoOVMy5qXEjR20/Lc1mrPhgOXTdKXQbqSLX2F8fQN+iUOUyY0ikQHmVO9uW3GEKSXfr2Itq8+eFI1LpcJSFRKdEYU2ktoLbSUlKSdRSLDYE728cB5H9DniJxmz5mVirZxzvS6nS6nl/1yo01dealzI1Q7Ud5uMlhtURsXW2ptSlEFKd774Y0bjZKbpWf+InEjjBmGHmagy6zDGQqWphlcOM04pthaGVo1uK7PS72qjp3J5DHsKHRKJAlPToFHgxpD9y68ywhC3Lm51KAud998Edy9l6ROdqb1Dpzsx1ssuyFRkFxbZFigqtci3TlgPB2U/SK4sZYpuYc+1HOUqvZV4fZphCqMszEVVb9JnQ7EGQ20jtS0+W1XSnu3Um5tfEfV+LfpQt5gi0KpZ+iUGpLoMHMNNVVq01TI7kmY+44plSFx1iWhlvs2S0kpUDvzIOPoFFoFBhQ1U6HRaexEcGlTDcdCUKA6FIFiMCfQqFV3GHKpR4E1UU3YU/HQ4Wj4p1A2+GAWpTk12BFXUEtpkqZQp9LatSA5YatJ2uL3tth5go0jZOkADpjtx44DuBjlxe18DUnlqH24DuBgutP1hv547qT9YfbgO4GOXHjgXHiN8B3Axy45XwNSfrD7cB3AxzUPEYFx4jAdxjdT/smf8RU/wA/ONkxjdT/ALJn/EVP8/OAQ9H5aG80caVuLSlJ4hyNyQB/tfCxr78GDLIdfZacJFgrxGMM4H5YpdbztxnlVRr1oI4gPobYe7zKP63wrqCOWo+JxrDuTEU+8nLNTlU1xJ1BntS5GUfBTar2H8EjASqqY03vFkPMW3GlZKfsOIucymoMxJLqAXnO0b1JGxsCQT5bffhah1Z2swn4NQQmPUY5UxKZQfZO4C0g76SNxhVKHlspiKHZyog7hHsOJtb47cx4kYAjKX5UT1IOglOh5rXtrR9U/eMPWlzWEaUUxlI8EOAfowxYaUuK0A0660n8S40fnGj1SfHBizET35VNmr8XXDcjz2O2ALJPbSw4+2pDmm3YvKOgjxSobA+/BlJp/qzshtt9p5kjUgOEKSenPa33YUILbF0j12AsXse8pA/SPLnhpICAntWXUvM20hwkk26trtvbwPTALlySkWkvz22yO93Eq296d8JTSh+MlqCystMArUrQQSeQt4nfc4VhVFxpIWVqdaQQlaFW1tX2G/0k74d1GSo/gjbui6Sp1Y5oQOfxOAjVPuLWpwFRccABCdiB0QD06k/DBUodbS4qM4hpsGzjiBpSfLWbqUfdgxKRYqTp7YW0p9pDXQfwldcGUVqJUpKErY5JUbtRh+V9ZWARCFoQpwJLYPM3LYWPNRutXwwo1GWpoFCAEjcqdBQ0P8E7q+OHMeO88oPRmQs3P4TJ3J80p6DGT+lFxHqfDDh0lGXO0nZ1zXNRl/LTRAKG5z6TZ8p+o0hKnCd/Y88BlmZZY9Ibj6YMZ8v5B4Qzfn3Fp7tUzLp20dC3GQo2HLtFHwxsC5DEVKWWwXF7pCEm6lXN9z09+Klwv4dxuHORaRkamOuqYgNFT8pwEyZ8hZKnpDh6KWtSlHyOLVLk0+iQzIWoAEhKUo3W6s8kp8SfDAHZjuKUX5aUlz2Up6Njw8z54ckG9r3UemIVDWY6kC45KTSWTbS02gOPW/KKgQk+QwY5fdI2zDV9f1y8k7+aSm33YB/PDnqytCFE3BUEjco+lbzwhJVHEdhUZRDHapvoNiE+fnhuzLqlMnx6fVHUSmJZKI8lCChSHALhKgDbe2xFuu2HT6mGZrJBDaXUkuk7JV5HzwCzMGI3322UHrqPeP34XN/t88M0IlRhaIlL0cm4QVWKSfBWDmcG1aZLDzXmtOpP2jAESpLkx31lQCmVWabUbC3jh4LEXuCVeBwm4zGlWU4228CNlqF7+7DGRHirc9VhNhL3MuINg2P9emAkHFFKFlKCohJNuV/LDViRPeZS6hEdSVcgFEafI+eFnJDMVCA89uACOZJPjbCcJBLj8hLam23CCkKFr+Jt0wHbVE7lcceWkn78Dsqh0nN+4tXw6094JSL3Nh54ZM1miyI0qbGrMB6PBW41KeblIU3HWj20uKBsgjqFEWwB/V5fWfpHihoDCjUVDK1KGpTivaWs3P8AowxkZqytEhIqUrM9JaiuMsSEPKmN6FNPL7NlwG+6FrISlQ2J2vhy/KW8VRoZF7lLjv0UWNjv49MB0yldotDEd1wINlFKgAD+nHUzmULCJDbjJPLWLA/HCzLCWG0sNpICB7PW/X/XphsKnSpFSeoKZ8dycxHTMeiBQUtLCiQlwo56SUkA+WAdhSVjUlQI6EG+G0tuLrDq5JZctYqbWASPA4Mqnwie7GRe3JJ/UfP8+DIhxGj3YzYPjp3+/AEprinEL1q7UJXYKO2pOEWHkR33fXFlLy17rOySnoB7sP8ASLdAPAbYQlutNxwXm0u6jpQgpvc/pwDeTOc0OKhhOlGxWobKP5OHTokFg9glvt9Atc2G43vhARJTqUiW+yltPe7NCbAHzJwZcvWsoiN9us7kD2PtwCEdqQymyac24TzUty6lHB22yw+qRIbajo0FISlexV4nCbxkLV2bjynXzv2LJ0hPvOOJp2j52Q62xfntc/5R54BSXKjSWwhhSlqSQUrTySfEnwwp64tw6IrXbKA3URZF/G+Gv4MSeyQ9LCTzcVpbB8TywO2Q4NUh9zshsOwQQ0n49cAsqC4UdoqX84F9q4sbgnoPhhJKWnSFNsPSyR7bpsnCzlOBSkxHOzbcHfSLlKk+IHQ+eHgATZAsQNh0sMA2Smoto7qYiQOSRfb7MdDDjqkvVF1Cko3CUghKfyjfmcdXPYSooQFPLH0UC5+3pjnYPy7Kk3Q2N+xHM/wj1HlgElqMnVJkHRGa3A+vb6RH5hhQOTHwOyZbjouBdzvE38unuwH1JekMwmyCEqC1AcrdE/6MZL6QmYa9XRRuAXDuStOauIalRXpDC7O0ijpt63MNuXc+bSeqljwwCno7UiTxy41V70gKo5IVlfKBfypk5CjZD7oWUzZyR0CiA0k89IVvj1miFAjAqSy2m30lbn7TitZDyPD4e5No+Q8pQY9Lo1DhtwojdtS9CBbUemo7k+JJOLCmlsrVqlurkq/4Q937OWAO7U4TR0KfBPgkFX5sJmoqd/E0+S4B10hI+/DpDbDCbNNoSPyQBbCTtThtK09sFq+qgaifswCaZNRNz8m6fDU8P0YzX0jXqkrg1mTtIjKEFEfftbn90N+WNKE+Uq/YUx0+GshGM39I1+evg1mIOwUoQW4+o9oCR+EN4DU0khPLBFOJHdUQL9MKITYA4zzN9O45rran8j1vKiKUtCQlipRXi82vqdaDZQPuGNbW5Y323TYMPv7cnNFfW3Za6lVKfRIMiqVaoRo0SKCt191YSltA6knpjI2s0cR+N5dGQVjK+TS6WvlxwKM+ooSbFUVGwaQeQWsE9QMScfg9nfO1TizuN2ZqVV4FPV2keiUqKtiGtzo48VqKnSOiTZI52xqzFPTGaQwwlLbbdghCQAlKRyAHhiP4807T0j9Z+bo0vpeF0icW2XNPjMb1p6RE7c1vWY5a+ETPWK9kjJUbItNVTINSqsxDrhcUuoznJSwo87KWbgE7288Y/VI+YOMHH3OvD6qcRcxZYomSYFMcgQKFO9RfnuSW1OLkuuga1oSUhASDpuFX549EutakhN+t8ULiDwN4ZcT6jGq+dMrNTKhCaUyzLZkOxnw0SCWytpSVKTfexJA323xNEREbQ5WTJbLeb36zP0/gxnNPpU5mynxBlcP8v5bh5qiRo1RjQ5Ed2T2pm0+L2zjL7ymw0txaUqBS2SUm1zvbEXVvTdqNVq8KFw4yaxUoNbU2ikVBYkPtvONQ0yZaOyYQpZUgutNDTtq7Qqtptjc6P6PXCSh5tZzzS8mxmKvHddfZeDzpS06432bi0tlWgKWnZRtdXM3OCP8Ao58HXcsQMmtZFhxqVS5j8+E3FcWw5GkPKUp1bbiFBaCoqN7HcbYNVNz3x+zplrgzlXidD4eP056tLZ+WG6o2+prLzRQS49ISyguFtJGm4TtcKNgMZ0/6S/GjLlXzzMfo2Vsywms3UXLuXWI85xkJTPaSpKy72Z1IstK7nfcgbWx6KzJwayFm7K1OyTmKjrl0WltBliMZjybthGjQtQWFOJKdiFEg73xHyPR54RyK2rMRyPBRNKoC7oWtCNcLaKrQlQSC2AADa9gByGAx2kelvm13O0yi1LIrIoNFqEihViqMMyuyhSo7JW7LW6pvshGDlk2vrAIVa2I6k+mDn+qtP0Gn5Fo8vMa69SaRAecdlRIEliotuOMv/Ot9rpTo37vfBuLXxu0zgDwuqGY6hmyVlRg1CrdsKgEPuoYl9q0plwusJUG3FKbUUlSk3It1GGuWfRs4OZTfak0TJbLL7EqHMQ85JedX20VKkx13Wok6EqKR0sbYDNuEnpJcRs4Zzy3Q86ZHodPpmY5dYorMinz3HXET6aVdstSVpA7FYSrQPaFhfCVUylxE445tz65ReLFbyiMuZnbo0ZmBJcQgxWoKVK1ISQNS3ZKl6jfZCMbZS+EmQKLJpk2l5ZisP0ibMqMNaVKJZkytXbuJuea9Sr323wvQOHtMy1m/Mmb6U/IbXmhUd+fE1DsVSWkdn24HRamw2lXQhtOA8ZOekBxZo9d4q5AGZZUurZwrcilcPHXEj8EdYfEWQ2jbfs29L9zexucNo3GzOtbouRxIzbXW5Sl8P3ajJNQHYv8ArEl1EjugAp16CVkqOoWBFsezY3B3hxEn0+qtZOp3rlKnTajCfKNS2JEu/rLiSdwpdzfERG9HPg5CajR4uRICWoYghlJUshIhrUuNzV9BS1EeN98Bg6/TazHGczS0jJlNq6IEaFUKHMiOyY0SoMyJ4ibOPNgr0khQWgFKt7eOLBH9JnifJzCzwoRkrLf7YKsxSaQtS6g8mlCOzDTKLoXoLhVoWlISU87nkMaTS/RW4EUb1tFP4ew0ImoaZdCnnVjsm3w+22kKUdKEugKCRYA4lMz+j/wpzgZq6/lCNIdn1FuqvPJccadMpDYaDiVoUFJPZgJIBsRcHngPNnBLjtxWm5Np+UZkONUHVUTMWZK3V5NUWp+M2xUpTKWIp0KCyNCQhRGkBN7bWMpl30s8+sUxcmHkmJUcvZVg5XXVZdQqql1KQKsyyoKRZsIWttThKidIULWAN8ehcvcCuF2U4iYeW8mwYLSaU/REobUvSmE86p11rn7KlrUo9bk4IxwE4UxqZUaOxkqEiJVm6ezMbC12dRCSlMUHf+1pQkC3hvgMNo/pAZ94/Vep5Ayll6o0CiVtyqUqnZlhsyQ/THYbhSl950pDdnShSQlCipG2rFx4awc5cMuMFM4aZjz/AFXNTVVyrInuv1CQtwtyI8wpS4nWSU6m30oIGxLYONKofBjh/ljN8rPeX6B6lV5inVvFmS6llS3PbWGNXZBSuqgm/wBuJCNkCms5/n8R3XpL9Um01mlNpccuzGYQtS1BpNtitRBUeulPhgLVjHKn/ZM/4ip/n5xsmMbqf9kz/iKn+fnAJ+jz/us40/8AGHI/zfCxs6hcEbfHGL+j0bZr40n/APuHI/zfCxpWZ82R8uMsF2G5JcklehtCkpulI1KJUogDbfANM5Q2oTbea41mZlOWhSlp27VkqAW2q3MW5eBGJuaw6sNvx/xzJ1AHktPVJ/16YxlVedqMmVNcmy1P+uKK4bxXpmtlxIDGgXShSOR3vcDocbQ/OjxhoeUdagNLYF1HyAGAaxokeWXHWJEhkk/OMpXpCVeY8cHeTIpY7dLynY4IDiV7qSPG+FoLLq33pr7XZF0JSG73IA8fPDlxpLzamXBrQvZQPhgGEltEB9mQwSht1elxI9nfrbphSVAbfSp+KotSNJKVoNtXkrocJoWuI6mnySHmnNm1EbjyOOSovyay5LguKQlsa1NXuhQ6gDpgItoMu1BuMElCX0OBSR9C19SQf4QBw9kxAyUxnHlOB27j61cy2jkPdywzikpqi5KxZtp89eQXqH5yMOJzyXlOvo/FuBLCFX2UASV2+zAIMhxx5b+r8IcUEtC2yXCOf+Cmw+3D6HFafdLftRoxAT+W59InxwyiBxaw2yrS46dBV1RdOpRHnYpGJR4+qMt0+CnS4sWRt7I6qOAPImLS56nESFvWAUfotg9T+rHkiv12JxC9KWuZjlOKk0bgzSl0qIjtNn61KSlyStCepbZLbfkVq88brxy4t0fgPw8kZhVGXUavLWmBRKU0QZFUqLps20gczudSjyCQT0x5w4T8M6jlDIIarkN+dWq6p6tzZ7DYc7eqvqUt8k2uG7nSnoU+/AaBV8wVKqSG2kxJkRAYW4Utyw2CoqAS9r2uAQUkdDbocTuUECrQ2K7VnFyZjSlMpWSC2kp7utA5AkdcFg5fRTnIy6hMcmdjH7GLEUlKtAVYqSTbcC1hfpbwxNogBaUpkqFkcmWxpbSPCw5nAOwtK+ovvyN8dBuSOowgqnQ3BoQ0GlG1lINiMchvuPsAufjEEoVfyOAZZlYfcphmR/xtOcTNQOhKLkj7CcSLRZmx0OpSFNOoCwFb7HcbYiqxJfqT7mW6bcFaAZci3dYbVcWA+kpW9h4A4e/jlGDG+aZjJDayk947bAeVsByM2j15XqYPZJSUOkeyV+CcHfldoVRIiQ44QQs/QQPP9WE1FK5CqdHX2LSE2VbYqHUA/pw8Q00y2GmkhCU8gNvt8cA0IWgMU1lzR83daxzCf9OHTTDLDYaZTpSN/efE4ap9ZZffeMJTmpWlKgsbI8MLIlgqSh5lxkqNgVjYnwuMAV5h9L6pcd1AUU7pXy28D0xwzwmAJimzqtugHkcEkXfmJiu6kNdlrty1qvuL4ehAQnskJOgbWt08DgGsSKy+43IkOl5y+vuObIHkBzx4lyjQc1PtVXhjARPRSeP9UqUxyY2FAU4MS32p4XuLa4jbWnxKjbfHt4wYyldophKFJ+m33d/eOvlgqGZkPS3D+dbauAweaBuVFu3I2J2wHiZifVJPCikUUsL9Wj5GymEtrjp7TtG6+to3WE6jshPdJsBuBffF6qfGzNDnGGoRcq5nqjNNltZniPUyfPYkPRpMKM4WX2ozbQMVAcbugLWsrTvbfHqOPIaeV2SGkIW2QgtqbCSLG426BKtwOV9+eE4SqVJcdqNLcgPOPPKS/JjhtWt5F21hSxuVAakm56HAeWZXEXjBkPKcSps5yqmZZeZOGwzK8qdEZWKTNbejtuOspbbTZCWpKlKbVe6kA9CMQdWzFUqZm7PlS4X8Z6nmSU/RcpQWcwPery3WC/VAhxF22w2oWWTptdNyL49hQSoduwCj5pRQg6d0tqANrc7C3Lltg8aNEgtpYgwo0ZpJBS20yhCdlagbAAe1uB0O/PAeYK5nbi/F4xVDI1Lz+00rLlQpcGlNVurRYyKzEUlBkPPMqZK5S3VKWhKmlJ0EJFuZx6ddnMlxQZZkLFza7Zv8b73x12FBkyGZUiHGefjErYddZSpbR2uUqNyn3jrjr8gRFNtutOhSxsEpubAb8vdYHAJ657vsNNMA9XDc/djqIulfbvOqdcAOm+wT5gDBfXd94UkH/wAHgKluLQQzCfJPLUAB8d8AnEhsyYrb7+talpCzdZtvhaQosttxoiQlbp0pSALAdT9mFIrRjxmmVc0ICd/LCE9qU68wqLtYqSs/VSRzwBGELXrZhrQhCSUre03U4r3YUXGhxU9rJUpah1d73wAOA+tUNppiMpCAVaC4RsgW9rB24bYUHnFqedtstZ/RywCZQueUrfCkR0ey2fp+ZHhh2NISWigFIG6Ry91sJPLcU0tDK0F0iyO8CScMI7Se2YDDLzb6VEvKUDbAOih6EVORwpxk82+ak+7ywQvGeRHjlSWk/jV6bH3YeiwOrra18c8L8h1A54ANJQygIaSEBI2sPvv44bqlOvr7GEoqV9NwjZPmPPHJClyHhCbPd06niDe3gMKOPMxG0ItYGwS0kbr8d8BF5izBQMh5aqeaswzExqZTWFy50hZ+ggXUT52FgB4jFc9D7JFbrDVZ9JbiBALOZuIvZmmRXEWVS6C3f1OOkc0lafnVjqVC/LFN4qUt/jFxVyZ6PinFGmzAczZuQgdxFIjL+ajqV07d7Sk+KUKx61Ya9aQmNCHq0FpIQOzTp1JAsAnwFttsA5dqGpxUaIgvPD2t+43/AAjjgp8h83qErWi9w22NKfieZwulEaCx3UpaabF73sB7zhuZcuaPwJAab6OuC5UPFKcB1yn0llJceaQhI6lRt+fCbU+IO5Tojjo8W27J+04UMKHHIky3O0UPpvKvb3DkMcE9botAiOPD6yu6n7+eA6Hqq57ENlu+/fdJI+wYzn0ixU1cGcyduI1uzj7I1X/dDfU40YRak/cyJwaSfoMp5fE4zf0jKcyzwazG6X5CyER9lukj90N9MBY+JnGPhxwbpcKscSszsUSFPf8AVI7zza1Bbukq09xJI2B3O2M7/q6vRR/vw03/AJNI/wCrxuTkViQlIeaQ4BuAtOq324L8m0/95R/4pP6sBh/9XV6KP9+Gm/8AJpH/AFeOH06vRRAJ/bhpu3/3aR/1eNx+Taf+8o/8Un9WOGm0/wDeUf8Aik/qwCdIrEDMFJhVukSA/CqDDcqO8kEBxpaQpKhffcEHGOZz44cQqdxNzHkTIXC6NmNrKNIiVapKXVxFffRI7XS3HSUFJWAyr2iLkgY2vQlhI06QkbW5ADGGZv4OcYJPFHNWeuHme8u0WLnCjwaRLM2mvSJcL1ftfnWCFhsqPbH2wR3RgJCl+l5wLmUykzJWbfUX6nTGqqqI/GeU7EZWOb5QhSW7G6bqIBINsaDI4m5Eis5hkv5kiJYym0l6tO6johIU12oKza34shW1zYjGHQPRxoHCLLnEJ0xpeZaBWMm07LyaVEidpPeMVuQha73spbq3wra2kgnDDK3BLOeT/QxzXlKsMy6xnrM9Emy6kFKDkl+W6zpbZJGylIbS22LbdzAatTPSZ4L1ul1ir0XNq57FCQy7MRHp0px4NOmzTrbIbLjrajsFtpUk254cwPSK4QVSRl6HTM2pmv5oZXIpzcWG+8VNIUUKW7pQewSFgpJd0WUCOe2MQd9F7jHm+mRsz1/iNRIFfbotIo7EODCkxYjtPjr7V1iYUOh0qcVYHs1JA02ANzhzkv0Sc65KhZLhUvNeWocjLdQmzJFUhQJLcpbD8pb6oKEl0oVHUF6Cl0KPNQIO+A2BPpJcIHMrS86sZgmyKLCliG5MYo8x1C3Deym9LRLjex+cQCgWNyMJ5g9J7gXldymt1riBCa+VYLNSjuNtuutCM8dLTjjiElDQWrZOsp1HYXxiz/ogcTXKTMpcXiJQ4FKm1Vc1eV4rU4UQMrYW2oBPbh5J1r7UoCw3qHs4znOfo/8AEzh1ld7hLkR2dXpOasv0ajVRaMtrchrXFe0pcallzTHT2V9aXAq1gUm5wHsaDxy4X1LPS+G0LNTLmYELWz6uGXQ2p5CNa2UvFPZLdSnvFsKKgNyMXpC0rF0na9sebOHvoqOZG4sO5uU7l6r0YVmXXYTktuZ8pwpD6SFJQoO+rkAqWNRbCik2N7Xx6SaSENpSOgwB8DAwMAMDAwMAMDAwMAMDAwMAMY3U/wCyZ/xFT/PzjZMY3U/7Jn/EVP8APzgG/AFWnMvG1QXotxAknV4f1uhb4jqf6/NeixXJikVD5QZWCG1OPSAQq0xC1G2nSd0WtYFJ3Iwfg6ZAm8evVdXa/s5m6NIN7/JsO3LGrUVyHMy5T10l1p0xWmbpSq5BSkXQfA+RwDWgUGpwjLflSEqkT3Q+++W0pUFBISNCBsk2A3OJhl+lwrhl3tnN9RHfWT78HevUaeTCUAb2KCd9uaVYDU6NHAafjGGeQunu/aNsAol6pPbMx0Mp6F03NvcMcMKUs/P1NzT1CEpR94wzSFJcUmo1CUi5ulaF6W1J6bjHZUKIIpdiRkTFA7lbpVYeP+jAL6qZBd+aR2j527l1rPvOCvrddbC6k6llpViGEbqV5E9fhhOFGMhm8eeG0jYpYbCbe+4vhd6nLYZWuGouSTYdo6u5A8j0wDREYPzkqeYATJ162jv3NItf4jAqadTwjtWShpKG0pA2SpagL292HECzMgtPRih54FQdKwsrt54buK7dTiBYOSJZTqP0Ut7/AKPvwAjhbSWJwaK0a3FOJRupOrYbeQtg1ZqMSlw1ZodktohQWHHZTiiAEMJF1rv+SAT9uAxIWh9a4jW0tV20HYAD2nD5HbHnf0oa7LzxVKN6KeVJ76JGZk/KmcZsfZUChIcGpFx7K5DmlpI+qVnAVzIK5fpDZ2f9IvODav2PNNvwOH1NdSQmLBN0O1JYPsvvi+k9G7eJxqOWpRTluO+6oEMJU0FJNtQQrSk2G24F8MIUBifDay9RWkwcvU9KYhLIt2wQNPZNeCBaxPliaU20t5qBGbSliMQVpSO4APZQMArDjFKFPP8A49w61A9EnkBhzc+O3h4YFzjl7b2wAIJBscNVqMJ9x5aPmH13JH0VH9GHQUCLpUD5gi2OKCVAoULhQsb+GAjZtKlGaalS5yYz60hDqVo1Mu2voKuoIudx44Zxq2lE1LNSbTFmou2+zewcHRbdyAofG/liTbU5ABQtC1MDdKhupPiLdMKyIsGoshEmMzIaO4DiAoffywCDrzUlIK4khWnktCCLfHCYfKB+7JrI/wCEa1D43wh+x4RFdpQqg/AUj+1lZWwr3pPL4Y6zUagtx2j1JttucWyppxoktyE9Sm/IjqMA+0zh3xMZXYXALeyvPbCSXVTvVW1gDtEh5ahtYjoPjhRpSHIBZi3bWlBbCeqDjjDaXo0d1j5tbSdKSByI2IPlgHD8dmQkIeQFdQeuG6okBoArfWg8rl6xGDlqbI+bfkNNpHPskm5+JwBFp0b2g2k+KrXP24DKI+aKnX+NtaydK4iN5apWVmIEqLCShlUmuJeSpTqyt4G7Xd7MBsXCuZ3GMWr/ABtzvnWmZ8pOVc5VhGXqzw9q+Y6LIeEIVCKY0hLaQ0iONTDa0qUizt3Li/dOPWU+iZOrk2JMqdGo8+bAOqG/Jitrdjm9/m1kEp332w3hZTyvluRKm0zLVIjpqPaInLZgtIcc7QjXqIT3kqsL352wGOZIzVmOs5yy7kSXxpTTIlJytR6s1VFsQ/W8xGRqCkpcWnsy01pS2QgFZUSSb4q/CvM2d8iysrvxsydvQ828RsxUFyiOwWg2wntJbofS4PnO0DjR5nTZVrY9DVTJOTDCpqFZTobkeg2XT466e0puKAbjshpsg33264nmoUJsMFqDEb9WeVKZ0sIsy6u5U4kW7qjqVcjc3OA8s5Pzbx1zM5kFT/GER1Z+ytVa/JW3RI14S4OgtNs7WKVhYCyq97HTa+GtP48ccc8v0lGVBEgTW8sUmsJYefgx41UlPqWH+0MlQV2N29ISybpK9+mPVNMiRo6GUNwozZpynWIpQwgdi2qwUlFh3UkAAgbG2GFZylkyosw3axlKizU0iyoIfp7TnqpvcdldPc38MBhn7ZnFmNmCRmZ7OjCabE4pRcmmginMqaTCkOttFfagalOtrXdJBKbDcHE76LSMwOcO8xuV3PU/MNQhZ0rbPYS1NlyAWpjw093v/OpsrSrYC2nbGzMU5lSFrlw4pU8+mX2fYIsl8EKDvL8YCEnVz2G+OwqRSaZImy6bS4cV+pOh+Y6wwltch0ADW4oC6lWAFzgHLTyHm0uNLuF+zvgygFcxfDN9HqJVKj7IUQl1s8tzzGHp2NsBwADkMDa/fISkcyTa2BdCQVLNkjmfDDQJNQUXXRaMk2SgfT8z5YA7kmAsdm5IaUlWxSs7WwkQ7TxraUXY53sTdSb9U+Iw5cEdltXbIbQgC26Rv5YYa4iFFUV99q/0UoJH2HACKzCdSGXEJD17pcSd3PMHDhS5USy3gZDI7pV9NPvHXDJDMPtD2774Qe9pLWlN/rX6YdpW9GSFtPplso5jbUB43HPAKGU6+ezhIueZW4kpA9w6456rJcB7We7b6QQkD/Th0lSVoCkG6VjVgEXFgL/HAJsstRmyGhpSTck879ScJRtEiW5LWAlpI0IWoch1P3HBHnRLdEVlWpCd3VjkB4YoPpH5wmZH4I5qq1I1JqkiF8l0pKBuqZKUGGLe5TgPnbAd9EGEviJO4hceZnarRnGurptLU7spNHgKUywhPgha+1c256sennXmYTGtWzaRYAD7AMVDgtw9icLeFmWMhRhtRqZHiuH6ziWwHFHzK9R+OLMjTMnlQTdmLsk9Cs8/swAbjPzD288AIG6GPojzV44M/JcU76lDQlTgF1qPstjz8/LB5z62wI8c3fe2Rfp4k+WOJSxSoZUrvEbqP0nFn9eAT9TjxwZM94vLG5W77KfcMdE+Q+B6lD7vRbp0JPuHO2Cx4anFJlzyVuncIJ7jfkB44il5mqNUfdi5TpiZaWiULmvuaIyVjmEkAqX52FvPATAYqjpJcntt3+i21+k88Zx6RkaWjgzmQqqC1jRHuFISP++G8XFNCzRMHaVLNrrCzzagR0IbHuKwpX34zv0g6DUYnCDML6M01GQhCY5WzIS2tKh6w3cbpuMBrFXrtKy9AXVK3U48GEzoS4/IcCG0lRCU3J5XJAHvwF16lIrDNAVU4wqMhhUpqIXB2q2UkBTiU8ykFQBPnjFfTcTJd9GnNLUNSW5C3qcllakawhz11nSop+kAbG3UYxviTlj0gRxpDauIcCZm1rhvUp2X5VHppghLjMyK4Y7gUtZWl3ToNrbHpgPb52G2IjLea8v5tZlycu1eNUGYMx2BIWwvUG5DRs42rwUk7EY8RMcb+IefI1E4mZg4iVvI3DXiNmQ00TW3EsrpUSJDKQEuqSQyZExLwLnVKEgWviG4Kzs4Veq5Tyhk3inmJilVXNud58yZDUht6sojOMOMdoopt3yd1JAJClW54D6BVaowqRT5FVqUluPEhsrkPuuGyW20C6lHyABvhOi1ek5kpEOvUWc3Np9RYRKjSGlXQ62sApWk+BBGPBEDi1nmtUuhyU8Uq1X8z5qp+YWM85QkJHq9DaaivnUhnQFxS0tLaE6lfOauvPEZH4n5yonDlMdfFWs5PqOUsg5cl5CosHSlrMMh2IlS9bZQTK1OgMltJGgeBN8B9GChJIPUC2OdkiwG4A8Dj558Q+N/EVuXWKpmji9mbKGeoub6JSIeS4pS3FVS3nYwddCezKlaw47d3X3SnSOWNY4TZu4x1zj4rgjXsyVhyJwwnVOqVypOEA1iFLINJZWoCxshbpUNt2BgPWobQOmB2ad9ueDYGAIW0nx+3ALSFCxG3vwfAwBA2gbgf6cGAA5Y7gYAYGBgYAYGBgYAYGBgYAYGBgYAYxup/wBkz/iKn+fnGyYxup/2TP8AiKn+fnAJej3/ALquNN+X7Ycj/N8LF+q8FVFqjGZ6VBWQLs1BmOganWiLhenqpKredicZ/wCjw6lWcONTOrvJ4hSFW8vk+FjaiLi2Ar8Gr0atOl6iVhlMk7qZ3CiPym1WI99sScN9cntWJLSO0aOlQBuk+GIPPkCKigyqy3FbTOgBMiO+BZQWki1yNyPLEy6xMDglw9Jd0hLiFmyVj3+W+AL6nLjlSIgYWwTqDbgPd8bHDZ1l1DmoU19h3o5GUFD4g4cOTKhF0GRGZCVuJQkJWSd8SDshlhBcecShI3uTgK+8C46C4VB07AuoUyv3BadsKuxXlIU4YDh0i6i/JO49ww7kPqqKFxokbtUq7pccFkDz8T8MKy1Jh01ba3LqS32aSo7rNsBGx0Okh+C0CllYcShR5BaOnxwSW0tjTHWdTnZErN9gVrAO/uviQpLekP8AXdLX+QkAjCTgZlPzXnVaW7IaSrzG+3iQTgKxxG4j5b4SZBzDxNzRIDcCjMqCW0+26pJ0tMIHMrWspSB1JGMF4H5LzHTaDXeKGf7Jz/n8mrVgXv6g32ZMaAjwQyggHxUVHDDM9Wf9IvjcGYoblcPOE8+0pAuWq1mNFxbwW3GQQfDtTbpfGusLa1FhQUWJevsza1r31J+3lgEKStEfLMJbCdNozYT71Dn8cSMdgMNJaPO+pZ8VdcQdKWtiiSKc4dS6XIMe/wBZKVBSP/RI+zFhPPAC19sMla5qy00pSIzZ76hzX4jD29tzhiw+zDU7Ffc0lCypKrcwcAZxtEGQ2uOkJQtXZuITy3Gx9+HekjY7m9sM2W1TXkyXgptpB+ZT1V+UrDs3tfkSdz4DAduQQnkSeuG9PI7N0J9hLp0e7/44IFu1DZpwtxvrWupfu8sOkIS0lLaEaQOSeuA7Y2FxiJrN/lOiq37T1xaR46eyXf8ARhWfW4UF71O6pMxY+bisDU4fM/V+NsJQ4FQlTG6vWVJDjQUI0VCu4xqFionmpRG3lgHrzDzcj1uMAtSkhLiOV7cjhFDzTBVJYStLKl6ZDZF9CzyIw/8APDdghE6WzsdRC/uwBC7Imq0tLU2ynZTnIqPgkeGFWoMVsXSwi/UnvG/xwvtYAAC2OYBCWiKlhan2QpIG4CbG55YQdS4KW3Hd/Gr0I+N7/mw7kNesMLZvzFwfAjDNxwvQo76trOpJHiRcYBScFOBmL0eWCr+CMPRbVcEA6rgHDaU06sJW0RrZN035EeGDR5TT4KUgodHNtXMHxwCVPKVNrN++t1ZUL78/DDh5pMhpTCr7/V5p88Juw4751qSUKHMpNjfCZTLikLDq5DXIpVYrHuwHQZzI0llD4TsF6wCR8cd9ddHt098W2ukBX34KZwXs3FkKUOQ0acGTMXqQ1IjrZ1ewSbgnzwCUtxD9PW4yVEXGoEbpIODmchSgiIhUlZF+6NgPMnHY90T5CPrBDluhw42AJB5fAYBARn5G8l+4G/ZoFk/HCj8hiMEpUkA2slI5qPgMIKlqfJRCbuRzcVslODIaYhD1iQ9rcPNxQuT5JHhgA2w486mRLSCr6LQ+h5nxw63va9z+fDPVKm91sKjsnqR3jgwpkID2Dr+v2h1e8YByRqBSRceGG7lPjqJLQLJULEtbA+8Y4pc9gWWgSEp9leuygPMYXbcQ62HUG6VC+A62hLTaWkckjThvMK3HmoiFEBwalWH0R0w6HPDYf7Zne2lkaftwC6GkNIKENhKUgn4dTjGOMSDnbizwV4cIDgjVHNhr0gAfjGKayp/vdNPaqZ2xr8vW+6mClVkEdo6sG3dHT44ynLchVb9OaJDRHeUMmcPVhtCh80Xp0pJKvIhtki46bYD1bPU4tTMJpWn1hRClDokDfDgdhBi32Q20n/X44QjRHu0TImPh1xPIJTpSm/hhJRNUeSlq/qrarqVa3aKHIDyHXAKQGXF6576fnHR3En6COg/ThNoGoyzIIvHYNmQPpK6q+GDznVurFPjkhxwXcUPoIwzrdXTSI7VNprYeqMkFESOPG1tavBA5k4BnXpcqs1AZUpL5aFg5UZKf7SyeSEn66vuG+J2ExCpcJuNGQ3Hix0BCEjZKQMNcvUZNDg9i44XZDqi9JfPN11XtK91+Q6AYPHb+U3VSnxqYQohlPRXmfHAK/K0ZSiGkPuW6oaJH24zf0ipjMngtmdLQVrQiOShaSkj8Ib3scaqkBICQAAPAYyb0nU6eElYcb2cJYSq30kdsi4OA1N+NEmRyxLjtPtGxUhxAUkkbi4Pniu57yTCzzluoUF2oz6QufGMVVRpzgamMtFQK0tu2JSCE2NvHEXxDnV1upZcotErblKNVkSEPyWWkOLCW2FOBICwQLlI3tiI+RM6/30qz/wAih/8AVYM7LlQck5Vy7leBkil0GI3Q6cwiLHhrZC2ktoHd2VcHxudySTiXZpNJjLbXHpsRpbersyhlKSnV7VrDa9hfxtjNvkTOv99Os/8AIof/AFWE5NJzvHjPyE8UaupTLSnEhUKIASkXANm72NrYGzTEUekNyJEtulw0vy06ZDqWEhbw8Fm11D34TfolHdciuOUiEtUEWiqVHQSxbojbu/C2GORKvJruSqDXZ5R6zUaZGlPaBZJcW0lSrD3k4mBMiGR6n6y12+jtOy1DXpvbVp5288GGX5q4AZfzpnePmzM+Zcy1CHFmx6g1RHJifk8SGCC0rQE6ylKgFaNWnULkE4m+HnCrL/DpyvT4EmoVGq5mnqqFUqVRe7aRIXyQjVYWbQnupSBZIxbHq7RI7C5Uirw2mWw4VuLfSEp0e3ck7aevhh21IYfaQ+y8hbbiQtC0m4Uki4IPhgD47gpcbSNSlgA9ScNo1WpcyRKixKhHeehKSiS2hwKUyoi4CwN0kgg2PQ4B3gYJ2qNWm+9r8sAuIHNXS+APgYJ2zWoJ1i5vYYbVCr0qksokVSox4jTjqGUrfcCApxRslIJ5knYDAPMDBQtB5Kxztm7gaxc4A+BgvaItfULfmwC4gEAqFz064A2BjgIO4N8dwAwMDAwAxjdT/smf8RU/z842TGN1P+yZ/wARU/z84Bh6PAIz9xsWAdIz68CfP1CGcbiVpTzvjF+ACg3mjjUs8v2wpFzy2+ToWL161U85PKbpMxcGitnQuW3s7KUDuGifZb/LG56YBXM01FfcGUqWouuuOIVOWn2WGQoE3PLUbWA88WhFh9n3YZ0yk0+iREwqcwlppO5HMk9Sonck+Jx12qxUAtsHt3OQbb3N/wBA88AWeSJsMuD5nUf4z6P6cEqDS0ymZbjZdYaSoFIFylR+lbrg7MaVJdbkziG+zOpDKDcA+JPU4cPymIybvKAsLADcn3DAMXZTj8lthqT2LDiNSHEc1nwueWFVQosNC5q0rfdbQTqWSo/ZywWHCQ9DWmUyAHXFOpR9Xe4PkcceNRpzKnQ6iS0gXIc7qreR5HAcgrLNLckpcC1Oa3jp33O9sYX6TXFWs5KyvS8hZEWlzPWdT8l0NATqEQrTeRPWOehls6geqikY1/MeYaJlGh5gzBmCoNQKTToSp8h91QShppKCVK+7Hl3hBBrXEzM1Z9IvPNLcg1PM8NuPlenv310ugNqu0Ck+w4+fnF9bFI5DAX7hxw2ofDDI1JyZS5Dzkalx7OvrVZUh5W7r7hHtLWslSid7nE8tbbsdEWI2pDSFBZeVsEAbkgnqcKvuGTGjLUTpeUjXva4PP9GGdZ/rrVYlDuVR9BkzEA2CkC2hs+RVv/g4BOjpTMj1aUm3YzJKy0u3toShICx5EpOJmK4X4zbpG6kJJ95GEpgCGERmjYLUllNhayR4Ww5QEoSEpGwFsAOW4xywIAUkKIPMgb47gYAAG5A5E3A8MNZP4S8iCFDSoangDvp8D4Xwaa8tpoBtYQVqCdR5J9+FWmGo4KWhufaUrdSv9GARnz4tKgqlyQoNoshKUjdRJslI8STYfHEeIVbq4C6nJVT46/8AvWMbLUnwcc/QB8cPatTzUoqGUSfV3WnEvsOc9DiDcX8QeR8jhm1WX4bzUPMcERFPfNtyGzqjuK+qDzSfI/bgF4KaLSmjHp7KGUau8UIJ1HxUTcn4nDn1+DY/hbY+OCx1GE96isktqSSybfak+eDOyojDobcUNQAV3U3sPswHPWn3u7DaOk83XBsPcMKR46Y6CNRWtZ1LWepwQ1KDzMkfEG+OfKMdRs0HHD0CUHAOcDDYzHUbuwXkp8RZX3Y78oQ9OoyE+7fV9mAUkOBhhx1ROyCR5nlbDdbDjUOPZOotLCnEjmodcdWpU5TaG2XA0lYWVrTYEDph4pekFZNjuT7sAmy82+kOIuQTa3I36j34JIiof0qSezdRulY5388Ep6T2bj529YcK0i3JNueFX5DMdJLqtzsE9VX8MByM+t9sh0AOIVpX5nxwr7ueEIjbgDkh0WU+rVbwFrYXwHUkgbn4nfDeoBK4jhV9GygfAg88L4QnIW5EcQ2LqNh998Ak46hiY2+8sIQ61pKjsL4CiueC22CiNexc5FfkPLzwq3IiyRpC0XSPYUNx9uElSXHgr1ZaWmE7Let+b9eAM8+GimPFSlT/AC02uE+/Cf4NEc1PrU/LO+lI1EeSR0GOMNuupUiMOyjqO7hHfeODpdZin1eCyXFj2inofylYAwVUHtwhqMD9bvKGO9nPBuJiFEfkc8AMTnBqemdmr6rSRYfE88cLM1s6m3w/b6LiQPsIwB2njIS5HfQEugWUlPJST1GE4b6WR6k/ZDjfdBPJSelvHBXXPWikWUxLb3Rr6+Iv1GFWlNzWyHGUlSDZbZG6VeXXAHdmRWPxj6L9QNyMJMONyJ6nmVBaENhBUBte+FmWGWvxLQT47frwdKUo2SNI8B1OAbmzNRbJse0aPP8AJ3/TjKfRqDVV9MHj3WVJklynxKFSGy8BskMqWSi30e8PvxqzKlPTXHyoBMcFoe/qcZx6JkVv9v70jZvauOuN5hpbY7Q94AwEEgfk7bfHAeoKo4pEB4AHvgIuPM22wqdMOKdCe6w2SByvYYarCZ0xUfUSxG0qWB1XzGD1RS3G24aT3n3An/BG5P2bYArCkQKc5UpTm4aL7y/IC/3DEXlCEuQHM0Txqm1RIWknm1H5ttjytufEnDnOMhmNlSqpcIQlUN1pPmpSSlI+JIGHtFjuxaTAjOpstqO2hYPQhABwBqspQjIaBIS64ltR8Ennh22hLaEoQLJSLD3Yb1RAXT3weiCR7xvhWMoqYbURuUAn7MAqTbGS+kUtMzhRmd0d5thqO0kjqoyG7nGlz5SnD6jEUS85YG30E9TjPfSLZRF4I5iaZSEhKI9vf6w3vgJLP5H7LclAbn1md/NF4eX8j9hw8zdkx7NLtLmxa6/S5dKecdZeaaQ5cLbLakkLBFiFHER+13mvpxKlD/zaxgzud38j9hw3qJ/rfL2P4hw7j8k4J+13mz++VK/k1jBXOG2aHm1su8SpmhxJSrTTmAbEWO9ttsDdJcLnEp4Y5SSb/wC0cG+21vV0b48jces0Zu4e+k/mTjHlaJMnHLtDpGWHojIUoOfKQk9mvTuO5IEVRt0J8ce1aBQo+XqHT8vxHFrjU2I1DaK9ypLaAkE+dhh0uCwsqUWWypZCiSkEkjkfO2DD5mmBX8hZYqXDCctMx+A5n9mdLeYDjy3TSWZKlIcIugKccXcJIBFsaCn0j88UnihQ8t5UzPLbpjUB6jy6LOMdSkOsUb1hEplhLXbJb1hNnVuFK90hPXHvFcCI5fXGZVqJJKkAk3Fj9owmKTB7cyTCjF1WxX2SdR6De3hgPBrHHvj5Qcnw6lM4gSqm7m7I9GzRKnP0tnTlv1mY21KeaQ2gBTbbTilBK9RGm5NsN8l5ozfK4i13L3DHjnOqTWauIVPpknNbcKK89Jioo7jpSkBsNE6kBPaJSBy5nHv35OihKkpjtAFHZ20C2n6vu8sJx6RBjJCGIcZpKSCkNspTYgWuLDwJGA8l+jxxb41Z24yv03NuaqUhpL9VYq+WZs2OibT22nAIqmIqWkvJ7tita1qSsOJKbWw2lcd6rThxKzPnXi3VqZXMvTKhAi5Dp7ERt6PDStLceYC62XFXCkul4kthJNwbY9fppsZD65SI7CH1iynUtgLI6Aq5nHHKTBcdL64cZbpToLimklVuVr2va19vPAfP7K3FDiDm7MHD6r5s4uyW4uXOIVWoIqEeTGVHnNmmdtGQ8+lpDbpWtfYhSUpCgrui4Bw8zRnmtcVuFdIqVa4ySV50n5uoaajlVqNHbGVnhUdICGCgPFSCnTqcUpKvatY496ppMFDYZRDjBsK1hAaSAFDkQLYHyTB7VT4hx+1WbqX2SQpR6EnrgPF7fEDjfV84R+EqeL1UgGBWM0092utU2KqZLZhR2X4xWC32YWC4pJKUgFIta++K+nj/AOkJl7IsfMv7LHswSc15Gg5lcD8BlpuhrVORHfca0o/FpaWVq7TXpUNRGkEY95/J0XX2gYaC7k3CBe55m/Pfr44HydF06OwaCdGiwQLafC3h5YDxDlvP3HnOFSyvktHHCO3T6ynMEhmt0RyDU5LrERphTTb7yWQwHUuLWDpRYo2IvuJn0d88Zszhx1oeac8cRp8d3M/DumzY9HWG24tRkocfQ+WmyLgjSlZ02N1eFsewY1IhREpTHiRmgi+gIaCQm/Pl47Xwf5MiApUmMylTYKW1BAugHmAeg8hgF2rb2+P/AMMKYI2goJubjpg+AGBgYGAGMbqf9kz/AIip/n5xsmMbqf8AZM/4ip/n5wFX4LtyMw8QOM+WUa2oLefnnqi4CQXUmBCCGQelwkk+WPQSksU2JdtAQ0wiyW0CwAHQDpjHvR4abTm7jU4EAKVxDf1G25tToVr42lSUrSUKAIIsQeuAjmors/S9OcOhQ1JZQrugeZ64eJYaYSA02lNuQSkDDb1B2KP63yi2m9w2sakDyHhjhmvMkCoRglB27VB1J+PUYBOMqZOS4XJKo5QvStpCRdPgL+7fbxwsY0KnIVKKSVJ5rV3lHy3wm84YUszSdUd9KUrI+gRyPuN8PnWGpLJbdGpKxvY4BoHanJSC0wiOkjZTh1K+wcsBVLS73p0l1/rpJsn7BgrkVUZKnTVZDbaRzUQUj7cZP6QHGaq8L8gRoeWo/wApZ3zbKNHytDKQO1krv8+sdG2kXcWeVk+eAzHjpUXPSH4sN8CaI4HMk5OdYmZ8fR+LmyrhyLTArrpIDjo6DSk+1bGmQtAYlLCkpsooJ5BISnSBboLDl0xVOGPDuHwnyJDyrGmrqFSkOrk1OpvD56oz3lFciQs9Spd/cAPDFsLDfbswlGzaWytYH01DxOASOg06GCkkBxtNvK+6sJUpIcrNakquVpebjpV1ShKQdP2qJwWtzTDZef7MKMdCBHa6FxRsL4e0qmJpkXsXHS684ouyHlG5ccsNSvut8MAeQdc5hCd+xu8r3chh0ee2GkDU645MV/bFWQPyU4dDlgBgYGBgOLQhxBQ4gLSeYPLDWCQlb7LTmtltdkKvffqL4POcUlkNIJC3joBH3/dhdllqO2llACUoFhbmcB383hhCdGiyYrzEpptTLjZDurlpsbE+FjyxydUIVMaDs+QhkHYalbqPgBzJ918RbgqGYk+rqjrp9Ncv2hd2eeGxsEjdINuu+AUoKHp2W4fbOKLhSS2tR7xsSEEn+CBhzGfBk9otKkKfT2bgPRxJ/MRh8hKEIS2hCUNoAASOSQBsBiNUV+ruzgO76yHAD1Snb82AkwB5ADyGOdokgkOosOuoYbVFSQ03qJLSlpKyPqnBvUIerV6q2PC3UYBylQULpKT5g74LobKtZbST423wgqnsEEsHsFDqnl8RgdlPAAMtsDyb3/VgHC1gJK3NKQncrO2GbjipxLbOpMYH5x0/TPgBhRMJC1apD63lH6JNk/ZywqtbUdIU4UoSnYb2GASnlxEfU0VJCFJCyjY6OoGDMRozVnGgFk7hau8SPeeWES4/OSW2gWmFd1Ti02v5DDxKEpSAj2UjSMAOpN+eBgYGAQmSFxme0QjUSoJ35AnxwUKqQv8AMMG/UOEX+7BpwCob4P8AczhVknsW9/oJ/NgGjzcp8Dt4cS/ipwk/cMAx+zSHZzqNCD3G0juA+7rh9c2528/DDNgeuvqluJ7iDpaSfL6XxwHfwmYkbKjsnkD7Z/Vhwyy0wjQ0iwHQdT4nxwa9r72v1w0cW5LeVHZJShGzih9bwBwB1zI7ailJU6s/RaTfHPXF81QZSQfyAf04XaQhlPZNJCQOo5n44NgGwdhzgWSFdoNwlYKVI8xhJpj1hx1DjykSI6hZ5OxI6EjCkxI9ZhOfS7Up1dbaTt92A4tMeeS4QhLyO6o9VYACWWFFqeQmxul0ewofoOFlSoqUdqZDVhuO+MH7qhcgKB8QCMETGipX2gjt6vHTgEGHUIlr0KCm5IKkqSdtXUYzn0YCmB6TvHmkFxalzG6BVVlXLUthxs/9AY0r1RlDwkpTYjoOX2fDGd8MZjNH9NrNlN3ScyZCp8sbWC1xZLiFW9wdGA9L027bkuOTdTb6lb9QrcH9GOkaqw2VfRjnSPO++OLuzWGzeyXmSi/ipJv+bHZd0VCI/wAkq1NKPgTuPzYCHzhZUugx3EJU09VWgtJGytKVqT9hAOLPY8r4ja5R2q3T1RlOrYcSQ6y8j2mnUm6Vfb06jEZTM1GO78kZoeYgVBnbUs6WpKei0KO2/VPMYCyPNpebU0sXSsEKHiMR/wAnG3ZrnPKYR3Utg2sPMjc4rud87LorcBikzGFOT3Vth0N9vpCUarBCSLk9NxyxUp+dqtNE2sQqu6H4SY7jMZkER1oNw6FhQuDq1JF+SgMBq8NuKylSYyECxsuyrqv4HzxlPpMV+C1wqrtEb7R+Y8iOS00NRaR6w3dbh+in34Qyy3IYqcSA0+7TpS478eruICi5dKrh0k3BUo3sob292EuO0OBSOC2YYlNipYbdEYqefWS8+fWGu9vuo+ZwG2J9ke7BH3m47Lj7q0oQ2krUpRsAALkk4On2R7sNatTo1XpcylTElTExhyO6AbEoWkpO/TY4fNid/B5tif7IDwbmZwby0INbap70n1RuuOMIEMrKtIWe/rDZVtq0+fLHphl7tQCCkgi4KeRHTHz7g/7HhxBTm5ukz82UVWUkvguTELWJjka47nY6dIUQNN9XiRvj3/DYRHbZjtbIabDaQfACw/NjscYw8Nwzi/s7JNomvxb+E+TlcMza7L7z8bSK7W2jbxjzOsY9xp44VfhznLKOQ6DR6MufmtMlxqdXqkqBAT2Gi7CXQhWp9faXSiw2STvyxsOMl4/8Lc3cXKE5k6l1TLEeiVGI7GqCKtRzMfQVbB+OrWAhxAuU3SdyDfHHdYd/0m+EEDNq8i1XNbUatRpzNLmt+rvFiJMdSC2y4/pDaSskBNyNXTEYn0quG1VqUVjK1Zh1CC3PlQapKd7ZnsDHjuvOhgdkQ+tAZUFJBTp8b93GR5O9GjiRUa7n7KtXzImmZKmZrpctJlU8vVCpMwWY5S43I1gJDimtKiUqIsSCL4vEP0VHGMkZXymrNzaf2Ozq9OLyIezxqLchGkAq20esXv10+eAuNF9KvgbX6RU67T88x/UqTDZqD63o7zJVGdXoacbC0gupWvupKAbkgDc4tHDji9kXiwxPkZHqy5gpMhMSe27GcYdjPKQFhtaHAFJVpINrdRjAuI3otLh5VplWazDVZj+T8qUmkQG6VShIkLlwJbb7cgMlQDiCUd5rmRyN7YuXoq5f4hR5Gfs9cRob0eZnCvtzIqHqd6g6WWoyGgoxitamhdJAClFRABPPAegMDAwMAMDAwMAMDAwMAMDAwMAMDAwMAMDAwMAMY3U/7Jn/ABFT/PzjZMY3U/7Jn/EVP8/OAT9Hn/dZxp/4w5H+b4WNoxi3o8kfsr40m/8A9Ycj/N8LG0XHK+AFhzwVaELQUKQCCNxbng+BgIyKgJMmlL7yGwNBO/dVfb4EHCtKeJjerrFnY57NSTzA6faLHBZP4PUmXh7MhPYrt48wfz4LNPq77VQbvYEIcT4pPI4Blm+t0nLFDk5ozFOYh0qjIXMnPvK0ttMoSSpZPkN8eWuGD87i3nud6S+dGlQflCOaZkqlyFd6nUa5PrKkHZLso2UfpJTpHU4keMte/qjuKLnA+iyFqyDkmSzMzvJbJ0VOcDqYpKVDZSE7OPD+AnqcaWr5NhISx2cdlDdkobQkX8gBgEkyUqdNQUFKbQeyjjqs9T/pwqhCmyuZMWNYBFk8m08yPPlzx1htTizMfAbQgHs0ctCPPDabNbLC5bxDcGKO0WvqsDkPME4CNrSl1BwQWmx63O7NSEat2mUKCu1UfhtiemudlGWr6RAaTtbc7YYUKJICHKvPRabUVJccTb8Siw0tDyA5+ajh21+FSlSP7UwdKB4r6nALstBlpDXVCQPj1wfHTzxzADA32sL774G/TDeY64hoJbNnHnNAV9XxPwwHEkSJ1hctx779NX/wvhtUqlKblopdIbQua6nWS5u3Hb+us/mHW2FpkiNQqY9JNwhhPIc1qOw95JthGh096JFXJmi86ce2kHzPJI8kjb4YA0Cgw4TvrTyly5nMyXzdV/EDkkeQw5cnxknSFl1QO6WxqP8AowmGvX31l5JLTKihtsHbUOZw5bbQ0kJbbSm3gMAgRMlAhQTFaPT2lkY5JXHZZEQIUsqRoS0Odul8Ky3JDbKlx2gpzwv08cIwTF7xSpS3lnUvtBZw/wCjAH09jTtD6knS0Qrrv78KRUqTFaSsd4NpN/I4QnhSUNuHvIZc1LT+Y/DDsbgWvYgEeeAGO7E+74455Y7bfYb4COQl8yVx5M59Kj3kFBASoeAFufjh03Bioc7VSS459ZairBKj3YinSO80Q4n3ggW+/Dm/ngO/oxzwHhgYGAGBgYGAb1A6Ybg6uDs0jxJw4QnQlCB9FIH3YbLJenoaPsMoLp81HYYdHy6bYBrPcWIxDR06ylu58zvhyhttpIbQBpQnSCOuG09faBEJH41wgpHgAeeHVjztgOfrthtTQBDTYfSN/M3OHJFtyOW+GikPQlKdaCnGVnUpI5pPjgHYAHLAwmzIZfF2nAT9XkR8MKYBGU0t1KFNEa2Va036+WOtutTWjrSLfTQr6J88K28Rhs82+1IMmM0lzWjQtBOnAF9Qcav6pJW0Pq6QU/C+OPOTomjtFsvBagm1rK3+7C8eS1IOyVJcR7TahZQ879cJL0rqehw/i2gWx5k74B3vyIAI2IvjIcxuO5a9MHg/mlLjgZr1JrOVXQANIV3JLe/idCx8Mam5KUtZbhguL6qPsA+N+uMg9JdAyxlrKfFPtXS9kXOVIrMp5A73qZd7CSlPgOyeUT/BwHrapKDYjyBuWnU28wrbDmVFblMKaVte1j4Ebg4j0vmroaEXeIShwvEbL3uAkeHniXBFvdgGcGSpZVGkCz7Wyh9YdFe7C0qDCmIDcyIy+kcg42FAfbhKVELxS+0vs32vZX9YeB8sJer1CTdMmShts7FLI3V7ycBC1bLWV5THqMWndjIQ4HELgJCHGljkb8hz64aIytRY7MdmVDZU6wtS0BSe3kKUpWpSio7Ak78rXxZZaEQIOiIkNXWlAI5p1EC/md8M1R2kSHYp1Ibba7ZZB77xJN7nwwDcKWFL9VbWlexcUFhaz/CWrZPwxnPpANg8H8wuC6xaPqUknQPwlvms95R922NM7EqRHC20uLf3aZGzTYtsSOuM99IdrRwhzE27qkvobj61W0oaHrDewGA11NyALnljpJAtv8cMqoiprgrTSFMpk7aC8Do872xBBniJ++aN/kOY3pj5o35oRXyTWfyrPoSeYHjjqAEna2Kz2XEX98UX/IXjnZcROsijeXza8bfhoj/VVFGptPTlstW98c0pJ1KAuDscNaWKiIiBVVMrk/TLKSEfC+PPfpdUytNLoud3ZHytlXLUKbKrWWmq+ukyZCe4UzGHEqT2jjQSoBCiASvYg2xF47LMTv1ej9CNu6NuWCudkLldgBtc7Y8sQ/SoqiokmPTEUxsMZ5oOW6YxLUpMl2mTmmV9q4hStRcCXDv+ST44zDM3ELiTxM/awzhxGzDlqnZZfn5lrL0JmBJW0GKaw4W1vhLoLygWysAWAPQ4MvfNhbAsm97C+PFeWPS/4mVuY5kJ1qixatUK5S6fTswVCF6tFYiTYrr6HXoweWQv5opQguDUVC9uWLFP9IjjWxxUe4eZfoFPr68tOUiPVnIkLS3UhKVd6Q24p78HQ2g3SLOa1JUm4wHrPAx594t8XOIeUuLNOycy7TaNlquU1cemVJyEqYuRV1IcUlhzQ4ksWCApJUkhe4uMZRw09ITjjEyZkJVTrlEzCJGSqnnCsyDEWqZIbiupSmO3pXpLp1WUogAeHiHtnAx40y/6VPF2ZlZNXzBTYlPTmFFNOXJqKR2wfnSSsqgIZEi7gShAUH1LQOd07WxFM+mHxqr2R/2T0ShZXgPUXJM7NVWZmIcdDz0SoORFMtltVkBfZFWrvafPAe4MDGI8CeJPELNWZc35O4hro706gtUufGk0xlbTa2JzBcDSkqJN0FBGoHvXvYY20YDuBgYGAGBgYGAGBgYGAGMbqf8AZM/4ip/n5xsmMbqf9kz/AIip/n5wDLgFIeZzbxo0RFuI/bCkEqQoE3+T4XQ42L1uc+fweApIP0nlWt8BjJfR6B/ZXxp/4w5H+b4WNoAOAYg1aObrQ1JSeiO4R9vPHFOVNQuintJ8lO/qGJDAwEemM+XUyJ8hJKDdCEiyEnzPXGMelLxfq+R8vQchcP0IlZ/zytdOorJ/7yaA+fqLg6NMJOq5sCrSOuNnrU5NLpsuqGLIk+qR3HyzHTrdc0JKtKE9VG1gPG2PDXAniZk3iRnDMnE7iFmqnQeJWZVOU2Jl2oqVElUKltKUGobTbwSpale26pAIUqw3AGA2bhvkGjcJch0/J9FW9LU135Ex83eqE103dkuq561qKlE9NsWuNEbZSAqy1r3Wsi5v5Ybx1Gd2KylYZYRq1FJ3Xa32W64eqcDaVOKIsjc4Bs4j1+UY6yQy0qzm+yleBxHPsisVr1BxWmHTUNvLZHJxxe6R7gBiTgIUI6FqHecJcPjcnb7sR6CmLmt1tfcE+IgtX21qQogj32I+w4CYWspSpQ8Cfu/+GG9OTaE0TzUCo+8m5w4/KFiN7eeG8P5hS4K9w0NSSPqk7fG98A4wL9MC1tvDCExxbTOloAuOkJRgF7b2O3jfDaahxTaX206iwvtLDe/iPswVFP7MWZlPN39vvX1Hx3wa8yP7QElA6jurH68ASfDZrMHsu2cbBKXkOI9pC0m48tjtY4Ymo1WjAGuBmTE2C5zA0lFzsXEcrb7kYdNerurUIclUdxRuW1Cwv7sKqkONoWxUo6SgjSpei6CD4jp8eYwBih+Otx+O2HWnLLU0DYJuPaSfAjHU1COe44tTRHPtARb48sRbMepUVNqRoqFO5pjOLs60PBCzsoeAPLlh3BrdNqbnqyVqbkjnGkN6XBb8lXMeY2wEghSXU6m1hY8Um4w2mJCJEd9KdJDvZlXUgi2CSWURNEyOSglxIUhPsrB8vHB6j+5+zSTqWpIR/CvgFJ6FKhvJAuSggDzwdpxC2UvBwBBSDqPhbBZDnYx3FlROlO3mbWw0DKlNxIDtglSe0d87dMAqZD8ruxEaEH+3LHMeQwFuS4SAp9YebKgnUdli+HYIVZRvY9LbDyw1QgSZi3F3Wlg9mlHQnqfhtgF32hIjrRq2UAAfA+OE4rqnUKbWLLZISq+DPvpjpSkp1OLNkJ6nz92ORmFsha3F6nXDdZ8fLALYGBgYAY7jmBzBHjgGzf8Atk7+S0hJ99ycOsNG1JjznkOEAPAKQo7cuYw6uDuFC3jfANHI8pMlbkct2fCUqUrmgDwx31eYj5xubqX9VaAEn7MOsDAIx31OrU26jQ637Sb3vfrha1972seeEJLLhUiTHt2rdwUnktPgcHYfTIbS6kWCr3T9UjmMBx6LFfN3WwVdFAaT9owmwHWZK4inytvs9YKtynDkEA3IJ25DDaNd6Y/IFikANo352O+AdHnjmO4GAapv8prA30spBt0N8LPR2XwC822sgXBV0w2BahTFqU7oZfSXCVfWGOjtahtpUiMDa52U57/LAdQ6t67VPShDKDpDtuvkMVrihkOPnzhvmjJ7q1uvVijy4jbih3kOKaOlSfAhWkg+IxbkhDaLApQ2jbbZKcN/XXVOgQUFwpNwvkgfHrfbASXov59HEL0fskZolPKMs0lqHPLh7wlx/mX9Xn2javtxorsxyUss05IWerx9lHu8Tjzb6KbbeUOIHFLgpOSsop9YTm2lJUNLRh1IKWtKB+S+l243AuMenWy00gJSEpHIDl8MAzNKKt5EuQ6evf0j7Bgj8D1FIlQQpKkEa0A3C0/HEklxKuR+3bHVW0kEXB2OAZykt1GApLK/bSFoI+0fmw0ed7dlmqIR+LSpDyOpRyV9nPCtNcEenrW4fmmlOaD+QCbYbspeeYEBv5sugvPK+olRuB7zgCrQpDYipWSUNqfiujnYW2P2jGfcenUPcG83PI2CxEUfeXWsaA4S7HkTkpIb0hloeDYO5+OM79IZbDPB7MqYVQjlDgjEtrNzft2hsb4DYU+yPdjuEg8gJ3vcWBwYOpJtv48sAfAwTtU7329+O9om9t8AbFVzjww4eZ/lRJedcj0WvPQARGcqENDymgSCQkqGwuAbeQxZ+0GrTY8r472ifP7MBS6hwf4X1PMbWb6hw5y/JrjBZLVRdp7SpCC1+KIXa4KBsD0GH6eHWR0xokJOTaOI8FuQ3Ga9Sb0spkAh9KRawCwTqA9q5viydonHA8k3uFC3iMBQYvAXg1DokvLcbhXllul1AsqlxRTGg2+pq5aKxbcoubE7jphdfBLhM5JpUtfDXLheoSENU1z5Pb1RUIVqSEG2wSrceB3xeO1Te1jjgeSq9gSAeY3wFbmcOMj1HNMfPU3J9JfzDEQER6m9FQuS0mxFgsi4sCR8ThKicMchZXdL+WskUamOFL6NcWIhs6XlBbw2HJawCRyJG+LUHEnyx0LSq2k3B6jlgM9a4A8GWYMymN8Kcroi1Bxp+UymmtBDjjZJQoi1rpJJHhc+OJWNwp4bxID1Ni8P6AzEkQ1011hFPaShcRbhcWwUgWLZWpSinkSSbYtpcSNzsBzOB2gPIH/XrgIqnZZodLqEqqU2jQ4kua2y1JfaZCFvIaSUtpURuQkEgA8r4l8Jh9BVpF7+7BgsE2wBsDAwMAMDAwMAMDAwMAMY3U/7Jn/EVP8APzjZMY3U/wCyZ/xFT/PzgEfR5Wn9lvGpPVPEN+/xp0LG1Yxb0eUj9lvGpVt1cQ37/CnwsbTgBgYGOYAqkaje5xU89cIuGfE2EKfxAyNRa80kEIM6Ghxxu/VCyNSD5pIOLaVbkDmMGwHmmZ6HE7KJEngJxrzXkwNBRapFSd+WqSCenYyCVoT5JWMV+o1T0pOHzDjPEHhBTs8UxttWusZKnBp1KB9JUCSQq5+q2s/HHrbDKpN6afJIUb6Cq999hgPLmT/SQ4S5klsZcmZiOXswFsFdHzFGcpktKhtbQ+E6v8EkYu2YEt1BVMp8Yocdkvh5p9KweyQ2dSlJIPX2fDfGo5y4bZA4k0c0nP8Ak2j5ghuoALVQhoeA8wVC6T5gjHn7MfocUbJ+YqRO4GcSs18P3ZC32G4Tcs1GloV2ZWkeqySoJTdNilCk4DTLlRKtNgT4HDb2KlqI9tiw8yFXxnE1PpY8OUlWZOHlA4mUxlHeqGVpPqFQUB9Iw5B0KPkh33DDKn+kvwoqlSay/XatMyXmRpQPyVmqC5THdfVsLdAbc96FEYDXfjfzw2kn8NiHluvc8rkWGDwpkaowmqhCdbdjuJBQ4hYUlV/AjbB3mWpCNCxcdOlvMYA5Ntj+bHRz5n4YamHIaIcizFAn6Dx1A/HngIntC7coBh1PtJUrb3g9cAs8y0+nS62k+dtx7j0wholxE/MFL7Y+g4qxA/hHn8cd9cU7+4o6n0/XvpT9/PHDHflKSmWtKGxuUI31eR/0YAqI8aW0iXHKmVL3StvulPvHX44Z1GM1U6VK+UG0reh6i26BZSFpF0qSeY8+mJV5xuK1rXayU7JSNyelh1wgy2mPBWqXYJIW46VGwsRyPwwDaFJcmRKTKkq1esR0vkn6TpQD+k4dOAO1BtDnstJUtPmo7X+zEZR0L/YnGUb3aQpTJOx0gnQfcU2xIyHAh2JLIISfm1e9Q/XgDVEaWmwd/nUA/bgPG9TYHUJWVDqB7sLSGUSElt0G2q4sbEEcjgrEVmPcoCipXNSlEk4BVQPZlKSASnbyOGTDxYpjbzYu64T8VE2v92Hbiw2ha1HZKSTiP3TEgx0j5xSwu3gLm+AeR4qWiXXFFx1W5Ueh8vDC+AeZ95xzADAwFqShsuKOlI3JVsMcQsOJ7RtQUgm1x44DuBgYGAK6008nQ6gKHn0w1W0IKw/HSotHurQTew8Rh2hxtxZbQtJUn2gFcsdsSSLgH7rYDja0ON9og3SRcHHcM4ASVvrZuI6l9xJ8ev34eYAFWgFZNgncnDenp0xEkpsXFKX8CcHlNrdjOto9pSSB54ER1LzCFJ5AAW+qRsRgBLeVHjOPJ9pCSoe+22DR2UxmUNI+iLE+JvvhOegrhPAfUN8KtrDjaFp5KAP24A2BgEpSNSiABe9za2G3ylEOyFLc8ShBIwDhbbS7XbSbctQvbAWpCEqWuyUJFz5Dyw3FRiXspakE/WQofowaU2ZUVaG1A6h3VDkT5YBJpozrSZIIQR822OVvEjrh3YJASlIATsANh5D44SiPduwlwp0qNwoeBG22GWYpj8WAliIrTInOpism1ykq5qt5AE4DJeKWZ2+GHHbhnxapYXITIfdyVX2G3AE+rTVJMVbqj3UaJQTuTyWbY9St0bNNTPb1bMBgJO6Y1PQBpP5TiwSr4ADGF8VOF8DPnCjMXDqGgMP1CEfUnQoBaZjfzjDl/rB1KTc+GNI9G7isni7weoGapQDVXaZ+Tq1FKrri1Jg9m+2sdDrSSB4KHjgLHMczNlZhdRenNVeAyEqeS60GpCEXsVak91VhvYjpiySpKDBdeSe6pslKh4EbfnweXDYnRH4b6SpqQ2ptafFKhY4rVKkVCmPR8qV1AfafQWoU1G3aJQm+hxJ9lYH22wEstF4sKmpSe/pLg/IAub/HHW1LhOv9rHdWHVlYcQnULdBtysMJsIMhllRldlIiqUjUbHUOW4PQ4M69KjAFVVZN9kpSxqP2A4BBEpEZJajTClsHZDsdRsPDGc+kJNDvB/MI7eIolMb2WVJP7ob6nGk+vVRY+aDrh8fVdI+84zn0hZFT/adzF64laBpjWHYgj90N9QcBpWZsvnM+X5tB+WalSjOZLQm057spLF/pNLsdKvOxx404W504iZfztKy1Qs25qzzmufmfMVGpkLMde0UuNTYCm9T7wQ1qW4ApKUkbkqJ5Y9xJ9ke7GQ1f0XOGNUbU7Fdr9JqIrc3MEeqUyquR5kaXLAEjsnB7KFgAFFiNsBmlJ9LTiLneqUbK3DvhNTpOYZVNq0qpsVGthtiBJp8wRnmUrQg9olRIKFWGyk364TpXpnZhz5Q5Ne4ZcMGKi1lvLyMw5nbn1UR1Ru86lUOOQgh10Fhw6yUotbxxL1D0OsvPZ/yy9l6rVjL2VaBlefSAaTWH41RdlSpaXnVreF1LC/nCok3KldMWuq+iFwbmxoEKBDrNFixKS3QZDFJqrsVFRp6VahHlaT88kkqJJ3OtW++Az2semPmWbSKvn3h9w1iVjJWXH6ZFqMudVPVpjj8xLK/mmghQ0NJkICtRBJ9nYXxIK9KrOb1OzxWmcoZapNKyvmFWWIMyr1l1HyhNS4ASlltpS7aFAhCQVqUCAOuInjH6J9Yz1m1UHJNBpOXMrz3qY7UJUaty2g8IhRYrp6Edk64lDaW0LKraRuDYY1ef6MXC+qUSZRw3WISpWZl5vRMi1FbcmNVFc3mXB7G1xbcWJwGXZV9LvPnEljJtK4f8LoUiv5pj1xUhuo1JceNBdpklDDtz2ZWULKjpuAoEi454pTXpNcRsyZ/XxLyFk4TI0Xh3KqVWoVQrC2Y0RyHUHG5XZlKSl109ktKFaU3FidjbGjT/AEMqBFzpk8ZYzFmGlZcy9CrypD0atvN1RcyovMuLWHx3lJUUu3B5atsaPQ/Rm4TZehOwKPSJUZiRlh7KbiES196A6pS3Lk7l1SlrUXD3iTgM7yn6Yac3vU2h0/IrqMx1qu06HApy5g+cpUuMJSKiTbZKWQu6frJtfpjnF7jTX8hcR8/ZjaZen0zhnlGny2KMiV2CJ0ufIWkuOGx9lDQAuDa6jiY4Zejk3lXjIriRU6fTo0LLGX42UMox2HVvSEwGhvIkuKA1OqHcAF7JvvviyZ+9HvLvEXOFSr1Zluop2ZKE3Qq9CaJQqUhl7toziHAboU2orHI3CrYDJqr6XPF3KMrMIzlwYo0WDkiRTF5jfh18ulEOeUhkxkltOtxGq6wopG22NK9I7j7N4JUiBUKTT6JUZb7T8xcGXNeRIfYZAUsMtstLJNie+rShJtc77WDNPo68Nc4tZtZrkSe4jOyaeirBuWpGtMIgsBFvYtpF7c+uOcUfR24fcW6nErOZXq5EmxadJpCnqXU3IipEF+xdju6PaQogG3iOeAsuX86x8z8Pqfn2i06S6zVKS3VY0QWDqgtoOJa8NRuE+/FH9HPjTVeNOXKjXKvTKTS5UOUGXKXFlOuS4JIuWZbbraC26DtsCk7kG2LRk7g9lfIkGdTcvT62iNOgRqd2b1SddTHZYZ7JvsQo2aVp3JSBdW53xX8oejRkPJldZzRBrebJlYTURUpE6dW3nnZq0sFhtuQdg62hCjpSRYHfc74CjcHFZiztlbjlRX82VVmSjO9cg0+cmUouwEpbbLXZKJOkINiEjbn4nGqcCc5T+InB7J+dqqhKZtYpMeTJ0jZTpSApXxIv8cVur+jrQWGK1KyNXqvQqvVp1TqhkGa64wJs5sNOurY1BLgSkXQk7JUAcaNknJ9JyHlGj5MoaFIgUWGzCjg89DaQAT5nngJ3AwMDADAwMDADAwMDADGN1P8Asmf8RU/z842TGN1P+yZ/xFT/AD84BP0ef91nGn/jDkf5vhY2jGL+jz/us40/8Ycj/N8LG0YDijYcwMMX5UxyQuNDS2OyA1qcva55Wth6oXFsMJoVFcFQYNuQeSeRT4/DAJerrjzoqlPLW44pfaEnunu9B4YlRywynNOuhEmKR2rJ1IBOygRuPiMLRJjMpBUgkKTstB5pPgcA4whOQXIT7YG6m1D7sL44rdJBF8A3hr7aKyu9wtsH7hiLzVTJk6NFkUtLSpkGUiU0lxelK9N9Sb9LpJHLD2mHs3ZEO5KWV3RvySrcDD8pCtiAcBBUnM0SoSlU+Sw/AqIGpUWSmyyOpQfZWnzScdzRknJ2e6W5Rs6ZXpVdgOgpVHqERD7djz2WDbDiv0OFXIojSQtDiD2jLzZ0uMuDktKuh+49cR9Brk5EpWXq/pTUWk6mn0ps3LbH00+Ch9JPQ78iMBh1c9DHJWXZL9X4MZzzVwyecABj0aaXqcD0JhPamtPiEhOIWfTfS74cFRnZZytxXpTYGmRR3/keqlNtyph0qYcVzNkKT7serQlDiO8kKCvHkcMKehtuRKYaALTbo0i+yVEbgeGA8sUz0oOGaaj8g5/brXDytjSlUHNlOXBBUejb5uy5/grONUhyYFSiom0+XGmx1i6HmHEvNqvvdKknljUswZby9mmnOUnM1Dp9VhODvx5sZDzZ/wAFQIxhtc9CrhIJT1S4bTsycM6lIWFLkZSqi4jSjzsqOdTBHiNGAnRNeXrUxCU6htSgTrSLW8sRlSzRFgiQ0p1pt6K2XVtKcCnNhfYDa+45nFBrOTvSu4Rx6hUabW8tcV6NB/CXIUqMaTWFs9dDqCWFqAHVKL7nFNy1xjy/mnM0nKdbRV8p5nqTiXEUKutCEVdpYPaSvuyE2tpU2VXFthgNRnV6UqoRafLEumS7KUpzSl4qUpN2tPQAm9wN9sS9L+UM0xUOVd9LUZhZaksNCynnW1EK1K+rtfbxthCLlGDAWs1qYmchTJYYa7EhZBIOs29pQsADtbE5EpqExm4qI6Y0RA7sZHOx8bffgFf9sClDVkxWtioC2vyHgBgSSh51qEgalEha/BKUm/24O8+UERYqQt7lpHsoHiTgzDCIiFqUsXPeccVvcj9GAW5m+Bhkqc4Vdulq0XUEi99ZJ6+7D6xA367g+WAa1L9xqHRSkJ/9IYIpSI84vST3VthLauidtxg88kJZ1GzQdSXT4J/+OHKgCNBSCnmBb7MBxK0KGpCkrTbmk8sElSEsNKUlOpw7IT4+eEHECJITJQnS0oaXAnbT4KOOrsqpRzcGzK1A+epO/wB/34Dnqo0dvPecd07lJNkfdzwantdnHCynSXiXCBsBfpbBZii6puENIL1yu3RA54d3ANhytYYAYby1uqUiIwbKfCrqP0Ugc8OMNnbJnxlkdFj7sAm60zGMV1pGltKu8rkbEW3+OFpjgaYcAPfKdKQPpE4Tqy3BDW0jSVukgauV0979GBEbckIbmy0DWQFIbBulNx+fALsNhphDYFrJAPv64UwMDAAmwKrXsCcNqckCMHOrpK1e/DocxhrTvxLg/wCFXbyF+WAVlEiK8R/c1fmwI20doDkEJ/NhOcolpLCTYvK0X8B1wukaEhNu6iwB8QMA3mBL7jEQgEKV2i/4I6H3nDoG1rbAcgNhhpFKn1uTFD8YAhsfkXwtKlR4cd2ZLdQ0yynWtSza3lgFCAoWKQb9CMM1CTDdIjNa0PbJF7hpWIh1U+fAfrNVmyqdAaQp1tiPZDimwL3WvnuOQAHnhpSK+9TVCNMpstEOU+kodVJS8pntQNAVbffc8zbywFpjMerspaPMXJ95NziNkgO5rpzT3sNxXnWr8i4SlJ+xP58ScyXHgsOSpTgbab3UVG1h5foHM9MRNNblVeoNVyY0YzLSFtQ2V+2pLmxWv6twBYYCaAKQBcgix25j44x3hKqRwd9LvMGTW0NNZY4wU9eYYV1kJarMVKEyW0Jvpu42oOGwubeWNi/+FvC22Mi9Jil1ePkqn8UcrwfWMw8M6rHzPEAFlOR2iRLZHU64/aC3UgYD10nkL+GI2u0RisRUILzseQw4Ho8ho99pzkFC+x57g7EYQynmmj5zyzSc35flpk02tw2Z0R1KrpW04gKSR8DiawFHhZijtLVAzHS3ZNWYWWXPVYK1h0c0uCwsEkW67G4w9ZrszvKpeRKilZ2BdDTA95JVe3wxatKRsAMCw8BgK2JOfHrKbp1Higjk5KccUPfZAH34zr0hFZyRwhr6p7dHVHIj9qWlOpUB6w3yBBvjabDwxmXpJHTwXzIU7fNx/wCcN4DSgohI3AAG+Dah44ZVKmxKvCVT5yVlp0DUELKTt5jfEH+1nlM7+qyv+Wvf0sBZ0pbSe7tjqiDtfFX/AGssp/vWV/y17+lgftZ5TG/qsr/lr39LAWdKUq6bWxWs68Ssi8OPkxGc8yw6QazMRAgB8m8iQsgJQkAHckjy3xO0umxaTERT4KVJYaFkhSysj4kk4zD0l8o13OeQoNNy3Rl1GcxmGjy9DYGtDLc1pbqgTysgE8+mAnahxm4b03PjHDSo5ths5jkltDcJQPtrBU22pwAoS4tKVFKCQogXAxdu3AUEl1IV9U7H3Y8lZz4V8Q52e8x5LiZLnvxczcQqLnJjM6VN+qxYcbsVOtrJUFh1BYKEoA3DgN7Xwj6RHA7OefM8cQ82UrLdWmzGqZlpnK8iNMW0UPJmO+vFiyxpWGSNR2ulVt98B661IcGoWWL7EG4/16Y4HmEhRDzfcNlKKht7/PHm3LPCriRlPJXHDIXD6NJy/HqEl1zJOuSdCFOw0dqWVFRLYL2q3LSok2xQHeHi1UTJaofo6ZyjZShvSkZxywSDLqc5cZKWJi/nvwlCHA5clVwpSV26gPaa5ASkHWm6lBIwxpuZaJV5lSgUysRZMmkPiNPbbWFGK6UBehduR0qB+OPDSfR24u1zLdaXnXL9dm1ek5AQcqJ+U1lcGqeuyFsshYXpVIaZ7BBWbiwtc3OHsvgvn2kVnO78bhTWnkV3MdDr9bNPWlHy7SjGaTNhIXrHzgf7Ra2+6FAGxscB7nEgKSClaTflY3uPHbAEhOtKC4i6x3d+Z8v9fHHibLmSeNGQKvEzrTeHGZ38rqq2Y2KLleLIQZdHgy47KYvaIUsAI7Zp1WnUezDgxaPRS4L50ouZaTmzixluqsVKj5Ko0KGudLLqI84duJVhrILoSpCSu17G1zgN+y3xbyXm+rGi0afKcmGZUIKQqI4lBdhLCJA1lOkWUoAXO/S++Ln6wkjUHUaRsTqGxx4Uzjwg48phZtRlrLVbZdnN55diCLJDZW/KkMKhLTZYAUtKVlJPKxuRhfPvALifQHq3QuHuV6m3kl6o5eqc+lNH1sTwmO6iYEMuOjtCHS0pxsrAXa+5GA9zocKtJBBuBe2+FMZb6OeVqpkvhVR6HVahWn1odkONN1eImPJjMrdUpDCm0rWEpQNkjUbJ0i+NSwAwMDAwAwMDAwAxjdT/ALJn/EVP8/ONkxjdT/smf8RU/wA/OAa+j5KaGc+NEVS7LPEJ9QuLA/1vhcjjbbjxxiXASMzKzNxpadBUP2w5BG9ik/J8Lkca76rVWzoRLZWgeypaO8B4bYCQuD1GGFUc7VKae13nH9j+SnqTgCnSVHU5UpF/KwGF40JuICWypa1HvLWbqOAWQAlNtrDliPITJqaHYqRpZB7R0fSv9EeOFKo7pbRGbKtb6wiyeenqcO2WkMthttGkJFhgDgWAF74B5csdwMAxlRXQ563EKQ8kWUFDZY8MKQpiZQUCgtuIsHG1DdJ/T78OTy5XwxpYKy/KX7brhHuCdgMAtOfZiR3JclYQ0whTjiidgkC5J+GIDLkaXWHkZsqzam3HkH1KMeUdlXIn8tQ3PwGHudY7snKtTaZvqDBXYfSCbKKfiAR8cSVOkszIEeVHADTzSFoA8CARgOS3kxIzjxHsJuB+b8+BAjliM2hf4wjUs/lHnhGsn8AdHiUD/wBIYfgi3PAdwVZSlJJIFtzfHTZQt44jdKqm+tDhIjMq06QfbWN738PLAFSkVB915TYVGW2WACPxgPMkeGM/zBwzyJnhj9rfihlCl5jpqUrkUhc9hLikNgi6Er9pDiL7KSQbW8MaNVahDo1PcqMxXZsR03OkXPkABzJNgBiIo0CqVKpnMtabDCg0WIkMEEstqN1KWeq1WG3TAefnfRUzvkWU/J4A8ZarS22FHTl7NiVVamKbNtKG3FESGU3uO6tXuxBVLi7xV4dvCm8duC1Xo8UbvZnyus1imBofSUlI9YZ89SDYdces2zoq60ukBRZTo/K3N/0YLp7SrrBSLBgAgjnc+f5sBh2QuIfDviFSzVOH2baTXIlu+qG+FrQr/hE7OIPkoA+OJuesOdjEVclawVC30BhxxA9FLgpxFqX7I5uUvkTMaAeyr9AfVTag2o/S7Vkp1/4YUPLGXDhN6UXDVUqXkXPFK4pUhtRbFMzOgQKtpTySma0Ozct07RAv433wGnyW0uxnWQmx7M2A6W33x2KsvR2nT7S0hSvef/hjIY/pNZSo9QTlzjLlmu8LqvpCUs5lYAhOk7WZmt6mF/FSTjUYCINThtzqVVTKhPpCmnIshK2lA8tKk3B+BwDuTIjtoU28pJBHeRfvEeQwWnJeSwrtLhCjdkK5hPng6I0ZlOpttOwuVHc7dScJpqUJRKTJTcfW2/PgHSggpIO6T3VDEYth1iShplwt7n1dSt9uqFeXUYdLnw0m3bBauiUd6/2YK2h2Q+2+82W0M3DaDzuepwCkeMhm61KLjqxdTh5+7C2BgYAKKUoUtakpSkXJPQYapK5UlEgtFDDRJSFe0o+OO1D8QlJNgpxCVfwb74dWIv3CNyfd5YBCREblIQl/VdBuO9hZKQgBI5DljtiSBbc8scwAx0W5nljnLfDeQ872yYkZKe0UnXrWeSfd44Ds11QbQy0bPP7JP1R1OFWmgy2llu2lItv18ThNiIlhRcWouOq5uK5+63TBKitaYighWnWQjV9W554AqbzpCHydLDBug/XPjg81SyluKg9982NvqdcLNoS0kNoRZIsAP0YQjfPSXpSjslXZNjwA5nAOQlKO4jkmwA8sQYSjMFYcdeUVwaatIbTbuOyBzV+UEbDfrfE06hSm1BpQSsJVoUfrW2J8r4iMrPsCmopZARNh6kSWVe32l/bI6hXO42wD2tPTI9KlvQYyZEhDRKGlDUFeNx12vt1xn2XIsBqsxVxVpnOCQEuMriONdg2RcugbIQAeh3xeMzpafo0mOqc3E1p9pa9IVbfSTz3tbbffFLbp9cRFFUchJjKcWtK5KJSlh9t0lDaQg+zp1AC/RN8BboLP7IJHyxOSFQ0K/AmlJ7irbdsoc739kdBiaHUm9zzv188Ejstxo7UVpOlDKEtpHkAP03wpgBgrjDMltcaSwh9l9KmnG1C6VpUCkgjrdJIwbCUlDj0R9pl0ocU0tKFjfSoggH4XwFC9EKtnJVXzh6MVXkjtckyhU8uBxVy9QJaitgA/S7JfaNHyCMen9Qva4vjwhmZFIpXpOcIolIo61Zsmet02uRmHAtb1IdQSqW46Ddvs3m0LRqsTqUADj1/R82RKRGbpmapLsWbFJaW/JaUlDwCiEuBy2k3AB533wFxxwkDmeeGcWtUecgOQ6pFeSeqHkn9OIvNebYGXYDMg9lIXIeEdtJeShFykklSjsAAk4CwXHiMZl6SZ/wDmWzJ/4OP/ADhvD17iLUVMetwKI2pmJDbmzgZAUUoUsjS0U7KISlR6bYznjrnqoVDhZVnnG4qqZVZCYkUNkh1Cm30EKUb2IUEk2tgPQqSNI92DYia21mBdMWMtvRGp1x2apSFKbA63AxV/U+NX/wBrZX/5K7/SwF+xw+eKF6nxq/8AtbK//JXf6WB6nxpOyqrlcjr+Cu/rwF92x3EZl9uutU5tGY3ojs7fWqIhSWrX2sFEnGQ+lm9V6ZkqgZhouaKxSH4GaKO2pECT2Tctt6Y00tt4AXUjSo7AjfxwG36kEXuLY6Skbm2PEnFDPedqVnPOWfKZm6pjNOWc+0rLtEy8mUfVXqa802VpMbksuhxxZcI1DSLEAWxL+jdmiuP8Qsgrbz5VcwOZ4yTPreaI0yoGQ3FnNyW+zUhtRPYAKdda0JsLIG22A9h3R0IwNSL21C4x43gce/SNzLWqbHo1SyPFh5rrmZaRTg/T31uQEUlx09quzg7VS0thJTsBz8sXqo+kHmyocGeF2aaFGotLr3EuVFp6plQKl0+luraWtxxQuCsXbUlCdQuVAXwHo66PEYF08rjHkjiB6Q3FrJUnNJZrWTpTXDKFS3qw1IjrZezEuUe96qA5ZhIBsj29SxY2w1jekVxjqmYRIS9luHQa5nGtZGp0b1NxUuI+xFddZlOLK9KrLbCVt6ep3wHsElCeZAwLote4tjw7ws9ILjrP4bZSo0zOmVnswysmTM5S63WoRQh5pl/s/UtCVgF3mVubaQU93EzE9MLPVUy5WMwN0ulUt1uTk9MCDJZPaBqp6PWQbqBXbUrSbCw3N8B7JK20jUpSQBvcnAKmxzUnHmOmcQOI+fvR54s50zVPpLEWMxmCnUdulsrZeZbiKkM9o6sqIKjoHK1rX67ZllLi7xSpmbYWSsiT6U1UM0Vyj0d2bWC/LQ0j9jrckupb7QWVqB7osFbk+OA906k3tcX546CDyN8eJH/S3431xug0HJOTIE3MDFLnzakG46VsVGREnuQyyz2jzfYtrLRUV3WUakgp649l0WTImwIs2XFXEfkMIcdjKIKmVqSCpCiNiQTb4YCRwMDAwAwMDAwAxjdT/smf8RU/z842TGN1P+yZ/wARU/z84BP0ef8AdZxp/wCMOR/m+FjaMYv6PP8Aus40/wDGHI/zfCxtGAGOEm9h4Y7gYCOZGqqSXFC6kJSlPkDiQBuL4YTgqJIRUG0qUB3HkpG5T0PwxxyppeKWKeQ86rmU+ygeJ/VgH5WkC5Nhgahe1jiNejraR2k6svJSfq6UD818Cm9m1JkNNKUW+4pJKyq6bcwT57YCSVcJuOmGDBMKeqOfxcm60fkqHMYkFC4IwxkDtKpHR/c0LcHvO36cAK33qPOGwBjOg3/gHCGUzqyzS1eMRr/ojHM2KU3lerKRuoQnrf5Bw6ozSGaRCabFkIjthPu0jAGqTLj8J5tpN1lN0jzG4++2EvX5agEM0x4m25WQkD7zh+Qeh+3At9mAY9lVnxZbzUcHnoGo28icLxYiIqNCSTvqKid1E9Thxgil6Rcg4CHzjT5NRoLyIadchhSJLSLX1qbUFBPxtb44e0WoRavTI9UhqJalIDibnlfmPgbjDHMtcXT46YcBtL9TlkoisX62/GK8EJ5k+WHOW6Q1QaJEo7StSYrYb1eJ5k/aTgFao00qOp9R0ONAqbc6pV09/uw3WtwKp8tYIdVZtYO19QufvGHNUZfdbbLKEudmsLLZVbWB0vgmlqrQwUqUhQVcHqhYOAfhQO2I92BLS845EkpQh6xWlab2PiMcXImwUhctTbrA9tSQQpPnbrhZyrQEDuPpdUeSWzqJPlgGc7K9Gq9LepFdpcOqRJIIfZmMpebdB53SoEHGEZk9CnIdPfk5h4J5nr/CusLu4fkCRqpzq+fzsF3U0QfyQk49AmoSlbtUx4j8ohJxz5TQqO+44wtvsLhaVWvy5be/AeT6tN9KThvGQznHh9TOJdGWAo1TKC/VqkGualOQXjpUfHsl3PgMTGU/SF4QZslmgIzS3R60yoNP0WuMqp05lZ5ILLwSVH+CVe/HpJiAtdPaRr0PNXW0oblN97efhit5w4b8MOKlM+TOJGS6FXk20lqoQ0OKbPXQpQ1J94IwFYQ2hCAppKNKuWkAhXuIwcC4J2Fhe3h+rFMmehdRcvqMjglxWztw9WVdp6nHn/KFOWeiVR5WsBPklScQc2lemFw+JNTylk/ihT213D9FlKpNS0ebDxUyo2+qtO+A07Axj7PpS8OaZNTSeJlNzJw2qClaQzm2lrhtqP5MgamFDwIXvjU6JW6LmWAiqZbq8Kqw3E6kvwpCH27fwkEjAOX2ESGy2u9rg7YS+T0XumVITfY2Xh1bluBffc44CDuMA3jOuNrXDeWVqQNTZPNSf9GHJwlIjCQkEHS42dTavA+Huwl6xMaAS9CW4rqpspsfgTtgHOEnoyHwCpRbcSbpcHMYTE7S4hD8V5nWbXXpsPsOHPLpgG7DzoeVFkgBaRqSr6wwpLSkxngvkG1K+I5YTmhKVx3id0OgX8j0waa265GcbZBUopIAHXfAGjKUqMyo+1pB8+WEqdvFBG51KKrdDfB477UhA7JVlADuHZST1uMB2E04suoUtlw/SbJH3YBbphjPo0GpKS+8hTUhAsiQyrQ4keAPh5HCijOjmy1CSny2X9nXHRUYvJZW2R0cQRgGsXLtMjuiQ8hUx8f26We1UPIX2HwF8GzDFdl0d1uM2VOtKbeQm+50LSrSPHYGwwv8pR+dnNH19BCR78OgbgEHz25H44BCBPi1OKiZDdDiVDvAHdCuqVDofLC9weWIqXRNMlVSo8gQpqvasm7T/wD4RPX34ESsPtvNQKzCVEfdJS0tJ1MOkbkIVyB8jbASt97eRJ8sZbxb4o5kpdXpvCrhDTI9b4kZib1xI79/VqVDCrLnyyPZaTewB3WRYYs/FDiPRuFOS52c6w07KEbQzDhMJ1PTpjitLEdpPNSlrIA+/CHBThNnbhzlKRn7Np9az/niSmpZrmxWw4/CZ0/MwoyVbdmykhJ89SrEnASeQ+BWU+CWR6iiVUV1/OuZVok1avyu9Nqc+4KNO5KGkK2S2nupT8cbQmcsNht+It4shKX1pAICtIJ2xE0ymUGJHjVqlj12XOCQidKUVuKBF7lR5e4AeGLBFjiMz2ZUVKUSpSiPaUeeAjHqDk+ckS5FIpbocNw44wjc/EYjapw+y/KiMnL8GnQJEeSiU04mOlSFLTcWUBzFifdzxZBS6eF9oIbVzzunb7MFVS4wUVx1OMKPMtK0g4CkjhQ0prUutusPv9p66WGghDyVrKtKR9EDUR42JxSvSCyblqm8McwVKPJdK2uweajdoFNocLzabgWuNirr9I42n5KYdP4RIkPjoFu7fdjPfSKjsRuC+Y0sMoR83H9kAX/CG+uA1BGyR7sGxTeJPFHLfCuhIrmZVPBpx1thtDTalFSlLSnoLC2q+/hi0x58aVEROZcuwtHaJXYju+NjvgHOBil5F4sZV4hVCuU2gOvKdoM0wn9bak6yG216hcDb5wC3PbHM+8Wsp8Op9CpuYX3UvV2cITIbbUrsyW3F61WHKzZG2+4wF1xG1yhUfMMVECuUyNOjJdbfDUhoOJDiFBaFWO1wpIIPQjD9p1LyEuI9lQCknxB5HGX8UeKeZaFnKh8L+G2XINZzbW4b9SPyjKUxCgwWVJQp55SEqUSpawlCAO8b7i2Atcjhzkafm5jP0zKFGfzHFb7FirLhoMptG40hy2q1ieuOZd4bZJyhVanWsq5Qo1InVlztZ8mFEQy5JVe91qSLq3JPx8cUqb6Q2VeHqIdA4wVOBTMz+rtyqnHpKZE2JAacdKG3XXuzHZNqI2U4EjY+F8TR4+8LgrT+yNJHy6vLQKWlkGoIa7VTQ23GjfVyOAsMTh/k6C7FeiZWpbK4L0qRGUiOkFp2SSX1p22LlzqPW5wlO4a5GqmUk5CqeUKRJy4Gw2KW7GSqMEg3ACCLbHceGMrrHpp8Go2SK7nSgTKjWk0WkKrSIjVPfacmRg4GitkuICVpC1JClJJCb74mD6WPB9hRZn1KpRX49JjVqe05S5AMCI+oJbcf7nzYKjYX52J5C+As54H8Iw/RprnDXLS38vNBmlOGnNFUJANwlokd0AkkeBO2JVrh/ktK2nGMrUpLkapOVhpSYyRonOJUlcgAf2xQUoFXM3OKpxG430ag5B4iZgyfIj1Kq8P4CpMtl1tzsEvdj2qG1KFtV0kEhJuLjEJlv0suEc7Kr1frNefp8mBFguzIT1PkNPKXKsGvV2lI1vJW4SlJSDcjAWuV6P8AwXm09ikyuFeV3YUWY5PZjKprXZokOG63ALc1W38dr4eVrhHwwr9dj5jr/D/L86rRGUMR5r8BtTrTbZ1IShRFwEncW5Yq/wDVW8Fy3SP/AJQShJrU+RS48M098SkS2NBeZda06mlIS4lR1AAJ717Yqz3pfZGm8SaTQaNUWDlZyl1Wp1Osy4z7DaG4egBcdxQCHm1KK06k3uU7YDbY+TssxqFKy3GoEBqlTi8qTDQwkMvF4ku6kcjqKlE+N8M2OGmQok5iqQ8m0dmbFfRJYfREQFtvIZ7FKwQLhQa7gP1dsUGR6W/BCFTGJ8uvz2VyKimktQl0uQJipSmi6hAY0dp3kAqSbWIxsbaw62lxIICwFAEWO/lgKRV+CXCjMESFBr3DrLs9mmvuyYSH6e2sMOuL1uLRcbKUslRI5nfF0ZYQyAhCEpQlISkDkAOgHTa2FcDADAwMDADAwMDADGN1P+yZ/wARU/z842TGN1P+yZ/xFT/PzgEvR5UDmzjSBzHEORf+T4WNpx5y4O5ry1Q8+8YolRzJS6bMPEF10plz2mSWzAhb6FKBN7bG2NnHErh7b/d7l0+P9c2P6eAs2Bis/tl8PP8Af5l3+U2P6eB+2Xw8/wB/mXf5TY/p4Cy2w3fWzAZckFCUhI1K0psT/pxBftl8PP8Af5l3+U2P6eGdS4jcP3W2kJz1l1QLyNQ+VGOX+XgLFFhF0iZUAFvKGyLd1A6C3jhSPS4sWSuUwkpUsWtfYb3NhiF/bL4eD/6eZd/lNj+ngftl8PP9/mXf5TY/p4CzYYAFVYuT7Mf86v8ARiI/bL4enlnzLv8AKjH9LDOXxFyC04mdHz3l0qbTpcSaoxujnt3+eAnsyNKXl+pNISVFcN5IA5nuHHctSUzMv06SkghyK0oEG49gdcRSeJfDl5sLGfculK03A+VGNx/lYrozXlShKcdytxHyyuMVFXydJqbHZpvuQ2sKuj3bgeAwGlY4SQCQL4otJ4z8PKitcOXm+hwJre62HapHO3ilQWQoe7EqOJfD0Cys+5cv1/rmx/TwDam1nOWYIXyhTWqTGZccW2hL4cW4gpUUkKAsL3Hjh0aFmedYVLNq2UnmiBGS0PgpWpQ+BxXMqcQshxJVciPZ5y6hKak480kVRjSGlpSQR3upvixftlcOx/8ATzLv8qMf08BIUrLFJo6i9EaWqQv8ZJeWXHXPeo7/AA5YlEICBYYrf7ZfDz/f5l3+U2P6eB+2Xw8/3+Zd/lNj+ngLBKDnYuFo2XoOk897Yb0tTRhNpZO4HfvzCut8QbvE/hu0CtzP2XB1/wBtGP6eGsXiJw/clvTHM85eZQ4kICDVWLm3U97AXIoSQQoBV9jcc8FSwy2O42hNvBIGK7+2Xw8658y7/KjH9LDNfFHJLylJiZ5y0G07F1yqs2v5AKwFomTWoqAFklazZCALqJ6f/HCUWIstvLmITqkm60dEi1rXxXWM98OI5L7vEDLzjp3Lq6ox9w17DBF8ScjzyW2s/wCXWI5BBV8psFS/d39hgLHSkrCn9BUYwXZoK5i3O3lhy/FivEl1htRPMkb/AG4rzPEXh0w2llGfMuhKAAAaoxf/AKeEKhxL4fqY7BrPmXe0eOgf10Y2B5n28BYaQVKggFepIWpKD+SCbYeaE25dMVlniLw5jsoYbz5l0JQAkf10Y6f4eD/tl8PP9/mXf5TY/p4CTnwaFmBl6kVaFDqLB7rseSyl1CvEFKgQcYrmf0JOA9VnOVzKdGqWQ6y4suev5TqDtOVrP0lNIPZK+KDjSoueOHMaUuT+2Fl9ZUVFINTj7aiCfpb8sPDxK4dq2OfMuHr/ALZsf08BgMng16VmRFa8l8WcvcQqe2TeDnCCYUxSfD1uKLKV0upvEW5xb4w5bIYz36KmfmnArQqRl5yLV49h9LuOJXbw7t/LHpI8SuHZ557y4f8Azmx/TxwcR+HQ5Z7y4P8Azox/TwHmVn0tuDLK0s5pm5gyk4okacw5fmQ0ptzJWpvSAPG+LplnjLwkzklH7FOKOVqp2hslLFVaN/gSD8MbFJz9wwmNlqZnLK76FApKXKhHUCPCxVjPc1cPfRAzmpb+aMvcMJzlrKdV6mhz/LSQfvwC88lcMdiCtL5SG1IAKVG+1j15dMOlAg2UNyL/AOnGUL9GD0UC+ahlTPMrJqw52kc0LPLrASfyWy8psDyAwi5whq1F7mRPTtnNoSsrMfMPyXVkEdE6iEOAf4WA1l9kSGi0pWm42PgfHCLEgahHkkNuJtpJNgseI88Y8qT6R+XikxuLXAjOLI1FQky3aW8tPQJKFuIB94Iw0f4g+kY82kOZM4KuX20niKLpHv8AV/zYDcXYkd9R7VsFXMKGyvuwiDLh98LXJaHNKj3gPI9cYWjiB6SsdQVHyjwaCNXeaPEYKsnxBLG2HUXiX6R0xKArJ/BePrc0do5xEC0Np+soJZufcN8BuTbrbyNTLmtJ3uen6sHsCNSuXieQ+3GGqpXpDVteudx54GZT+cuVUsPTXCjzLryEk+ekYducKM1TmHlZn9PhZaWQeyoUWlU9V/q9oS4v78Bs5S4tJHZFYO1juD/r7sRFRqFJy68yqbVYkBp5RuiVJQ0m3UgrItigtejdwBqCAvOvpEZtzG44E9siZxAUyhW3IpYWgW92H9C9Gv0HaZMckPUPJtTWy4A05Vq56+u9rk3eeV9+APW/SL4B5bITW+MmUYqtWgo+VW1qKvqjQSb4o0nj9J4lVxmjej9kiqcQH46wW5wYch0Zp/o7IluJA7NHggKUo8sbS/TPRzytDbpGRKZw2psuoudkl2KiCjsU81uE+IHLzIxfaPnbhdRacxTKdnbLTUeOkJQE1NgXt12V1N8BlPDv0Za/KzXSuKHH3PBzZmOjOKkUmkwWjGolHdUCNTLJ7zzgCiA66SrqAMehgkkc7YrX7ZHDvn+zzLvj/tox/Twb9svh5/v8y7/KbH9PARlPq1KyyuZlbMMhDTTDpdhKcBIdYWSQBtzSbpPw8cSlOqlCqTqmqBWB6wkawxrULjx0KsSPMYSPEThuV9oc85bKwLBRqTFwP8ryxAZzz5w99Rj1WJnbLqpsOXHU0tFTYCgkuJSoX1+zYm4wF0lPmXTg+NQCFjtgk2O3MYURTGNIchvvNKUAQoLKh9h2xW2uI3DyJKcaXnvLhYlKKkKNUYsFdR7fW2DQOI/D+MVw157y6pCO+0s1RjdJ6e30wFk9TnrN11NSQNu40kfnxm3pGQlN8GcxuKnSHLIj7KULfuhvpi5ftlcPP9/mXf5UY/p4zX0ieImRZ3CPMFOg5wokmQ62wUIaqLKibSGyfpdBv8MBsk2mQqpFMSoR25DCilRbcSFJJSQQbHwIB94w60DfzxV08VOGekf/ADiZY5f/AGux/Sx39tThl/fEyx/K7H9LAT0alQITkh6HFbZXLe9YfKEgdo5pCdavE6UpF/ADAl0unz1xnJkRt5cN71hhS03LTukp1p8DZSh8TiB/bU4Zf3xMsfyux/SwP21OGX98TLH8rsf0sBZ0ICL264y3ilwjruZ850LiXkDNrOXM10OK9T+2lQvW4k2C6pKlsPNBSVGykpUlSVAg+/Fr/bU4Zf3xMsfyux/Sxw8U+GRO/ETLH8rsf0sBi2avRiz/AJqmV+Y9xRgR15/pMWj5yLVG/dDTBWEKh3WewUW3FIOrWOoAO+Gh9EOqxs0tuUviG0zlSJmf9lTFLXTdckSzF9XU2X9di3ayh3b35m2N0HFPhkP/AKxcsfyux/SwUcT+GAJP7YmWLn/8XY/pYDF3/RCYmZNoGTpOcldnRMjT8nqcTF0l1UlTahIACtglTXsdfHEo9whqmVKBxBzLmptzPE7NWWYNGl0ukQgwuSYzTrPzQWs21B0HdW2knGqnijwxP/1iZYH/AJ3Y/pY5+2hwwtY8Q8sW8Pldj+lgMi4Y+jjUqN6L03g5mWuuDMWaqfINaqSyH3BKfQE3JuNZbQltAPI6PPERL9ErNVcnQs2Zk4ox5Oa8uM0yLlyZHpAaixGoLqnG+2ZKyXVOFRCrKFvo6cbmOJvC5LnajiHljURpv8rscvD2sG/bR4Zbf/OLljb/APF2P6WAyXLXosuwc5pz7mbOCKlVZ9QrFRrAYh9g085OiNRQlkaiWwhtlIBJKiSd8VN70K61W6BT8lZu4oom5coGXKhlilMxqSGJCIz5QW3HXCshbiOyQDYAKAO1yTj0N+2jwyIseIuWP5XY/pY5+2hwx/vi5Z53/wBt2P6WAxrJnopO5frWVMwz6pl5qZlmvGsKXSKS5HM5HqTkZKHS46s6h2hVqBt0Ax6QHLFV/bQ4YWt+2Hle17/7bsf0sG/bU4Zf3xMsfyux/SwFpwMVb9tThl/fEyx/K7H9LA/bU4Zf3xMsfyux/SwFpwMVb9tThl/fEyx/K7H9LA/bU4Zf3xMsfyux/SwFpwMVb9tThl/fEyx/K7H9LA/bU4Zf3xMsfyux/SwFpxjdT/smf8RU/wA/OL0ripw00nTxEywT0/rux/SxmLOZcvZi9JV12gZgp9TQxkZCHVQpTbwQoziQFFJNiRgPK2ZAKnXJlQqQ9blPuJW6+/8AOOOKCALqUdybADfoMRZgwv3mx/FjAwMAX1KH+9Gf4sYHqUP96M/xYwMDAD1KH+9Gf4sYOiDC/ebHP+5jAwMAT1KF+9Gf4sYHqUP96M/xYwMDAdEKFf8AcjP8WMH9Rg6v3Gxz/uYwMDAAQYWpX4Gx7R/tYwX1GEn2YbA26NjAwMAZUGF2X7jY25fNjbBEwoW34Iz/ABYwMDAdMGFt+BscrfixyucF9Sh/vRn+LGBgYAepQ/3oz/FjA9Sh/vRn+LGBgYBRMGFb9xsfxYwBAg6T+BMc/wC5jxwMDAFMGFq/cbH8WMd9RharepsW/wDBjAwMB1MGFqP4Gx/FjA9ShAG0Rnn/AHMYGBgCmDC0j8EY/ixgJgwtz6mx/FjAwMBwwoW34Iz/ABYxz1KF+9Gf4sYGBgDGDC0/uRj+LGC+pQ/3oz/FjAwMAPUof70Z/ixgepQ/3oz/ABYwMDAD1KH+9Gf4sYMIMIkXhscv7mMDAwHVQYQULQ2OX9zGOepQ7AeqM/xYwMDAAwYVv3Gx0H4sYM5BglJvDY6f2sYGBgOKgwf3mxz/ALmMd9Qg61/gTHI/2sYGBgCNwYR2MNjn/cxg6oMLWn8DY6/2sYGBgCmDCsfwNjc/3MYMYMK37jY5/wBzGBgYDqIUMXIiM32/tYwmYMIXtDY5/wBzHgMDAwHPUof70Z/ixgepQ/3oz/FjAwMB1MKFqH4Iz/FjB0wYWv8AcbH8WMDAwATBg6bepsWsP7WMcVBhaT+Bse0f7WMDAwBPUof70Z/ixgyYMK6B6mxbV/cxgYGAN8nU+w/AY/8AFJ/VgfJ1P/eMf+KT+rAwMAPk6n/vGP8AxSf1YHydT/3jH/ik/qwMDAD5Op/7xj/xSf1YHydT/wB4x/4pP6sDAwA+Tqf+8Y/8Un9WB8nU/wDeMf8Aik/qwMDAD5Op/wC8Y/8AFJ/VgfJ1P/eMf+KT+rAwMAPk6n/vGP8AxSf1YHydT/3jH/ik/qwMDAD5Op/7xj/xSf1YHydT/wB4x/4pP6sDAwA+Tqf+8Y/8Un9WB8nU/wDeMf8Aik/qwMDAD5Op/wC8Y/8AFJ/VgfJ1P/eMf+KT+rAwMAPk6n/vGP8AxSf1YHydT/3jH/ik/qwMDAD5Op/7xj/xSf1YHydT/wB4x/4pP6sDAwAFOp/7xj/xSf1YtXD+fOy89MeoE1+mOPIQlxUNwslYBUQFFFri5PPxwMDAf//Z height=546 alt aria-describedby=cn0020><ol class=u-margin-s-bottom><li data-moz-translations-id=0><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr4_lrg.jpg target=_blank download title="Download high-res image (596KB)" data-moz-translations-id=1><span class=anchor-text data-moz-translations-id=2>Download : <span class=download-link-title data-moz-translations-id=3>Download high-res image (596KB)</span></span></a><li data-moz-translations-id=4><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr4.jpg target=_blank download title="Download full-size image" data-moz-translations-id=5><span class=anchor-text data-moz-translations-id=6>Download : <span class=download-link-title data-moz-translations-id=7>Download full-size image</span></span></a></ol></span><span class="captions text-s"><span id=cn0020><p id=sp0035><span class=label data-moz-translations-id=0>Fig. 4</span>. Map of the Milandre catchment with its main characteristics. The <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/catchment-area title="Learn more about catchment area from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=1>catchment area</a> is about 13 km2 large and encloses the Milandre underground stream.</p></span></span></figure></div><p id=p0325><span data-moz-translations-id=0>The mean annual precipitation is 1070 mm as measured by the MeteoSwiss <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/weather-stations title="Learn more about weather station from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=1>weather station</a> at Fahy, located 7 km away from the center of the catchment area. The mean annual effective precipitation (precipitation minus real evapotranspiration) is 520 mm. An overview of the hydrogeological settings of the area is provided by </span><a class="anchor u-display-inline anchor-paragraph" href=#b0330 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0330 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Kovacs and Jeannin (2003)</span></a>. The aquifer is hosted by Upper Jurassic limestone and is underlain by the Oxfordian marls, which act as a regional aquiclude. As the active conduits lie almost directly above this impervious formation, there is no deep phreatic zone and the system is qualified as a shallow karst system (<a class="anchor u-display-inline anchor-paragraph" href=#b0455 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0455 data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Perrin, 2003</span></a>).<p id=p0330>The downstream part of the catchment contains a 10.5 km speleological network, the Milandre cave, which hosts a 4 km long perennial cave stream, the Milandrine. This is the main drainage axis of the catchment. The stream flows into a sump around ~ 500 m upstream from the Saivu spring. Two main underground tributaries feed the Milandrine and each of them contributes to 25 to 35% to the cumulated discharge of the springs (<a class="anchor u-display-inline anchor-paragraph" href=#b0215 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0215 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Grasso and Jeannin, 1994</span></a>).<p id=p0335>The underground stream is monitored along its course at three places for discharge, temperature and electrical conductivity. Several drillholes located around the main karst conduits, have been monitored showing the groundwater behavior around the conduit network. This data will be important for the next steps of the challenge.</p></section><section id=s0130><h2 id=st150 class="u-h4 u-margin-l-top u-margin-xs-bottom">4. Methods</h2><section id=s0135><h3 id=st155 class="u-h4 u-margin-m-top u-margin-xs-bottom">4.1. Workflow of the karst modelling challenge (KMC)</h3><p id=p0340>For step 1 of KMC, dedicated to the relationship between <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/meteorological-parameter title="Learn more about meteorological parameters from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=0>meteorological parameters</a> and spring hydrograph, the discharge rates of the three spring of the system (Saivu, Font and Bâme) were simply summed in order to provide one single discharge time series for the whole KHS.<section id=s0140><h4 id=st160 class="u-margin-m-top u-margin-xs-bottom">4.1.1. Calibration data</h4><p id=p0345>In February 2017, each team received 4 years of <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/meteorological-data title="Learn more about meteorological data from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=0>meteorological data</a>, from 1.1.1992 to 31.12.1995. For the main meteorological station (Fahy), located 7 km south-west of center of the catchment area, hourly precipitation, hourly temperature and daily PET were given. Hourly precipitation data were also given for the Maira station, which is located in the middle of the catchment area. Daily precipitation were also given for a third station (Mormont) located ~ 2 km south-east of the catchment. Each team also received ~ 2.5 years of hourly discharge data (sum of Saivu, Font and Bâme springs), from 24.9.1992 to 28.3.1995.<p id=p0350>This data set was used for a first calibration of the models, as well as for adjusting criteria for the comparison. All teams returned their results, and a first comparison was made in 2018. Based on this first exercise, it was then decided to provide a second dataset for a further evaluation of model performances.</p></section><section id=s0145><h4 id=st165 class="u-margin-m-top u-margin-xs-bottom">4.1.2. Model evaluation</h4><p id=p0355>In June 2019 a new dataset was sent to all teams with two years of data (2014–2015) for the meteorological station of Fahy and for the total combined discharge rates of Saivu, Font and Bâme springs. Data from Maira and Mormont were not provided because they were declared as not useful by the teams after the analysis of the first dataset.<p id=p0360>Teams were asked to apply their model to the second dataset with the parameters calibrated with the first dataset, to run the quality criteria, and to write a comment on it. If they wished, they had the possibility to improve the calibration using years 2014–2015.</p></section><section id=s0150><h4 id=st170 class="u-margin-m-top u-margin-xs-bottom">4.1.3. Test data</h4><p id=p0365>Finally, all teams were invited to forecast discharge time series for the year 2016, for which they only received input data. That meant that they could use their calibration data based on the 1992 to 1995 time series data for the simulation (forecast) of 2016, or based on the 2014–2015 time series, or a combination of both. They sent their best model to the first author, who applied the same evaluation criteria to all received time series.<p id=p0370>Among the 15 teams originally interested in the challenge, 8 of them sent their results and 5 additional teams joined afterwards, making a total of 13 modelling teams comparing their approaches and results.</p></section></section><section id=s0155><h3 id=st175 class="u-h4 u-margin-m-top u-margin-xs-bottom">4.2. Uncertainty of the data</h3><p id=p0375>We briefly discuss here two different types of uncertainties, which are attached to measured data used for the challenge:<section id=s0160><h4 id=st180 class="u-margin-m-top u-margin-xs-bottom">4.2.1. Measurement uncertainty</h4><p id=p0380>Air temperature is measured with a high precision, better than + -0.1 °C. Precipitation is measured with a high-quality <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/rain-gage title="Learn more about rain gage from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=0>rain gage</a> (calibrated tipping bucket of MeteoSwiss), with a precision in the order of 0.2 mm. Both are measured every 10 min and averaged (T) or cumulated (P) over one hour.<p id=p0385>For the spring discharges the water level is measured every 15 or 30 min and is transformed into discharge using a rating curve established empirically for the measurement section. The obtained discharge rates are averaged over one hour. For this step 1 of the challenge, the output discharge rate is obtained by summing the three outlet points (springs) of the MKHS. Rating curves were derived by taking discharge rate measurements at the springs across a range of various rates (or water levels). Most measurements were made by salt dilution method. Measurements are repeated every few years in order to look at potential changes in the rating curves.<p id=p0390>Uncertainties on discharge rate data are in the order of ±20% in absolute values. However, this uncertainty must be considered as global and not as a noise related to every single value.</p></section><section id=s0165><h4 id=st185 class="u-margin-m-top u-margin-xs-bottom">4.2.2. Uncertainty related to spatial heterogeneity</h4><p id=p0395>Meteorological parameters are measured at an official meteorological station of the Swiss Meteorological Agency (SMA). The station is located 7 km away from the center of the catchment area, at a similar elevation. Summer showers locally can be quite different from what is measured at the rain gage. However, for most major events, the rain distribution is rather uniform, as given by a comparison with other meteorological stations in the area, including the one located in the middle of the catchment area, which is rather small (10–15 km<sup data-moz-translations-id=0>2</sup>) and rather flat. This latter rain gage was compared but not used for the challenge, as its measurements appeared to be less liable than those of the official SMA station.<p id=p0400>Land-use is not uniform. As detailed previously the catchment area is covered by forest (30%), pastures and cultivated land (60%), and about 10% of urban area. The effect on recharge of the differences in RET models for these respective regions is not directly measurable, inducing another source of spatial uncertainty.</p></section></section><section id=s0170><h3 id=st190 class="u-h4 u-margin-m-top u-margin-xs-bottom">4.3. Evaluation of model performances</h3><p id=p0405>The only aim in the present step of the challenge was to simulate the observed discharge rate at the system output “as well as possible”. The quality criteria (or efficiency criteria) must therefore result from the comparison between the observed and the simulated (forecasted) time series. Six methods or objective functions were considered, providing an evaluation of model performances as a function of different objectives (<a class="anchor u-display-inline anchor-paragraph" href=#b0385 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0385 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Madsen et al., 2002</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0430 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0430 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Moriasi et al., 2007</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0045 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0045 data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Biondi et al., 2012</span></a>): Mean Squared Error (MSE), Normalized Mean Squared Error (NMSE), Variance (VAR), Nash criteria (NASH), Kling-Gupta Efficiency criteria (KGE), and Volume Conservations Coefficient (VCC). Each method summarizes the difference between the forecasted and the observed curves into one single value, which is obtained with a different calculation.<p id=p0410>During the first exercise using data from the 1990 s, discussions between participants of the challenge showed that only NASH, KGE and VCC were applicable for the comparison. The other criteria are not normalized, and can therefore hardly be compared from one simulation to another. Hence, although they have been calculated, they will not be further discussed in this paper.<div><p id=p0415>VCC is the ratio between the forecasted volume of flow and the observed one. Ideally the value must be 1. Quality classes are given in <a class="anchor u-display-inline anchor-paragraph" href=#t0010 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0010 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Table 2</span></a> as well as the range between very good to low.<div class="tables colsep-0 frame-topbot rowsep-0" id=t0010><span class="captions text-s"><span id=ce.caption_fd4_yrq_zpb><p id=ce.simple-para_hmw_yrq_zpb><span class=label data-moz-translations-id=0>Table 2</span>. Quality classes for VCC, NASH and KGE criteria, and for the necessary effort.</p></span></span><div class=groups><table><tbody><tr class=valign-top><td class=align-left><figure class=inline-figure><img src="data:image/jpeg;base64,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" height=102 alt></figure></table></div></div></div><p id=p0420>NASH takes into account the ratio between the mean squared error (MSE) and the variance (VAR) as defined by <a class="anchor u-display-inline anchor-paragraph" href=#b0440 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0440 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Nash and Sutcliffe (1970)</span></a>. The NASH coefficient ranges from -∝ to 1 (1 being the ideal value). For NASH less than 0, the mean of the observed values is a better indicator than the calibrated model. For NASH = 0, the results provided by the model are as accurate as the mean of the observed values. Usually, the model may be considered as reliable when NASH coefficient is higher than ~0.75.<p id=p0425>The Nash criterion (least squares) is well known for giving a lot of weight to flood periods, but in return being less sensitive with regards to the simulation of low water levels. This motivated <a class="anchor u-display-inline anchor-paragraph" href=#b0225 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0225 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Gupta et al. (2009)</span></a> to introduce an improved criteria (KGE) taking into account a normalized distance between the observed and forecasted curves. KGE ranges between 1 and 0.<p id=p0430>These three criteria were applied to the whole time series. However, in order to evaluate more closely the apparent strengths and weaknesses of the respective models, we also split the observed curve into four components: peak rising limb, peak recession, base flow, and undetermined. We calculated the quality criteria for the overall time series, as well as for each of its four components.<p id=p0435>In addition, a fourth criteria was used to assess the applicability of the respective models, i.e. the effort necessary to set up, calibrate and run the model. Classes are given in <a class="anchor u-display-inline anchor-paragraph" href=#t0010 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0010 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Table 2</span></a>.<p id=p0440>A “final note” was arbitrarily calculated using a weighted mean between KGE (weight 2/5), VCC (weight 2/5) and Nash (weight 1/5).<p id=p0445>Relative errors are also used for characterizing the difference between the forecasted and the observed values. For each value (hour) the difference is calculated and divided by the larger value of observed or forecasted discharge rate. Doing so, a forecasted value 3 times lower than the observed one makes a relative error of − 300%, and a forecasted value 3 times larger than the observed one a relative error of + 300%.</p></section><section id=s0175><h3 id=st195 class="u-h4 u-margin-m-top u-margin-xs-bottom">4.4. Time step</h3><p id=p0450>Hourly Discharge Measurements (HMs) of the Milandre springs show fast fluctuations on a time-scale which is shorter than a day which would suggest that model simulations should be computed at an hourly time-step.<p id=p0455>However, some models are designed for daily time-steps and provide daily values (DVs). These may be compared to the reference (observed) time series (HMs) either by taking daily instantaneous values (DIVs), e.g. the measured value at noon, or by averaging values over 24 h (DAVs).<p id=p0460>In the framework of KMC, results of the simulations had to be compared to HMs for computing the efficiency criteria. Therefore, simulated DVs were retransformed into hourly values (HVs). A linear decomposition of DVs into HVs was thus necessary.<p id=p0465>Conversions from hourly values to daily values and then to hourly values again may significantly affect the quality of the simulation. Two biases were tested: the effect of resampling/decomposing, and the way to resample (from instantaneous or from average values).<div><p id=p0470>One question raised by participants was if it was better to use average or instantaneous values. Therefore, we made a comparison between reference HMs and hourly values issued from the resampling of DIVs and DAVs. The new time-series are called HDIVs and HDAVs respectively (<a class="anchor u-display-inline anchor-paragraph" href=#f0025 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0025 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Fig. 5</span></a>).<figure class="figure text-xs" id=f0025><span><img src=data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAcQBxAAD/2wBDAAMCAgICAgMCAgIDAwMDBAYEBAQEBAgGBgUGCQgKCgkICQkKDA8MCgsOCwkJDRENDg8QEBEQCgwSExIQEw8QEBD/2wBDAQMDAwQDBAgEBAgQCwkLEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBD/wAARCACSAt4DASIAAhEBAxEB/8QAHQABAAICAwEBAAAAAAAAAAAAAAcIBQYBAwQCCf/EAEUQAAEDBAIBAgQDAwkIAQIHAAECAwQABQYRBxIhCBMUFSIxFkFRIzJTF0JXYZKWtNHVCSQzN1JicXQYQ4ElNDhjc3Z3/8QAFAEBAAAAAAAAAAAAAAAAAAAAAP/EABQRAQAAAAAAAAAAAAAAAAAAAAD/2gAMAwEAAhEDEQA/AP1TpSlApSlApSlApSlApSlApSlApSlApSlApSuCpKddlAbOhs/c0HNKUoK5WDjDjfkL1FczOZ5geP39cZFgaZduVuakLZQqCoqCFrSSj9fBHnzURxuc+TcV4sfteFZTcJa8QhX2ZB9m0i5PTIES4yWIbk2S+Q2zGLMfXud/dc6KIPgbtJk3p/4wy3JLjlt2t14aud3baanuQMguEFEpLSOiA43HeQhekkp8pPjxXF/9PPDmTCE3dcJj/DwLaLM3FjPvRozkAb6xnmWlpbeaBJIQ4lQBJ0Bs0EC5rm11zPKoqZES3Q+1648uDTkeKkOh2UZC1Bbn7zqUkDqFfYb/AFNSZwRy7m/JWRqsV6MNCsUtC4mUBloAm+CY6yEp1+4n24zjnX9Hm/y1W8w+D+MICY/wuMI7xDbCytyU+4oG3dvgyVKWSfb7q1sne/O68/F+KWjBH7+Z93sbuSZXfHrndBASGEuSS0gJQltSlL2lhDROySdlXgGgjjkvmHkPDMo5YvsS5MvWHjbG4l0iWZEFClTpMhl4/tnv30tpU2hWkaOire/Faxb+UvU2u23S32iy5JfVLtca4JutwxNu2PxHPiG0SmYjC1pRKV7K1uMpX522UqUvYFWTVguJOXK+3Z6xx3pOSxmod2LoLiZbDaVpQ2tCiU9QlxY1ryFed1pcX04cLWmBKgDHZBbnNMREuyrzMeeYbaX3ZbjPOPFyOErAKQypGiBr7Cgig83Zq/YouOWrkW7T78/fJkFxtGElrIWEMxkO+yuGsfDp8utlUhYQ2G3EaBUe1YXFudOceS7bhrVvyy3Y0/cMEvOQXR5u0tSXHJkGYlhHQKUUISryVgFQ8nqfsanhz048QOW2LbVY3LBiTHZ6JibvNTOW+4gNuKclh333QpCUoKVrUkpSkEaSAPdjfBXE+HJZGOYhGt7cWDNtjDbbzvtsxJboefZQkq0hClgHQACfsnQ8UFarp6iudLBgUHKH8mssy45dx2rLoTS7YhqNaZiJEVv20/V2daUmUd+4rsCjYPnQzHK+UZ7bpsjifOspjZOtq54XkUW4s29uIpgO35hlyOtDZKSns32bUfq0VAlWt1K3JvprwLP+MnMJxyJDtTjePHGrVMPuSG4tuU40tTPTuO6T7KBsnfj7/ethtHAXFFltsq2RcYLqJs2FcJDsqdIkvOPQ3EuRduuuKc6NLQkob7dE+QE6J2EEWX1G8y3e9u5lbcbyO42JGRzrU/YmsY6w2Lcw64yZIuBIKn0qa9xQ8pIUpATsA0s115Hyzkz068i5hmVuuMLLRcrszao0BDKbYt20uOIbadB7uoCFlKi5s9kgjQJFT3/ITxb+L1Zv+GlfMlTfmZR8dI+DMzWvifhPc+H97/8Ac9vtvzvfmvPYvTvw/jWXQs4suJfD3e2OSHLeszpK2YJfCg8I7CnC0ylfc7ShKUk6OtgEBHnJnMnI+H51fuLrfKhLvuRS7KrCFmKD0jSXC1MLid/tCx7L7pP5JWjdYK58+chxeYrFCtd2cueM3HNVYjKabsoatrSA24NomOdXXpSXW1dw2FNJ6qSda7Gw92wHEL5ltjzu7WKPJv2NtyWrVNXvvFTISEvBOjo9gkDyDr8tbrULj6fuE/xCnK7ljaG57l4au0dS7nIQ01c+4UHmGvcDbTq1D6i2kFzagrsFEEIY4O5HyWwWdGLQZcVuAzi+U5AhDjQK/i2bw+hCuxP7oB8p+1Y571F82ZGpuPizN4M6y4hZb0tq14obizd7hMjl5Tb69j4Zn6AgdCFbUs70kCp+vHp04bv0GDbrhhyfh7emY2ylmbIYJaluFySytTbiS404s9lNrJQTrx4rm9+nfiDIGbdHuGLOIbtltbszSYlxlRQ7Ab/cjP8AsuJ+IaHnSHew+pXj6jsIwncscu27kGBIzeZccIsVwnWli3MLxxE+1vtym2wtiXMQr3I0r31ONDZSgK9nwsKO7L1oEjgjiyTkjWUu40sSmXmZIjonSEQVPspSGnVww4I63EBCOq1NlQKEEHaRrf6BSlKBSlKBSuCpKddlAbOhs/euaBSlKCj8648qMcG+oOJj+I4xLx1V/wAt9+dLvz7EtAPb3CmOmItCiB9gXR2/PrUhXDkjk1cLImMJyWDYYPF+F2u8Ljy7ch9N5ediuPdHFqILTIQx023pXZSj28AVOiOL8ERjeQYinH2haMpflybvG91zUpyV/wAdRPbY77/mka/LVa/lnEPCeVXOD+J7VCclwYzVrSyLk6x8RGSC43FkNocSJLYCFKDboWnXc60TQRjZvUHnF8xNWSNvQ4y5HItnsDLC46SWoEpERa2j58rAfX9f3/q8VgZmRZ/m6MMyXJ8oYeC+XH7Va4bMBttuE3FNwZbWVD6nFqCElXY62PAFTa7wTwvkeRM8hJxqPLmOTI13afjznvhVy2UpSzKDKHPZLoSlKfc69ikAEkeKzrXFmCRocCFGx5lDVrvLuQw0+44Q1cXFOKW/+9skqecOj4+r7fagjfgblvOOTMgNmvRio/Ctm+Cyb2mkjvffiltFKdf8MBqOXen/AEyW/wBK1/kfmfkvDbnzHlEK5sybTx2xbo9rsqLelZkyZcdpXuPOD9opKHHQoJQU7AIP5al3i3jr8BR77OnLt7t6ym8PXq7PQY5ZZW+sJQkJSpSlaDbaBskkkE1lXePcMkOZI5Jx+LI/F6UIvaHgXETUoZDKUrSolOvbAToAUFeIXJvqWNpyK2Wix5JeyxEgTY14uWLNWyY2Fv8ASazHjLUluS4hr9q0Fa2dpUVHWy+bs0kWGHjlo5Fu0+/v3mdCdQ3hRbyFlMdhpz2Vwlj4dOi8hSn1dEe243oEqKhLDXAHCtkt8mzv2h0C9qjxg9Ovkx6WVMkuMNx5Drxea6EFaUtKT1IJH613OenLiBy3RLcrGpQMOW7ORMTd5gnOPOoShwuyg777oWhCEKStaklKEJI0kABBWOc5c4ck2vD/AJZlltxl6dx/dMlujjVqalLdmxJSWUJQFqKEoV5KwCf+0j71j7r6hedsfweFkT2T2OXcct4+by6E25bEtRbTL+LhtFoEK7OMqTLO/cV2BRsK86qyeNcFcUYehprG8QjwW49vl2phtt50oZhyXfdeYQkqISgr8gDXX7J0PFa3yl6acIzvjl3B7HBi2d9mwpxq3SnEuPpi273mXFsdCsd0q+HQNk78ff77CJuQsnz6BnNm4pzrKo+TriX/ABLIotyat7cNTQfmvMuR1obJSQFM90K/e6qIJOtlhXqN5kv02FnaccyO5WCfcrixLs6cYLMO3wGVPJbfbuBO3HQWU9wdpJcUEpHUGp3sfAXFOPx/ZhYwXHDcIl0VIlTpEl9UmL/+XJddcUsob2ejfbonZ0nya+4XA/FdvywZpExlSLimY5cUNmdIVDaluAhyQiIXPh0Oq7K2tLYUSpR3snYQJZLlyRlHKfp45BzPNLbdImVs3W6NWyLAQyi2uO2xS0tsuA9nWwhfUlzZ7JBGt6G5cj8x8kYlnV94rgTIKr/kVwsv4JWqKD1hyVFE1S0/ZwxxHkOk/wDSpINSDj/p24exfLIWb2PEfhrtbFyFW9ZnSVtQQ+FB5MdlThaZQvsdoQlKd6OtgVsF8xHALjm2P5lfrfb15LZGZbdmlPOdXmm3UpEjoNjsOoTs6Ogfy2dhAU7nrkJHMVkgW66OXHGbpmb+JSGm7MGrayhDT+i3Nc6uvSkuMKC0thTSSFp/IKOD4U5RyrGeL02O3TIjUSycXyskiJcZClJmpmykhaiT5RpCfp+1TgeBeDrlla7+mwMvXhi4N5ChDd0kdYktSyoSmmA70ZU4oK7KQlPufUFdgTX3ePTXwrfbZbrPccKQYdrgvWxhpqbJZC4bq+7kd3o4C80VfV0c7J351QQhf/UJzReLvLtuFxL18VjmM2e5+xbcVNxYvNwlxhIU2+7sfDMkANjoQoFS1E6SBW0QuVuW43JduRyDOuOG2u73eLDtkB7HES7VKYeZSpLLk9tXuMTCvujSylAWkAJUCCZNybgvh7J5luYvli1JjW5NsZaj3STFVKgNa0w8lpxPxLSd/uOhaR3V4+o79LXBHFrOTs5a1jS0TI8lE1pgTpHwKJKEhKXxD9z4cOgAaX7fYEA735oN/pSlApSlApSlApXAUlRISoHXg6P2rmgUpSgUrE27LMVu90lWS05Laptxg7+Khx5jbj7Gjo90JJUnz48geay1ApSlApSlApXytaG0KccWEoQCpSlHQAH3Jrym8WgRI083SJ8NMUhMd730+28V/uBCt6UVflr7/lQeyleWHdbXcVSUW+4xZSobpYkBl5Kyy4BsoXo/SoAjwfPmubfcrdd4iJ9qnxpsVzYQ9HdS42rRIOlJJB0QR/5FB6aUpQKV0TJsO3RXJ1wlsxY7KezjzzgQhA/UqPgCjs+Cw+xGfmMNvSu3sNrcAU71G1dQfKtDydfYUHfStftvIWA3m6fI7RnGPzrjtQ+DjXNl1/af3h0Soq8aO/Hisii/WN2S3DavMFch5xxltpMhBWtxsbcQE72VJH7w+4/Og99K6DOhJmJtypjIlrbLqWC4PcUgEAqCfuQCQN/byK76BSlKBSlKBSlKBUST805jv/I2VYjx/CwxiBi/wLbj14VKU8+5IYDpIDWkhIB1+ZqW6i7jv/nTy3/7Fk/wCaB19T38Ti/+zcP86dfU9/E4v/s3D/OpRpQRd19T38Ti/wDs3D/OnX1PfxOL/wCzcP8AOpRpQRd19T38Ti/+zcP86dfU9/E4v/s3D/OpRpQRd19T38Ti/wDs3D/OnX1PfxOL/wCzcP8AOpRpQRd19T38Ti/+zcP86iz1P/y5p4MytWYOYP8ACCJ/u/yVN0+Y/GbHw3wvtnt73u9OuvG/v43VpK4UlKtdkg9TsbH2NBRj0LK/2jZZgjnpNoGH9Ro5L2+e+3oa6e153/8Az/Vur0UpQKUpQfLiihClAbIBOv1qoGG5nkVrwXjjKoE9CLjm8rK77eHFMNuLVJEaS42kKWCUhsobQACPpQE+R4q4NaBjHDOKY+3Khy4ka6Qk3W4XK1MyY4JtqZoPxLLaiTtClLe14Gkr6+dbIQXhuZ87M222w2OTWb5ecx4p/FcZy9xY0eNAuyVRkj2y0hPVpQk+Q530pAO9EitXv11y3JLpi2HZJyByVab3bM3xqVItl9jWkSmESPiAHW5MVtTL7KlNLCRoKSptW/BAFtl8a4A7HYiPYfaXGItoVYWWlxUKSi3K6bigEa9o+2j6ft9I/SsND4E4bgWV7HovHVmRb5Etie6yWO3aQyQWnOx+rsgj6fPjzr7mggi180ciSOacRch5DeZuMZRlF3x9YmR4Ue2PNxmZRSILSe0xTjbkXTjrqkoJ31RpaeuCs+Zct3XA8bmZJyvdbijkfCclkzWhDiMiA7FaSphcUpa2lXVSkr79wonYCftVlk8G8ORr65lg48sLd0+NF0M0xUhaJSST7yT/ADFbJJI1vZ3uvVZ8X4neTbbZY7ZjrwsUWRGgMRvaX8JHeAS8hCRvqlYACh9j4oKmZLyVyJgfDFmhYNnOUSZeH8cwcikONMW5DDK3EKU2q4yH0/WyUtKQhqO17h6nssFQVW25pyXy6p/PM2tXIsm2xcNh4xPh2ZqDHVFkKloQqUh9S0F1SFAkAJWkpPnZ+1TaOBeAprVsg/ydYxIbtkNyLBYMdC0txSslTYT9lN9lK8EEAnxqs8zxlxo3b5uOM4jZ/hJ0WJGmRPYSQ8xHT1jpWn80oCdJ39tUFc3OWud77nmRXrG7bk64uPZqnHm7ckWhqxLgNvNtumQ488mYH1oWt1KkgDZaSlCwSVe+JylyUY9k5Tczp/rf8/exFzEXIsf4WNEEh6OOhCPf+JSGQ+pSllJBUOgGiJ3uvDXFd8y1rOrvgdml35hbbiJzsZKnO7Y02s/kpSR+6ogkaGj4Fd8fifjaLmbvIcfCrS3kb5KnLiIyfeKikJK9/buUgJKv3iPG6CAMf5h5SyHFuL0WTM7Yq/ZRx9fbs+5PS0mO7cWExQw891SOiErcWD10PJ2DqsdJv+R3PjHlDGM7yPNGMksOJt5KzAyqLbi5BkNpdW1MiS4ADbjXvMaAUAtJRvwFaqwNs4J4cs1xmXW2ca4/HlT25LUlxMJB9xuQQX0EEaCFkAqSPBP5Vi7j6eeO04fc8JxGzRMat9/dYTeDCY/azIqFgrjlZO0pWkFH3IAUrQ80G64Tdpd+wyw3ye37cm42yLLeRrXVxxpKlDX/AJJrNV8MstR2UR2G0tttJCEISNBKQNAAfpqvugVwd68feuaUEZSR6lviXfg3OM/Y7q9r3Uz+/Tfjto63r76rr6+p7+Jxf/ZuH+dSjSgi7r6nv4nF/wDZuH+dOvqe/icX/wBm4f51KNKCp3qz/l4TwNkpyVeG9vaR8s/D6bmLp8y7D4X4Toe3u+511+Wt7+ndav6GT/tETFg//IdNk/Cntjqb7v5916jrr2fH/n3vq++6uwUpVoqSDo7Gx9jXNApSlAqm9okOXO2cdZ3LIVeMg5Yv82YtQClIUzCusdlrzvw2yw0gD/tP6mrkVoeP8PYzZLhcnn2GbhCkX17I7dFksBXyya+0pEhTSv0cLjqvsCC855IPgKs27lPmadx3kGcQeTZNu/BGAY9kjFuj22GI82W98Up9L/7Lt7S0spT1bKCPuCPtW0Zpy5zrcs/zx3CLdlCk4NPhQrdb4abQ3aJQVHZfcNwclvIkD3feUlKmuqUBKSO57CrHM8T8ax7VPsTGEWdu33S3s2qZGTFSG34bIWGmFj80JDi9D8uxroyThnirML9HyfJ8Cs1zukVLSG5MiMlailtXZsK/JYSo7SFA6/Kgr3cOa+Rk8x45LtN+vT+O3XPV4lIakR4LFoLaW3ErZjo+qY9IbcbUVPEob+k6SRqpZ9OF1y/JcFl5xl+YT73KuV0ujLUVxlhpiGzGnyGW0NBtCVElDaOxWpWyNjXmtkk8F8PzMgfyqVx1Y3btJmNXByWqKkuGU2pKkPA/zXApKT2GideSa2qx2Cy4zbk2jH7XGt8JDjryWI7YQgLccU44rQ/NS1qUf1KjQUvyDJ81yW3cK81X7O5F0fu+YyZv4TTHjpjxHWYU/wBthgpR7wcb9v23O619lKJ0nwK5s3PXqEsuAt8rvQshuMO74pcLzLN9RaW7ZEkpY95hcBEV4yltJUShaHApXXSipBB3aNfCnDcTLByC5gFgavyZnxqLkqMgOokqHUupJ8JWoEgkaKt+d19Wbhvh3F79LvtnwPH4N0uDb4edTGQFLbcO3gEnwlKifq6gA780EWZZN5D4+x6wWuNzhc72/nV3tFqXc5kOCXrM3IDhdlMe22lHVzoltsOpWErUk7X+6dTdzjlqdyzB4MictXJuFDy2XaZF/ZhRFXCVEFmbmhpZLRZDzbrnXulsDrrad+am3H+JvT8xbLtiWO4libkO8tpTcITCWnfebQdpChskJSTtIGgk+Ro1m8f444ssCbNGxzF7HFNnckS7WGG0d2XHR0edQfuVKB6qV5J+xNBVq1cs80WfDcZzO48nzrrIyEZZaHYz0GI2wz8tjTFRpSAhsK9/tEQVkqLau6tIT4AyS+Y+VuMMZtOQyOQXeQJWT8aXDLXGX4sZLFvmRkR1JeZ+HQlXwyjJUkpWVH9mCFDyKsPkPD/H16xZ7DIFktltU1GuHy9xiMgrt7s1t1t6Q2nxpSvec2fG+yv1rq4t4K444nsDNpxzGLYiUu2sW24TUxEpXOQ2jqe486So9lFA+najQQhkmT844tek8c4jydKzCXesahZEZUlMCPPZ/wB8bafahL9tMcKeaWssJeSoBbZBUQfGs5dn2T33HpmHHOMsDzNuzGFdE32229i7wHWLaw+2yt1hCmVjTvcONaCkOAHZTurM2/gPhi12K44zb+NbCxa7sptUyMmInq6Wztv+sdD5TojqftqvbauH+L7JCi2614LZ48eGmUhlAjJPUSUhMjydk+4kBKt72AB9hQVoicjZ3gePfh60ZM64r8L4HAYusyLHW7ANylOsPS1lKEpX7aCnqF/QClOx5VvK5Bm/K1o5EHCNu5XuL7LWV2OGcgchxF3BMWdBnPPxXNNBn3EqjIWhYbBSl1HYK19VhLfxDxharPJx+Bgtnatsy3N2iRF+FSW3YTZWUMKB3tCS45pP2HY6rmx8ScaY1Bg22xYVaoce2z/mkVLbA21M6KR74UfJX0WpPYnejr7UFfLjluV/yfWPJ7zfDdciwrlkYzEubrLbcibCVcPg1ocDYSnsth36uqQCUA6FWxrRbvxFjdzu9jlMMswbbaL09kj1ujsBKJtzWFdZDp/MpUtS/t5X1JPjzvVArzXL5l8vk/J/hvjvaV8N8T29r3NfT36+eu9b15r00oIu6+p7+Jxf/ZuH+dOvqe/icX/2bh/nUo0oIu6+p7+Jxf8A2bh/nTr6nv4nF/8AZuH+dSjSg/NT1NL9bf8A8lbOn07KSrLPlzf4kGPCQLL7e/8Ad/jfjP2Hu9O/2+rr1/PVXt4V/ls/A8X+XsYsMo/+r+Hve+H66/ne5/O/Xr4rfAhKSSlIGzs6H3NfVAqNPUrcMktXA2cXDE3ZDVzYtDym3Y5IeaRr9qtvXnulvuRr8wKkuuFJStJQtIUlQ0QRsEUEJ3ey+nvBrNgWRW+022NIbkNNYi5aErD899cdfVsKY2p5tbfZS++0aHZX23UOX3mTlvG+Mcc5NZ5MeuE3kPGL9cnLW7DjezZX2La9LZVECGwsBlbaWlh5TnYkb6nwbN4zwtxRht/XlGL4DZrbdVhxIksRglTYWdrDY+zYJ+4SBuviNwhxFDl3qdF47sbb+RMPxrmoRE/7y09v3kKH2CV7PYDXb890FcLvkfNlqdyKJ/LpfXflPGLGfpX8tgBRuX7cFgfsdCIfaG2ztzyNODXn5m8tcsSbRyPysjlVyKjB37K9b8XYiRDHkiRCiPOx3ypsvH3VvrQ2UrSoHR+r7VaiRx7g8pcpcnFra4qbaRYZBVHSfdtw7ajK/Vr61/T9vqNaHjnpk4ys2eXzP7lYbbdZ9xuMWdbg/CSPliY8ZphptvyQoJ9oKSSPpJ8a+9BE0fmvkZ/mXE5UC+3l/G8jzOfjDzUqPCj2pbbLUn9nEbHaY4+05GIceWpDZIV1QQpOsWzyjzKnjvjyV+KsrvVx5KvsxiW/aYtsRJgR4ofUiPDEgNspW4G0dlOqWSEOFCdkAWQXwZxAvIXcsXx1Yzd3ZzdzVMMVPuCWhXZL6T/NXvyVDRP57rIXHizjq7Yi3gdxw21v4+y4HWreqOPZacCysLQB+6rsSdjR2TQaJxjfOQb3w9lDPJUGY1Ptq7nBjvT1wzMlREtktOSURFrZQ9pXVQSRso7dU9tCv9uRyufT7wAL+MO/DAveIfDiGmV8cB2R7fbv+z3/ANWv69VcjHMGxDEce/CeM45AttoIcCocdkJbX333Kh/OKtnZOyfzr5OA4WbFacYOMW75TYXIz1sh+wn2oi4+vZU2n7JKNDrr7UFMsPv9x47yLllmyPmPK5Qyi92i2ELCdXpqYlhK07+6/h5AXr8xEVXr4ujcj4fwridh4/lZIjFrPNypme1iRtxvKFs3V9LDobnAtuRwlKwpKNL7qRrY2Kt0OMePRJizPwbaS/Cu7t+jOGKkqauLqSlyUk68OqClAq+52axd44K4fv8Abotpu3HdlfiQXpD8dv4YJ9tchwuPkFOjpa1FSh9iTsig0zL+Rb7D4Pw/mPFsvVcbbBXa7xepaoCWPmdocATJWpoglkhDnvaSQQWtb1sVohzLnTOMWtec4/eL2/j+TX+6zmIeOKtzd4bsqAG7euKJifacbV0LzoV9f7dHU6HU2YkY7YpVgcxWRaIi7O9FMFcEsp9hUcp6FrprXXr419tVgcg4i4yymw2vGL/hNql2uyIS3bYymAlMNCUdAlrrooT1AGgdEAfpQQty7kzea+hPJsjj5BIvvxuMun46dBTEedWlfUh5lH0oWlSSlQT42kkeCK6ciTyr/Ltw0eRPwiYw+e/CfJRJ9wL+Wq3397x11+nndWCk4Nh8zEVYFJxq3OY4qMIZtZYT8MWB9m+n26+PtXon4xj9zuVsvFws8WROs3um3yHGwVxfdR0c6H+b2R9J19xQVAt+NYlB9BMPKWbLb4mSRISpdmnsRkNzPmyZyvhPbWAFlaneidAnsFEaIOq7INkmNcWZHytBibvnGnK14ycBofUuOl7pPaGhshUZx7x+ZSmrF2D09cJ4teI9+x/jWyQp0N5UiO42x4ZdJJK0JJ6pVsk7A2N+K262Ypjdlhz7farJDixrpJflzWW2gESHnjt1ax/OUs/cn70FY5E3OOQrhm3O/GEn3FS7pBxKxOR3Y6JkmyxHCqcq3rkgse86+t0I7jqfYG/yImP0/ZTJybFLozcMhyK5z7LeZNslt5FBjRrlBcQEq+Hf+G/YukJWlSXG/CkLT+YJrYnOJuNXMIj8bKwm0DGIgSI9rTGSmOz1V2SUJH7pCiSCNHZ+9ZTE8PxfBbM3j+IWOJabc0tTiY8Zvqnuo7Uo/mVE/cnZoMzSlKBSlKBSlKBUXcd/86eW/wD2LJ/gE1KNRdx3/wA6eW//AGLJ/gE0Eo0pSgUpSgUpSgUpSgUpSgUpSgUpSgUpSgUpSg1DmH/lLmv/APXbl/hnKrnNwnDMI4A4jzzB8UtFkzES8VahyrbDbjSJqpDjDchhxTYBdQ4yt0qSrsPHbW0gi2NztsG822XZ7pGRIhzmHI0hlf7rjS0lKkn+ogkVouI+n3iPBrnBu+OYopqTa0FuAZNxlS0QwU9dsofcWho9dp2gAhJI3okUFcOPYkfC+KuGud2FJZZxe73CyXt3wAbPcZzrKlLP/S1IEZ3f2CQs1m7fL5Susa884YOxdUN57lC2X3rVGjSbvHx2E2qPCMRmVtpaVupW+saJ6vkpG/Isg1xlgTOCSOMW8YhjFpUd+K9bCCWVtPFRcSdnelFaj9/zrzXziHjnIsbtOJXTGWvllhShNsbjvuxnIYQjoA060pLiPo+k6V5Hg7oPLwjlbmacb2y+v5O7f3lrfYemP2v5dI9xp1bam34+yG3kFJQsDSSpJIABAre6xWMYvj+G2SNjmMWtm3W2GkpZYaB0Nkkkk7KlEkkqJJJJJJJrK0ClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUEIeskzBwbKNvaYclC+2IsIfWUNqc+Zx+oWpIUQnetkAkD8jWg5tI5Kkc6Ww8jWXGbetPHGUiJ8luUiX3G4nbv7rDXX8ta3vzvVWXyfFMezO1Gx5RambjBL7MksO76+6y4lxtXgjylaEqH9Yrpu2EYrfLyzkN2sseTcY8CTbGpC99kxZHX3mvvrqrojf/igqgMJw3DfSZxjyJhmKWi0ZtHaxhy2zrfCbjy5kl52O2tha2wFOpdbW4laVdgQSoglOx14XBaxPg7hvnaLpv8AA93mRbq4APNlnTXo8nsf+ltZYeP6BpRqw+J+nfh7Cbhb7njuIlp60jVvTIuMqU1D+nqCy086tDRCSUgpSCASAQCa2SJx1hMHCHuN4uOREYy/GfiOW3RLKmXiouoOzvSu6t+fzoK3MzOTr65k3OmAM3DpluTos6XrZHjyLozjdvS4y2uE1K/YrLkn3XiCD+zcUUgkDU68GZY9mfHkW7ysokX6Q3KlQ35Mu1C3SkOMvKQpmRHB6peQU9VFISkkbCQCBXuunD/G94xG04LNxdkWWxJbTbGI7zrC4QbT0T7TrakuIPUlJIUCQSDvZrN4rieO4TY4+OYrambfbo3YoZb2dqUSpS1KUSpa1KJKlKJUSSSSaDL0pSgUpSgUpSgUpSgUpSgUpSgUpSgUpSgUpSgUpSgUpSgUpSgUpSgUpSgUpSgUpSgUpSgUrjYrmgUpSgUpSgVF3HZH8tXLY39pFk/wCalGuhmDCjyZE2PEZbkSykvupQAp0pGk9j9zoeBv8qDvpSlApSuKDmlKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUFEZea8rtekjM48bC3XrSm8XlAyI5IlMhtsXl0dgz09wdf3QkL+w8eKl3K+cOSrYxm+cY6qxpxbjS4RbTOtUuI4qZdT7bC5DqJIcCWOqZCfbT7a+xQdkdvErnhXj48c3Dik2t/8ADl0ekPyY/wAW73Ut+QZDhDnbsNuKJ8Hx9h4rx5BwBxtk2SLyW6wJ6lSVxnZ8Bq4vNwLi7H17DkmMlQbeUjqnRUD+4ne9Cg0u1845hNj4465Htm7tybc8Pe6srGoMczPbUn6/Dv8Au7e1fbyrwN+I6Oe8x5paOL87k3e0G8Xq7XtVnt8SG61HaWzbpqWkP7dJfCltoUR9PUkgb0DU4tenPjBnM2s5ahXREyPeXMhjxRdJAhMXFxtTbshEbv7aVrStXbx5JJ+9ZKLw/h9itGOw8etJ9zDHZMuwtvzHQhuQ824hXuK8lSSHVjyFa3sDwKDBcIcwXPmKVcrvGhR2LDCt1rQkhJ935m8x78ponetNBxhGtb7d9nxUcZHnNy9P2R8vWJMh+WvIIrWVYbGedKy5PlqRCdiN7OwkTDHWQPAEn7eKmbhnjSLxXhScfajQWJUydLu9wTCCgwJcp5Tzob7aUUBS+qdgHSR4H2r2ZhxVg+eZFjGVZPZ/irlh8xU60vB1SPadUnqewSQFp+x6q2OyUnWwKCtmS8r5x6d7JbsFx67Rb0MIh2dq7x12uZcH57spxHxT0qf2SzBO3FLQlRcWrsCEaIFbXxlyHkVp5SnYi062/ByPkPJWZBkFa3Gm48CO62lo9tJHYnY0Ro+NVIud+m7i/kW6Xe55JFu5F+QwLnEi3eTGiy3WAAw+402sJLzYSjqvWx0T+gruufp643ubYPsXaHMReX783cId1kMS25jzIZeUl5CgoJW2AlSd9SPy3QQvI9Ume3SJiNigux7Rdr3Cu91m3GPilwvjaGYs5cVllEWISsFZAKnFqAHXQBKgB2SvUbzAuLZr/kluZ45tT9rjSDIvOKXCZDlTC+tqQ3IfQpC7a2Orakqeb8oeCj+6U1Lw9N/GUXHMfxqxs3qxt4umQ3a5lqvEmNMYafWVvNe+lfdTa1EEpUSNpSfuBXFz9N/HF1ixrbIdyNNtaiNwZVvRkEz4W4sIJITKbLhD++yuxV5UFEHYoPN6gLpNdteE4VFlLYbzfKoNonPMLIPwgbdkvoCh+TiIxaP2PVw681DjWX8pW61+oeFjuCqvdsYv92QLi5kaYq4Y+XM7S20pClAJ/eGiNk+NVYflXj9/Occt8ayyWIV4sF0h3qzvug+23Ijr30VoEhC2y40ogEhLh15r1QuL8Pg27K7axb3Es5tJfmXpPxDh9515lLThSSdoBQhI0nWtb+9BUrjzK7pivNuI5rcb9NNma45xO1XVt6QtTIRMYmKS+pJOgv347AKz50ojfmu3jDOeXbH/ACr5fZ48+4XG+5BYrrKSuE9cV2S3TYnud2oKFpW8WmiyFNNkHZUQDrRsZdfTXxHe7HdMcuVgkPQLxY7bjstv454FUGApSoyAoK2lSStW1D6jvyTWRuPB2BznrzLYbuttl3x6FIkSrbdJEV5DkRkMsKaU2oe3psdSB4OzsGgiSRyZeZmP4ByejLrbfZkLOm8VmvQIEm2h6JMe+GcYkQnyVsvtuFpZQokgtJIOlEVZuomXwHZ403FLdan1ixWS+O5TcjMkuSZt0uoR1YcddXsr6lXcqJ3tpoDwDUs0ClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUCo6wS73Sby1yda5c996Jbn7QmIwte0MBcIKWED8tq8n+upFqLuO/+dPLf/sWT/AJoJRpSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlAqLuO/+dPLf/sWT/AJqUai7jv/AJ08t/8AsWT/AACaCUaUpQKUpQKUpQKUpQKUpQKUpQcbH61zVb7VxfgfJ/qH5eTnuPM3j5aixNQlSHF7ipXCUVe1pQ6bPnY0d+ajNn1Gcm4fxm5AsORtXSdirF9cZU/ZpV4m3aLBuEiPHVIcQptuK0WmPqkuubWpKylJ0dhduuaqPmnIV2yzKo4jRItrU5eeP5qHo/cOrMwvrU28rsPcQkgADSfBVve/EmcJczZbybf2bFc7fBjuWKyK/E/tIUC1eBMdjhlG1HqnUV9ejs6U358+QmylV95G5vzrCMn5QuqflbuM8a49Fuot6Ya1S7jIkMvFKC8F6aQlbafIQokKP6DepwOfOe1267QrbjkzJpKbZGuSbq1glztjduJkNtymkxpRSZqkNrLraW1pUsNrSQDrYWupVX1eoDMF4xEh27kKx3C7y73Kt3xEfDLn8zbDLCXSyqzlXuJd7LT2WtxDaW1JWTtQFYnHvUTzZyLbsQRjz2NY9KumEXfI7o7MtjsoplQZaWAhttL6QlKz2JBUop34KteQttSqdXD1Pc22TCIuVS28WmTcqwE5haIbcJ1tq3SEvxmzHdcLpL6FCUD2AQQUnwd1lOU875Jtz73FufXu2XC4tXTD7/EuNmiOQUpjvXxhh2M4hTqydKT4UFDslRBSNeQtjSql2v1WZ/ccgXkkOwz7hjYyGZaHLJFw65qdjwmHXGTON00YylBbRWpsDqEKICipPn7tWUcs5dyb6fs5yy+2X5HmBuV0j2u3R3WVwfctTjrTLjpcUJI6K8qKEaWkaGj4C2NKr/yNzjnmGZdknHUWJa3sguMyyJwsKYWUvR5rnsvKeSFgrLC2ZC1FJT9HTevucHcvUXm8LmOz49Ck266Y/cMu/CcpiLZZXtRf2S9Om5rUlpx/3EEKYaQ4EgKClAjdBZylVV4Q5Tyq1WdGMIdYkRmcbybIUuSQtx4yWLu+22kqKvLfXx11v7eRWKneqrk+5lpnHIaG5toxS0XyVFjYfc7um8TpjBfMZLkXsmG31T1C191dl71pB2Fv6VW1/nHkxrkWBEyRUfBrNcZlsatkK84zMeZuLMppBUldzbX7UWSHVONBlxA+tCB57g1ZKgUpSgUpSgUpSgUpSgUqk8/MeVYnCnPsWzYR8ytTV9ytAvK8kDD0ZI7b6MlsqHT8gFDf5aqRJ3LHJ4h3yNgUyxxIfGeIWy83Fu6xHH3Lw89FW97KXQ4kR0Btkj3CFnsv7aT5CylKrnaPUVlt9xVWURIVuaQ9n9px2O04yolFvloirV2+vy6A+odvt4H01rs7N+S82Th+R3q921iOvld2zWyHBiONe03F+PZCn3C6fe7+2lRR1SBr8/yC11KhLhDmXLuT76zZ7lb4cZVisXXJvbbUOl6+KWz7Te1HogJjOudTs9Xmjv8AXA8hc7chYTdeW8gQLVIx/jhiAzDtiYLq5M6VMjtqQpx5KyUoQ46n6UNqUpOwPOthYulVYh87c7KtGRQLbjc/JJEGLAnMXtGDXK1pYbcf9uW2mFKUlUtxlv8AbIS24C4CU6BA36HfUDl7mLwYds5BsdwvEy8TYHxEbDbkbkgMMoX7KrMVe6h3s4kKWtxDaUKQon6xQWfpVTLF6hebOQ7biRxx7GcekXLBLllF0XMtrksplQ5IZ9ptCXkhKVnsTtSuv5FVeG4epnnCyYbFySU3ikyflOCN5haIiITzTNuf+KiMmM64XSX0KTLB76QUlP2INBcGlVPz/OuSoOZWXizkC92u4z41/wAVv0e42eG7BQY0iY6y5GcbU6vfVbJIV20pKvKQR5+MQ9Vmf326Q8rNhnXDGrjcZ8Z20RcPuba7bCZLoblquak/DPHbI7oAAHu6SpRR5C2dKqdacn5cyrlPgHNMwvtkNmy5u6XNm12yO6yqCXLapxtlbhcUJSQhQ2ooRpSdgaPjcOQucM+xDLsj43iw7U7kVwuNkawwKZWUvxZqy2+t5AXtZYLEpxRSUjoE/b7kLA0qsk71E5sxzFaMdhyLfccfuWWPYpIYi2aV7UTq08Uu/M1qS06/3ZIUw0hYR9SVLBTs4XhXl7LrJxi3aGFxZDdm41k5Ow7KStx1cxMySgBxRX9SNNp8eD/XQW1pVScl9UXJT90k2/E4RTKx7HLTdZENnD7ndhep0uMJBjJeigpho66Sla+6uyySNI+rZ7bzdyW/ydBteWOR8Ltt0ukaJbLTd8ZlqRcIzrAWkpuiF+y3KKitPsLQCFNlPnYUQsfSlKBSlKBSlKBSlKBSlKBSlKBSlKBSlKBSlKBSlKBSlKBSlKBSlKBSlKBSlKBSlKBSlKBUXcd/86eW/wD2LJ/gE1KNRBMxrmjF+S8uyrBbFht3tmUfAOgXW9SYT7C48cNKT1bivJUDrYPYff7UEv0qLvnPqa/o841/vdO/06nzn1Nf0eca/wB7p3+nUEo0qLvnPqa/o841/vdO/wBOp859TX9HnGv97p3+nUEo0qLvnPqa/o841/vdO/06nzn1Nf0eca/3unf6dQSjSou+c+pr+jzjX+907/TqfOfU1/R5xr/e6d/p1BKNKi75z6mv6PONf73Tv9OqLvU3kXOkHg7K5+VWDDLBFiwy8xcbPmVwTPYlgj4cxwm3grdLvQJRvS99T4JoLRUqi/oW5F/2huVswUc6YFbHMSUkf/jF+Jt92KNDRS02kl0/f/iIRv8A6qvRQRpkXAGF5FlV2zI3nLbVcb62w3cvlGRS4LUoMo6N922lhOwgkbABrzX30zcQX5mLCdsEqDAj2cWB2Bbrg/EjS7cCopjSG21gPIBWsjts7Urz5NSpSgjiHwBxrB+Hcatkx16IbQptx+4PuLKrZ3+DKlFW1de6t7/e39W69HEvGxwIZPebhEtjF7zG9vXu6C3dywHFJS2gJUsBSvobSSSBtSlnXmt/pQaw/wAbYbLuWS3SdZkSnMuhs2+8NvrU41KjtIWhKCgnqB1cWDoDe/Nanb/Tvx5AiS7Ym7ZZIcejsMNPSMonOyILLS+7QjrLvZkBSQdp8nrokgaqU6q8M8ulrg8m8hsXYw79c88RhsBaIKp0lqNHLbLbEVgAhbqip51KVfR2cKleN7CTFemrjQx43VeRN3GPNenqvDd9lpuT7rrSWnA7KC/ccQptttBQT102jx9Ir14n6d+KsJTFaxuxSIzUG2z7PFbM59aWIcx4PPMoClHSe42n/pHgaFQlh/qM5GskJGT8k3X2sZxvMbji9/euMKOxLDC4jb8KQ+GCW23EukNLSjQ06NgKSa6X+bOeLg5iOKSlX613e8Yw9l81+w40zcpDXvylJiQlNOEIS202UhxX76yE6UnySEmcq+lvEcx4wXhGKMN2+dCxlWKWl+VIecajwFuMrW2sAkqJ9hH1Hahr7+TWyW307caQIUuK/EulxenzLdNkTLhdZEmUpUF5L0RsOuLKw024nYbB6+VbB7GotxrmLl25z73kGcXeJhsXEsGgX+42aTBQtv455MwOe+4kLdS0Cw2sIbPf8tn7VqUT1G8yWSRlGPT5suQ+7bseftE/IbKxBdhvXK4/BLeMZkg/DoKu6EvaWeh2SDugsO1wLx+xlTmVMIvLRdnquq7W3eJKLYqaofU+YgX7RWSSo/ToqJJGzuvBj/pw4pxXMLNmFsjXVE2wrlGyRXrzJch24PoUl5uNGUsttoKVH6QnQ0Na0KhLOuZubuLM1yXjiRmbuVrccxmJaZ7FhjqnQhcHJgkOKYaCW3nAmLptJAT9SOwP1dvblGbcmHiDJ8ty+1Xdu5ceZDbLhjV5vFrat8yeyt1lLqHGGldUq6uvsEgJStKknr96Cxd84ywvI85x3ke8WdL+QYq3KatUr3FD2EyEhLu0g9VbA8dgdbOtbNapK9NHFcvIkZK7DvAfYvacjiR0XmUmLDuXfuuQywF9G1LJV30NKC1gj6juUm1+42leiOyQdH8q+qCKbt6ZeJbtBg29dsucRuAzPipVCu0mO49Gmul2THdWhYU40tZ7dFEgflqu25+nDjO4tW9mO3fLUmBaWLEr5Ve5UIy4DI00xILSwXkpBUAVbIC1efJqUaUEdT+BsBueQM32Z87caakR5htRvMo21yQx19l1cUr9tSkFttQJH7yEq+43Ui0rg714+9BzSoyk3j1JJkuph8f8crYC1BpTmVzUrUjfgqAt5AOvuAT/AOTXX859TX9HnGv97p3+nUEo0qLvnPqa/o841/vdO/06nzn1Nf0eca/3unf6dQSjSqoerDJuebZwPk9zyCyYjjxiMJdt1wsWX3A3Fu4BQ+GEdCYALrinOqfbJ0oKIOhsjVPQzyD/ALQTK40FPqBwG0pxZTY1d7wr4C7qTodSGG0n3N/96Wz9/q/Kgu1SlKDTEcRYK3ieT4Sm1u/KcwkzpV3Z+Jc7POS/+OQrt2R234CSNflqsLlfA3FmWTIzl4jzWlNQGrPKYi3V+M3cYTeyiNLQhYEhsDv9KwfClj7E1JtU6tkx+8xOPuRJqg5dcl5WvkqQV/V0bjwbrGjsef5rbTDadfbfY/zjQTnK9OfE12yZGZC3TUufM4t8RFi3R9uAZ8dKUNSvh0LDRcCEITvXkAA1nGOHsFiQbbAjWt0NWi/vZPDSZTv0XFxTqluE72QVPufSdpG/t4FVctPM/NKuPbzl9iy21WqDg+CY/kotMeyMBmc+/wDEqfZUdbaaUhgABvqUk7B1sHacy525ql5/mjOA2XI5LOE3CHAiWaDj7MmHdVGOy+98VLcUFsFQe6tlBSE9UqV32U0E48R8anAWsku1wi21m95fe375dBbisse6tKUJSlSwFK0htOyQNknxWUk8XYRNXlirhZUy0ZulpF7afcUtuSltkMpHUnSNIAH068jf381AE/nvkZvmGwtWq6y5uNXPNTicqOLO01a2Ue24CG5S9PvS0uIPbptodVDXjdSj6eMizjMMKmZxmuUfMlzrnco8WE1CaYahsxp0hhASUjutSkNo7FRPkeNUHqicAYFbrbNtr15yyQ5chHZRMmZPOdlsBhXdlMd1TvZrqravp+/ne66VemnjIsRig5A3cY8x+cu8N32Um5SHHm0Nuh6SF+44hTbTSClRI6toGvpFV9yfNORcpt/C/NuQ5WxMtd1zB64NYuxBaR8EGoM8sttvAe6twJbKXAsqBWo9QgDVdtm9S3PFrwkcoz7Fe7xbLljE29SG7jj7Vvt1okJZ96MI76F+6+wd+2rv2WdJUCnyKCxOKennivCWo7GO2STHZh2qbZIzap77iWIMp73nWUBSjpPf9380jwNCtY5Y9L2KZnxmrCsVZbt86JjacUtj8uQ840xbvfjuLbWASVEiOgdjtQ/Xyax2UXnmTj+wWODJ5WjX2dnN4tdli3F2xx2zZlvpcW++lLZCHUFLfVpLiSQtQKlLH01qT/KHNcjlGBwZF5EitPxsok2iXkSbRHXJkw/lDc5v9kR7KH0Lc6EhPUp0euzQTDZ/TtxraR7zkS6XCcq4QbkufcbrIlSlOwyTGR7riyr2mypXVvfX6lHWya77bwHx7aMmGSwEXlpKZr1xbtQvEn5W1Kd7e46mH39oKUVrJ+nW1qOtndV8tPOfOFsxLHMzvebwrmciGUWowxaWWWmHbZHlrYlpUPrLi1RAXEElv6z1SnQrJu86cvcXY5bMiy7J4ObO5Nx5PyxqMxbWozdumxUR16Qpo9lxlfEgHuSoFvYV9WgEx436a+LMVy20ZlaIV3EvHjJFmjvXiU7DtiJCVJdbjx1LLbaCFfuhOhoa1oVs9944wS+59jnIt6tTLuSY0zKYtMlTqkqaQ+kJd+kHSvpHgkHr2VrWzUDZJnfqFw+6nj61ZU1ml1uuORcjTMj2uKxOhNCW2zLRGZ2GXSW3CtlLuyVIUCV7ArVcw5LyvIsedsLWdS5c9q2ZhFlTbljDFvvVrcj25l1DK0kFCVkOb7tJSlba0eCR2ITu96buJpeTIyAs3b4uJeRksSM3epKY0K4KWpa5DLAX0bLpU530NL7rB+5r4uvpb4fu1pt9kdtNzjRLfa37J0h3eTHMm3vLLi4z6kLBebKyVdVE6JOvvUM23k7OsDs3yeBcoc26O4xgtui3iVbWfeZeucp2OZD5bCfdQ0lSSlBITsf9yiczkHJnM9kzxPCkXkKNJmpyaywvxG7aY5kGDPhznXGlspAZDyFRNoUEgdVo7JV57BLl+9P/ABvfJUNxar1bHWLYzZ3Ra73KhGdBZGm2ZHtLSXkpBUAVbIC1Dfk16FcD4CvJo+Su/OnRFmNXFm2O3mS5bUS20gIfEVSy33ToKB1+8O3381DNxznL3MHsGb365R52TYVyj+FPmLbCWFXCEuf8E4FoRpIUtpxKlJSAns2CANCrVUClK81yXcW7fJXaGI785LSjHakOqaaW5r6QtaUqKUk62QkkfoaD00qLvnPqa/o841/vdO/06nzn1Nf0eca/3unf6dQSjSou+c+pr+jzjX+907/TqfOfU1/R5xr/AHunf6dQSjSvzW9TnJPrVxr1JWWBwDaW3somW5teQ2CxXCReLSG96YdlCRGZbiuKT38pUCpKUk68E3s4VunM93weLM51xfHrDkyv+LFss5clnrr7q7JHRW/5oUsf935UG+18PPNR2lPvuobbQOylrUAlI/Uk/avuoU5njsZLy9xjgGUtJexG7C7S5cR1ZEe4TY7TZjx3k704jqt5z21bSot+QetBM0aXFmsiRDktPtK2AttYUk6/rHiu2q5ZtPwrhi7ysN4UmRLBkuW3Sy2+XAjtd4FjbkrdbTPREGmW3FhtadDQWtKFKSdHtrT/AChza9zRC9P8bkiO2qNeJUWVkabPHVKkRflTc1tJaI9lDyFrKSQnqU9SU7oLZUql0LnvnK04JjudXjNYNydye1ZZHENNoZZZiSbVGkrYlII2tSlqi7WhRKP2h6hIAFbQ5zJyjxvEgO5ryJab8cnwWTk7UhdpS2xaJjKoyeqExtreYcMtKUJV2WVN67nt4C1NKp2zztzXFt2c425epzNytN1xVm13C/WaLHmts3OWlh4OxmT0AACygKAXo7Vvwa9+bcwc6W/NcsxPDHcnu8jj6PAZZRFxmNJZv8txhL7hmPbQI6FhQbT7PTqQpRJ8JAWzW62319xxKe6uqex1s/oP66+HZUZjv70hpv20919lgdU/qf0H9dQR6nJ2Qv49xVcbJaIvzl3PLO6zBuEpTDSXiw+S2662hwpA2QSlKvI+1QZ6gzm13u/MkbkiyWO2yXuL7WyGbPc3pjSmFXZ4ElbjDJSryoaCT415/KgvW5IYaQlx19tCFkBKlKABJ+wB/rrsqhXKN3ueQ4Lg3GkmepTnFmR21V4/abW441dmYVuC/wA1d2Vre3+ZbH61NVr5D5TtnMcay8h5HcrFCumQSbda4Rx9mRZLhF6LVHQzPbV7zUpSEdiHTrulaQjRSaCxdKr7znzLnfFOU3eyw34sj8T482nCGFMJ7fPRJTGWyT/9QEyozgB/JDn5A1jr/nvMOH8ksw85yy42WwJuVrt1vnR8cYm2a4peS2hwS3kEPxZC31OIT5Q2nsydKBUCFkC60lxLSnEha9lKSfJ199Cut6dCjocckS2W0MkBxS3AAgn7bJ+33FQNzhIzGN6heIXcGtVouN0+X5GEsXWe7DYKPajdj7jbTquw/IdNH9RUMZXHvN8jcqw+Q7HZkS5nLOGMz4EWQqdCW2pu2jp2dabKwpJ+oKbA8keR5oLvw7hAuCFOQJrElCT1UplwLAP6EivozIiUurMloJYOnSVjSD9/q/T/AO9Vo5Mk4P6cOV8byjCbFb7Dbn8bv8nIrZaY6I7EmNFabcYeUy2Aguh9SGUL12Pv9dnwKjLhmBJi5Uvj/k7Esrt0TnfHpicjN6t5YYVkenXXEMLUpQPaM6ttPjeoaD5JoLzrdabR7jjqEo8fUogDz9vNfdVBxe933lCBxnwJeHRLvGEzpMrMg6o9HPkqw1DD2vsJD6or4390IX962bjLlTkiLkwsnLubS7FepVtflu2m9Y2y3b/cbfbQtdumx1gPMp9xI04paylxtR0QoELMUpSgUpSgUpSgUpSgUpSgUpSgUpSgUpSgV1vxo8kJTIYbdCFBaQtIUAofYjf512UoFKUoFKUoFKUoFRnI4Lxi53bLkX5CZ1gyudEvKrftxlyJc2UBCpDLza0qbKg20radKCgs9tK0JMpQRy36fOF4uL3zDTg8I2TJnmpN3iPOurRNfb69XXCpRJcJQgqXvsojaiT5rK5pxPx9yMiArJ7KX3LYlbUSREmPw32W1gBbYdjrQv21BKdo7dT1TseBUdep6x2fJ77w9jmRW2PcbVcM6Q1LhyUBbL6BbpiglaD4UNgHR/MCtFzzJbV6X8+ymLxtZRHs0nBDdkY9BQSwm9fGJiw/ZYT4SuQp3oUoAK/aB0SKCwEHibjO2wp9vgYhbWYdztTNjlx0o/ZOwWgsNsFG+vUB1z7Df1HdaZefTDxuzj12iYVYbdAvFztvypU27fEXRD8b3ErDEhLr3d1sdNJ+sKbBJbKTVeOMMcduuP536YHJeWxpGSY6xlNhuN+tsu3v/OmkoTNU18SlKlAS0MSPp8D3lVvjXIuUc73PCZuH2xM244Tiz+XXK2uyCyw5kKkORIkJ1QOh1eRMUQft1Sf0NBvHG3pXxrHouUqzxu3XuRlzcJiazGTKSyhuIpamFpckPuyS8FOFXul3snq2EdQgVsVy9PeHGxw8Sx9kW6yPXyNer6h5x2XJuxjkLabckPLU4R7jbOyoq+hHUaH2wnAPJOW5ZfLnj2a5S4/dIlujTXrPcsUeslwhLWpaVFPZam5DHZJSFoKvqSfqIIqb6BSlKBSlKBSlKBSlKBSlKDrejx5HT4hht321BaO6Qeqh9iN/Y/112UpQKUpQKjnHeFMds86f8wQ1cbcnIpOTWWO4hSVWyXJaWiT1UFaUlSnn1AEePeWPPjUjUoNHj8JcWRLJc8bj4dERbbza49lnRwtzq9CYCwyyfq2AkOua0d/UfNdWT8FcVZjfvxLkWJtyZyksIf6yn2mZaWTtoSGULDcgIP291KtDx9q32lBHMn08cOTMhVlMjDGlXI3NF5Q58XICGJyVBXxDTYX0acUR9SkJSVgqCuwJB3LGsYsOH2hFixu3NwYDbr76WGySA486p1xXkk/Uta1f/espSgjlv088NtZhHzlvCo6bzEnuXSK58S/7UaWsEOPNMd/abWvsruUoHYnatnzXNm4B4bx24yLpbsMiJW+1IjezIfdfjMNP/wDGbYYdWpphK9kKS2lII8Hx4qPuR7vnNs9V2NfgbG4N9krwS4+7EnXhVvaSj46P9YUGXeyvsNdR4J8/lUPW+3uZvdbPZOSMeguIuPPNzauVoXJM6JoWd5Qa7KQgOICkpUNoA2B48boLPW708cNQbHcsfjYgh6DdW2WZAkzpElxKGVdmUNOuOKWyltX1IDakhCvKdHzWTsHDnGOMLtUiy4vHZfskuRNiSVvOuviS+37bzrjq1FbrikfSVOFR0APyFQdlOR2H0w8i5c1glpEfHfwEb8vH4ST7CLumWI8UMMjwhckudClIHctg6JBNR5xPYXLrCzL01XyRl0VzkPFhkUO4Xy1zLe6jIUJSmeY5kpSVAOmM+kI8D6t6oLR5JwVgFzww4paLLFtiojV0+UP6WsQZM5p5t94JKvqKvfcJBP8AOOteK83Ffp4434vx2PaoWPxpc1dlYsk+VILjyZEdCAFtIbdWsMsrV2UWkaRs+QdVD0DkDKec5HHdts0JMq74bY5WV5DAXILLK77HS5CiwnVJ+wMtEhzR/JkH9K3zgPkvNsoySbjefZMs3dq1Mz5FiuOKPWebDcU4ULU0srU1KjhQKO6CryAexChQbXb/AE5cM2yzTrFDwxAjTwwlxa5slyQ2lhXZhLL6nC6wltWyhLakhBJKQN17bbwVxTaYzMaLiDCwyJo9yQ+9Idd+LbS1ILrji1LdK20IQSsqISlIGgBW+0oNDhcGcUwbJMx1rD47tvn2uPZZLUl51/3YTClqZaKnFKVpBcWUnexsaPga7LHwpxjjrMNq14ugLg3RN6akPyXpEhU1LSmkvOPOrU46pLa1IHdSgE6A0ANbxSgju8cMY/cbpY/gkMwbLbcheyudAQ2VGfc1dih1S1K+kJcWXCAPKko+wGjIlKUClKUClKUClKUHW3GjtOOOtMNoW6ezikpAKz9tk/nXZSlArBZjhGK5/aPkeXWdq4REuofbClKQ4y8k7S604ghbTifyWghQ/I1naUGhQuCuKIGL3XD2sQYdtt8cS9cjJfekSZTqdFDjklxanlLR1T0UV9kdU9SNCu7HuFuMcVkWybZMVZZl2iRJlxZbj7r0j35DYbedcdcUpbq1IAT2cKjoADWhW70oIzyzgHAb3gTmF2SzRbUuLAu0azSAFuC3PXBl5t91KSodt++skE/mdarz4X6a+J8RxJ7FlYtGnC42hmz3J19x53346E/8NsOLUWGu21htspSkkEDYBqVKUEb2v068N2h2TIh4agvzTDVLffmyH3pKoj4ejLdcccUpxTbgBSpRJAATvqNV7sy4Q4vz+9JyDK8XTMmllEZ9aJT7CJbKFFSWpDba0okIBKtJdCgOyhrRO96pQYa94fjWRotLd6tDMlFjnM3K3pO0iPJaSpLbiQkjykKUAD48/asXk3FHHuYy7nOyXGI09+821q0TluKWC/DbdLqGjojwHFFXjR2fvW20oNKmcMcX3CberjLw2CuTkUuBOujv1BUp+EUmKtRB+7ZQkjXjx53XXB4R4wt2XfjeJjATdUynJzfaW+qM1JcBC324yllht1XZW3EoCj2Ud/Ud7zSgwOR4LiOXXGx3bJLDFuEzGp3zK0vPJJVEk9FI9xH9fVah52PsfuAawlx4S4xu2W/ja4YyHboZDUxf+9viM7JaADb7kYLDDjqeqNOKQVDogg/SNbzSgxE/E8eueRWrLZ1sbeu9kbkM2+USrswh8JDoAB19QQnewftWAyHhfjPKol8h3vGG3m8knxrpc1NyHmXH5cdLaWHgttaVIWgNN6KSP3RW7UoI5g+nviKFGlRlYqub8Y5GW+7cbjKnPOCO6HmUF19xa/bS4kL9vt07eSDW25Jh2NZeLZ+I7S1NNnuDN0gKUVJVHlNb6OoKSCCNkfoQSDsHVZmlBrls46wiz5Rfs0tuNxGL1k7bLN3mBJK5aGklLaVgnWgkkeAN/nusNjHBnFeH3B26WPFG0vuMmOgSZT8puOyVBRaYbeWpDCCUp+hsJT9KfH0jW+UoFKUoFKUoFKUoFKUoFKUoFKUoFKUoFKUoFKUoFKUoFKUoFKUoFKUoKa/7T++XrG+J8IveO3iba7jFzNhTEuFIWw+0TDlAlK0EKTsEjwfsaiL0xXa65djOD5Fldzl3q7XDlyHGmT7g8qTIkNMW19xhtxxwlS0tr+pAJISryNGlKC23N4DXOfAkhv6HVX+7sFafCi0q1PqUjf36koQSPsSlJ/IVG/C0OGInqqQIjIScyurZAbGij4FtXX/x2Ws6/VSj+ZpSg03/AGZeRZBmSs3v2X324Xy5xmoEFibcpS5L7UbTivZS44SpLfYk9Qdb86q9dKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUEK3b/APWJj3/+e3H/AB8aqceqy+XvGuNb7e8cvE61XGLzzPLEyFIWw+0TbVAlK0EKTsEjwfsTSlB7vTldbplnGuL5FlNylXm63Pl+zxZ064PKkSJTLEda2W3XFkqWhtf1ISokJV5GjVs+fwGuVOCJTY6PfjOQz7ifCvbXa5fdG/v1PVOx9jofpSlBoXCkKH+NPVW38Iz1Xkq21J9saKTbG1FJH6FS1nX6qUfzNRp/szsiyDMb7mt2y6+3C9zrfBhQYcm5SlyXY8YrcUWW1uElDZKQeoIGwDrxSlBfilKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUH//Z height=146 alt aria-describedby=cn0025><ol class=u-margin-s-bottom><li data-moz-translations-id=0><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr5_lrg.jpg target=_blank download title="Download high-res image (102KB)" data-moz-translations-id=1><span class=anchor-text data-moz-translations-id=2>Download : <span class=download-link-title data-moz-translations-id=3>Download high-res image (102KB)</span></span></a><li data-moz-translations-id=4><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr5.jpg target=_blank download title="Download full-size image" data-moz-translations-id=5><span class=anchor-text data-moz-translations-id=6>Download : <span class=download-link-title data-moz-translations-id=7>Download full-size image</span></span></a></ol></span><span class="captions text-s"><span id=cn0025><p id=sp0040><span class=label data-moz-translations-id=0>Fig. 5</span>. Workflow applied for testing the effect of resampling and of using daily average values or instantaneous values (e.g. the measure value at noon).</p></span></span></figure></div><div><p id=p0475><a class="anchor u-display-inline anchor-paragraph" href=#f0030 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0030 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Fig. 6</span></a> shows that significant differences do exist between HMs, HDIVs and HDAVs. Most of the time, instantaneous values (HDIVs) show a better fit with HMs than average values (HDAVs): HDAVs systematically underestimate peak-flows and often overestimate the rising limb of the floods. HDIVs usually show a better fit with peak-flow except when variations of peak-flow are clearly shorter than one day (e.g. the flood on Dec. 5th, 1992). For this case, the averaged value may provide a better fit. HDIVs better reproduce low-flows than HDAVs.<figure class="figure text-xs" id=f0030><span><img src=data:image/jpeg;base64,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 height=300 alt aria-describedby=cn0030><ol class=u-margin-s-bottom><li data-moz-translations-id=0><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr6_lrg.jpg target=_blank download title="Download high-res image (234KB)" data-moz-translations-id=1><span class=anchor-text data-moz-translations-id=2>Download : <span class=download-link-title data-moz-translations-id=3>Download high-res image (234KB)</span></span></a><li data-moz-translations-id=4><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr6.jpg target=_blank download title="Download full-size image" data-moz-translations-id=5><span class=anchor-text data-moz-translations-id=6>Download : <span class=download-link-title data-moz-translations-id=7>Download full-size image</span></span></a></ol></span><span class="captions text-s"><span id=cn0030><p id=sp0045><span class=label data-moz-translations-id=0>Fig. 6</span>. Zoom on the three time-series HMS, HDIVs and HDAVs (period 1/11/1992 to 31/12/1992).</p></span></span></figure></div><p id=p0480>In summary HDAVs show a more systematic bias than HDIVs.<div><p id=p0485>HDIVs and HDAVs are thus compared to HMs using the provided efficiency criteria (see <a class="anchor u-display-inline anchor-paragraph" href=#t0015 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0015 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Table 3</span></a>). In both cases it appears that VCC (volume conservation coefficient) is well conserved. NASH and KGE are about ~0.9 to ~0.95. meaning that conversion from hourly to daily and then to hourly values again degrades the time series in the same order of magnitude of what is usually targeted (and obtained) with (the best) simulations.<div class="tables colsep-0 frame-topbot rowsep-0" id=t0015><span class="captions text-s"><span id=cn0060><p id=sp0075><span class=label data-moz-translations-id=0>Table 3</span>. Comparison of the efficiency criteria for HDIVs and HDAVs.</p></span></span><div class=groups><table><thead><tr class=valign-top><td scope=col class=align-left><span class=screen-reader-only data-moz-translations-id=0>Empty Cell</span><th scope=col class="align-left rowsep-1" colspan=2>Flow components<td scope=col class=align-left><span class=screen-reader-only data-moz-translations-id=0>Empty Cell</span><td scope=col class=align-left><span class=screen-reader-only data-moz-translations-id=0>Empty Cell</span><td scope=col class=align-left><span class=screen-reader-only data-moz-translations-id=0>Empty Cell</span><tr class="rowsep-1 valign-top"><th scope=col class=align-left>HDIVs<th scope=col class=align-left>Rising limb<th scope=col class=align-left>Base flow<th scope=col class=align-left>Flood recession<th scope=col class=align-left>Undet. Flow<th scope=col class=align-left>Total<tbody><tr class=valign-top><th class=align-left scope=row>VCC<td class=align-left>0.95<td class=align-left>1.01<td class=align-left>1.01<td class=align-left>1.04<td class=align-left>1.002<tr class=valign-top><th class=align-left scope=row>NASH<td class=align-left>0.816<td class=align-left>0.985<td class=align-left>0.917<td class=align-left>0.927<td class=align-left>0.92<tr class=valign-top><th class=align-left scope=row>KGE<td class=align-left>0.86<td class=align-left>0.96<td class=align-left>0.95<td class=align-left>0.93<td class=align-left>0.95</table><table><thead><tr class=valign-top><td scope=col class=align-left><span class=screen-reader-only data-moz-translations-id=0>Empty Cell</span><th scope=col class="align-left rowsep-1" colspan=2>Flow components<td scope=col class=align-left><span class=screen-reader-only data-moz-translations-id=0>Empty Cell</span><td scope=col class=align-left><span class=screen-reader-only data-moz-translations-id=0>Empty Cell</span><td scope=col class=align-left><span class=screen-reader-only data-moz-translations-id=0>Empty Cell</span><tr class="rowsep-1 valign-top"><th scope=col class=align-left>HDAVs<th scope=col class=align-left>Rising limb<th scope=col class=align-left>Base flow<th scope=col class=align-left>Flood recession<th scope=col class=align-left>Undet. Flow<th scope=col class=align-left>Total<tbody><tr class=valign-top><th class=align-left scope=row>VCC<td class=align-left>0.97<td class=align-left>1.01<td class=align-left>0.99<td class=align-left>1.06<td class=align-left>0.995<tr class=valign-top><th class=align-left scope=row>NASH<td class=align-left>0.818<td class=align-left>0.972<td class=align-left>0.918<td class=align-left>0.934<td class=align-left>0.91<tr class=valign-top><th class=align-left scope=row>KGE<td class=align-left>0.83<td class=align-left>0.96<td class=align-left>0.90<td class=align-left>0.91<td class=align-left>0.92</table></div></div></div><p id=p0490>It should be observed that NASH and KGE for HDIVs are slightly better than those computed for HDAVs. VCC is a bit lower for HDAVs, which makes sense as HDVAs systematically underestimate peak flows.<p id=p0495>Our advice for KMC was then to work at hourly time-step as far as possible for simulations at the Milandre test site. But if this was not possible, then teams were encouraged to use DIVs from HMs to compare daily values obtained with their models.<p id=p0500>Finally, as all teams were required to compute their final efficiency criteria at an hourly time step, their simulated results, if calculated at daily time-step, needed to be resampled into hourly data using a linear decomposition.</p></section></section><section id=s0180><h2 id=st200 class="u-h4 u-margin-l-top u-margin-xs-bottom">5. Results</h2><section id=s0185><h3 id=st205 class="u-h4 u-margin-m-top u-margin-xs-bottom">5.1. Overall performance</h3><div><p id=p0505>As shown on <a class="anchor u-display-inline anchor-paragraph" href=#f0035 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0035 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Fig. 7</span></a> the different model forecasts generally seem to reproduce the correct order of magnitude of discharge rates for most of the prediction period. Models tend to underestimate discharge during recessions occurring after significant high-water conditions (e.g. March and June on <a class="anchor u-display-inline anchor-paragraph" href=#f0035 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0035 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Fig. 7</span></a>).<figure class="figure text-xs" id=f0035><span><img src=data:image/jpeg;base64,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 height=274 alt aria-describedby=cn0035><ol class=u-margin-s-bottom><li data-moz-translations-id=0><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr7_lrg.jpg target=_blank download title="Download high-res image (195KB)" data-moz-translations-id=1><span class=anchor-text data-moz-translations-id=2>Download : <span class=download-link-title data-moz-translations-id=3>Download high-res image (195KB)</span></span></a><li data-moz-translations-id=4><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr7.jpg target=_blank download title="Download full-size image" data-moz-translations-id=5><span class=anchor-text data-moz-translations-id=6>Download : <span class=download-link-title data-moz-translations-id=7>Download full-size image</span></span></a></ol></span><span class="captions text-s"><span id=cn0035><p id=sp0050><span class=label data-moz-translations-id=0>Fig. 7</span>. Forecasted discharge rates in red (envelope of results coming from all models) compared to the observed rates (Black line). Most observed peaks are forecasted. Models tend to underestimate discharge during recessions occurring after significant high-water conditions (e.g. March and June). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</p></span></span></figure></div><p id=p0510>Given that most models provided reasonable results, quality criteria were then applied in order to identify strengths and weaknesses of the respective models. The following comparison takes into account only the best prediction for the year 2016 provided by the participating teams. Most are obtained with calibration on years 2014–2015, but some used a mix with 1992–1995 as well.<div><p id=p0515>In <a class="anchor u-display-inline anchor-paragraph" href=#t0020 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0020 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Table 4</span></a> model results have been classified according to their “final score” as defined in chapter 3. Effort is given as indication. Concerning KGE and VCC all models are acceptable to good, but none is very good.<div class="tables colsep-0 frame-topbot rowsep-0" id=t0020><span class="captions text-s"><span id=ce.caption_ikl_1sq_zpb><p id=ce.simple-para_gm5_1sq_zpb><span class=label data-moz-translations-id=0>Table 4</span>. Comparison of the model performances. All models produced acceptable results. Color ratings are given in <a class="anchor u-display-inline anchor-paragraph" href=#t0010 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0010 data-moz-translations-id=1><span class=anchor-text data-moz-translations-id=2>Table 2</span></a>.</p></span></span><div class=groups><table><tbody><tr class=valign-top><td class=align-left><figure class=inline-figure><img src=data:image/jpeg;base64,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 height=258 alt></figure></table></div></div></div><div><p id=p0520>Forecasted curves for the year 2016 are given for all individual models in <a class="anchor u-display-inline anchor-paragraph" href=#f0040 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0040 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Fig. 8</span></a>. The log scale provides a better view of the relative errors between the forecasted and the observed curve than the linear scale.<figure class="figure text-xs" id=f0040><span><img src="data:image/jpeg;base64,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" height=463 alt aria-describedby=cn0040><ol class=u-margin-s-bottom><li data-moz-translations-id=0><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr8_lrg.jpg target=_blank download title="Download high-res image (592KB)" data-moz-translations-id=1><span class=anchor-text data-moz-translations-id=2>Download : <span class=download-link-title data-moz-translations-id=3>Download high-res image (592KB)</span></span></a><li data-moz-translations-id=4><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr8.jpg target=_blank download title="Download full-size image" data-moz-translations-id=5><span class=anchor-text data-moz-translations-id=6>Download : <span class=download-link-title data-moz-translations-id=7>Download full-size image</span></span></a></ol></span><span class="captions text-s"><span id=cn0040><p id=sp0055><span class=label data-moz-translations-id=0>Fig. 8</span>. Forecasted <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/hydrograph title="Learn more about hydrographs from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=1>hydrographs</a> of the thirteen models applied for KMC-step 1 compared to the observed curve (pale red). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</p></span></span></figure></div><p id=p0525>All models reproduce the overall shape of the curve of discharge variations, covering periods of time of low flow and other periods with peaks. The amplitude of the peaks is in the right order of a magnitude. Low flow conditions are correctly identified. However, a closer look shows that low flow conditions are rather poorly forecasted by most models whereby often the forecasted discharge rate is too low and too flat.<p id=p0530>It appears that curves predicted by reservoir models and semi-distributed models reproduce somehow better the observed behavior with many peaks from January to June, and less peaks from July to October. InfoWorks and KRM1 are semi-distributed models with their recharge processes based on reservoir models; their curves are thus quite similar to those of reservoir models. VarKarst is based on a different approach (it explicitly considers spatial variability), and it can be seen that the peaks are pretty well predicted, but the simulation does not reflect the observed behavior well during low flow conditions. RCD-seasonal oversimplifies recession during summer with just a single long recession. The distributed models fail at forecasting low flow conditions as too many little peaks are produced, and the subsequent recessions are much too quick. Peaks are underestimated in winter and overestimated in Summer. CHLEM, which is a reservoir model, produces curves which are similar to those of distributed models. <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/lumped-model title="Learn more about Lumped models from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=0>Lumped models</a> are not bad for the first half of the year, but have a significant drift in the second part of the year, with, ANN-2 performing better overall than the three others.<div><p id=p0535>Relative errors are given in <a class="anchor u-display-inline anchor-paragraph" href=#f0045 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0045 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Fig. 9</span></a><span data-moz-translations-id=2>. Errors on peaks are of short duration, but may be of considerable intensity (usually larger than 400%, positive or negative, for some events along the year). The flow-rate during the recession in March is underestimated by most models. CNN is the only model providing a prediction close to observations for this event. VarKarst, KarstMOD and CHLEM are particularly low. The main recession in July tends also to be underestimated by most models with <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/gardenia title="Learn more about Gardenia from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=3>Gardenia</a>, Karstflow and KRM1 performing better than the others.</span><figure class="figure text-xs" id=f0045><span><img src="data:image/jpeg;base64,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" height=970 alt aria-describedby=cn0045><ol class=u-margin-s-bottom><li data-moz-translations-id=0><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr9_lrg.jpg target=_blank download title="Download high-res image (1MB)" data-moz-translations-id=1><span class=anchor-text data-moz-translations-id=2>Download : <span class=download-link-title data-moz-translations-id=3>Download high-res image (1MB)</span></span></a><li data-moz-translations-id=4><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr9.jpg target=_blank download title="Download full-size image" data-moz-translations-id=5><span class=anchor-text data-moz-translations-id=6>Download : <span class=download-link-title data-moz-translations-id=7>Download full-size image</span></span></a></ol></span><span class="captions text-s"><span id=cn0045><p id=sp0060><span class=label data-moz-translations-id=0>Fig. 9</span>. Relative errors of the respective models for year 2016. All predictions show periods of time with errors larger than 200%. The green band shows the domain with + -50% of relative error. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</p></span></span></figure></div><p id=p0540>It is noticeable that most models using neural-derived approaches display a significant drift in the last months of the simulations except the <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/self-organizing-systems title="Learn more about MLP from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=0>MLP</a> model.<div><p id=p0545>By classifying the relative errors, we can display the percentage of the forecasted data (hours/year) for which the error of the model is lower than a value (<a class="anchor u-display-inline anchor-paragraph" href=#f0050 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0050 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Fig. 10</span></a>). For instance, the LSTM model (intense pale blue) has 22% of the forecasted values with an error lower than 50% (e.g. between 67 and 150 L/s instead of 100 L/s). Gardenia has 72%: the higher is the curve, the better the performance of the model.<figure class="figure text-xs" id=f0050><span><img src="data:image/jpeg;base64,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" height=389 alt aria-describedby=cn0050><ol class=u-margin-s-bottom><li data-moz-translations-id=0><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr10_lrg.jpg target=_blank download title="Download high-res image (366KB)" data-moz-translations-id=1><span class=anchor-text data-moz-translations-id=2>Download : <span class=download-link-title data-moz-translations-id=3>Download high-res image (366KB)</span></span></a><li data-moz-translations-id=4><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr10.jpg target=_blank download title="Download full-size image" data-moz-translations-id=5><span class=anchor-text data-moz-translations-id=6>Download : <span class=download-link-title data-moz-translations-id=7>Download full-size image</span></span></a></ol></span><span class="captions text-s"><span id=cn0050><p id=sp0065><span class=label data-moz-translations-id=0>Fig. 10</span>. Percentage of forecasted data (of time) as a function of relative errors. The best model has 72% of forecasted data within a relative error of + -50%. The worse model has only 22% of forecasted data with a relative error lower than 50%. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</p></span></span></figure></div></section><section id=s0190><h3 id=st210 class="u-h4 u-margin-m-top u-margin-xs-bottom">5.2. Performance by flow components</h3><p id=p0550>The observed times series was divided into four components: rising limb of the peaks, recession, base flow and undetermined flow. Base-flow was empirically determined when the discharge rate is decreasing over five successive hours with a rate lower than 1 L/s per hour. The latter component includes points which are not clearly related to one of the other three classes.<div><p id=p0555>For the year 2016 the significance of the four components is given in <a class="anchor u-display-inline anchor-paragraph" href=#t0025 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0025 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Table 5</span></a> (in hours). Variations from one year to the other are in the order of ±5%:<div class="tables colsep-0 frame-topbot rowsep-0" id=t0025><span class="captions text-s"><span id=cn0065><p id=sp0080><span class=label data-moz-translations-id=0>Table 5</span>. Part of the total hydrograph in the respective components.</p></span></span><div class=groups><table><thead><tr class="rowsep-1 valign-top"><th scope=col class=align-left>Flow component<th scope=col class=align-left>Percentage in hours<th scope=col class=align-left>Percentage in volume<th scope=col class=align-left>Mean discharge rate<tbody><tr class=valign-top><td class=align-left>Rising limb<td class=align-left>11<td class=align-left>21<td class=align-left>608 L/s<tr class=valign-top><td class=align-left>Recession<td class=align-left>33<td class=align-left>57<td class=align-left>555 L/s<tr class=valign-top><td class=align-left>Base flow<td class=align-left>9<td class=align-left>9<td class=align-left>106 L/s<tr class=valign-top><td class=align-left>Undetermined<td class=align-left>28<td class=align-left>13<td class=align-left>156 L/s</table></div></div></div><div><p id=p0560>Each value of the observed curve is attributed to one single flow component. For the forecasted curves, the components of the observed curve are used for the separation, meaning that all models are compared on the basis of the same components. As can be seen on <a class="anchor u-display-inline anchor-paragraph" href=#f0055 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0055 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Fig. 11</span></a>, most values belonging to the undetermined flow correspond in fact to base-flow conditions. They could not be attributed to base-flow because they include slight increases of the discharge rate, which may be related to some technical problems (e.g. temperature variations on the electronics, or changes of the atmospheric pressure which are not perfectly corrected).<figure class="figure text-xs" id=f0055><span><img src=data:image/jpeg;base64,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 height=334 alt aria-describedby=cn0055><ol class=u-margin-s-bottom><li data-moz-translations-id=0><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr11_lrg.jpg target=_blank download title="Download high-res image (153KB)" data-moz-translations-id=1><span class=anchor-text data-moz-translations-id=2>Download : <span class=download-link-title data-moz-translations-id=3>Download high-res image (153KB)</span></span></a><li data-moz-translations-id=4><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-gr11.jpg target=_blank download title="Download full-size image" data-moz-translations-id=5><span class=anchor-text data-moz-translations-id=6>Download : <span class=download-link-title data-moz-translations-id=7>Download full-size image</span></span></a></ol></span><span class="captions text-s"><span id=cn0055><p id=sp0070><span class=label data-moz-translations-id=0>Fig. 11</span>. Decomposition of the time series of observed discharge-rates. The undetermined component mainly corresponds to base flow. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</p></span></span></figure></div><p id=p0565>The respective models have specific results for each component. Gardenia has among the best performances for most criteria and components. This is less the case for Varkarst, especially concerning volume conservation (VCC). Most models have one component with KGE close to or below 0.5. Nash criteria is low for base flow in most models. CHLEM, CFP-modified and RCD_seasonal, three models with the lowest scores, are better than most other models for base-flow according to VCC and KGE.<p id=p0570>The Nash criteria is quite poor for most models as this criterium cannot be good for a trending time series (as the mean value is taken as reference in the denominator of the Nash-Sutcliff formula).<div><p id=p0575>Other quality criteria have also been calculated for the respective models, mainly Mean squared error and the variance test. However, these criteria do not provide more information than those presented in <a class="anchor u-display-inline anchor-paragraph" href=#t0030 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0030 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Table 6</span></a>.<div class="tables colsep-0 frame-topbot rowsep-0" id=t0030><span class="captions text-s"><span id=ce.caption_zkg_dsq_zpb><p id=ce.simple-para_eq3_dsq_zpb><span class=label data-moz-translations-id=0>Table 6</span>. Table showing the three criteria applied to the four components of the 13 models compared for the challenge. Models are sorted according to their global performance (see <a class="anchor u-display-inline anchor-paragraph" href=#t0020 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0020 data-moz-translations-id=1><span class=anchor-text data-moz-translations-id=2>Table 4</span></a>).</p></span></span><div class=groups><table><tbody><tr class=valign-top><td class=align-left><figure class=inline-figure><img src=data:image/jpeg;base64,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 height=303 alt></figure></table></div></div></div></section><section id=s0195><h3 id=st215 class="u-h4 u-margin-m-top u-margin-xs-bottom">5.3. Comparison of calibrated parameters</h3><p id=p0580>Given that the models are based on different concepts, their parameters are all different and cannot be compared directly. Some aspects, however, can be roughly compared for several models, i.e. catchment size, storage capacity, conduit/diffuse flow components.<section id=s0200><h4 id=st220 class="u-margin-m-top u-margin-xs-bottom">5.3.1. Catchment size</h4><p id=p0585>In karst regions catchment size is never known exactly, and so this was considered to be a calibration parameter by some of the models. The calibrated size area varies between 11.6 and 16.24 km<sup data-moz-translations-id=0>2</sup>, which ranges between-9 and + 28% of the assessed surface area (12.7 km<sup data-moz-translations-id=1>2</sup>). The main reason for these variations is related to the model applied for assessing evapotranspiration: the size was calibrated to adjust the water balance on the calibration period.<p id=p0590>Neural networks models do not provide any catchment size. The role of this parameter is approached by a series of unidentified functions and parameters.</p></section><section id=s0205><h4 id=st225 class="u-margin-m-top u-margin-xs-bottom">5.3.2. Storage capacity</h4><p id=p0595>The attenuation between recharge and discharge is related to the characteristics of the storage in all models. However, the way it is generated is different in the respective models. Most models have quick and slow storages included somehow, which reflects what is commonly accepted in karst aquifers since the 1970s as summarized e.g. by <a class="anchor u-display-inline anchor-paragraph" href=#b0025 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0025 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Bakalowicz (2005)</span></a>. Their number and characteristics are, however, different and hardly comparable. It may be only mathematical, or based on the infilling of a reservoir, or on the routing of water, etc. Somehow, all models generate peaks within 0.2–5 days after a rain event, and a base-flow usually corresponding to the emptying of some kind of reservoir (or mathematical function) with slow recession keeping water flowing out of the system over more than 6 months without recharge. The storage volume of this slow component is in the order of 100–250 mm.</p></section><section id=s0210><h4 id=st230 class="u-margin-m-top u-margin-xs-bottom">5.3.3. Conduit/diffuse flow components</h4><p id=p0600>Another long-lasting debate among karst hydrogeologist (see e.g., <a class="anchor u-display-inline anchor-paragraph" href=#b0025 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0025 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Bakalowicz, 2005</span></a>) is about the ratio between the quick-flow component (assumed to be related to conduits) and the slow-flow component (assumed to be related to matrix or diffuse flow). From our results we were hoping to be able to compare the amount of recharge flowing through both components of the respective models. However, as every model has a different architecture and concept, this could hardly be done. The only thing we can compare is the amount of peak flow (rising limb and recession) and base-flow (including undetermined flow) for each model. The comparison shows, however, that differences between models are not very significant, the amount of peak flow ranging between 64 and 78%, and the volume of base flow ranging between 22 and 36%.</p></section></section></section><section id=s0215><h2 id=st235 class="u-h4 u-margin-l-top u-margin-xs-bottom">6. Discussion and model comparison</h2><p id=p0605>The comparison shows that forecasted curves with similar values for their quality criteria may have different shapes. This illustrates the fact that quality criteria for model comparison could still be improved. Our first assessment of the results was that all models produced fair to good simulations. However, this assessment is probably rather qualitative than quantitative. Indeed, when looking at relative errors, we realized that most models produce values with errors larger than 50% for about 50% of the time or more. Results are clearly worse during low-flow conditions. Absolute differences are low during these periods of time, but relative ones are significant.<p id=p0610>By looking at the respective curves (<a class="anchor u-display-inline anchor-paragraph" href=#f0040 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0040 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Fig. 8</span></a>) and at the relative errors (<a class="anchor u-display-inline anchor-paragraph" href=#f0045 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0045 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Fig. 9</span></a>), one can say that the three criteria we selected are not sufficient to characterize the overall quality of the results. For instance, the shape of the curve forecasted by the VarKarst model reproduces very well the peaks, be very poorly the values during the dry-season. Gardenia is better for base-flow, but still include significant differences in March and October of the simulated year. The fact that most models obviously tend to underestimate or poorly reproduce low-flow (e.g., LSTM or VarKarst) could be related to the way calibration is implemented. It is usually mainly targeted on minimising the squared errors metric, which gives much weight to the peaks and not much to low-flow values. A criterium minimizing relative errors instead of absolute errors could possibly improve the weighting of base-flow conditions in the overall results. Another option could also be to calibrate the parameters which control base-flow first as they not dependent much on the recharge model, and then to calibrate the other parameters on the residuals of this first calibration step. In the case of RCD_seasonal and possibly VarKarst, the main reason for the poor forecasting of low-flow conditions seems to be related to the recharge model, which completely fails to produce any recharge between June and November or December. The water deficit in the recharge reservoir is obviously too high over this period compared to the reality.<p id=p0615>Interestingly the spatial distribution of precipitation appeared not to be significant and was abandoned along the process in agreement with all participants. Obviously, the number of recharge event for which differences are significant (e.g. summer storms) is low. This may be the case for the Milandrine KHS, which is small and located in a temperate and hilly region. This may be different for a larger catchment with more relief and contrasting climate.<p id=p0620>KRM_1 model is the only model for which land-use was taken into consideration. However, this does not appear to improve the results in a way to produce better results than the other models.<p id=p0625>The number of parameters in the respective models ranges between less than 10 and several thousands. Despite automatic calibration/optimization procedures, models with less parameters tend to be better than the models with many parameters, i.e. distributed models did not provide better results than global ones. Not considering catchment characteristics in the respective models could be a reason for this. Therefore, it seems that the transformation between meteorological parameters and discharge rates is mainly controlled by a few general characteristics of the system (such as size, recharge process and one or two storages) rather than by the detailed spatial distribution of parameters controlling these). Modellers using neural networks, with many parameters, claimed that data delivered for calibration were not sufficient. As no basic structure is provided in their models, compared to the models based upon a more physical function, they have to build this structure using data. They require therefore at least 15 years of data without any gap, which is challenging!<p id=p0630>These observations raise the question of uncertainty and overfitting associated with numerical models. In fact, we can probably conclude that the best models are as good as the data we provided.<p id=p0635>MODFLOW-CFPv2 and KarstFLOW are fully distributed models. These models are based on the principle that the transformation of the input signal into the output one is mainly related to the characteristics of the aquifer. This approach is more deductive than for the other models because these models are constructed from the supposed characteristics of the catchment and of the aquifer. A consequence is that the recharge part of the models is simplified. This is the main reason why peaks in the summer season are strongly exaggerated with both models (see <a class="anchor u-display-inline anchor-paragraph" href=#f0040 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0040 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Fig. 8</span></a>). For the given exercise, this approach is also not the most efficient as it requires more work for digitizing a lot of data, but at the end the results include more bias than many other models because recharge was not considered properly. Would a spatial discretization of the model parameters improve the results? Whilst this was not explicitly investigated in this research, we can argue that the main discrepancy of these models is related to (too) large peaks in summer, which seem to be more related to an oversimplification of the recharge model, rather than to the spatialization of the model parameters. Recharge seems therefore really to be the key factor in the present case.<p id=p0640>On the contrary, lumped parameter models are more inductive because they include less or no constraining hypotheses. The disadvantage is that none of their parameters can be compared somehow with natural characteristics (not even the size of the catchment area!). They provide purely functional results. Another problem is that they require long and continuous data sets for learning the input–output relationship of a natural flow system.<p id=p0645>In semi and fully distributed models, the spatial distribution of recharge (land-use, vegetation, precipitation) can be taken into account. However, as the performance of these models turned out to be no better than results of similar models (reservoir) which did not take it into account, one can infer that, at least in the present case-study, the role of spatial distribution of the recharge is low. The effect of spatial distribution was, however, not analysed in any depth and so could be further investigated (e.g., <a class="anchor u-display-inline anchor-paragraph" href=#b0050 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0050 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Bittner et al., 2018</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0210 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0210 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Gill et al., 2020</span></a>). As the Milandre site is rather flat, small and without allogenic recharge, the use of a complimentary catchment area with more contrasted conditions would probably be better for this analysis. It should be noted that the relative significance of the spatial distribution of recharge function in any models will vary according to the characteristics of the karst catchment being simulated: for example, whether there is significant allogenic recharge or not, the size of the catchment area, and/or the steepness of the topography may all be important factors.<p id=p0650><span data-moz-translations-id=0>An evaluation of the number of parameters hydrologically constrained in a rainfall-runoff model in <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/surface-water-hydrology title="Learn more about surface hydrology from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=1>surface hydrology</a> was investigated by </span><a class="anchor u-display-inline anchor-paragraph" href=#b0270 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0270 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Jakeman and Hornberger (1993)</span></a>. They show that most hydrological systems are well described by models with 4 to 6 parameters. The contribution of any supplementary parameter seems to be within the data uncertainty related to monitoring data. This explains somehow the fact that, in our study, models with many parameters (greater than20) do not provide better results than those with less parameters. In karst, it is difficult to obtain reasonable results with less than 6 parameters: three for the routing and three for the transformation of precipitation to recharge. Because of some visible threshold processes in karst systems, 9 parameters seems to be a more reasonable minimum. <a class="anchor u-display-inline anchor-paragraph" href=#t0005 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0005 data-moz-translations-id=4><span class=anchor-text data-moz-translations-id=5>Table 1</span></a> indicates that some of the models had less than 9 parameters, however they include implicit assumptions fixing some of the parameters (e.g. distribution functions, fixed topology or geometry, etc.). <a class="anchor u-display-inline anchor-paragraph" href=#b0270 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0270 data-moz-translations-id=6><span class=anchor-text data-moz-translations-id=7>Jakeman and Hornberger (1993)</span></a> also suggest that a standardized simple model could be applied systematically to any hydrological system. This would help comparing systems’ characteristics and identifying drifts in data related to technical or natural reasons (e.g. climate change). The model comparison presented in our paper could therefore be a good basis for defining such a standardized karst model.</p></section><section id=s0220><h2 id=st240 class="u-h4 u-margin-l-top u-margin-xs-bottom">7. Conclusion and outlook</h2><section id=s0225><h3 id=st245 class="u-h4 u-margin-m-top u-margin-xs-bottom">7.1. Conclusion</h3><p id=p0655>Before making a conclusion, it is important to have in mind that the comparison between the models was only carried out a one-year simulation period. Due to the non-stationary nature of hydrologic phenomena, some models could be very good for one kind of situation and not so good for others. The presented interpretations must thus be considered as “snapshot” interpretations.<p id=p0660>Compared to a similar exercise conducted for surface catchment (<a class="anchor u-display-inline anchor-paragraph" href=#b0260 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0260 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Holländer et al., 2009</span></a>) all models performed reasonably well! Although they are based on strongly different modelling approaches all models lead to reasonable and somehow similar results for the proposed exercise (input–output hydrological modelling). Despite considerable simplifications with respect to reality, Gardenia provides excellent results in the present case. It fits the observed data almost within the range of uncertainty on the data themselves. However, despite very good scores, the forecasted hydrograph displays significant differences with the observed one, especially in low water conditions. This is even more obvious for results issued from VarKarst model.<p id=p0665>Apart from the different aspects of the modelling approach it can also be concluded from the modelling exercise that recharge is crucial in the modelling of any precipitation-discharge relationship. More specifically it can be observed that the real evapotranspiration must be carefully approached, otherwise models will fail at reproducing discharge rates from precipitation data, at least in temperate regions. It can even be inferred that hydrogeological processes taking place within the aquifer are subsidiary compared to recharge processes taking place in the soil and epikarst.<p id=p0670>Lumped parameters models are interesting options if they can include non-linear functions and adequate parameters for taking evapotranspiration into account. Regarding ANN models, their limitation is that they require long and complete datasets for calibration and validation, and that they usually do not provide any physical indication about the real system, even if this “knowledge extraction” has been successfully attempted (as suggested by <a class="anchor u-display-inline anchor-paragraph" href=#b0320 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0320 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Kong-A-Siou et al., 2013</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0090 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0090 data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Darras et al., 2015</span></a>) these models remain mainly functional. Taking into account the limited duration covered by the available data, several ANNs show an acceptable behavior.<p id=p0675>From the comparison, as well as from tests of some models on other sites, it also seems that the order and priority in which the calibration of the models is carried out has a strong effect on the result. Calibrating base-flow first and peak-flow afterwards appears to improve the results. This point was not properly investigated in this study and should be further examined. Some physical reasons could support this idea as storage characteristics of karst aquifer are probably relevant concerning base-flow. As this part of the hydrograph is more independent on recharge processes, it may be meaningful to calibrate the storage components of the models first using one or two storage reservoirs. This is also supported by ideas derived from <a class="anchor u-display-inline anchor-paragraph" href=#b0335 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0335 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Kovacs et al. (2005)</span></a>. Then the recharge model must focus on modelling the difference between precipitation and the assessed base-flow.<p id=p0680>It should also be highlighted here that the scoring scheme used to evaluate of strengths and weaknesses needs to be adapted for the purpose for the model is being developed for. Models used to provide flood assessments may not be assessed in the same way as those models focussing on the implications of low flow/drought conditions. The scores for the respective flow components (<a class="anchor u-display-inline anchor-paragraph" href=#t0025 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0025 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1>Table 5</span></a>) shows the differences between the respective models.<p id=p0685>It should be noted here that some of the modelling approaches could be combined in order to use their respective strengths. For instance, semi-distributed models could be used in combination with neural methods in order to improve their recharge assessment component. Such optimizations will need to be adjusted to specific conditions of the site, available data and question to be addressed.</p></section><section id=s0230><h3 id=st250 class="u-h4 u-margin-m-top u-margin-xs-bottom">7.2. Outlook</h3><p id=p0690>In the case of the Milandre Catchment area, only one model (KRM1) within the challenge attempted to take the spatial distribution of evapotranspiration (land-use) into account. This did not improve the model results in a measurable way. However, as real evapotranspiration is very significant, and as we know that this differentiation is necessary in catchments with stronger altitudinal gradients, it would be meaningful to further investigate this aspect.<p id=p0695>This report covers the very first step of a model comparison. It was focused only on the temporal discharge response of the spring to recharge events. We would like to expand the challenge to further aspects of the modelling of karst <a href=https://www.sciencedirect.com/topics/earth-and-planetary-sciences/hydrogeology title="Learn more about hydrogeology from ScienceDirect's AI-generated Topic Pages" class=topic-link data-moz-translations-id=0>hydrogeology</a>. The next step is to take into account the spatial distribution of flow and heads within the aquifer. A first question with this respect is the fact that the discharge rate given at the spring for the challenge in reality corresponds to the flow-rate coming out of three different springs: Font, Saivu and Bâme, which are located several hundred meters apart. We also have head measurements at various locations within the aquifer, and it would be interesting to see how various modelling approaches could reproduce those data. For this new exercise, it seems that lumped models will be hardly usable, and that distributed models will be more adequate. It will be therefore interesting to see how the respective teams will adapt their approach for this new challenge.<p id=p0700>Once heads and discharge rates will be approached, we will try to simulate flow velocities and some transport processes for which we also acquired data. This may also result in better performance of the models to the overall flows simulated in step 1 as some parameters will be better constrained.</p></section></section><section id=s0240><h2 id=st260 class="u-h4 u-margin-l-top u-margin-xs-bottom">CRediT authorship contribution statement</h2><p id=p0710><strong data-moz-translations-id=0>Pierre-Yves Jeannin:</strong> Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Visualization, Supervision, Project administration. <strong data-moz-translations-id=1>Guillaume Artigue:</strong> Software, Validation, Formal analysis, Investigation. <strong data-moz-translations-id=2>Christoph Butscher:</strong> Software, Validation, Formal analysis, Writing - review & editing. <strong data-moz-translations-id=3>Yong Chang:</strong> Software, Validation, Formal analysis. <strong data-moz-translations-id=4>Jean-Baptiste Charlier:</strong> Methodology, Software, Validation, Formal analysis, Writing - review & editing. <strong data-moz-translations-id=5>Lea Duran:</strong> Software, Validation, Formal analysis. <strong data-moz-translations-id=6>Laurence Gill:</strong> Methodology, Software, Validation, Formal analysis, Writing - review & editing. <strong data-moz-translations-id=7>Andreas Hartmann:</strong> Methodology, Software, Validation, Formal analysis, Writing - review & editing. <strong data-moz-translations-id=8>Anne Johannet:</strong> Methodology, Software, Validation, Formal analysis, Writing - review & editing. <strong data-moz-translations-id=9>Hervé Jourde:</strong> Software, Validation, Formal analysis. <strong data-moz-translations-id=10>Alireza Kavousi:</strong> Software, Validation, Formal analysis, Writing - review & editing. <strong data-moz-translations-id=11>Tanja Liesch:</strong> Software, Validation, Formal analysis. <strong data-moz-translations-id=12>Yan Liu:</strong> Software, Validation, Formal analysis. <strong data-moz-translations-id=13>Martin Lüthi:</strong> Software, Validation, Formal analysis. <strong data-moz-translations-id=14>Arnauld Malard:</strong> Methodology, Software, Validation, Formal analysis, Writing - review & editing. <strong data-moz-translations-id=15>Naomi Mazzilli:</strong> Software, Validation, Formal analysis, Writing - review & editing. <strong data-moz-translations-id=16>Eulogio Pardo-Igúzquiza:</strong> Methodology, Software, Validation, Formal analysis, Writing - review & editing. <strong data-moz-translations-id=17>Dominique Thiéry:</strong> Software, Validation, Formal analysis. <strong data-moz-translations-id=18>Thomas Reimann:</strong> Methodology, Software, Validation, Formal analysis, Writing - review & editing. <strong data-moz-translations-id=19>Philip Schuler:</strong> Software, Validation, Formal analysis. <strong data-moz-translations-id=20>Thomas Wöhling:</strong> Software, Validation, Formal analysis. <strong data-moz-translations-id=21>Andreas Wunsch:</strong> Methodology, Software, Validation, Formal analysis, Writing - review & editing.</p></section></div><section id=coi005><h2 id=st265 class="u-h4 u-margin-l-top u-margin-xs-bottom">Declaration of Competing Interest</h2><p id=p0715>The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.</p></section><section id=s0245><h2 id=st270 class="u-h4 u-margin-l-top u-margin-xs-bottom">Acknowledgements</h2><p id=p0720>This work was supported by the Swiss National Research Foundation (SNF) for Swisskarst and Thermokarst projects (Grant Numbers 406140-125962 and 200021_188636), the Deutsche Forschungsgemeinschaft, DFG for the iKarst project (Grant Numbers: LI 727/31-1 and RE 4001/2-1). The work of E. Pardo-Igúzquiza was supported by research project PID2019-106435 GB-I00 of the Ministerio de Ciencia e Innovación of Spain. This TCD-Dublin was conducted within the Irish Centre for Research in Applied Geosciences (ICRAG), supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number 13/RC/2092. The authors would like to thank the French Karst National Observatory Service (SNO KARST) initiative at the INSU/CNRS for their diffusion of KarstMod platform. It strengthens dissemination of knowledge and promote cross-disciplinarily research on karst systems at the French national scale.<p id=p0725>The data used in the project were acquired along several projects supported by the Federal Roads Office (FEDRO) and by the University of Neuchâtel. Marc Hessenauer (MFR SA, Delémont) and Pierre-Xavier Meury (Géo & Environment, Delémont) also participated to the data collection. The caving club (Spéléo-Club Jura was also very supportive). We also would like to thank Prof. Carol Wicks and an anonymous reviewer as well as Dr. Junbing Pu (associate editor) for their constructive comments.</p></section><div class=Appendices><section id=s0255><h2 id=st280 class="u-h4 u-margin-l-top u-margin-xs-bottom">Appendix. Supplementary data</h2><span class=download-all-supplemental-data><span class=article-attachment><form method=post action=/sdfe/pdf/download/S0022169421005552/attachments target=download_all><button class="button-link download-all-button button-link-secondary u-font-sans button-link-icon-left" type=submit><svg focusable=false viewBox="0 0 98 128" width=18.375 height=24 class="icon icon-download" data-moz-translations-id=0><path d="m77.38 56.18l-6.6-7.06-16.78 17.24v-40.36h-1e1v40.34l-17.72-17.24-7.3 7.08 29.2 29.32 29.2-29.32m10.62 17.82v2e1h-78v-2e1h-1e1v3e1h98v-3e1h-1e1" data-moz-translations-id=1></path></svg><span class=button-link-text data-moz-translations-id=2><span class=download-all-title data-moz-translations-id=3>Download all </span>supplementary files<span class=u-show-inline-from-sm data-moz-translations-id=4> included with this article</span></span></button></form><a class="anchor help-link u-font-sans u-margin-s-left anchor-default anchor-has-colored-icon anchor-icon-left" href=https://service.elsevier.com/app/answers/detail/a_id/19286/supporthub/sciencedirect/ target=_blank title="What’s this? (Opens in new window)"><svg focusable=false viewBox="0 0 114 128" width=20 height=20 class="icon icon-help"><path d="m57 8c-14.7 0-28.5 5.72-38.9 16.1-10.38 10.4-16.1 24.22-16.1 38.9 0 30.32 24.68 55 55 55 14.68 0 28.5-5.72 38.88-16.1 10.4-10.4 16.12-24.2 16.12-38.9 0-30.32-24.68-55-55-55zm0 1e1c24.82 0 45 20.18 45 45 0 12.02-4.68 23.32-13.18 31.82s-19.8 13.18-31.82 13.18c-24.82 0-45-20.18-45-45 0-12.02 4.68-23.32 13.18-31.82s19.8-13.18 31.82-13.18zm-0.14 14c-11.55 0.26-16.86 8.43-16.86 18v2h1e1v-2c0-4.22 2.22-9.66 8-9.24 5.5 0.4 6.32 5.14 5.78 8.14-1.1 6.16-11.78 9.5-11.78 20.5v6.6h1e1v-5.56c0-8.16 11.22-11.52 12-21.7 0.74-9.86-5.56-16.52-16-16.74-0.39-0.01-0.76-0.01-1.14 0zm-4.86 5e1v1e1h1e1v-1e1h-1e1z"></path></svg><span class=anchor-text>What’s this?</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link"><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z"></path></svg></a></span></span><p id=p0735>The following are the Supplementary data to this article:<span class=display data-moz-translations-id=0><span class="e-component e-component-mmc1" id=m0005 data-moz-translations-id=1></span></span><div class="article-attachment u-margin-xs-bottom" data-moz-translations-id=2><a class="anchor download-link u-font-sans u-display-inline anchor-default anchor-icon-left" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-mmc1.pdf target=_blank download title="Download Acrobat PDF file (3MB)" data-moz-translations-id=3><svg focusable=false viewBox="0 0 95 128" width=20 height=20 class="icon icon-pdf-download" data-moz-translations-id=4><path d="m82 108h-7e1v-49c0-6.08 4.92-11 11-11h17v-2e1h-6c-2.2 0-4 1.8-4 4v6h-7c-3.32 0-6.44 0.78-9.22 2.16 2.46-5.62 7.28-11.86 13.5-17.1 2.34-1.98 5.3-3.06 8.32-3.06h46.4v4e1h1e1v-5e1h-56.4c-5.38 0-10.62 1.92-14.76 5.4-9.1 7.68-18.84 20.14-18.84 32.1v70.5h9e1v-18h-1e1v8zm-25.94-39.4c-0.84-0.38-1.84-0.56-2.98-0.56h-9.04v23.6h5.94v-9h2.18c2.48 0 4.36-0.62 5.66-1.84s1.92-3.06 1.92-5.5c0-1.04-0.12-2-0.4-2.88-0.26-0.88-0.66-1.64-1.22-2.3s-1.22-1.14-2.06-1.52zm-3.12 8.94c-0.4 0.46-0.98 0.7-1.74 0.7h-1.22v-5.76h1.22c1.56 0 2.34 0.96 2.34 2.88 0 0.98-0.2 1.72-0.6 2.18zm21.84-8.52c-0.96-0.66-2.32-0.98-4.06-0.98h-8.72v23.6h8.72c1.74 0 3.1-0.32 4.06-0.98s1.7-1.52 2.18-2.62 0.78-2.34 0.88-3.76 0.16-2.9 0.16-4.44-0.06-3.02-0.16-4.44-0.4-2.68-0.88-3.76c-0.48-1.1-1.2-1.96-2.18-2.62zm-2.78 14.66c-0.06 0.96-0.18 1.72-0.38 2.24s-0.48 0.88-0.84 1.04-0.86 0.24-1.48 0.24h-1.32v-14.74h1.32c0.62 0 1.12 0.08 1.48 0.24s0.64 0.52 0.84 1.04 0.32 1.28 0.38 2.24c0.06 0.98 0.08 2.24 0.08 3.84s-0.02 2.9-0.08 3.86zm13.98-6.58v-4.02h7.74v-5.04h-13.7v23.6h5.96v-9.72h7.26v-4.82z" data-moz-translations-id=5></path></svg><span class=anchor-text data-moz-translations-id=6>Download : <span class=download-link-title data-moz-translations-id=7>Download Acrobat PDF file (3MB)</span></span></a></div><span class="captions text-s" data-moz-translations-id=8><span data-moz-translations-id=9><p data-moz-translations-id=10><span class=label data-moz-translations-id=11>Supplementary Data 1</span>. </p></span></span><p><p id=ce.para_l2x_btq_zpb><span class=display><span class="e-component e-component-mmc2" id=m0006></span></span><div class="article-attachment u-margin-xs-bottom"><a class="anchor download-link u-font-sans u-display-inline anchor-default anchor-icon-left" href=https://ars.els-cdn.com/content/image/1-s2.0-S0022169421005552-mmc2.zip target=_blank download title="Download zip file (7MB)" data-moz-translations-id=0><svg focusable=false viewBox="0 0 95 128" width=20 height=20 class="icon icon-document" data-moz-translations-id=1><path d="m35.6 1e1c-5.38 0-10.62 1.92-14.76 5.4-9.1 7.68-18.84 20.14-18.84 32.1v70.5h9e1v-108zm0 1e1h46.4v88h-7e1v-49c0-6.08 4.92-11 11-11h17v-2e1h-6c-2.2 0-4 1.8-4 4v6h-7c-3.32 0-6.44 0.78-9.22 2.16 2.46-5.62 7.28-11.86 13.5-17.1 2.34-1.98 5.3-3.06 8.32-3.06z" data-moz-translations-id=2></path></svg><span class=anchor-text data-moz-translations-id=3>Download : <span class=download-link-title data-moz-translations-id=4>Download zip file (7MB)</span></span></a></div><span class="captions text-s"><span><p><span class=label data-moz-translations-id=0>Supplementary Data 2</span>. </p></span></span><p></p></section></div></div><div class="related-content-links u-hide-from-md sf-hidden"></div><div class="Tail text-s"></div><section class="bibliography u-font-serif text-s" id=bi005><h2 class="section-title u-h4 u-margin-l-top u-margin-xs-bottom">Referencias</h2><section class=bibliography-sec id=bs005><ol class=references id=reference-links-bs005><li data-moz-translations-id=0><span class="label u-font-sans" data-moz-translations-id=1><a class="anchor anchor-default" href=#bb0005 id=ref-id-b0005 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=2><span class=anchor-text data-moz-translations-id=3>Anderson et al., 2015</span></a></span><span class=reference id=h0005 data-moz-translations-id=4><div class=other-ref data-moz-translations-id=5><span data-moz-translations-id=6>Anderson M. P, Woessner W. W., Hunt R. J., 2015. Modelado de aguas subterráneas aplicada, Simulaton de flujo y transporte advítivo. Elsevier, Amsterdam: 564 p.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=7><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Anderson%20M.%20P.%2C%20Woessner%20W.%20W.%2C%20Hunt%20R.%20J.%2C%202015.%20Applied%20Groundwater%20Modelling%2C%20Simulaton%20of%20Flow%20and%20Advective%20Transport.%20Elsevier%2C%20Amsterdam%3A%20564%20p." target=_blank rel="noopener noreferrer" data-moz-translations-id=8><span class=anchor-text data-moz-translations-id=9>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=10><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=11></path></svg></a></div></span><li data-moz-translations-id=12><span class="label u-font-sans" data-moz-translations-id=13><a class="anchor anchor-default" href=#bb0010 id=ref-id-b0010 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=14><span class=anchor-text data-moz-translations-id=15>Annable y Sudicky, 1998</span></a></span><span class=reference id=h0010 data-moz-translations-id=16><div class=contribution data-moz-translations-id=17><div class="authors u-font-sans" data-moz-translations-id=18>W.L. Annable, E.A. Sudicky</div><div id=ref-id-h0010 class="title text-m" data-moz-translations-id=19>Simulación de la génesis kárstica: interacciones hidrodinámicas y geoquímicas de agua en conductos parcialmente llenos</div></div><div class="host u-font-sans" data-moz-translations-id=20>Bull. Hydrogéol., 16 (1998), pp. 211 a 221</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=21><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Simulation%20of%20karst%20genesis%3A%20hydrodynamic%20and%20geochemical%20rock-water%20interactions%20in%20partially-filled%20conduits&publication_year=1998&author=W.L.%20Annable&author=E.A.%20Sudicky" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0010 data-moz-translations-id=22><span class=anchor-text data-moz-translations-id=23>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=24><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=25></path></svg></a></div></span><li data-moz-translations-id=26><span class="label u-font-sans" data-moz-translations-id=27><a class="anchor anchor-default" href=#bb0015 id=ref-id-b0015 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=28><span class=anchor-text data-moz-translations-id=29>Atkinson, 1977</span></a></span><span class=reference id=h0015 data-moz-translations-id=30><div class=contribution data-moz-translations-id=31><div class="authors u-font-sans" data-moz-translations-id=32>T.C. Atkinson</div><div id=ref-id-h0015 class="title text-m" data-moz-translations-id=33>Flujo difuso y flujo de conducto en terreno de piedra caliza en las colinas de Mendip, Somerset (Gran Bretaña)</div></div><div class="host u-font-sans" data-moz-translations-id=34>J. Hydrol., 35 (1o2) (1977), pp. 93-110</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=35><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Diffuse%20flow%20and%20conduit%20flow%20in%20limestone%20terrain%20in%20the%20Mendip%20Hills%2C%20Somerset%20&publication_year=1977&author=T.C.%20Atkinson" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0015 data-moz-translations-id=36><span class=anchor-text data-moz-translations-id=37>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=38><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=39></path></svg></a></div></span><li data-moz-translations-id=40><span class="label u-font-sans" data-moz-translations-id=41><a class="anchor anchor-default" href=#bb0020 id=ref-id-b0020 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=42><span class=anchor-text data-moz-translations-id=43>Artigue et al., 2012</span></a></span><span class=reference id=h0020 data-moz-translations-id=44><div class=other-ref data-moz-translations-id=45><span data-moz-translations-id=46>Artigue G., Johannet A., Borrell V., Pistre S., 2012. Pronóstico de inundaciones en cuencas mal calibradas utilizando redes neuronales: estudio de caso de la cuenca del Gardon de Mialet (Francia sur). - Nat. Peligros la Tierra Syst. Sci. 12, 3307-3324. 10/f4gt8k.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=47><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Artigue%20G.%2C%20Johannet%20A.%2C%20Borrell%20V.%2C%20Pistre%20S.%2C%202012.%20Flash%20flood%20forecasting%20in%20poorly%20gauged%20basins%20using%20neural%20networks%3A%20case%20study%20of%20the%20Gardon%20de%20Mialet%20basin%20(southern%20France).%20%E2%80%94%20Nat.%20Hazards%20Earth%20Syst.%20Sci.%2012%2C%203307%E2%80%933324.%2010%2Ff4gt8k." target=_blank rel="noopener noreferrer" data-moz-translations-id=48><span class=anchor-text data-moz-translations-id=49>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=50><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=51></path></svg></a></div></span><li data-moz-translations-id=52><span class="label u-font-sans" data-moz-translations-id=53><a class="anchor anchor-default" href=#bb0025 id=ref-id-b0025 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=54><span class=anchor-text data-moz-translations-id=55>Bakalowicz, 2005</span></a></span><span class=reference id=h0025 data-moz-translations-id=56><div class=contribution data-moz-translations-id=57><div class="authors u-font-sans" data-moz-translations-id=58>M. Bakalowicz</div><div id=ref-id-h0025 class="title text-m" data-moz-translations-id=59>Aguas subterráneas krastrasas: un reto para los nuevos recursos</div></div><div class="host u-font-sans" data-moz-translations-id=60>Hidrogeol. J., 13 (2005), págs. 148 a 160</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=61><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Karst%20groundwater%3A%20a%20challenge%20for%20new%20resources&publication_year=2005&author=M.%20Bakalowicz" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0025 data-moz-translations-id=62><span class=anchor-text data-moz-translations-id=63>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=64><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=65></path></svg></a></div></span><li data-moz-translations-id=66><span class="label u-font-sans" data-moz-translations-id=67><a class="anchor anchor-default" href=#bb0030 id=ref-id-b0030 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=68><span class=anchor-text data-moz-translations-id=69>Barron, 1993</span></a></span><span class=reference id=h0030 data-moz-translations-id=70><div class=contribution data-moz-translations-id=71><div class="authors u-font-sans" data-moz-translations-id=72>A.R. Barron</div><div id=ref-id-h0030 class="title text-m" data-moz-translations-id=73>Limitaciones de aproximación universal para las superposiciones de una función sigmoidal</div></div><div class="host u-font-sans" data-moz-translations-id=74>Inf. Teoría IEEE Trans. El 13 de diciembre de 1994, págs. 930-945</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=75><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Universal%20approximation%20bounds%20for%20superpositions%20of%20a%20sigmoidal%20function&publication_year=1993&author=A.R.%20Barron" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0030 data-moz-translations-id=76><span class=anchor-text data-moz-translations-id=77>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=78><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=79></path></svg></a></div></span><li data-moz-translations-id=80><span class="label u-font-sans" data-moz-translations-id=81><a class="anchor anchor-default" href=#bb0595 id=ref-id-b0595 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=82><span class=anchor-text data-moz-translations-id=83>Bengio y otros, 1994</span></a></span><span class=reference id=h0035 data-moz-translations-id=84><div class=other-ref data-moz-translations-id=85><span data-moz-translations-id=86>Bengio Y., Simard P., Frasconi P., 1994. Aprender dependencias a largo plazo con descenso de gradiente es difícil, IEEE Trans. Neural Netw., 5(2): 157o 166, 10/bmf3dr, 1994.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=87><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Bengio%20Y.%2C%20Simard%20P.%2C%20Frasconi%20P.%2C%201994.%20Learning%20long-term%20dependencies%20with%20gradient%20descent%20is%20difficult%2C%20IEEE%20Trans.%20Neural%20Netw.%2C%205(2)%3A%20157%E2%80%93166%2C%2010%2Fbmf3dr%2C%201994." target=_blank rel="noopener noreferrer" data-moz-translations-id=88><span class=anchor-text data-moz-translations-id=89>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=90><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=91></path></svg></a></div></span><li data-moz-translations-id=92><span class="label u-font-sans" data-moz-translations-id=93><a class="anchor anchor-default" href=#bb0040 id=ref-id-b0040 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=94><span class=anchor-text data-moz-translations-id=95>Bezes, 1976</span></a></span><span class=reference id=h0040 data-moz-translations-id=96><div class=contribution data-moz-translations-id=97><div class="authors u-font-sans" data-moz-translations-id=98>C. Bezes</div><div id=ref-id-h0040 class="title text-m" data-moz-translations-id=99>Contribución a la modélisation des syst.mes aquif.res karstiques</div></div><div class="host u-font-sans" data-moz-translations-id=100>Université des Sciences et Techniques du Languedoc, Montpellier, Thése (1976)</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=101><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Contribution%20%C3%A0%20la%20mod%C3%A9lisation%20des%20syst%C3%A8mes%20aquif%C3%A8res%20karstiques&publication_year=1976&author=C.%20Bezes" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0040 data-moz-translations-id=102><span class=anchor-text data-moz-translations-id=103>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=104><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=105></path></svg></a></div></span><li data-moz-translations-id=106><span class="label u-font-sans" data-moz-translations-id=107><a class="anchor anchor-default" href=#bb0045 id=ref-id-b0045 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=108><span class=anchor-text data-moz-translations-id=109>Biondi et al., 2012</span></a></span><span class=reference id=h0045 data-moz-translations-id=110><div class=contribution data-moz-translations-id=111><div class="authors u-font-sans" data-moz-translations-id=112>D. Biondi, G. Freni, V. Iacobellis, G. Mascaro, A. Montaniri</div><div id=ref-id-h0045 class="title text-m" data-moz-translations-id=113>Validación de modelos hidrológicos: Base conceptual, enfoques metodológicos y una propuesta de código de prácticas</div></div><div class="host u-font-sans" data-moz-translations-id=114>El físico. Chem. Tierra, 42o44 (2012), pp. 70 a 76</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=115><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Validation%20of%20hydrological%20models%3A%20Conceptual%20basis%2C%20methodological%20approaches%20and%20a%20proposal%20for%20a%20code%20of%20practice&publication_year=2012&author=D.%20Biondi&author=G.%20Freni&author=V.%20Iacobellis&author=G.%20Mascaro&author=A.%20Montaniri" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0045 data-moz-translations-id=116><span class=anchor-text data-moz-translations-id=117>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=118><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=119></path></svg></a></div></span><li data-moz-translations-id=120><span class="label u-font-sans" data-moz-translations-id=121><a class="anchor anchor-default" href=#bb0050 id=ref-id-b0050 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=122><span class=anchor-text data-moz-translations-id=123>Bittner et al., 2018</span></a></span><span class=reference id=h0050 data-moz-translations-id=124><div class=other-ref data-moz-translations-id=125><span data-moz-translations-id=126>Bittner D., Narany T. S., Kohl B., Disse M., Chiogna G., 2018. Modelar el impacto hidrológico del cambio de uso de la tierra en un sistema kárstico dominado por dolomitas. Journal of Hydrology, 567, diciembre de 2018: 267-279.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=127><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Bittner%20D.%2C%20Narany%20T.%20S.%2C%20Kohl%20B.%2C%20Disse%20M.%2C%20Chiogna%20G.%2C%202018.%20Modeling%20the%20hydrological%20impact%20of%20land%20use%20change%20in%20a%20dolomite-dominated%20karst%20system.%20Journal%20of%20Hydrology%2C%20567%2C%20December%202018%3A%20267-279." target=_blank rel="noopener noreferrer" data-moz-translations-id=128><span class=anchor-text data-moz-translations-id=129>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=130><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=131></path></svg></a></div></span><li data-moz-translations-id=132><span class="label u-font-sans" data-moz-translations-id=133><a class="anchor anchor-default" href=#bb0055 id=ref-id-b0055 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=134><span class=anchor-text data-moz-translations-id=135>Boegli, 1980</span></a></span><span class=reference id=h0055 data-moz-translations-id=136><div class=other-ref data-moz-translations-id=137><span data-moz-translations-id=138>Boegli A., 1980. Hidrología Karst y Especuletología Física. Springer Verlag: 284 p.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=139><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Boegli%20A.%2C%201980.%20Karst%20Hydrology%20and%20Physical%20Speleology.%20Springer%20Verlag%3A%20284%20p." target=_blank rel="noopener noreferrer" data-moz-translations-id=140><span class=anchor-text data-moz-translations-id=141>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=142><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=143></path></svg></a></div></span><li data-moz-translations-id=144><span class="label u-font-sans" data-moz-translations-id=145><a class="anchor anchor-default" href=#bb0060 id=ref-id-b0060 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=146><span class=anchor-text data-moz-translations-id=147>Bonacci, 1987</span></a></span><span class=reference id=h0060 data-moz-translations-id=148><div class=other-ref data-moz-translations-id=149><span data-moz-translations-id=150>Bonacci O., 1987. Hidrología Karst, con referencia especial al Dinaric Karst. Springer Verlag: 184 p.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=151><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Bonacci%20O.%2C%201987.%20Karst%20hydrology%2C%20with%20Special%20Reference%20to%20the%20Dinaric%20Karst.%20Springer%20Verlag%3A%20184%20p." target=_blank rel="noopener noreferrer" data-moz-translations-id=152><span class=anchor-text data-moz-translations-id=153>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=154><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=155></path></svg></a></div></span><li data-moz-translations-id=156><span class="label u-font-sans" data-moz-translations-id=157><a class="anchor anchor-default" href=#bb0065 id=ref-id-b0065 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=158><span class=anchor-text data-moz-translations-id=159>Borghi et al., 2016</span></a></span><span class=reference id=h0065 data-moz-translations-id=160><div class=contribution data-moz-translations-id=161><div class="authors u-font-sans" data-moz-translations-id=162>A. Borghi, P. Renard, F. Cornaton</div><div id=ref-id-h0065 class="title text-m" data-moz-translations-id=163>Puede uno identificar la geometría de redes de conductos kársticos y propiedades de los datos de pruebas hidráulicas y trazadoras?</div></div><div class="host u-font-sans" data-moz-translations-id=164>Adv. Retorno del agua, 90 (2016), pp. 99-115</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=165><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Can%20one%20identify%20karst%20conduit%20networks%20geometry%20and%20properties%20from%20hydraulic%20and%20tracer%20test%20data&publication_year=2016&author=A.%20Borghi&author=P.%20Renard&author=F.%20Cornaton" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0065 data-moz-translations-id=166><span class=anchor-text data-moz-translations-id=167>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=168><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=169></path></svg></a></div></span><li data-moz-translations-id=170><span class="label u-font-sans" data-moz-translations-id=171><a class="anchor anchor-default" href=#bb0070 id=ref-id-b0070 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=172><span class=anchor-text data-moz-translations-id=173>Bredehoeft y Pinder, 1970</span></a></span><span class=reference id=h0070 data-moz-translations-id=174><div class=contribution data-moz-translations-id=175><div class="authors u-font-sans" data-moz-translations-id=176>- J.D. Bredehoeft, G.F. Pinder</div><div id=ref-id-h0070 class="title text-m" data-moz-translations-id=177>Análisis digital del flujo areal en sistemas de aguas subterráneas multiagilantes. Un modelo cuasi tridimensional</div></div><div class="host u-font-sans" data-moz-translations-id=178>Retorno del agua. Res., 6 3) (1970), pp. 883-888</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=179><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Digital%20analysis%20of%20areal%20flow%20in%20multi-aquifer%20groundwater%20systems.%20A%20quasi%20three-dimensional%20model&publication_year=1970&author=J.D.%20Bredehoeft&author=G.F.%20Pinder" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0070 data-moz-translations-id=180><span class=anchor-text data-moz-translations-id=181>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=182><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=183></path></svg></a></div></span><li data-moz-translations-id=184><span class="label u-font-sans" data-moz-translations-id=185><a class="anchor anchor-default" href=#bb0075 id=ref-id-b0075 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=186><span class=anchor-text data-moz-translations-id=187>Brown, 1970</span></a></span><span class=reference id=h0075 data-moz-translations-id=188><div class=contribution data-moz-translations-id=189><div class="authors u-font-sans" data-moz-translations-id=190>M.C. Marrón</div><div id=ref-id-h0075 class="title text-m" data-moz-translations-id=191>Hidrología Karst de la cuenca inferior de Maligne</div></div><div class="host u-font-sans" data-moz-translations-id=192>Jasper, Alberta, Cave Stud., 13 (1970), pp. 179-193</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=193><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Karst%20hydrology%20of%20the%20lower%20Maligne%20Basin&publication_year=1970&author=M.C.%20Brown" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0075 data-moz-translations-id=194><span class=anchor-text data-moz-translations-id=195>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=196><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=197></path></svg></a></div></span><li data-moz-translations-id=198><span class="label u-font-sans" data-moz-translations-id=199><a class="anchor anchor-default" href=#bb0085 id=ref-id-b0085 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=200><span class=anchor-text data-moz-translations-id=201>Brown, 1973</span></a></span><span class=reference id=h0080 data-moz-translations-id=202><div class=contribution data-moz-translations-id=203><div class="authors u-font-sans" data-moz-translations-id=204>M.C. Marrón</div><div id=ref-id-h0080 class="title text-m" data-moz-translations-id=205>Equilibrio masivo y análisis espectral aplicado a redes hidrológicas kársticas, Resour del agua</div></div><div class="host u-font-sans" data-moz-translations-id=206>Res, 9 (1973), pp. 749-752</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=207><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Mass%20balance%20and%20spectral%20analysis%20applied%20to%20karst%20hydrologic%20networks%2C%20Water%20Resour&publication_year=1973&author=M.C.%20Brown" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0080 data-moz-translations-id=208><span class=anchor-text data-moz-translations-id=209>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=210><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=211></path></svg></a></div></span><li data-moz-translations-id=212><span class="label u-font-sans" data-moz-translations-id=213><a class="anchor anchor-default" href=#bb0080 id=ref-id-b0080 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=214><span class=anchor-text data-moz-translations-id=215>Butscher y Huggenberger, 2008</span></a></span><span class=reference id=h0085 data-moz-translations-id=216><div class=contribution data-moz-translations-id=217><div class="authors u-font-sans" data-moz-translations-id=218>C. Carnicero, P. Huggenberger</div><div id=ref-id-h0085 class="title text-m" data-moz-translations-id=219>Evaluación de la vulnerabilidad intrínseca en las áreas kársticas: un enfoque de modelado numérico</div></div><div class="host u-font-sans" data-moz-translations-id=220>Retorno del agua. Res., 44 (2008), pág. W03408</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=221><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Intrinsic%20vulnerability%20assessment%20in%20karst%20areas%3A%20a%20numerical%20modeling%20approach&publication_year=2008&author=C.%20Butscher&author=P.%20Huggenberger" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0085 data-moz-translations-id=222><span class=anchor-text data-moz-translations-id=223>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=224><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=225></path></svg></a></div></span><li data-moz-translations-id=226><span class="label u-font-sans" data-moz-translations-id=227><a class="anchor anchor-default" href=#bb0125 id=ref-id-b0125 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=228><span class=anchor-text data-moz-translations-id=229>Campbell y Sullivan, 2002</span></a></span><span class=reference id=h0090 data-moz-translations-id=230><div class=contribution data-moz-translations-id=231><div class="authors u-font-sans" data-moz-translations-id=232>C.W. Campbell, S.M. Sullivan</div><div id=ref-id-h0090 class="title text-m" data-moz-translations-id=233>Simulando el flujo de cuevas y los niveles de agua usando el Modelo de Gestión del Agua Tormenta</div></div><div class="host u-font-sans" data-moz-translations-id=234>Eng. Geol., 65 (2002), pp. 133-139</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=235><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Simulating%20time-varying%20cave%20flow%20and%20water%20levels%20using%20the%20Storm%20Water%20Management%20Model&publication_year=2002&author=C.W.%20Campbell&author=S.M.%20Sullivan" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0090 data-moz-translations-id=236><span class=anchor-text data-moz-translations-id=237>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=238><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=239></path></svg></a></div></span><li data-moz-translations-id=240><span class="label u-font-sans" data-moz-translations-id=241><a class="anchor anchor-default" href=#bb0120 id=ref-id-b0120 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=242><span class=anchor-text data-moz-translations-id=243>Castany, 1968</span></a></span><span class=reference id=h0095 data-moz-translations-id=244><div class=other-ref data-moz-translations-id=245><span data-moz-translations-id=246>Castany G., 1968. Prospection et exploitation des eaux souterraines: Dunod P. (Ed.): 718 p.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=247><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Castany%20G.%2C%201968.%20Prospection%20et%20exploitation%20des%20eaux%20souterraines%3A%20Dunod%20P.%20(Ed.)%3A%20718%20p." target=_blank rel="noopener noreferrer" data-moz-translations-id=248><span class=anchor-text data-moz-translations-id=249>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=250><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=251></path></svg></a></div></span><li data-moz-translations-id=252><span class="label u-font-sans" data-moz-translations-id=253><a class="anchor anchor-default" href=#bb0115 id=ref-id-b0115 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=254><span class=anchor-text data-moz-translations-id=255>Chang et al., 2019</span></a></span><span class=reference id=h0100 data-moz-translations-id=256><div class=contribution data-moz-translations-id=257><div class="authors u-font-sans" data-moz-translations-id=258>Y. Chang, J. Wu, G. Jiang, L. Liu, T. Reimann, M. Sauter</div><div id=ref-id-h0100 class="title text-m" data-moz-translations-id=259>Modelado la descarga de muelles y el transporte de soluto en conduits mediante el acoplamiento CFPv2 a un depósito epikarst para un acuífero kárstico. -</div></div><div class="host u-font-sans" data-moz-translations-id=260>J. Hydrol., 569 (2019), pp. 587-599, <a class="anchor anchor-default" href=https://doi.org/10.1016/j.jhydrol.2018.11.075 target=_blank rel="noreferrer noopener" data-moz-translations-id=261><span class=anchor-text data-moz-translations-id=262>10.1016/j.jhydrol.2018.11.075</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=263><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=264></path></svg></a></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=265><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Modelling%20spring%20discharge%20and%20solute%20transport%20in%20conduits%20by%20coupling%20CFPv2%20to%20an%20epikarst%20reservoir%20for%20a%20karst%20aquifer.%20&publication_year=2019&author=Y.%20Chang&author=J.%20Wu&author=G.%20Jiang&author=L.%20Liu&author=T.%20Reimann&author=M.%20Sauter" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0100 data-moz-translations-id=268><span class=anchor-text data-moz-translations-id=269>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=270><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=271></path></svg></a></div></span><li data-moz-translations-id=272><span class="label u-font-sans" data-moz-translations-id=273><a class="anchor anchor-default" href=#bb0110 id=ref-id-b0110 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=274><span class=anchor-text data-moz-translations-id=275>Chemin, 1974</span></a></span><span class=reference id=h0105 data-moz-translations-id=276><div class=other-ref data-moz-translations-id=277><span data-moz-translations-id=278>Chemin J., 1974. Essai dáun mod-le mathématique conceptuel au calcul du bilan hydrique de l-aquif-re karstique de la source du Lez (región N de Montpellier). Thse, Université de Montpellier: 67 p.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=279><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Chemin%20J.%2C%201974.%20Essai%20d%E2%80%99application%20d%E2%80%99un%20mod%C3%A8le%20math%C3%A9matique%20conceptuel%20au%20calcul%20du%20bilan%20hydrique%20de%20l%E2%80%99aquif%C3%A8re%20karstique%20de%20la%20source%20du%20Lez%20(region%20N%20de%20Montpellier).%20Th%C3%A8se%2C%20Universit%C3%A9%20de%20Montpellier%3A%2067%20p." target=_blank rel="noopener noreferrer" data-moz-translations-id=280><span class=anchor-text data-moz-translations-id=281>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=282><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=283></path></svg></a></div></span><li data-moz-translations-id=284><span class="label u-font-sans" data-moz-translations-id=285><a class="anchor anchor-default" href=#bb0105 id=ref-id-b0105 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=286><span class=anchor-text data-moz-translations-id=287>Chen y Goldscheider, 2014</span></a></span><span class=reference id=h0110 data-moz-translations-id=288><div class=contribution data-moz-translations-id=289><div class="authors u-font-sans" data-moz-translations-id=290>Z. Chen, N. Goldscheider</div><div id=ref-id-h0110 class="title text-m" data-moz-translations-id=291>Modelado de comportamiento hidráulico espacial y temporal variado de un sistema kárstico plegado con drenaje dominante en la escala de cuenca, Hochifen-Gottesacker, Alpes</div></div><div class="host u-font-sans" data-moz-translations-id=292>J. Hydrol., 514 (2014), pp. 41 - 52</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=293><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Modeling%20spatially%20and%20temporally%20varied%20hydraulic%20behavior%20of%20a%20folded%20karst%20system%20with%20dominant%20conduit%20drainage%20at%20catchment%20scale%2C%20Hochifen-Gottesacker%2C%20Alps&publication_year=2014&author=Z.%20Chen&author=N.%20Goldscheider" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0110 data-moz-translations-id=294><span class=anchor-text data-moz-translations-id=295>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=296><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=297></path></svg></a></div></span><li data-moz-translations-id=298><span class="label u-font-sans" data-moz-translations-id=299><a class="anchor anchor-default" href=#bb0100 id=ref-id-b0100 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=300><span class=anchor-text data-moz-translations-id=301>Coutouis et al., 2016</span></a></span><span class=reference id=h0115 data-moz-translations-id=302><div class=other-ref data-moz-translations-id=303><span data-moz-translations-id=304>Coutouis A., Johannet A., Pistre S., Ayral P.-A., Cadilhac L. 2016. Hacia una herramienta de predicción basada en redes neuronales dedicada a la predicción de bajo nivel de agua. Estudio de caso sobre el acuífero kárstico de Méjannes-le-Clap (Francia). 8o Congreso Internacional de Modelado Medioambiental y Software (iEMSs), Jul 2016, Toulouse, Francia.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=305><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Coutouis%20A.%2C%20Johannet%20A.%2C%20Pistre%20S.%2C%20Ayral%20P.-A.%2C%20Cadilhac%20L.%202016.%20Towards%20a%20neural%20networks-based%20prediction%20tool%20devoted%20to%20low%20water-levels%20forecasting.%20Case%20study%20on%20the%20M%C3%A9jannes-le-Clap%20karst%20aquifer%20(France).%20%E2%80%94%208th%20International%20Congress%20on%20Environmental%20Modelling%20and%20Software%20(iEMSs)%2C%20Jul%202016%2C%20Toulouse%2C%20France.%20%E3%80%88hal-02127821%E3%80%89." target=_blank rel="noopener noreferrer" data-moz-translations-id=306><span class=anchor-text data-moz-translations-id=307>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=308><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=309></path></svg></a></div></span><li data-moz-translations-id=310><span class="label u-font-sans" data-moz-translations-id=311><a class="anchor anchor-default" href=#bb0095 id=ref-id-b0095 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=312><span class=anchor-text data-moz-translations-id=313>Darcy, 1856</span></a></span><span class=reference id=h0120 data-moz-translations-id=314><div class=other-ref data-moz-translations-id=315><span data-moz-translations-id=316>Darcy H., 1856. Les fontaines publiques de la ville de Dijon : exposition et application des principes . suivre et des formulas . patron dans les questions de distribution d'eau, Paris, Victor Dalmont,</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=317><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Darcy%20H.%2C%201856.%20Les%20fontaines%20publiques%20de%20la%20ville%20de%20Dijon%20%3A%20exposition%20et%20application%20des%20principes%20%C3%A0%20suivre%20et%20des%20formules%20%C3%A0%20employer%20dans%20les%20questions%20de%20distribution%20d%27eau%2C%20Paris%2C%20Victor%20Dalmont%2C." target=_blank rel="noopener noreferrer" data-moz-translations-id=318><span class=anchor-text data-moz-translations-id=319>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=320><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=321></path></svg></a></div></span><li data-moz-translations-id=322><span class="label u-font-sans" data-moz-translations-id=323><a class="anchor anchor-default" href=#bb0090 id=ref-id-b0090 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=324><span class=anchor-text data-moz-translations-id=325>Darras et al., 2015</span></a></span><span class=reference id=h0125 data-moz-translations-id=326><div class=contribution data-moz-translations-id=327><div class="authors u-font-sans" data-moz-translations-id=328>T. Darras, V.B. Estupina, L. Kong-A-Siou, B. Vayssade, A. Johannet, S. Pistre</div><div id=ref-id-h0125 class="title text-m" data-moz-translations-id=329>Identificación de las contribuciones espaciales y temporales de las precipitaciones a inundaciones repentinas mediante modelos de red neuronal: estudio de caso sobre la cuenca de Lez (sur de Francia)</div></div><div class="host u-font-sans" data-moz-translations-id=330>Hidrol. Tierra Syst. Sci., 19 (10) (2015), pp. 4397-4410</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=331><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Identification%20of%20spatial%20and%20temporal%20contributions%20of%20rainfalls%20to%20flash%20floods%20using%20neural%20network%20modelling%3A%20case%20study%20on%20the%20Lez%20basin%20&publication_year=2015&author=T.%20Darras&author=V.B.%20Estupina&author=L.%20Kong-A-Siou&author=B.%20Vayssade&author=A.%20Johannet&author=S.%20Pistre" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0125 data-moz-translations-id=332><span class=anchor-text data-moz-translations-id=333>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=334><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=335></path></svg></a></div></span><li data-moz-translations-id=336><span class="label u-font-sans" data-moz-translations-id=337><a class="anchor anchor-default" href=#bb0130 id=ref-id-b0130 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=338><span class=anchor-text data-moz-translations-id=339>Dewandel et al., 2003</span></a></span><span class=reference id=h0130 data-moz-translations-id=340><div class=contribution data-moz-translations-id=341><div class="authors u-font-sans" data-moz-translations-id=342>B. Dewandel, P. Lachassagne, M. Bakalowicz, Ph Weng, A. Al-Malki</div><div id=ref-id-h0130 class="title text-m" data-moz-translations-id=343>Evaluación de 17 espesores acuíferos mediante el análisis de hidrografías de recesión. Aplicación al acuífero de roca dura de Omán</div></div><div class="host u-font-sans" data-moz-translations-id=344>J. Hydrol., 74 (2) (2003), pp. 248-269</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=345><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Evaluation%20of%2017%20aquifer%20thickness%20by%20analysing%20recession%20hydrographs.%20Application%20to%20the%20Oman%20ophiolite%2018%20hard-rock%20aquifer&publication_year=2003&author=B.%20Dewandel&author=P.%20Lachassagne&author=M.%20Bakalowicz&author=Ph%20Weng&author=A.%20Al-Malki" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0130 data-moz-translations-id=346><span class=anchor-text data-moz-translations-id=347>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=348><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=349></path></svg></a></div></span><li data-moz-translations-id=350><span class="label u-font-sans" data-moz-translations-id=351><a class="anchor anchor-default" href=#bb0135 id=ref-id-b0135 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=352><span class=anchor-text data-moz-translations-id=353>Dodge, 1983</span></a></span><span class=reference id=h0135 data-moz-translations-id=354><div class=contribution data-moz-translations-id=355><div class="authors u-font-sans" data-moz-translations-id=356>E.D. Esquivo</div><div id=ref-id-h0135 class="title text-m" data-moz-translations-id=357>Hidrología des aquifáres karstiques du Causse Comtal (Aveyron, Francia)</div></div><div class="host u-font-sans" data-moz-translations-id=358>Univ, Bruselas (1983)</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=359><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Hydrog%C3%A9ologie%20des%20aquif%C3%A8res%20karstiques%20du%20Causse%20Comtal%20%20Th%C3%A8se%20Sci&publication_year=1983&author=E.D.%20Dodge" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0135 data-moz-translations-id=360><span class=anchor-text data-moz-translations-id=361>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=362><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=363></path></svg></a></div></span><li data-moz-translations-id=364><span class="label u-font-sans" data-moz-translations-id=365><a class="anchor anchor-default" href=#bb0140 id=ref-id-b0140 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=366><span class=anchor-text data-moz-translations-id=367>Doherty, 2015</span></a></span><span class=reference id=h0140 data-moz-translations-id=368><div class=contribution data-moz-translations-id=369><div class="authors u-font-sans" data-moz-translations-id=370>J. Doherty</div><div id=ref-id-h0140 class="title text-m" data-moz-translations-id=371>Análisis de calibración e incertidumbre para modelos ambientales complejos</div></div><div class="host u-font-sans" data-moz-translations-id=372>Watermark Numerical Computing, Brisbane, Australia (2015)</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=373><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Calibration%20and%20Uncertainty%20Analysis%20for%20Complex%20Environmental%20Models&publication_year=2015&author=J.%20Doherty" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0140 data-moz-translations-id=374><span class=anchor-text data-moz-translations-id=375>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=376><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=377></path></svg></a></div></span><li data-moz-translations-id=378><span class="label u-font-sans" data-moz-translations-id=379><a class="anchor anchor-default" href=#bb0145 id=ref-id-b0145 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=380><span class=anchor-text data-moz-translations-id=381>Doherty et al., 1994</span></a></span><span class=reference id=h0145 data-moz-translations-id=382><div class=contribution data-moz-translations-id=383><div class="authors u-font-sans" data-moz-translations-id=384>J. Doherty, L. Brebber, P. Porte</div><div id=ref-id-h0145 class="title text-m" data-moz-translations-id=385>Placa: Estimación del parámetro independiente del modelo</div></div><div class="host u-font-sans" data-moz-translations-id=386>Watermark Computing, Corinda, Australia, 122 (1994), <a class="anchor anchor-default" href=https://doi.org/10.1016/j.jhydrol.2011.02.004 target=_blank rel="noreferrer noopener" data-moz-translations-id=387><span class=anchor-text data-moz-translations-id=388>10.1016/j.jhydrol.2011.02.004</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=389><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=390></path></svg></a></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=391><div class="link u-margin-m-right u-display-inline" data-moz-translations-id=392><els-view-pdf-element class="universal-pdf-button pending" data-config='{"seamlessAccessEnabled":false,"states":[{"name":"found","label":"View PDF","iconSize":"small"},{"name":"found-doi","label":"View article","icon":"<!-- no icon -->","iconSize":"small","show":true}],"integratorId":8301}' doc-id=10.1016/j.jhydrol.2011.02.004 doc-type=doi tooltip-placement=left data-moz-translations-id=393><template shadowroot=open></template></els-view-pdf-element></div><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Pest%3A%20Model-independent%20parameter%20estimation&publication_year=1994&author=J.%20Doherty&author=L.%20Brebber&author=P.%20Whyte" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0145 data-moz-translations-id=394><span class=anchor-text data-moz-translations-id=395>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=396><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=397></path></svg></a></div></span><li data-moz-translations-id=398><span class="label u-font-sans" data-moz-translations-id=399><a class="anchor anchor-default" href=#bb0150 id=ref-id-b0150 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=400><span class=anchor-text data-moz-translations-id=401>Dreiss, 1982</span></a></span><span class=reference id=h0150 data-moz-translations-id=402><div class=contribution data-moz-translations-id=403><div class="authors u-font-sans" data-moz-translations-id=404>S.J. Dreiss</div><div id=ref-id-h0150 class="title text-m" data-moz-translations-id=405>Pelos lineales para acuíferos kársticos</div></div><div class="host u-font-sans" data-moz-translations-id=406>Retorno del agua. Res., 18 (4) (1982), pp. 865-876</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=407><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Linear%20kernels%20for%20karst%20aquifers&publication_year=1982&author=S.J.%20Dreiss" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0150 data-moz-translations-id=408><span class=anchor-text data-moz-translations-id=409>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=410><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=411></path></svg></a></div></span><li data-moz-translations-id=412><span class="label u-font-sans" data-moz-translations-id=413><a class="anchor anchor-default" href=#bb0155 id=ref-id-b0155 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=414><span class=anchor-text data-moz-translations-id=415>Dreiss, 1983</span></a></span><span class=reference id=h0155 data-moz-translations-id=416><div class=contribution data-moz-translations-id=417><div class="authors u-font-sans" data-moz-translations-id=418>S.J. Dreiss</div><div id=ref-id-h0155 class="title text-m" data-moz-translations-id=419>Funciones de respuesta de unidad lineal como indicadores de las zonas de recarga para grandes muelles kársticos</div></div><div class="host u-font-sans" data-moz-translations-id=420>J. Hydrol., 61 (1983), pp. 31 a 44</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=421><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Linear%20unit-response%20functions%20as%20indicators%20of%20recharge%20areas%20for%20large%20karst%20springs&publication_year=1983&author=S.J.%20Dreiss" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0155 data-moz-translations-id=422><span class=anchor-text data-moz-translations-id=423>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=424><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=425></path></svg></a></div></span><li data-moz-translations-id=426><span class="label u-font-sans" data-moz-translations-id=427><a class="anchor anchor-default" href=#bb0160 id=ref-id-b0160 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=428><span class=anchor-text data-moz-translations-id=429>Dreyfus, 2005</span></a></span><span class=reference id=h0160 data-moz-translations-id=430><div class=contribution data-moz-translations-id=431><div class="authors u-font-sans" data-moz-translations-id=432>G. Dreyfus</div><div id=ref-id-h0160 class="title text-m" data-moz-translations-id=433>Redes neuronales: Metodología y aplicaciones, Reimpresión suave de tapa dura, 1a ed</div></div> <div class="ReferenceLinks u-font-sans" data-moz-translations-id=435><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Neural%20Networks%3A%20Methodology%20and%20Applications%2C%20Softcover%20reprint%20of%20hardcover%2C%201st%20ed&publication_year=2005&author=G.%20Dreyfus" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0160 data-moz-translations-id=436><span class=anchor-text data-moz-translations-id=437>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=438><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=439></path></svg></a></div></span><li data-moz-translations-id=440><span class="label u-font-sans" data-moz-translations-id=441><a class="anchor anchor-default" href=#bb0165 id=ref-id-b0165 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=442><span class=anchor-text data-moz-translations-id=443>Drogue, 1980</span></a></span><span class=reference id=h0165 data-moz-translations-id=444><div class=contribution data-moz-translations-id=445><div class="authors u-font-sans" data-moz-translations-id=446>C. Drogue</div><div id=ref-id-h0165 class="title text-m" data-moz-translations-id=447>Essai d'un type de structure de magasins carbonatés fissurés. Aplicación - linterprétation de certains aspects du fonctionnement hydrogéologique</div></div><div class="host u-font-sans" data-moz-translations-id=448>Mémoires hor série Société Géologique de France, 11 (1980), pp. 101 a 108</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=449><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Essai%20didentification%20dun%20type%20de%20structure%20de%20magasins%20carbonat%C3%A9s%20fissur%C3%A9s.%20Application%20%C3%A0%20linterpr%C3%A9tation%20de%20certains%20aspects%20du%20fonctionnement%20hydrog%C3%A9ologique&publication_year=1980&author=C.%20Drogue" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0165 data-moz-translations-id=450><span class=anchor-text data-moz-translations-id=451>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=452><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=453></path></svg></a></div></span><li data-moz-translations-id=454><span class="label u-font-sans" data-moz-translations-id=455><a class="anchor anchor-default" href=#bb0170 id=ref-id-b0170 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=456><span class=anchor-text data-moz-translations-id=457>Duran y Gill, 2021</span></a></span><span class=reference id=h0170 data-moz-translations-id=458><div class=contribution data-moz-translations-id=459><div class="authors u-font-sans" data-moz-translations-id=460>L. Duran, L.W. Gill</div><div id=ref-id-h0170 class="title text-m" data-moz-translations-id=461>Modelado de flujo primaveral de una captura kárstica irlandesa usando Modflow-USG con CLN</div></div><div class="host u-font-sans" data-moz-translations-id=462>J. Hydrol., 597 (2021), <a class="anchor anchor-default" href=https://doi.org/10.1016/j.jhydrol.2021.125971 target=_blank rel="noreferrer noopener" data-moz-translations-id=463><span class=anchor-text data-moz-translations-id=464>10.1016/j.jhydrol.2021.125971</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=465><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=466></path></svg></a></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=467><div class="link u-margin-m-right u-display-inline" data-moz-translations-id=468><els-view-pdf-element class="universal-pdf-button pending" data-config='{"seamlessAccessEnabled":false,"states":[{"name":"found","label":"View PDF","iconSize":"small"},{"name":"found-doi","label":"View article","icon":"<!-- no icon -->","iconSize":"small","show":true}],"integratorId":8301}' doc-id=10.1016/j.jhydrol.2021.125971 doc-type=doi tooltip-placement=left data-moz-translations-id=469><template shadowroot=open></template></els-view-pdf-element></div><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Modeling%20spring%20flow%20of%20an%20Irish%20karst%20catchment%20using%20Modflow-USG%20with%20CLN&publication_year=2021&author=L.%20Duran&author=L.W.%20Gill" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0170 data-moz-translations-id=470><span class=anchor-text data-moz-translations-id=471>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=472><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=473></path></svg></a></div></span><li data-moz-translations-id=474><span class="label u-font-sans" data-moz-translations-id=475><a class="anchor anchor-default" href=#bb0175 id=ref-id-b0175 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=476><span class=anchor-text data-moz-translations-id=477>Edijatno y Michel, 1989</span></a></span><span class=reference id=h0175 data-moz-translations-id=478><div class=contribution data-moz-translations-id=479><div class="authors u-font-sans" data-moz-translations-id=480>C. Edijatno, C. Michel</div><div id=ref-id-h0175 class="title text-m" data-moz-translations-id=481>Unmod-le pluie-débit - trois paramátres</div></div><div class="host u-font-sans" data-moz-translations-id=482>La Houille Blanche, 2 (1989), pp. 113 a 121</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=483><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Un%20mod%C3%A8le%20pluie-d%C3%A9bit%20%C3%A0%20trois%20param%C3%A8tres&publication_year=1989&author=C.%20Edijatno&author=C.%20Michel" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0175 data-moz-translations-id=484><span class=anchor-text data-moz-translations-id=485>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=486><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=487></path></svg></a></div></span><li data-moz-translations-id=488><span class="label u-font-sans" data-moz-translations-id=489><a class="anchor anchor-default" href=#bb0180 id=ref-id-b0180 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=490><span class=anchor-text data-moz-translations-id=491>Enemark et al., 2019</span></a></span><span class=reference id=h0180 data-moz-translations-id=492><div class=contribution data-moz-translations-id=493><div class="authors u-font-sans" data-moz-translations-id=494>T. Enemark, L.J. Peeters, D. Mallants, O. Batelaan</div><div id=ref-id-h0180 class="title text-m" data-moz-translations-id=495>Construcción y pruebas de modelos conceptuales hidrogeológicos: una revisión</div></div><div class="host u-font-sans" data-moz-translations-id=496>J. Hydrol., 569 (2019), pp. 310 a-29, <a class="anchor anchor-default" href=https://doi.org/10.1016/j.jhydrol.2018.12.007 target=_blank rel="noreferrer noopener" data-moz-translations-id=497><span class=anchor-text data-moz-translations-id=498>10.1016/j.jhydrol.2018.12.007</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=499><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=500></path></svg></a></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=501><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Hydrogeological%20conceptual%20model%20building%20and%20testing%3A%20a%20review&publication_year=2019&author=T.%20Enemark&author=L.J.%20Peeters&author=D.%20Mallants&author=O.%20Batelaan" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0180 data-moz-translations-id=504><span class=anchor-text data-moz-translations-id=505>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=506><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=507></path></svg></a></div></span><li data-moz-translations-id=508><span class="label u-font-sans" data-moz-translations-id=509><a class="anchor anchor-default" href=#bb0185 id=ref-id-b0185 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=510><span class=anchor-text data-moz-translations-id=511>Fiorillo, 2014</span></a></span><span class=reference id=h0185 data-moz-translations-id=512><div class=contribution data-moz-translations-id=513><div class="authors u-font-sans" data-moz-translations-id=514>F. Fiorillo</div><div id=ref-id-h0185 class="title text-m" data-moz-translations-id=515>La recesión de los hidrografías de primavera, centrada en los acuíferos kársticos</div></div><div class="host u-font-sans" data-moz-translations-id=516>Retorno del agua. Gestionar., 28 (2014), pp. 1781-1805, <a class="anchor anchor-default" href=https://doi.org/10.1007/s11269-014-0597-z target=_blank rel="noreferrer noopener" data-moz-translations-id=517><span class=anchor-text data-moz-translations-id=518>10.1007/s11269-014-0597-z</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=519><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=520></path></svg></a></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=521><div class="link u-margin-m-right u-display-inline" data-moz-translations-id=522><els-view-pdf-element class="universal-pdf-button pending" data-config='{"seamlessAccessEnabled":false,"states":[{"name":"found","label":"View PDF","iconSize":"small"},{"name":"found-doi","label":"View article","icon":"<!-- no icon -->","iconSize":"small","show":true}],"integratorId":8301}' doc-id=10.1007/s11269-014-0597-z doc-type=doi tooltip-placement=left data-moz-translations-id=523><template shadowroot=open></template></els-view-pdf-element></div><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=The%20recession%20of%20spring%20hydrographs%2C%20focused%20on%20karst%20aquifers&publication_year=2014&author=F.%20Fiorillo" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0185 data-moz-translations-id=524><span class=anchor-text data-moz-translations-id=525>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=526><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=527></path></svg></a></div></span><li data-moz-translations-id=528><span class="label u-font-sans" data-moz-translations-id=529><a class="anchor anchor-default" href=#bb0190 id=ref-id-b0190 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=530><span class=anchor-text data-moz-translations-id=531>Ford y Williams, 1989</span></a></span><span class=reference id=h0190 data-moz-translations-id=532><div class=other-ref data-moz-translations-id=533><span data-moz-translations-id=534>Ford D., Williams P. W., 1989. Karst Geomorfología e Hidrología. Chapman & Hall: 601 p.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=535><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Ford%20D.%2C%20Williams%20P.%20W.%2C%201989.%20Karst%20Geomorphology%20and%20Hydrology.%20Chapman%20%26%20Hall%3A%20601%20p." target=_blank rel="noopener noreferrer" data-moz-translations-id=536><span class=anchor-text data-moz-translations-id=537>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=538><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=539></path></svg></a></div></span><li data-moz-translations-id=540><span class="label u-font-sans" data-moz-translations-id=541><a class="anchor anchor-default" href=#bb0195 id=ref-id-b0195 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=542><span class=anchor-text data-moz-translations-id=543>Forkasiewicz y Paloc, 1967</span></a></span><span class=reference id=h0195 data-moz-translations-id=544><div class=contribution data-moz-translations-id=545><div class="authors u-font-sans" data-moz-translations-id=546>J. Forkasiewicz, H. Paloc</div><div id=ref-id-h0195 class="title text-m" data-moz-translations-id=547>Le régime de tarissement de la Foux de la Vis</div></div><div class="host u-font-sans" data-moz-translations-id=548>La Houille Blanche (BRGM), 1 (1967), pp. 29 a 36</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=549><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Le%20r%C3%A9gime%20de%20tarissement%20de%20la%20Foux%20de%20la%20Vis&publication_year=1967&author=J.%20Forkasiewicz&author=H.%20Paloc" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0195 data-moz-translations-id=550><span class=anchor-text data-moz-translations-id=551>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=552><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=553></path></svg></a></div></span><li data-moz-translations-id=554><span class="label u-font-sans" data-moz-translations-id=555><a class="anchor anchor-default" href=#bb0200 id=ref-id-b0200 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=556><span class=anchor-text data-moz-translations-id=557>Ghasemizadeh, 2012</span></a></span><span class=reference id=h0200 data-moz-translations-id=558><div class=contribution data-moz-translations-id=559><div class="authors u-font-sans" data-moz-translations-id=560>R. Ghasemizadeh, <em data-moz-translations-id=561>et al.</em></div><div id=ref-id-h0200 class="title text-m" data-moz-translations-id=562>Revisión: Flujo de aguas subterráneas y modelado de los acuíferos kársticos, con especial referencia al sistema acuífero de la costa norte de Puerto Rico</div></div><div class="host u-font-sans" data-moz-translations-id=563>Hidrogeol. J., 20 (2012), pp. 1441-1461</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=564><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Review%3A%20Groundwater%20flow%20and%20transport%20modeling%20of%20karst%20aquifers%2C%20with%20particular%20reference%20to%20the%20North%20Coast%20Limestone%20aquifer%20system%20of%20Puerto%20Rico&publication_year=2012&author=R.%20Ghasemizadeh" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0200 data-moz-translations-id=565><span class=anchor-text data-moz-translations-id=566>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=567><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=568></path></svg></a></div></span><li data-moz-translations-id=569><span class="label u-font-sans" data-moz-translations-id=570><a class="anchor anchor-default" href=#bb0205 id=ref-id-b0205 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=571><span class=anchor-text data-moz-translations-id=572>Gill et al., 2013</span></a></span><span class=reference id=h0205 data-moz-translations-id=573><div class=contribution data-moz-translations-id=574><div class="authors u-font-sans" data-moz-translations-id=575>L.W. Gill, O. Naughton, P.M. Johnston</div><div id=ref-id-h0205 class="title text-m" data-moz-translations-id=576>Modelando una red de turbulras en kárstico de tierras bajas</div></div><div class="host u-font-sans" data-moz-translations-id=577>Retorno del agua. Res., 49 (6) (2013), pp. 3487-3503</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=578><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Modelling%20a%20network%20of%20turloughs%20in%20lowland%20karst&publication_year=2013&author=L.W.%20Gill&author=O.%20Naughton&author=P.M.%20Johnston" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0205 data-moz-translations-id=579><span class=anchor-text data-moz-translations-id=580>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=581><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=582></path></svg></a></div></span><li data-moz-translations-id=583><span class="label u-font-sans" data-moz-translations-id=584><a class="anchor anchor-default" href=#bb0210 id=ref-id-b0210 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=585><span class=anchor-text data-moz-translations-id=586>Gill et al., 2020</span></a></span><span class=reference id=h0210 data-moz-translations-id=587><div class=contribution data-moz-translations-id=588><div class="authors u-font-sans" data-moz-translations-id=589>L.W. Gill, P. Schuler, L. Duran, P.M. Johnston, P. Morrissey</div><div id=ref-id-h0210 class="title text-m" data-moz-translations-id=590>Evaluación de modelos semidistribuidos-pipe-red y de diferencia-terminada distribuida para simular sistemas kársticos</div></div><div class="host u-font-sans" data-moz-translations-id=591>Hydrogeology Journal, 29 (2020), pp. 259-279, <a class="anchor anchor-default" href=https://doi.org/10.1007/s10040-020-02241-8 target=_blank rel="noreferrer noopener" data-moz-translations-id=592><span class=anchor-text data-moz-translations-id=593>10.1007/s10040-020-02241-8</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=594><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=595></path></svg></a></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=596><div class="link u-margin-m-right u-display-inline" data-moz-translations-id=597><els-view-pdf-element class="universal-pdf-button pending" data-config='{"seamlessAccessEnabled":false,"states":[{"name":"found","label":"View PDF","iconSize":"small"},{"name":"found-doi","label":"View article","icon":"<!-- no icon -->","iconSize":"small","show":true}],"integratorId":8301}' doc-id=10.1515/9780822386001-015 doc-type=doi tooltip-placement=left><template shadowroot=open></template></els-view-pdf-element></div><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=An%20evaluation%20of%20semi-distributed-pipe-network%20and%20distributed-finite-difference%20models%20to%20simulate%20karst%20systems&publication_year=2020&author=L.W.%20Gill&author=P.%20Schuler&author=L.%20Duran&author=P.M.%20Johnston&author=P.%20Morrissey" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0210 data-moz-translations-id=599><span class=anchor-text data-moz-translations-id=600>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=601><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=602></path></svg></a></div></span><li data-moz-translations-id=603><span class="label u-font-sans" data-moz-translations-id=604><a class="anchor anchor-default" href=#bb0215 id=ref-id-b0215 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=605><span class=anchor-text data-moz-translations-id=606>Grasso y Jeannin, 1994</span></a></span><span class=reference id=h0215 data-moz-translations-id=607><div class=contribution data-moz-translations-id=608><div class="authors u-font-sans" data-moz-translations-id=609>A. Grasso, P.Y. Jeannin</div><div id=ref-id-h0215 class="title text-m" data-moz-translations-id=610>Etude critique des méthodes d'analyse de la réponse globale des syst-mes karstiques. Aplicación au site de Bure (JU, Suisse)</div></div><div class="host u-font-sans" data-moz-translations-id=611>Bulletin d'Hydrogéologie, 13 (1994), pp. 87-113</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=612><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Etude%20critique%20des%20m%C3%A9thodes%20danalyse%20de%20la%20r%C3%A9ponse%20globale%20des%20syst%C3%A8mes%20karstiques.%20Application%20au%20site%20de%20Bure%20&publication_year=1994&author=A.%20Grasso&author=P.Y.%20Jeannin" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0215 data-moz-translations-id=613><span class=anchor-text data-moz-translations-id=614>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=615><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=616></path></svg></a></div></span><li data-moz-translations-id=617><span class="label u-font-sans" data-moz-translations-id=618><a class="anchor anchor-default" href=#bb0220 id=ref-id-b0220 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=619><span class=anchor-text data-moz-translations-id=620>Guinot et al., 2015</span></a></span><span class=reference id=h0220 data-moz-translations-id=621><div class=other-ref data-moz-translations-id=622><span data-moz-translations-id=623>Guinot V., Savéan M., Jourde H., Neppel L., 2015. Modelo conceptual de precipitación con una función de transferencia de tiempo característica de dos parámetros, infinito. Procesos hidrológicos, Wiley, 2015, 29 (22): 4756-4778.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=624><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Guinot%20V.%2C%20Sav%C3%A9an%20M.%2C%20Jourde%20H.%2C%20Neppel%20L.%2C%202015.%20Conceptual%20rainfall-runoff%20model%20with%20a%20two-parameter%2C%20infinite%20characteristic%20time%20transfer%20function.%20Hydrological%20Processes%2C%20Wiley%2C%202015%2C%2029%20(22)%3A%204756-4778." target=_blank rel="noopener noreferrer" data-moz-translations-id=625><span class=anchor-text data-moz-translations-id=626>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=627><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=628></path></svg></a></div></span><li data-moz-translations-id=629><span class="label u-font-sans" data-moz-translations-id=630><a class="anchor anchor-default" href=#bb0225 id=ref-id-b0225 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=631><span class=anchor-text data-moz-translations-id=632>Gupta et al., 2009</span></a></span><span class=reference id=h0225 data-moz-translations-id=633><div class=contribution data-moz-translations-id=634><div class="authors u-font-sans" data-moz-translations-id=635>H.V. Gupta, H. Kling, K.K. Yilmaz, G.F. Martínez</div><div id=ref-id-h0225 class="title text-m" data-moz-translations-id=636>Decomposición del Error de Plaza de la Media y Criterios de Desempeño NSE: Implicaciones para mejorar el modelado hidrológico</div></div><div class="host u-font-sans" data-moz-translations-id=637>J. Hydrol., 377 (2009), pp. 80 a 91</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=638><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Decomposition%20of%20the%20Mean%20Squared%20Error%20and%20NSE%20Performance%20Criteria%3A%20Implications%20for%20Improving%20Hydrological%20Modeling&publication_year=2009&author=H.V.%20Gupta&author=H.%20Kling&author=K.K.%20Yilmaz&author=G.F.%20Martinez" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0225 data-moz-translations-id=639><span class=anchor-text data-moz-translations-id=640>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=641><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=642></path></svg></a></div></span><li data-moz-translations-id=643><span class="label u-font-sans" data-moz-translations-id=644><a class="anchor anchor-default" href=#bb0230 id=ref-id-b0230 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=645><span class=anchor-text data-moz-translations-id=646>Halihan y Wicks, 1998</span></a></span><span class=reference id=h0230 data-moz-translations-id=647><div class=contribution data-moz-translations-id=648><div class="authors u-font-sans" data-moz-translations-id=649>T. Halihan, C.M. Wicks</div><div id=ref-id-h0230 class="title text-m" data-moz-translations-id=650>Modelado de las respuestas de tormenta en acuíferos de flujo de conductos con embalses</div></div><div class="host u-font-sans" data-moz-translations-id=651>J. Hydrol., 208 (1998), pp. 82 a 91</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=652><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Modeling%20of%20storm%20responses%20in%20conduit%20flow%20aquifers%20with%20reservoirs&publication_year=1998&author=T.%20Halihan&author=C.M.%20Wicks" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0230 data-moz-translations-id=653><span class=anchor-text data-moz-translations-id=654>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=655><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=656></path></svg></a></div></span><li data-moz-translations-id=657><span class="label u-font-sans" data-moz-translations-id=658><a class="anchor anchor-default" href=#bb0235 id=ref-id-b0235 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=659><span class=anchor-text data-moz-translations-id=660>Hartmann et al., 2012</span></a></span><span class=reference id=h0235 data-moz-translations-id=661><div class=other-ref data-moz-translations-id=662><span data-moz-translations-id=663>Hartmann A., Lange J., Weiler M., Arbel Y., & Greenbaum N., 2012. Un nuevo enfoque para modelar la variabilidad espacial y temporal de la recarga a los acuíferos kársticos. Ciencias del Sistema de Hidrología y Tierra, 16 (7), 2219-2231. 10.5194/hess-16-2219-2012.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=664><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Hartmann%20A.%2C%20Lange%20J.%2C%20Weiler%20M.%2C%20Arbel%20Y.%2C%20%26%20Greenbaum%20N.%2C%202012.%20A%20new%20approach%20to%20model%20the%20spatial%20and%20temporal%20variability%20of%20recharge%20to%20karst%20aquifers.%20Hydrology%20and%20Earth%20System%20Sciences%2C%2016(7)%2C%202219%E2%80%932231.%2010.5194%2Fhess-16-2219-2012." target=_blank rel="noopener noreferrer" data-moz-translations-id=665><span class=anchor-text data-moz-translations-id=666>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=667><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=668></path></svg></a></div></span><li data-moz-translations-id=669><span class="label u-font-sans" data-moz-translations-id=670><a class="anchor anchor-default" href=#bb0240 id=ref-id-b0240 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=671><span class=anchor-text data-moz-translations-id=672>Hartmann et al., 2013</span></a></span><span class=reference id=h0240 data-moz-translations-id=673><div class=contribution data-moz-translations-id=674><div class="authors u-font-sans" data-moz-translations-id=675>A. Hartmann, J.A. Barberá, J. Lange, B. Andreo, M. Weiler</div><div id=ref-id-h0240 class="title text-m" data-moz-translations-id=676>Avances en la simulación hidrológica de las áreas de recarga de variantes del tiempo de los sistemas kársticos .Ejemplificado en un resorte kárstico en el sur de España</div></div><div class="host u-font-sans" data-moz-translations-id=677>Adv. <a class="anchor anchor-default" href=https://doi.org/10.1016/j.advwatres.2013.01.010 target=_blank rel="noreferrer noopener" data-moz-translations-id=678><span class=anchor-text data-moz-translations-id=679>Resucitar</span></a> del agua, 54 (2013), <a class="anchor anchor-default" href=https://doi.org/10.1016/j.advwatres.2013.01.010 target=_blank rel="noreferrer noopener" data-moz-translations-id=678><span class=anchor-text data-moz-translations-id=679>10.1016/j.advwatres.2013.01.010</span></a></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=682><div class="link u-margin-m-right u-display-inline" data-moz-translations-id=683><els-view-pdf-element class="universal-pdf-button pending" data-config='{"seamlessAccessEnabled":false,"states":[{"name":"found","label":"View PDF","iconSize":"small"},{"name":"found-doi","label":"View article","icon":"<!-- no icon -->","iconSize":"small","show":true}],"integratorId":8301}' doc-id=10.1016/j.advwatres.2013.01.010 doc-type=doi tooltip-placement=left data-moz-translations-id=684><template shadowroot=open></template></els-view-pdf-element></div><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Progress%20in%20the%20hydrologic%20simulation%20of%20time%20variant%20recharge%20areas%20of%20karst%20systems%20%20Exemplified%20at%20a%20karst%20spring%20in%20Southern%20Spain&publication_year=2013&author=A.%20Hartmann&author=J.A.%20Barber%C3%A1&author=J.%20Lange&author=B.%20Andreo&author=M.%20Weiler" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0240 data-moz-translations-id=685><span class=anchor-text data-moz-translations-id=686>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=687><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=688></path></svg></a></div></span><li data-moz-translations-id=689><span class="label u-font-sans" data-moz-translations-id=690><a class="anchor anchor-default" href=#bb0245 id=ref-id-b0245 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=691><span class=anchor-text data-moz-translations-id=692>Hartmann et al., 2014</span></a></span><span class=reference id=h0245 data-moz-translations-id=693><div class=contribution data-moz-translations-id=694><div class="authors u-font-sans" data-moz-translations-id=695>A. Hartmann, M. Mudarra, B. Andreo, A. Marín, T. Wagener, J. Lange</div><div id=ref-id-h0245 class="title text-m" data-moz-translations-id=696>Modelado impactos espatiotemporal de los extremos hidroclimáticos en la recarga subterránea en un acuífero cárstico mediterráneo</div></div><div class="host u-font-sans" data-moz-translations-id=697>Retorno del agua. Res., 50 (8) (2014), <a class="anchor anchor-default" href=https://doi.org/10.1002/2014WR015685 target=_blank rel="noreferrer noopener" data-moz-translations-id=698><span class=anchor-text data-moz-translations-id=699>10.1002/2014WR015685</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=700><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=701></path></svg></a></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=702><div class="link u-margin-m-right u-display-inline" data-moz-translations-id=703><els-view-pdf-element class="universal-pdf-button pending" data-config='{"seamlessAccessEnabled":false,"states":[{"name":"found","label":"View PDF","iconSize":"small"},{"name":"found-doi","label":"View article","icon":"<!-- no icon -->","iconSize":"small","show":true}],"integratorId":8301}' doc-id=10.1002/2014WR015685 doc-type=doi tooltip-placement=left data-moz-translations-id=704><template shadowroot=open></template></els-view-pdf-element></div><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Modeling%20spatiotemporal%20impacts%20of%20hydroclimatic%20extremes%20on%20groundwater%20recharge%20at%20a%20mediterranean%20karst%20aquifer&publication_year=2014&author=A.%20Hartmann&author=M.%20Mudarra&author=B.%20Andreo&author=A.%20Mar%C3%ADn&author=T.%20Wagener&author=J.%20Lange" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0245 data-moz-translations-id=705><span class=anchor-text data-moz-translations-id=706>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=707><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=708></path></svg></a></div></span><li data-moz-translations-id=709><span class="label u-font-sans" data-moz-translations-id=710><a class="anchor anchor-default" href=#bb0250 id=ref-id-b0250 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=711><span class=anchor-text data-moz-translations-id=712>Hartmann et al., 2021</span></a></span><span class=reference id=h0250 data-moz-translations-id=713><div class=contribution data-moz-translations-id=714><div class="authors u-font-sans" data-moz-translations-id=715>A. Hartmann, Y. Liu, T. Olarinoye, R. Berthelin, V. Marx</div><div id=ref-id-h0250 class="title text-m" data-moz-translations-id=716>Integración de la labor sobre el terreno y el modelado a gran escala para mejorar la evaluación de los recursos hídricos kársticos</div></div><div class="host u-font-sans" data-moz-translations-id=717>Hidrogeol. J., 29 (1) (2021), pp. 315-329, <a class="anchor anchor-default" href=https://doi.org/10.1007/s10040-020-02258-z target=_blank rel="noreferrer noopener" data-moz-translations-id=718><span class=anchor-text data-moz-translations-id=719>10.1007/s10040-020-02258-z</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=720><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=721></path></svg></a></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=722><div class="link u-margin-m-right u-display-inline" data-moz-translations-id=723><els-view-pdf-element class="universal-pdf-button pending" data-config='{"seamlessAccessEnabled":false,"states":[{"name":"found","label":"View PDF","iconSize":"small"},{"name":"found-doi","label":"View article","icon":"<!-- no icon -->","iconSize":"small","show":true}],"integratorId":8301}' doc-id=10.1007/s10040-020-02258-z doc-type=doi tooltip-placement=left data-moz-translations-id=724><template shadowroot=open></template></els-view-pdf-element></div><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Integrating%20field%20work%20and%20large-scale%20modeling%20to%20improve%20assessment%20of%20karst%20water%20resources&publication_year=2021&author=A.%20Hartmann&author=Y.%20Liu&author=T.%20Olarinoye&author=R.%20Berthelin&author=V.%20Marx" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0250 data-moz-translations-id=725><span class=anchor-text data-moz-translations-id=726>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=727><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=728></path></svg></a></div></span><li data-moz-translations-id=729><span class="label u-font-sans" data-moz-translations-id=730><a class="anchor anchor-default" href=#bb0255 id=ref-id-b0255 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=731><span class=anchor-text data-moz-translations-id=732>Hochreiter y Schmidhuber, 1997</span></a></span><span class=reference id=h0255 data-moz-translations-id=733><div class=other-ref data-moz-translations-id=734><span data-moz-translations-id=735>Hochreiter S., Schmidhuber J., 1997. Largo memoria a corto plazo, Ciputación Neural, 9(8): 1735-1780, 10/bxd65w, 1997.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=736><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Hochreiter%20S.%2C%20Schmidhuber%20J.%2C%201997.%20Long%20Short-Term%20Memory%2C%20Neural%20Computation%2C%209(8)%3A%201735%E2%80%931780%2C%2010%2Fbxd65w%2C%201997." target=_blank rel="noopener noreferrer" data-moz-translations-id=737><span class=anchor-text data-moz-translations-id=738>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=739><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=740></path></svg></a></div></span><li data-moz-translations-id=741><span class="label u-font-sans" data-moz-translations-id=742><a class="anchor anchor-default" href=#bb0260 id=ref-id-b0260 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=743><span class=anchor-text data-moz-translations-id=744>Holl-nder et al., 2009</span></a></span><span class=reference id=h0260 data-moz-translations-id=745><div class=other-ref data-moz-translations-id=746><span data-moz-translations-id=747>Holl-nder H. M., Blume T., Bormann H., Buytaert W., Chirico G. B., Exbrayat J.-F., Gustafsson D., H. H., Kraft P., Stamm C., Stoll S., Bl-schl G., Flúhler H., 2009. Predicciones comparativas de descarga de una cuenca artificial (Chicken Creek) usando datos escasos, Hydrol. Tierra Syst. Sci., 13: 2069-202094, doi 10.5194.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=748><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Holl%C3%A4nder%20H.%20M.%2C%20Blume%20T.%2C%20Bormann%20H.%2C%20Buytaert%20W.%2C%20Chirico%20G.%20B.%2C%20Exbrayat%20J.-F.%2C%20Gustafsson%20D.%2C%20H%C3%B6lzel%20H.%2C%20Kraft%20P.%2C%20Stamm%20C.%2C%20Stoll%20S.%2C%20Bl%C3%B6schl%20G.%2C%20Fl%C3%BChler%20H.%2C%202009.%20Comparative%20predictions%20of%20discharge%20from%20an%20artificial%20catchment%20(Chicken%20Creek)%20using%20sparse%20data%2C%20Hydrol.%20Earth%20Syst.%20Sci.%2C%2013%3A%202069%E2%80%932094%2C%20doi%2010.5194." target=_blank rel="noopener noreferrer" data-moz-translations-id=749><span class=anchor-text data-moz-translations-id=750>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=751><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=752></path></svg></a></div></span><li data-moz-translations-id=753><span class="label u-font-sans" data-moz-translations-id=754><a class="anchor anchor-default" href=#bb0265 id=ref-id-b0265 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=755><span class=anchor-text data-moz-translations-id=756>Hornik et al., 1989</span></a></span><span class=reference id=h0265 data-moz-translations-id=757><div class=contribution data-moz-translations-id=758><div class="authors u-font-sans" data-moz-translations-id=759>K. Hornik, M. Stinchcombe, H. Blanco</div><div id=ref-id-h0265 class="title text-m" data-moz-translations-id=760>Las redes de alimentación multicapa son aproximadores universales</div></div><div class="host u-font-sans" data-moz-translations-id=761>Neural Netw., 2 (5) (1989), pp. 359 a 366</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=762><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Multilayer%20feedforward%20networks%20are%20universal%20approximators&publication_year=1989&author=K.%20Hornik&author=M.%20Stinchcombe&author=H.%20White" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0265 data-moz-translations-id=763><span class=anchor-text data-moz-translations-id=764>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=765><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=766></path></svg></a></div></span><li data-moz-translations-id=767><span class="label u-font-sans" data-moz-translations-id=768><a class="anchor anchor-default" href=#bb0270 id=ref-id-b0270 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=769><span class=anchor-text data-moz-translations-id=770>Jakeman y Hornberger, 1993</span></a></span><span class=reference id=h0270 data-moz-translations-id=771><div class=contribution data-moz-translations-id=772><div class="authors u-font-sans" data-moz-translations-id=773>A.J. Jakeman, G.M. Hornberger</div><div id=ref-id-h0270 class="title text-m" data-moz-translations-id=774>Qué tan compleja se ha librado en un modelo de carreras de lluvias? -</div></div><div class="host u-font-sans" data-moz-translations-id=775>Retorno del agua. Res., 29 (8) (1993), págs. 2637 a 2649</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=776><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=How%20Much%20Complexity%20Is%20Warranted%20in%20a%20Rainfall-Runoff%20Model%20&publication_year=1993&author=A.J.%20Jakeman&author=G.M.%20Hornberger" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0270 data-moz-translations-id=777><span class=anchor-text data-moz-translations-id=778>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=779><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=780></path></svg></a></div></span><li data-moz-translations-id=781><span class="label u-font-sans" data-moz-translations-id=782><a class="anchor anchor-default" href=#bb0275 id=ref-id-b0275 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=783><span class=anchor-text data-moz-translations-id=784>Jeannin et al., 2015</span></a></span><span class=reference id=h0275 data-moz-translations-id=785><div class=contribution data-moz-translations-id=786><div class="authors u-font-sans" data-moz-translations-id=787>P.Y. Jeannin, A. Malard, D. Rickerl, E. Weber</div><div id=ref-id-h0275 class="title text-m" data-moz-translations-id=788>Evaluar los peligros kárstico-hidráulicos en la tunelización del sistema de muelles Brunnmáhle. Jura Bernese, Suiza. -</div></div><div class="host u-font-sans" data-moz-translations-id=789>En el medio ambiente. Earth Sci., 74 (12) (2015), pp. 7655-7670</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=790><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Assessing%20karst-hydraulic%20hazards%20in%20tunneling%20%20the%20Brunnm%C3%BChle%20spring%20system%20%20Bernese%20Jura%2C%20Switzerland.%20&publication_year=2015&author=P.Y.%20Jeannin&author=A.%20Malard&author=D.%20Rickerl&author=E.%20Weber" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0275 data-moz-translations-id=791><span class=anchor-text data-moz-translations-id=792>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=793><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=794></path></svg></a></div></span><li data-moz-translations-id=795><span class="label u-font-sans" data-moz-translations-id=796><a class="anchor anchor-default" href=#bb0280 id=ref-id-b0280 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=797><span class=anchor-text data-moz-translations-id=798>Jeannin, 1996</span></a></span><span class=reference id=h0280 data-moz-translations-id=799><div class=other-ref data-moz-translations-id=800><span data-moz-translations-id=801>Jeannin P.-Y., 1996. Estructura et compportement hydraulique des aquifáres karstiques. - Thse Sc. Univ. Neuchátel: 270 p.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=802><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Jeannin%20P.-Y.%2C%201996.%20Structure%20et%20comportement%20hydraulique%20des%20aquif%C3%A8res%20karstiques.%20%E2%80%93%20Th%C3%A8se%20Sc.%20Univ.%20Neuch%C3%A2tel%20%3A%20270%20p." target=_blank rel="noopener noreferrer" data-moz-translations-id=803><span class=anchor-text data-moz-translations-id=804>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=805><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=806></path></svg></a></div></span><li data-moz-translations-id=807><span class="label u-font-sans" data-moz-translations-id=808><a class="anchor anchor-default" href=#bb0285 id=ref-id-b0285 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=809><span class=anchor-text data-moz-translations-id=810>Jeannin, 2001</span></a></span><span class=reference id=h0285 data-moz-translations-id=811><div class=contribution data-moz-translations-id=812><div class="authors u-font-sans" data-moz-translations-id=813>P-Y. Jeannin</div><div id=ref-id-h0285 class="title text-m" data-moz-translations-id=814>El modelado fluye en conductos kársticos y epifreáticos en la Cueva de Hálloch (Muotathal, Suiza). -</div></div><div class="host u-font-sans" data-moz-translations-id=815>Retorno del agua. Res., vol. 37 (2) (2001), pp. 191-200</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=816><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Modelling%20flow%20in%20phreatic%20and%20epiphreatic%20karst%20conduits%20in%20the%20H%C3%B6lloch%20Cave%20.%20&publication_year=2001&author=P.-Y.%20Jeannin" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0285 data-moz-translations-id=817><span class=anchor-text data-moz-translations-id=818>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=819><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=820></path></svg></a></div></span><li data-moz-translations-id=821><span class="label u-font-sans" data-moz-translations-id=822><a class="anchor anchor-default" href=#bb0290 id=ref-id-b0290 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=823><span class=anchor-text data-moz-translations-id=824>Jeannin y Maréchal, 1995</span></a></span><span class=reference id=h0290 data-moz-translations-id=825><div class=other-ref data-moz-translations-id=826><span data-moz-translations-id=827>Jeannin P.-Y., Maréchal J.-C., 1995. Lois de Pertes de charge dans les conduits karstiques. Base théorique et observaciones. Boletín d'Hydrogéologie No 14, Neuchátel : 149-176.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=828><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Jeannin%20P.-Y.%2C%20Mar%C3%A9chal%20J.-C.%2C%201995.%20Lois%20de%20Pertes%20de%20charge%20dans%20les%20conduits%20karstiques.%20Base%20th%C3%A9orique%20et%20observations.%20%E2%80%93%20Bulletin%20d%27Hydrog%C3%A9ologie%20No%2014%2C%20Neuch%C3%A2tel%20%3A%20149-176." target=_blank rel="noopener noreferrer" data-moz-translations-id=829><span class=anchor-text data-moz-translations-id=830>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=831><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=832></path></svg></a></div></span><li data-moz-translations-id=833><span class="label u-font-sans" data-moz-translations-id=834><a class="anchor anchor-default" href=#bb0295 id=ref-id-b0295 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=835><span class=anchor-text data-moz-translations-id=836>Kaufmann et al., 2016</span></a></span><span class=reference id=h0295 data-moz-translations-id=837><div class=contribution data-moz-translations-id=838><div class="authors u-font-sans" data-moz-translations-id=839>G. Kaufmann, F. Gabrovsek, J. Turco</div><div id=ref-id-h0295 class="title text-m" data-moz-translations-id=840>Correa de maqueamiento del río Pivka subterráneo en Postojnska Jama, Eslovenia</div></div><div class="host u-font-sans" data-moz-translations-id=841>Acta Carsol., 45 (1) (2016), pp. 57-70</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=842><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Modelling%20flow%20of%20subterranean%20Pivka%20river%20in%20Postojnska%20Jama%2C%20Slovenia&publication_year=2016&author=G.%20Kaufmann&author=F.%20Gabrovsek&author=J.%20Turk" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0295 data-moz-translations-id=843><span class=anchor-text data-moz-translations-id=844>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=845><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=846></path></svg></a></div></span><li data-moz-translations-id=847><span class="label u-font-sans" data-moz-translations-id=848><a class="anchor anchor-default" href=#bb0300 id=ref-id-b0300 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=849><span class=anchor-text data-moz-translations-id=850>Kavousi et al., 2020</span></a></span><span class=reference id=h0300 data-moz-translations-id=851><div class=contribution data-moz-translations-id=852><div class="authors u-font-sans" data-moz-translations-id=853>A. Kavousi, T. Reimann, R. Liedl, E. Raeisi</div><div id=ref-id-h0300 class="title text-m" data-moz-translations-id=854>Caracterización del acuífero de Karst mediante aplicación inversa de MODFLOW-2005 CFPv2 discrecre-continuum flujo y modelo de transporte. -</div></div><div class="host u-font-sans" data-moz-translations-id=855>J. Hydrol., 587 (2020), <a class="anchor anchor-default" href=https://doi.org/10.1016/j.jhydrol.2020.124922 target=_blank rel="noreferrer noopener" data-moz-translations-id=856><span class=anchor-text data-moz-translations-id=857>10.1016/j.jhydrol.2020.124922</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=858><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=859></path></svg></a></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=860><div class="link u-margin-m-right u-display-inline" data-moz-translations-id=861><els-view-pdf-element class="universal-pdf-button pending" data-config='{"seamlessAccessEnabled":false,"states":[{"name":"found","label":"View PDF","iconSize":"small"},{"name":"found-doi","label":"View article","icon":"<!-- no icon -->","iconSize":"small","show":true}],"integratorId":8301}' doc-id=10.1016/j.jhydrol.2020.124922 doc-type=doi tooltip-placement=left data-moz-translations-id=862><template shadowroot=open></template></els-view-pdf-element></div><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Karst%20aquifer%20characterization%20by%20inverse%20application%20of%20MODFLOW-2005%20CFPv2%20discrete-continuum%20flow%20and%20transport%20model.%20&publication_year=2020&author=A.%20Kavousi&author=T.%20Reimann&author=R.%20Liedl&author=E.%20Raeisi" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0300 data-moz-translations-id=863><span class=anchor-text data-moz-translations-id=864>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=865><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=866></path></svg></a></div></span><li data-moz-translations-id=867><span class="label u-font-sans" data-moz-translations-id=868><a class="anchor anchor-default" href=#bb0305 id=ref-id-b0305 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=869><span class=anchor-text data-moz-translations-id=870>Király, 1975</span></a></span><span class=reference id=h0305 data-moz-translations-id=871><div class=other-ref data-moz-translations-id=872><span data-moz-translations-id=873>Király, L. 1975. Rapport sur létat actuel des connaissances dans le domaine des caract'res physiques des roches karstiques. Hidrogeología de los terrenos kársticos. En: A. Burger & L. Dubertret (eds), int. Unión de Geol. Ciencias, B, 3 : 53.-67.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=874><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Kir%C3%A1ly%2C%20L.%201975.%20Rapport%20sur%20l%E2%80%99%C3%A9tat%20actuel%20des%20connaissances%20dans%20le%20domaine%20des%20caract%C3%A8res%20physiques%20des%20roches%20karstiques.%20Hydrogeology%20of%20karstic%20terrains.%20In%20%3A%20A.%20Burger%20%26%20L.%20Dubertret%20(eds)%2C%20int.%20Union%20of%20geol.%20Sciences%2C%20B%2C%203%20%3A%2053.-67." target=_blank rel="noopener noreferrer" data-moz-translations-id=875><span class=anchor-text data-moz-translations-id=876>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=877><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=878></path></svg></a></div></span><li data-moz-translations-id=879><span class="label u-font-sans" data-moz-translations-id=880><a class="anchor anchor-default" href=#bb0310 id=ref-id-b0310 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=881><span class=anchor-text data-moz-translations-id=882>Király y Morel, 1976</span></a></span><span class=reference id=h0310 data-moz-translations-id=883><div class=other-ref data-moz-translations-id=884><span data-moz-translations-id=885>Király L., Morel G., 1976. Etude de régularisation de l-Areuse par mod-le mathématique. Bulletin d-Hydrogéologie de l-Université de Neuchátel, 1: 19-36.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=886><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Kir%C3%A1ly%20L.%2C%20Morel%20G.%2C%201976.%20Etude%20de%20r%C3%A9gularisation%20de%20l%E2%80%99Areuse%20par%20mod%C3%A8le%20math%C3%A9matique.%20Bulletin%20d%E2%80%99Hydrog%C3%A9ologie%20de%20l%E2%80%99Universit%C3%A9%20de%20Neuch%C3%A2tel%2C%201%3A%2019-36." target=_blank rel="noopener noreferrer" data-moz-translations-id=887><span class=anchor-text data-moz-translations-id=888>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=889><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=890></path></svg></a></div></span><li data-moz-translations-id=891><span class="label u-font-sans" data-moz-translations-id=892><a class="anchor anchor-default" href=#bb0315 id=ref-id-b0315 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=893><span class=anchor-text data-moz-translations-id=894>Király y otros, 1995</span></a></span><span class=reference id=h0315 data-moz-translations-id=895><div class=contribution data-moz-translations-id=896><div class="authors u-font-sans" data-moz-translations-id=897>L. Király, P. Perrochet, Y. Rossier</div><div id=ref-id-h0315 class="title text-m" data-moz-translations-id=898>Efecto del epikarst en el hidrografía de los muelles kársticos: un abordaje numérico</div></div><div class="host u-font-sans" data-moz-translations-id=899>Bull. d'hydrogéologie, 14 (1995), pp. 199 a 220</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=900><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Effect%20of%20the%20epikarst%20on%20the%20hydrograph%20of%20karst%20springs%3A%20a%20numerical%20approach&publication_year=1995&author=L.%20Kir%C3%A1ly&author=P.%20Perrochet&author=Y.%20Rossier" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0315 data-moz-translations-id=901><span class=anchor-text data-moz-translations-id=902>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=903><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=904></path></svg></a></div></span><li data-moz-translations-id=905><span class="label u-font-sans" data-moz-translations-id=906><a class="anchor anchor-default" href=#bb0320 id=ref-id-b0320 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=907><span class=anchor-text data-moz-translations-id=908>Kong-A-Siou et al., 2013</span></a></span><span class=reference id=h0320 data-moz-translations-id=909><div class=contribution data-moz-translations-id=910><div class="authors u-font-sans" data-moz-translations-id=911>L. Kong-A-Siou, K. Cros, A. Johannet, Estupina V. Borrell, S. Pistre</div><div id=ref-id-h0320 class="title text-m" data-moz-translations-id=912>Método KnoX, o Knowledge eXtraction de la red neuronal. Estudio de caso sobre el acuífero kárstico de Lez (sur de Francia)</div></div><div class="host u-font-sans" data-moz-translations-id=913>J. Hydrol., 507 (2013), pp. 19 a 32</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=914><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=KnoX%20method%2C%20or%20Knowledge%20eXtraction%20from%20neural%20network%20model.%20Case%20study%20on%20the%20Lez%20karst%20aquifer%20&publication_year=2013&author=L.%20Kong-A-Siou&author=K.%20Cros&author=A.%20Johannet&author=Estupina%20V.%20Borrell&author=S.%20Pistre" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0320 data-moz-translations-id=915><span class=anchor-text data-moz-translations-id=916>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=917><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=918></path></svg></a></div></span><li data-moz-translations-id=919><span class="label u-font-sans" data-moz-translations-id=920><a class="anchor anchor-default" href=#bb0325 id=ref-id-b0325 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=921><span class=anchor-text data-moz-translations-id=922>Kovács, 2003</span></a></span><span class=reference id=h0325 data-moz-translations-id=923><div class=contribution data-moz-translations-id=924><div class="authors u-font-sans" data-moz-translations-id=925>A. Kovács</div><div id=ref-id-h0325 class="title text-m" data-moz-translations-id=926>Estimación de la geometría de la red de conductos de un acuífero kárstico mediante el modelado de flujo de agua subterránea (Bure, Suiza)</div></div><div class="host u-font-sans" data-moz-translations-id=927>Bol. Geol. Min., 114 (2) (2003), págs. 183 a 192</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=928><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Estimation%20of%20conduit%20network%20geometry%20of%20a%20karst%20aquifer%20by%20the%20means%20of%20groundwater%20flow%20modeling%20&publication_year=2003&author=A.%20Kov%C3%A1cs" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0325 data-moz-translations-id=929><span class=anchor-text data-moz-translations-id=930>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=931><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=932></path></svg></a></div></span><li data-moz-translations-id=933><span class="label u-font-sans" data-moz-translations-id=934><a class="anchor anchor-default" href=#bb0330 id=ref-id-b0330 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=935><span class=anchor-text data-moz-translations-id=936>Kovacs y Jeannin, 2003</span></a></span><span class=reference id=h0330 data-moz-translations-id=937><div class=contribution data-moz-translations-id=938><div class="authors u-font-sans" data-moz-translations-id=939>A. Kovacs, P-Y. Jeannin</div><div id=ref-id-h0330 class="title text-m" data-moz-translations-id=940>Panorama hidrogeológica de la Meseta Bure, Ajoie, Suiza. Ecolgae geol</div></div><div class="host u-font-sans" data-moz-translations-id=941>Helv., 96 (2003), pp. 367 a 379</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=942><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Hydrogeological%20overview%20of%20the%20Bure%20Plateau%2C%20Ajoie%2C%20Switzerland.%20%20Ecolgae%20geol&publication_year=2003&author=A.%20Kovacs&author=P.-Y.%20Jeannin" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0330 data-moz-translations-id=943><span class=anchor-text data-moz-translations-id=944>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=945><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=946></path></svg></a></div></span><li data-moz-translations-id=947><span class="label u-font-sans" data-moz-translations-id=948><a class="anchor anchor-default" href=#bb0335 id=ref-id-b0335 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=949><span class=anchor-text data-moz-translations-id=950>Kovács et al., 2005</span></a></span><span class=reference id=h0335 data-moz-translations-id=951><div class=contribution data-moz-translations-id=952><div class="authors u-font-sans" data-moz-translations-id=953>A. Kovács, P. Perrochet, L. Kiraly, P-Y. Jeannin</div><div id=ref-id-h0335 class="title text-m" data-moz-translations-id=954>Método cuantitativo para la caracterización de los acuíferos kársticos a partir del análisis de hidrógrafos de resorte. -</div></div><div class="host u-font-sans" data-moz-translations-id=955>J. Hydrol., 303 (2005), pp. 152 - 164</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=956><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=A%20quantitative%20method%20for%20the%20characterisation%20of%20karst%20aquifers%20based%20on%20spring%20hydrograph%20analysis.%20&publication_year=2005&author=A.%20Kov%C3%A1cs&author=P.%20Perrochet&author=L.%20Kiraly&author=P.-Y.%20Jeannin" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0335 data-moz-translations-id=957><span class=anchor-text data-moz-translations-id=958>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=959><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=960></path></svg></a></div></span><li data-moz-translations-id=961><span class="label u-font-sans" data-moz-translations-id=962><a class="anchor anchor-default" href=#bb0340 id=ref-id-b0340 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=963><span class=anchor-text data-moz-translations-id=964>Kovács y Sauter, 2007</span></a></span><span class=reference id=h0340 data-moz-translations-id=965><div class=other-ref data-moz-translations-id=966><span data-moz-translations-id=967>Kovács A., Sauter M., 2007. Modelado hidrodinámica kárstico. En: D. Goldscheider N & Drew, ed. Métodos en Karst Hydrogeology, Taylor & Francis, Londres: 201-222.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=968><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Kov%C3%A1cs%20A.%2C%20Sauter%20M.%2C%202007.%20Modelling%20karst%20hydrodynamics.%20In%3A%20D.%20Goldscheider%20N%20%26%20Drew%2C%20ed.%20Methods%20in%20Karst%20Hydrogeology%2C%20Taylor%20%26%20Francis%2C%20London%3A%20201-222." target=_blank rel="noopener noreferrer" data-moz-translations-id=969><span class=anchor-text data-moz-translations-id=970>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=971><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=972></path></svg></a></div></span><li data-moz-translations-id=973><span class="label u-font-sans" data-moz-translations-id=974><a class="anchor anchor-default" href=#bb0345 id=ref-id-b0345 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=975><span class=anchor-text data-moz-translations-id=976>Lauritzen et al., 1985</span></a></span><span class=reference id=h0345 data-moz-translations-id=977><div class=contribution data-moz-translations-id=978><div class="authors u-font-sans" data-moz-translations-id=979>S.-E. Lauritzen, J. Abott, R. Arnesen, G. Crossley, D. Grepperud, A. Ive</div><div id=ref-id-h0345 class="title text-m" data-moz-translations-id=980>Morfología e hidráulica de un conducto fratical activo</div></div><div class="host u-font-sans" data-moz-translations-id=981>Cave Sci., 12 (4) (1985), pp. 139 a 146</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=982><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Morphology%20and%20hydraulics%20of%20an%20active%20phreatic%20conduit&publication_year=1985&author=S.-E.%20Lauritzen&author=J.%20Abott&author=R.%20Arnesen&author=G.%20Crossley&author=D.%20Grepperud&author=A.%20Ive" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0345 data-moz-translations-id=983><span class=anchor-text data-moz-translations-id=984>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=985><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=986></path></svg></a></div></span><li data-moz-translations-id=987><span class="label u-font-sans" data-moz-translations-id=988><a class="anchor anchor-default" href=#bb0350 id=ref-id-b0350 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=989><span class=anchor-text data-moz-translations-id=990>Liedl et al., 2003</span></a></span><span class=reference id=h0350 data-moz-translations-id=991><div class=contribution data-moz-translations-id=992><div class="authors u-font-sans" data-moz-translations-id=993>R. Liedl, M. Sauter, D. H-ckinghaus, T. Clemens, G. Teutsch</div><div id=ref-id-h0350 class="title text-m" data-moz-translations-id=994>Simulación del desarrollo de acuíferos kársticos mediante un modelo de flujo de tubería continuo acoplado</div></div><div class="host u-font-sans" data-moz-translations-id=995>Retorno del agua. Res., 39 (2003), págs. 1-11, <a class="anchor anchor-default" href=https://doi.org/10.1029/2001WR001206 target=_blank rel="noreferrer noopener" data-moz-translations-id=996><span class=anchor-text data-moz-translations-id=997>10.1029/2001WR001206</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=998><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=999></path></svg></a></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1000><div class="link u-margin-m-right u-display-inline" data-moz-translations-id=1001><els-view-pdf-element class="universal-pdf-button pending" data-config='{"seamlessAccessEnabled":false,"states":[{"name":"found","label":"View PDF","iconSize":"small"},{"name":"found-doi","label":"View article","icon":"<!-- no icon -->","iconSize":"small","show":true}],"integratorId":8301}' doc-id=10.1029/2001WR001206 doc-type=doi tooltip-placement=left data-moz-translations-id=1002><template shadowroot=open></template></els-view-pdf-element></div><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Simulation%20of%20the%20development%20of%20karst%20aquifers%20using%20a%20coupled%20continuum%20pipe%20flow%20model&publication_year=2003&author=R.%20Liedl&author=M.%20Sauter&author=D.%20H%C3%BCckinghaus&author=T.%20Clemens&author=G.%20Teutsch" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0350 data-moz-translations-id=1003><span class=anchor-text data-moz-translations-id=1004>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1005><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1006></path></svg></a></div></span><li data-moz-translations-id=1007><span class="label u-font-sans" data-moz-translations-id=1008><a class="anchor anchor-default" href=#bb0355 id=ref-id-b0355 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1009><span class=anchor-text data-moz-translations-id=1010>Livre, 1915</span></a></span><span class=reference id=h0355 data-moz-translations-id=1011><div class=other-ref data-moz-translations-id=1012><span data-moz-translations-id=1013>Livre L., 1915. Le probláme hydrologique de la Haute Ajoie et le Creux-Genaz. Contribución l'étude de la circulación souterraine en terrenos calcaires. Actes de la Société Jurassienne d'émulation, années 1915-1916: 75-111.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1014><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Li%C3%A8vre%20L.%2C%201915.%20Le%20probl%C3%A8me%20hydrologique%20de%20la%20Haute%20Ajoie%20et%20le%20Creux-Genaz.%20Contribution%20%C3%A0%20l%27%C3%A9tude%20de%20la%20circulation%20souterraine%20en%20terrains%20calcaires.%20Actes%20de%20la%20Soci%C3%A9t%C3%A9%20Jurassienne%20d%27%C3%A9mulation%2C%20ann%C3%A9es%201915-1916%20%3A%2075-111." target=_blank rel="noopener noreferrer" data-moz-translations-id=1015><span class=anchor-text data-moz-translations-id=1016>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1017><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1018></path></svg></a></div></span><li data-moz-translations-id=1019><span class="label u-font-sans" data-moz-translations-id=1020><a class="anchor anchor-default" href=#bb0360 id=ref-id-b0360 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1021><span class=anchor-text data-moz-translations-id=1022>Lióvre, 1940</span></a></span><span class=reference id=h0360 data-moz-translations-id=1023><div class=other-ref data-moz-translations-id=1024><span data-moz-translations-id=1025>Livre L., 1940. Le karst jurassien. Hydrologie de la Haute-Ajoie et découverte dáune riviére souterraine du Jura bernois. Le Jura S.A. imprimerie, Porrentruy: 159 p.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1026><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Li%C3%A8vre%20L.%2C%201940.%20Le%20karst%20jurassien.%20Hydrologie%20de%20la%20Haute-Ajoie%20et%20d%C3%A9couverte%20d%E2%80%99une%20rivi%C3%A8re%20souterraine%20du%20Jura%20bernois.%20%E2%80%94%20Le%20Jura%20S.A.%20imprimerie%2C%20Porrentruy%3A%20159%20p." target=_blank rel="noopener noreferrer" data-moz-translations-id=1027><span class=anchor-text data-moz-translations-id=1028>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1029><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1030></path></svg></a></div></span><li data-moz-translations-id=1031><span class="label u-font-sans" data-moz-translations-id=1032><a class="anchor anchor-default" href=#bb0365 id=ref-id-b0365 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1033><span class=anchor-text data-moz-translations-id=1034>Lin y otros, 1996</span></a></span><span class=reference id=h0365 data-moz-translations-id=1035><div class=contribution data-moz-translations-id=1036><div class="authors u-font-sans" data-moz-translations-id=1037>T. Lin, B.G. Horne, P. Tiño, C.L. Giles</div><div id=ref-id-h0365 class="title text-m" data-moz-translations-id=1038>Aprender dependencias a largo plazo en redes neuronales recurrentes de NARX. -</div></div><div class="host u-font-sans" data-moz-translations-id=1039>IEEE Trans. Neural Networks, 7 (6) (1996), pp. 1329-1338</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1040><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Learning%20long-term%20dependencies%20in%20NARX%20recurrent%20neural%20networks.%20&publication_year=1996&author=T.%20Lin&author=B.G.%20Horne&author=P.%20Ti%C3%B1o&author=C.L.%20Giles" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0365 data-moz-translations-id=1041><span class=anchor-text data-moz-translations-id=1042>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1043><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1044></path></svg></a></div></span><li data-moz-translations-id=1045><span class="label u-font-sans" data-moz-translations-id=1046><a class="anchor anchor-default" href=#bb0370 id=ref-id-b0370 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1047><span class=anchor-text data-moz-translations-id=1048>Lin et al., 1996b</span></a></span><span class=reference id=h0370 data-moz-translations-id=1049><div class=other-ref data-moz-translations-id=1050><span data-moz-translations-id=1051>Lin T., Horne B. G., Tiño P., Giles C. L., 1996b. Aprender dependencias a largo plazo no es tan difícil con las redes NARX. Avances en Sistemas de Procesamiento de Información Neural: 577o583.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1052><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Lin%20T.%2C%20Horne%20B.%20G.%2C%20Ti%C3%B1o%20P.%2C%20Giles%20C.%20L.%2C%201996b.%20Learning%20long-term%20dependencies%20is%20not%20as%20difficult%20with%20NARX%20networks.%20%E2%80%94%20Advances%20in%20Neural%20Information%20Processing%20Systems%3A%20577%E2%80%93583." target=_blank rel="noopener noreferrer" data-moz-translations-id=1053><span class=anchor-text data-moz-translations-id=1054>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1055><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1056></path></svg></a></div></span><li data-moz-translations-id=1057><span class="label u-font-sans" data-moz-translations-id=1058><a class="anchor anchor-default" href=#bb0375 id=ref-id-b0375 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1059><span class=anchor-text data-moz-translations-id=1060>Liu et al., 2021</span></a></span><span class=reference id=h0375 data-moz-translations-id=1061><div class=contribution data-moz-translations-id=1062><div class="authors u-font-sans" data-moz-translations-id=1063>Y. Liu, T. Wagener, A. Hartmann</div><div id=ref-id-h0375 class="title text-m" data-moz-translations-id=1064>Evaluar la sensibilidad del flujo de arroyos a la variabilidad de la precipitación en las cuencas de influencia kárstica con equilibrios de agua no cerrados</div></div><div class="host u-font-sans" data-moz-translations-id=1065>Retorno del agua. Res., 57 (1) (2021), <a class="anchor anchor-default" href=https://doi.org/10.1029/2020WR028598 target=_blank rel="noreferrer noopener" data-moz-translations-id=1066><span class=anchor-text data-moz-translations-id=1067>10.1029/2020WR028598</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1068><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1069></path></svg></a></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1070><div class="link u-margin-m-right u-display-inline" data-moz-translations-id=1071><els-view-pdf-element class="universal-pdf-button pending" data-config='{"seamlessAccessEnabled":false,"states":[{"name":"found","label":"View PDF","iconSize":"small"},{"name":"found-doi","label":"View article","icon":"<!-- no icon -->","iconSize":"small","show":true}],"integratorId":8301}' doc-id=10.1029/2020WR028598 doc-type=doi tooltip-placement=left data-moz-translations-id=1072><template shadowroot=open></template></els-view-pdf-element></div><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Assessing%20streamflow%20sensitivity%20to%20precipitation%20variability%20in%20karst-influenced%20catchments%20with%20unclosed%20water%20balances&publication_year=2021&author=Y.%20Liu&author=T.%20Wagener&author=A.%20Hartmann" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0375 data-moz-translations-id=1073><span class=anchor-text data-moz-translations-id=1074>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1075><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1076></path></svg></a></div></span><li data-moz-translations-id=1077><span class="label u-font-sans" data-moz-translations-id=1078><a class="anchor anchor-default" href=#bb0380 id=ref-id-b0380 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1079><span class=anchor-text data-moz-translations-id=1080>Largo, 2009</span></a></span><span class=reference id=h0380 data-moz-translations-id=1081><div class=contribution data-moz-translations-id=1082><div class="authors u-font-sans" data-moz-translations-id=1083>A.J. Largo</div><div id=ref-id-h0380 class="title text-m" data-moz-translations-id=1084>Separación del hidrógrafo para cuencas kársticas utilizando un modelo de descargo de precipitación de dos dominios,</div></div><div class="host u-font-sans" data-moz-translations-id=1085>J. Hydrol., 364 (3o4) (2009), pp. 249 a 256</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1086><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Hydrograph%20separation%20for%20karst%20watersheds%20using%20a%20two-domain%20rainfalldischarge%20model%2C%20&publication_year=2009&author=A.J.%20Long" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0380 data-moz-translations-id=1087><span class=anchor-text data-moz-translations-id=1088>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1089><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1090></path></svg></a></div></span><li data-moz-translations-id=1091><span class="label u-font-sans" data-moz-translations-id=1092><a class="anchor anchor-default" href=#bb0385 id=ref-id-b0385 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1093><span class=anchor-text data-moz-translations-id=1094>Madsen y otros, 2002</span></a></span><span class=reference id=h0385 data-moz-translations-id=1095><div class=contribution data-moz-translations-id=1096><div class="authors u-font-sans" data-moz-translations-id=1097>H. Madsen, G. Wilson, H.C. Ammentorp</div><div id=ref-id-h0385 class="title text-m" data-moz-translations-id=1098>Comparación de diferentes estrategias automatizadas para la calibración de modelos de precipitación. -</div></div><div class="host u-font-sans" data-moz-translations-id=1099>J. Hydrol., 261 (2002), pp. 48 - 59</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1100><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Comparison%20of%20different%20automated%20strategies%20for%20calibration%20of%20rainfall-runoff%20models.%20&publication_year=2002&author=H.%20Madsen&author=G.%20Wilson&author=H.C.%20Ammentorp" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0385 data-moz-translations-id=1101><span class=anchor-text data-moz-translations-id=1102>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1103><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1104></path></svg></a></div></span><li data-moz-translations-id=1105><span class="label u-font-sans" data-moz-translations-id=1106><a class="anchor anchor-default" href=#bb0390 id=ref-id-b0390 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1107><span class=anchor-text data-moz-translations-id=1108>Maillet, 1905</span></a></span><span class=reference id=h0390 data-moz-translations-id=1109><div class=other-ref data-moz-translations-id=1110><span data-moz-translations-id=1111>Maillet E. T., 1905. Essais d'hydraulique souterraine et fluviale. - Hermann A. (Ed.) París: 218 p.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1112><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Maillet%20E.%20T.%2C%201905.%20Essais%20d%27hydraulique%20souterraine%20et%20fluviale.%20%E2%80%94%20Hermann%20A.%20(Ed.)%20Paris%20%3A%20218%20p." target=_blank rel="noopener noreferrer" data-moz-translations-id=1113><span class=anchor-text data-moz-translations-id=1114>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1115><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1116></path></svg></a></div></span><li data-moz-translations-id=1117><span class="label u-font-sans" data-moz-translations-id=1118><a class="anchor anchor-default" href=#bb0395 id=ref-id-b0395 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1119><span class=anchor-text data-moz-translations-id=1120>Malard, 2018</span></a></span><span class=reference id=h0395 data-moz-translations-id=1121><div class=other-ref data-moz-translations-id=1122><span data-moz-translations-id=1123>Malard A., 2018. Caracterización hidrogeológica de los acuíferos kársticos en Suiza utilizando un enfoque pragmático. Doctorado en Tesis, Universidad de Neuchátel, Suiza: 369 p.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1124><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Malard%20A.%2C%202018.%20Hydrogeological%20characterization%20of%20karst%20aquifers%20in%20Switzerland%20using%20a%20pragmatic%20approach.%20%E2%80%93%20PhD-Thesis%2C%20University%20of%20Neuch%C3%A2tel%2C%20Switzerland%3A%20369%20p." target=_blank rel="noopener noreferrer" data-moz-translations-id=1125><span class=anchor-text data-moz-translations-id=1126>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1127><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1128></path></svg></a></div></span><li data-moz-translations-id=1129><span class="label u-font-sans" data-moz-translations-id=1130><a class="anchor anchor-default" href=#bb0400 id=ref-id-b0400 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1131><span class=anchor-text data-moz-translations-id=1132>Mangin, 1970</span></a></span><span class=reference id=h0400 data-moz-translations-id=1133><div class=other-ref data-moz-translations-id=1134><span data-moz-translations-id=1135>Mangin A., 1970. Contribución . l'étude d'aquifáres karstiques . partir de l'analyse de courbes de décrue et de tarissement. Annales de Speléologie, Volumen 25(3): 581-609.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1136><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Mangin%20A.%2C%201970.%20Contribution%20%C3%A0%20l%27%C3%A9tude%20d%27aquif%C3%A8res%20karstiques%20%C3%A0%20partir%20de%20l%27analyse%20de%20courbes%20de%20d%C3%A9crue%20et%20de%20tarissement.%20%E2%80%94%20Annales%20de%20Spel%C3%A9ologie%2C%20Volume%2025(3)%3A%20581-609." target=_blank rel="noopener noreferrer" data-moz-translations-id=1137><span class=anchor-text data-moz-translations-id=1138>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1139><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1140></path></svg></a></div></span><li data-moz-translations-id=1141><span class="label u-font-sans" data-moz-translations-id=1142><a class="anchor anchor-default" href=#bb0405 id=ref-id-b0405 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1143><span class=anchor-text data-moz-translations-id=1144>Mangin, 1975</span></a></span><span class=reference id=h0405 data-moz-translations-id=1145><div class=other-ref data-moz-translations-id=1146><span data-moz-translations-id=1147>Mangin A., 1975. Contribución de létude hidrodinomicique des aquifáres karstiques. Annales de Spéléologie, 19 (3, 29 (4), 30 (1) : 172 p.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1148><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Mangin%20A.%2C%201975.%20Contribution%20%C3%A0%20l%E2%80%99%C3%A9tude%20hydrodynamique%20des%20aquif%C3%A8res%20karstiques.%20%E2%80%94%20Annales%20de%20Sp%C3%A9l%C3%A9ologie%2C%2019%20(3%2C%2029%20(4)%2C%2030%20(1)%20%3A%20172%20p." target=_blank rel="noopener noreferrer" data-moz-translations-id=1149><span class=anchor-text data-moz-translations-id=1150>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1151><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1152></path></svg></a></div></span><li data-moz-translations-id=1153><span class="label u-font-sans" data-moz-translations-id=1154><a class="anchor anchor-default" href=#bb0410 id=ref-id-b0410 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1155><span class=anchor-text data-moz-translations-id=1156>Martel, 1921</span></a></span><span class=reference id=h0410 data-moz-translations-id=1157><div class=other-ref data-moz-translations-id=1158><span data-moz-translations-id=1159>Martel E.A., 1921. Le Nouveau traité des eaux souterraines. París, 0. Doin, G. Doin: 838 p.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1160><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Martel%20E.-A.%2C%201921.%20Le%20Nouveau%20trait%C3%A9%20des%20eaux%20souterraines.%20%E2%80%94%20Paris%2C%200.%20Doin%2C%20G.%20Doin%20%3A%20838%20p." target=_blank rel="noopener noreferrer" data-moz-translations-id=1161><span class=anchor-text data-moz-translations-id=1162>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1163><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1164></path></svg></a></div></span><li data-moz-translations-id=1165><span class="label u-font-sans" data-moz-translations-id=1166><a class="anchor anchor-default" href=#bb0415 id=ref-id-b0415 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1167><span class=anchor-text data-moz-translations-id=1168>Mazzilli et al., 2017</span></a></span><span class=reference id=h0415 data-moz-translations-id=1169><div class=contribution data-moz-translations-id=1170><div class="authors u-font-sans" data-moz-translations-id=1171>N. Mazzilli, D. Bertin, V. Guinot, H. Jourde, N. Lecoq, D. Labat, B. Arfib, C. Baudement, C. Danquigny, L. Dal Soglio</div><div id=ref-id-h0415 class="title text-m" data-moz-translations-id=1172>KarstMod: una plataforma de modelado para la precipitación - análisis de descarga y modelización dedicado a los sistemas kársticos. -</div></div><div class="host u-font-sans" data-moz-translations-id=1173>En el medio ambiente. Modelol. Software, 122 (2017), p. 10.1016</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1174><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=KarstMod%3A%20a%20modelling%20platform%20for%20rainfall%20-%20discharge%20analysis%20and%20modelling%20dedicated%20to%20karst%20systems.%20&publication_year=2017&author=N.%20Mazzilli&author=D.%20Bertin&author=V.%20Guinot&author=H.%20Jourde&author=N.%20Lecoq&author=D.%20Labat&author=B.%20Arfib&author=C.%20Baudement&author=C.%20Danquigny&author=L.%20Dal%20Soglio" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0415 data-moz-translations-id=1175><span class=anchor-text data-moz-translations-id=1176>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1177><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1178></path></svg></a></div></span><li data-moz-translations-id=1179><span class="label u-font-sans" data-moz-translations-id=1180><a class="anchor anchor-default" href=#bb0420 id=ref-id-b0420 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1181><span class=anchor-text data-moz-translations-id=1182>Mazzilli et al., 2019</span></a></span><span class=reference id=h0420 data-moz-translations-id=1183><div class=other-ref data-moz-translations-id=1184><span data-moz-translations-id=1185>Mazzilli N., Guinot V., Jourde H., Lecoq N., Labat D., et al., 2019. KarstMod: Una plataforma de modelado para lluvias - análisis de descarga y modelización dedicado a sistemas kársticos. Modelado y Software Ambiental, Elsevier, 2019: 122 p.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1186><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Mazzilli%20N.%2C%20Guinot%20V.%2C%20Jourde%20H.%2C%20Lecoq%20N.%2C%20Labat%20D.%2C%20et%20al.%2C%202019.%20KarstMod%3A%20A%20modelling%20platform%20for%20rainfall%20-%20discharge%20analysis%20and%20modelling%20dedicated%20to%20karst%20systems.%20%E2%80%94%20Environmental%20Modelling%20and%20Software%2C%20Elsevier%2C%202019%3A%20122%20p." target=_blank rel="noopener noreferrer" data-moz-translations-id=1187><span class=anchor-text data-moz-translations-id=1188>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1189><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1190></path></svg></a></div></span><li data-moz-translations-id=1191><span class="label u-font-sans" data-moz-translations-id=1192><a class="anchor anchor-default" href=#bb0425 id=ref-id-b0425 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1193><span class=anchor-text data-moz-translations-id=1194>Milanovic, 1976</span></a></span><span class=reference id=h0425 data-moz-translations-id=1195><div class=other-ref data-moz-translations-id=1196><span data-moz-translations-id=1197>Milanovic P., 1976. Régimen de agua en dee kkarst: estudio de caso de la zona de drenaje de Ombla Spring. . . . . . . . . . . . . . . . . . . . . . . . . . . . Karst Hydrology y Recursos Hígados, Vol. 1, Karst Hydrology, Fort Collins, Colorado, Recursos hídricos Publicaciones: 165-186.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1198><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Milanovic%20P.%2C%201976.%20Water%20regime%20in%20dee%20karst%3A%20case%20study%20of%20Ombla%20Spring%20drainage%20area.%20%E2%80%94%20in%3A%20Yevjevich%20V.%20(ed.).%20Karst%20Hydrology%20and%20Water%20Resources%2C%20Vol.%201%2C%20Karst%20Hydrology%2C%20Fort%20Collins%2C%20Colorado%2C%20Water%20Resources%20Publications%3A%20165%E2%80%93186." target=_blank rel="noopener noreferrer" data-moz-translations-id=1199><span class=anchor-text data-moz-translations-id=1200>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1201><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1202></path></svg></a></div></span><li data-moz-translations-id=1203><span class="label u-font-sans" data-moz-translations-id=1204><a class="anchor anchor-default" href=#bb0430 id=ref-id-b0430 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1205><span class=anchor-text data-moz-translations-id=1206>Moriasi et al., 2007</span></a></span><span class=reference id=h0430 data-moz-translations-id=1207><div class=contribution data-moz-translations-id=1208><div class="authors u-font-sans" data-moz-translations-id=1209>D.N. Moriasi, J.G. Arnold, M.W. Van Liew, R.L. Bingner, R.D. Harmel, <em data-moz-translations-id=1210>et al.</em></div><div id=ref-id-h0430 class="title text-m" data-moz-translations-id=1211>Directrices de evaluación modelo para cuantificar sistemáticamente la exactitud de las simulaciones de cuencas hidrográficas</div></div><div class="host u-font-sans" data-moz-translations-id=1212>Suelo y agua. - Estoy. Soc. Agric. Biol. Eng., 50 (3) (2007), pp. 885-900</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1213><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Model%20evaluation%20guidelines%20for%20systematic%20quantification%20of%20accuracy%20in%20watershed%20simulations&publication_year=2007&author=D.N.%20Moriasi&author=J.G.%20Arnold&author=M.W.%20Van%20Liew&author=R.L.%20Bingner&author=R.D.%20Harmel" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0430 data-moz-translations-id=1214><span class=anchor-text data-moz-translations-id=1215>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1216><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1217></path></svg></a></div></span><li data-moz-translations-id=1218><span class="label u-font-sans" data-moz-translations-id=1219><a class="anchor anchor-default" href=#bb0435 id=ref-id-b0435 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1220><span class=anchor-text data-moz-translations-id=1221>Morrissey et al., 2020</span></a></span><span class=reference id=h0435 data-moz-translations-id=1222><div class=contribution data-moz-translations-id=1223><div class="authors u-font-sans" data-moz-translations-id=1224>P. Morrissey, T. McCormack, O. Naughton, P.M. Johnston, L.W. Gill</div><div id=ref-id-h0435 class="title text-m" data-moz-translations-id=1225>Modelando las inundaciones subterráneas en una cuenca kárstica de tierras bajas. -</div></div><div class="host u-font-sans" data-moz-translations-id=1226>J. Hidrol., 580 (2020)</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1227><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Modelling%20groundwater%20flooding%20in%20a%20lowland%20karst%20catchment.%20&publication_year=2020&author=P.%20Morrissey&author=T.%20McCormack&author=O.%20Naughton&author=P.M.%20Johnston&author=L.W.%20Gill" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0435 data-moz-translations-id=1228><span class=anchor-text data-moz-translations-id=1229>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1230><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1231></path></svg></a></div></span><li data-moz-translations-id=1232><span class="label u-font-sans" data-moz-translations-id=1233><a class="anchor anchor-default" href=#bb0440 id=ref-id-b0440 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1234><span class=anchor-text data-moz-translations-id=1235>Nash y Sutcliffe, 1970</span></a></span><span class=reference id=h0440 data-moz-translations-id=1236><div class=contribution data-moz-translations-id=1237><div class="authors u-font-sans" data-moz-translations-id=1238>J.E. Nash, J.V. Sutcliffe</div><div id=ref-id-h0440 class="title text-m" data-moz-translations-id=1239>Pronosticando el flujo de río a través de modelos conceptuales parte I. Una discusión de principios. -</div></div><div class="host u-font-sans" data-moz-translations-id=1240>J. Hydrol., 10 (3) (1970), pp. 282-290</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1241><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=River%20flow%20forecasting%20through%20conceptual%20models%20part%20I%20%20A%20discussion%20of%20principles.%20&publication_year=1970&author=J.E.%20Nash&author=J.V.%20Sutcliffe" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0440 data-moz-translations-id=1242><span class=anchor-text data-moz-translations-id=1243>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1244><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1245></path></svg></a></div></span><li data-moz-translations-id=1246><span class="label u-font-sans" data-moz-translations-id=1247><a class="anchor anchor-default" href=#bb0445 id=ref-id-b0445 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1248><span class=anchor-text data-moz-translations-id=1249>Pardo-Igúzquiza et al., 2018</span></a></span><span class=reference id=h0445 data-moz-translations-id=1250><div class=contribution data-moz-translations-id=1251><div class="authors u-font-sans" data-moz-translations-id=1252>E. Pardo-Igúzquiza, P. Dowd, A. Pulido-Bosch, J.A. Luque-Espinar, J. Heredia, J.J. Durán-Valsero</div><div id=ref-id-h0445 class="title text-m" data-moz-translations-id=1253>Un modelo distribuido parsimonioso para simular el flujo de agua transitoria en un acuífero kárstico de alto alivio. -</div></div><div class="host u-font-sans" data-moz-translations-id=1254>Hidrogeol. J., 26 (8) (2018), págs. 2617-2627</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1255><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=A%20parsimonious%20distributed%20model%20for%20simulating%20transient%20water%20flow%20in%20a%20high-relief%20karst%20aquifer.%20&publication_year=2018&author=E.%20Pardo-Ig%C3%BAzquiza&author=P.%20Dowd&author=A.%20Pulido-Bosch&author=J.A.%20Luque-Espinar&author=J.%20Heredia&author=J.J.%20Dur%C3%A1n-Valsero" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0445 data-moz-translations-id=1256><span class=anchor-text data-moz-translations-id=1257>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1258><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1259></path></svg></a></div></span><li data-moz-translations-id=1260><span class="label u-font-sans" data-moz-translations-id=1261><a class="anchor anchor-default" href=#bb0450 id=ref-id-b0450 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1262><span class=anchor-text data-moz-translations-id=1263>Penman, 1948</span></a></span><span class=reference id=h0450 data-moz-translations-id=1264><div class=other-ref data-moz-translations-id=1265><span data-moz-translations-id=1266>Penman H. L., 1948. Evaporación natural de aguas abiertas, suelo desnudo y hierba. - Proc. Roy. Soc., Londres, Reino Unido, vol. A193, no 1032, 1948: 120-145.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1267><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Penman%20H.%20L.%2C%201948.%20Natural%20evaporation%20from%20open%20water%2C%20bare%20soil%2C%20and%20grass.%20%E2%80%94%20Proc.%20Roy.%20Soc.%2C%20London%2C%20U.K.%2C%20vol.%20A193%2C%20no%201032%2C%201948%3A%20120%E2%80%93145." target=_blank rel="noopener noreferrer" data-moz-translations-id=1268><span class=anchor-text data-moz-translations-id=1269>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1270><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1271></path></svg></a></div></span><li data-moz-translations-id=1272><span class="label u-font-sans" data-moz-translations-id=1273><a class="anchor anchor-default" href=#bb0455 id=ref-id-b0455 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1274><span class=anchor-text data-moz-translations-id=1275>Perrin, 2003</span></a></span><span class=reference id=h0455 data-moz-translations-id=1276><div class=other-ref data-moz-translations-id=1277><span data-moz-translations-id=1278>Perrin J., 2003. Un modelo conceptual de flujo y transporte en un acuífero kárstico basado en variaciones espaciales y temporales de trazador natural. Doctorado en Tesis, Universidad de Neuchátel: 227 p.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1279><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Perrin%20J.%2C%202003.%20A%20conceptual%20model%20of%20flow%20and%20transport%20in%20a%20karst%20aquifer%20based%20on%20spatial%20and%20temporal%20variations%20of%20natural%20tracer.%20%E2%80%94%20PhD-Thesis%2C%20University%20of%20Neuch%C3%A2tel%3A%20227%20p." target=_blank rel="noopener noreferrer" data-moz-translations-id=1280><span class=anchor-text data-moz-translations-id=1281>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1282><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1283></path></svg></a></div></span><li data-moz-translations-id=1284><span class="label u-font-sans" data-moz-translations-id=1285><a class="anchor anchor-default" href=#bb0460 id=ref-id-b0460 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1286><span class=anchor-text data-moz-translations-id=1287>Perrin et al., 2003</span></a></span><span class=reference id=h0460 data-moz-translations-id=1288><div class=contribution data-moz-translations-id=1289><div class="authors u-font-sans" data-moz-translations-id=1290>J. Perrin, P.-Y. Jeannin, F. Zwahlen</div><div id=ref-id-h0460 class="title text-m" data-moz-translations-id=1291>Almacenamiento epikarst en un acuífero kárstico: un modelo conceptual basado en datos isotópicos, sitio de pruebas Milandre, Suiza</div></div><div class="host u-font-sans" data-moz-translations-id=1292>J. Hydrol., 279 (2003), pp. 106-124</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1293><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Epikarst%20storage%20in%20a%20karst%20aquifer%20%3A%20a%20conceptual%20model%20based%20on%20isotopic%20data%2C%20Milandre%20test%20site%2C%20Switzerland&publication_year=2003&author=J.%20Perrin&author=P.-Y.%20Jeannin&author=F.%20Zwahlen" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0460 data-moz-translations-id=1294><span class=anchor-text data-moz-translations-id=1295>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1296><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1297></path></svg></a></div></span><li data-moz-translations-id=1298><span class="label u-font-sans" data-moz-translations-id=1299><a class="anchor anchor-default" href=#bb0465 id=ref-id-b0465 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1300><span class=anchor-text data-moz-translations-id=1301>Peterson y Wicks, 2006</span></a></span><span class=reference id=h0465 data-moz-translations-id=1302><div class=contribution data-moz-translations-id=1303><div class="authors u-font-sans" data-moz-translations-id=1304>E.W. Peterson, C.M. Wicks</div><div id=ref-id-h0465 class="title text-m" data-moz-translations-id=1305>Evaluando la importancia de la geometría del conducto y los parámetros físicos en los sistemas kársticos utilizando el modelo de gestión del agua de tormenta (SWMM). -</div></div><div class="host u-font-sans" data-moz-translations-id=1306>J. Hydrol., 329 (2006), pp. 294 a 405</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1307><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Assessing%20the%20importance%20of%20conduit%20geometry%20and%20physical%20parameters%20in%20karst%20systems%20using%20the%20storm%20water%20management%20model%20.%20&publication_year=2006&author=E.W.%20Peterson&author=C.M.%20Wicks" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0465 data-moz-translations-id=1308><span class=anchor-text data-moz-translations-id=1309>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1310><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1311></path></svg></a></div></span><li data-moz-translations-id=1312><span class="label u-font-sans" data-moz-translations-id=1313><a class="anchor anchor-default" href=#bb0470 id=ref-id-b0470 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1314><span class=anchor-text data-moz-translations-id=1315>Pianosi et al., 2016</span></a></span><span class=reference id=h0470 data-moz-translations-id=1316><div class=contribution data-moz-translations-id=1317><div class="authors u-font-sans" data-moz-translations-id=1318>F. Pianosi, K. Beven, J. Freer, J. Hall, J. Rougier, D. Stephenson, T. Wagener</div><div id=ref-id-h0470 class="title text-m" data-moz-translations-id=1319>Análisis de sensibilidad de los modelos ambientales: Una revisión sistemática con flujo de trabajo práctico. -</div></div><div class="host u-font-sans" data-moz-translations-id=1320>En el medio ambiente. Modelol. Software, vol. 79 (2016), pp. 214-232, <a class="anchor anchor-default" href=https://doi.org/10.1016/j.envsoft.2016.02.008 target=_blank rel="noreferrer noopener" data-moz-translations-id=1321><span class=anchor-text data-moz-translations-id=1322>10.1016/j.envsoft.2016.02.008</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1323><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1324></path></svg></a></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1325><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Sensitivity%20analysis%20of%20environmental%20models%3A%20A%20systematic%20review%20with%20practical%20workflow.%20&publication_year=2016&author=F.%20Pianosi&author=K.%20Beven&author=J.%20Freer&author=J.%20Hall&author=J.%20Rougier&author=D.%20Stephenson&author=T.%20Wagener" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0470 data-moz-translations-id=1328><span class=anchor-text data-moz-translations-id=1329>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1330><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1331></path></svg></a></div></span><li data-moz-translations-id=1332><span class="label u-font-sans" data-moz-translations-id=1333><a class="anchor anchor-default" href=#bb0475 id=ref-id-b0475 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1334><span class=anchor-text data-moz-translations-id=1335>Rango y Martinec, 1995</span></a></span><span class=reference id=h0475 data-moz-translations-id=1336><div class=contribution data-moz-translations-id=1337><div class="authors u-font-sans" data-moz-translations-id=1338>A. Rango, J. Martinec</div><div id=ref-id-h0475 class="title text-m" data-moz-translations-id=1339>Revisando el método de grado-día para los cálculos de derretimiento de la nieve. -</div></div><div class="host u-font-sans" data-moz-translations-id=1340>J. - Sí. Retorno del agua. Assoc., 31 (1995), pp. 657-669, <a class="anchor anchor-default" href=https://doi.org/10.1111/j.1752-1688.1995.tb03392.x target=_blank rel="noreferrer noopener" data-moz-translations-id=1341><span class=anchor-text data-moz-translations-id=1342>10.1111/j.1752-1688.1995.tb03392.x</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1343><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1344></path></svg></a></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1345><div class="link u-margin-m-right u-display-inline" data-moz-translations-id=1346><els-view-pdf-element class="universal-pdf-button pending" data-config='{"seamlessAccessEnabled":false,"states":[{"name":"found","label":"View PDF","iconSize":"small"},{"name":"found-doi","label":"View article","icon":"<!-- no icon -->","iconSize":"small","show":true}],"integratorId":8301}' doc-id=10.1111/j.1752-1688.1995.tb03392.x doc-type=doi tooltip-placement=left data-moz-translations-id=1347><template shadowroot=open></template></els-view-pdf-element></div><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Revisiting%20the%20degree-day%20method%20for%20snowmelt%20computations.%20&publication_year=1995&author=A.%20Rango&author=J.%20Martinec" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0475 data-moz-translations-id=1348><span class=anchor-text data-moz-translations-id=1349>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1350><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1351></path></svg></a></div></span><li data-moz-translations-id=1352><span class="label u-font-sans" data-moz-translations-id=1353><a class="anchor anchor-default" href=#bb0480 id=ref-id-b0480 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1354><span class=anchor-text data-moz-translations-id=1355>Reimann et al., 2014</span></a></span><span class=reference id=h0480 data-moz-translations-id=1356><div class=contribution data-moz-translations-id=1357><div class="authors u-font-sans" data-moz-translations-id=1358>T. Reimann, M. Giese, T. Geyer, R. Liedl, J.C. Maréchal, W.B. Zapatero</div><div id=ref-id-h0480 class="title text-m" data-moz-translations-id=1359>Representación de la abstracción de agua de un conducto kárstico con modelos numéricos discretos-continuum. Hidrohimero</div></div><div class="host u-font-sans" data-moz-translations-id=1360>Tierra Syst. Sci., 18 (2014), pp. 227-241, <a class="anchor anchor-default" href=https://doi.org/10.5194/hess-18-227-2014 target=_blank rel="noreferrer noopener" data-moz-translations-id=1361><span class=anchor-text data-moz-translations-id=1362>10.5194/hess-18-227-2014</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1363><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1364></path></svg></a></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1365><div class="link u-margin-m-right u-display-inline" data-moz-translations-id=1366><els-view-pdf-element class="universal-pdf-button pending" data-config='{"seamlessAccessEnabled":false,"states":[{"name":"found","label":"View PDF","iconSize":"small"},{"name":"found-doi","label":"View article","icon":"<!-- no icon -->","iconSize":"small","show":true}],"integratorId":8301}' doc-id=10.5194/hess-18-227-2014 doc-type=doi tooltip-placement=left data-moz-translations-id=1367><template shadowroot=open></template></els-view-pdf-element></div><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Representation%20of%20water%20abstraction%20from%20a%20karst%20conduit%20with%20numerical%20discrete-continuum%20models.%20%20Hydrol&publication_year=2014&author=T.%20Reimann&author=M.%20Giese&author=T.%20Geyer&author=R.%20Liedl&author=J.C.%20Mar%C3%A9chal&author=W.B.%20Shoemaker" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0480 data-moz-translations-id=1368><span class=anchor-text data-moz-translations-id=1369>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1370><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1371></path></svg></a></div></span><li data-moz-translations-id=1372><span class="label u-font-sans" data-moz-translations-id=1373><a class="anchor anchor-default" href=#bb0485 id=ref-id-b0485 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1374><span class=anchor-text data-moz-translations-id=1375>Reimann et al., 2018</span></a></span><span class=reference id=h0485 data-moz-translations-id=1376><div class=other-ref data-moz-translations-id=1377><span data-moz-translations-id=1378>Reimann T., Liedl R., Birk S., Bauer S., 2018. Modificaciones y mejoras de las subrutinas de flujo CFPM1 y adición de subrutinas de transporte. Accesible en: <a class="anchor u-display-inline anchor-default" href=http://tu-dresden.de/die_tu_dresden/fakultaeten/fakultaet_forst_geo_und_hydrowissenschaften/fachrichtung_wasserwesen/igw/forschung/downloads/cfpv2 target=_blank rel="noreferrer noopener" data-moz-translations-id=1379><span class=anchor-text data-moz-translations-id=1380>http://tu-dresden.de/die-tu-dresden/fakultaeten/fakultaet.forst.geo.und-hydrowissenschaften/fachrichtung-w-w/forschung/downloads/cfpv2</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1381><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1382></path></svg></a>.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1383><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Reimann%20T.%2C%20Liedl%20R.%2C%20Birk%20S.%2C%20Bauer%20S.%2C%202018.%20Modifications%20and%20enhancements%20to%20CFPM1%20flow%20subroutines%20and%20addition%20of%20transport%20subroutines.%20%E2%80%94%20Accessible%20at%3A%20http%3A%2F%2Ftu-dresden.de%2Fdie_tu_dresden%2Ffakultaeten%2Ffakultaet_forst_geo_und_hydrowissenschaften%2Ffachrichtung_wasserwesen%2Figw%2Fforschung%2Fdownloads%2Fcfpv2." target=_blank rel="noopener noreferrer" data-moz-translations-id=1384><span class=anchor-text data-moz-translations-id=1385>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1386><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1387></path></svg></a></div></span><li data-moz-translations-id=1388><span class="label u-font-sans" data-moz-translations-id=1389><a class="anchor anchor-default" href=#bb0490 id=ref-id-b0490 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1390><span class=anchor-text data-moz-translations-id=1391>Schoeller, 1962</span></a></span><span class=reference id=h0490 data-moz-translations-id=1392><div class=other-ref data-moz-translations-id=1393><span data-moz-translations-id=1394>Schoeller H., 1962. Les eaux souterraines. - Ed. Masson, París: 642 p.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1395><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Schoeller%20H.%2C%201962.%20Les%20eaux%20souterraines.%20%E2%80%94%20Ed.%20Masson%2C%20Paris%20%3A%20642%20p." target=_blank rel="noopener noreferrer" data-moz-translations-id=1396><span class=anchor-text data-moz-translations-id=1397>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1398><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1399></path></svg></a></div></span><li data-moz-translations-id=1400><span class="label u-font-sans" data-moz-translations-id=1401><a class="anchor anchor-default" href=#bb0495 id=ref-id-b0495 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1402><span class=anchor-text data-moz-translations-id=1403>Schuler et al., 2020</span></a></span><span class=reference id=h0495 data-moz-translations-id=1404><div class=other-ref data-moz-translations-id=1405><span data-moz-translations-id=1406>Schuler P., Duran L., Johnston P.M., Gill L.W., 2020. Cuantificando y representando numéricamente los componentes de recarga y flujo en un acuífero carbonato kkarstificado. Investigación de Recursos Hídricos 56, e2020WR027717.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1407><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Schuler%20P.%2C%20Duran%20L.%2C%20Johnston%20P.M.%2C%20Gill%20L.W.%2C%202020.%20Quantifying%20and%20numerically%20representing%20recharge%20and%20flow%20components%20in%20a%20karstified%20carbonate%20aquifer.%20%E2%80%94%20Water%20Resources%20Research%2056%2C%20e2020WR027717." target=_blank rel="noopener noreferrer" data-moz-translations-id=1408><span class=anchor-text data-moz-translations-id=1409>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1410><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1411></path></svg></a></div></span><li data-moz-translations-id=1412><span class="label u-font-sans" data-moz-translations-id=1413><a class="anchor anchor-default" href=#bb0500 id=ref-id-b0500 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1414><span class=anchor-text data-moz-translations-id=1415>Sherman, 1932</span></a></span><span class=reference id=h0500 data-moz-translations-id=1416><div class=contribution data-moz-translations-id=1417><div class="authors u-font-sans" data-moz-translations-id=1418>L. Sherman</div><div id=ref-id-h0500 class="title text-m" data-moz-translations-id=1419>Corriente de flujo de Rainfall por el Método de Gráfico Unidad. Ingeniería Newa</div></div><div class="host u-font-sans" data-moz-translations-id=1420>Récord, 108 (1932), pp. 501-505</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1421><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Stream%20Flow%20from%20Rainfall%20by%20the%20Unit%20Graph%20Method.%20%20Engineering%20Newa&publication_year=1932&author=L.%20Sherman" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0500 data-moz-translations-id=1422><span class=anchor-text data-moz-translations-id=1423>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1424><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1425></path></svg></a></div></span><li data-moz-translations-id=1426><span class="label u-font-sans" data-moz-translations-id=1427><a class="anchor anchor-default" href=#bb0505 id=ref-id-b0505 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1428><span class=anchor-text data-moz-translations-id=1429>Zapatero y al., 2008</span></a></span><span class=reference id=h0505 data-moz-translations-id=1430><div class=other-ref data-moz-translations-id=1431><span data-moz-translations-id=1432>Shoemaker W.B., Kuniansky E.L., Birk S., Bauer S., Swain E.D., 2008. Documentación de un proceso de flujo de conductos (CFP) para MODFLOW-2005 Producto del Programa de Recursos de Agua Terrestre. - UU. Estudio Geológico. 10.3133/tm6A24.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1433><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Shoemaker%20W.B.%2C%20Kuniansky%20E.L.%2C%20Birk%20S.%2C%20Bauer%20S.%2C%20Swain%20E.D.%2C%202008.%20Documentation%20of%20a%20Conduit%20Flow%20Process%20(CFP)%20for%20MODFLOW-2005%20A%20product%20of%20the%20Ground-Water%20Resources%20Program.%20%E2%80%94%20U.S.%20Geological%20Survey.%2010.3133%2Ftm6A24." target=_blank rel="noopener noreferrer" data-moz-translations-id=1434><span class=anchor-text data-moz-translations-id=1435>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1436><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1437></path></svg></a></div></span><li data-moz-translations-id=1438><span class="label u-font-sans" data-moz-translations-id=1439><a class="anchor anchor-default" href=#bb0510 id=ref-id-b0510 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1440><span class=anchor-text data-moz-translations-id=1441>Inteligente, 1988</span></a></span><span class=reference id=h0510 data-moz-translations-id=1442><div class=other-ref data-moz-translations-id=1443><span data-moz-translations-id=1444>Inteligente C. C., 1988. Técnicas Artificial Tracer para la Determinación de la Estructura de los Acuíferos de Condetuzos. Agua subsuelo, Vol 26 (4): 445-453.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1445><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Smart%20C.%20C.%2C%201988.%20Artificial%20Tracer%20Techniques%20for%20the%20Determination%20of%20the%20Structure%20of%20Conduit%20Aquifers.%20%E2%80%94%20Groundwater%2C%20Vol%2026%20(4)%3A%20445-453." target=_blank rel="noopener noreferrer" data-moz-translations-id=1446><span class=anchor-text data-moz-translations-id=1447>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1448><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1449></path></svg></a></div></span><li data-moz-translations-id=1450><span class="label u-font-sans" data-moz-translations-id=1451><a class="anchor anchor-default" href=#bb0515 id=ref-id-b0515 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1452><span class=anchor-text data-moz-translations-id=1453>Smart y Friederich, 1986</span></a></span><span class=reference id=h0515 data-moz-translations-id=1454><div class=other-ref data-moz-translations-id=1455><span data-moz-translations-id=1456>Smart P. L. & Friederich H. 1986. Movimiento y almacenamiento de agua en la zona insaturada de un aquictor de carbonato madridificado. Mendip Hills, Inglaterra. Actas de la Conferencia sobre Problemas Ambientales en Karst Terranes y sus Soluciones, Bowling Green, Kentucky, 28-30 de octubre, publicado por National Water Well Association, Dublin, Ohio : 59-87.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1457><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Smart%20P.%20L.%20%26%20Friederich%20H.%201986.%20Water%20movement%20and%20storage%20in%20the%20unsaturated%20zone%20of%20a%20maturely%20karstified%20carbonate%20aquiter.%20Mendip%20Hills%2C%20England.%20%E2%80%93%20Proceedings%20of%20the%20Conference%20on%20Environmental%20Problems%20in%20Karst%20Terranes%20and%20their%20Solutions%2C%20Bowling%20Green%2C%20Kentucky%2C%20October%2028-30%2C%20published%20by%20National%20Water%20Well%20Association%2C%20Dublin%2C%20Ohio%20%3A%2059-87." target=_blank rel="noopener noreferrer" data-moz-translations-id=1458><span class=anchor-text data-moz-translations-id=1459>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1460><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1461></path></svg></a></div></span><li data-moz-translations-id=1462><span class="label u-font-sans" data-moz-translations-id=1463><a class="anchor anchor-default" href=#bb0520 id=ref-id-b0520 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1464><span class=anchor-text data-moz-translations-id=1465>Inteligente y Hobbs, 1986</span></a></span><span class=reference id=h0520 data-moz-translations-id=1466><div class=contribution data-moz-translations-id=1467><div class="authors u-font-sans" data-moz-translations-id=1468>P.L. Smart, S.L. Hobbs</div><div id=ref-id-h0520 class="title text-m" data-moz-translations-id=1469>Caracterización de los acuíferos carbonato: Una base conceptual. Actas de la Conferencia sobre Problemas Ambientales en Karst Terranes y sus Soluciones, Bowling Green, Kentucky, 28-30 de octubre, publicado por la Asociación Nacional del Pozo del Agua</div></div><div class="host u-font-sans" data-moz-translations-id=1470>1o14, Dublín, Ohio (1986)</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1471><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Characterisation%20of%20carbonate%20aquifers%3A%20A%20conceptual%20base.%20%20Proceedings%20of%20the%20Conference%20on%20Environmental%20Problems%20in%20Karst%20Terranes%20and%20their%20Solutions%2C%20Bowling%20Green%2C%20Kentucky%2C%20October%202830%2C%20published%20by%20National%20Water%20Well%20Association&publication_year=1986&author=P.L.%20Smart&author=S.L.%20Hobbs" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0520 data-moz-translations-id=1472><span class=anchor-text data-moz-translations-id=1473>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1474><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1475></path></svg></a></div></span><li data-moz-translations-id=1476><span class="label u-font-sans" data-moz-translations-id=1477><a class="anchor anchor-default" href=#bb0525 id=ref-id-b0525 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1478><span class=anchor-text data-moz-translations-id=1479>Teutsch y Sauter, 1991</span></a></span><span class=reference id=h0525 data-moz-translations-id=1480><div class=other-ref data-moz-translations-id=1481><span data-moz-translations-id=1482>Teutsch G., Sauter M., 1991. Modelado de aguas subterráneas en terranes kársticos: Efectos de escala, adquisición de datos y validación de campo. Tercera Conferencia sobre Hidrogeología, Ecología, Monitoreo y Gestión del Agua Subterránea en Karst Terranes. National Ground Water Association, Dublin, Ohio: 17-35.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1483><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Teutsch%20G.%2C%20Sauter%20M.%2C%201991.%20Groundwater%20modeling%20in%20karst%20terranes%3A%20Scale%20effects%2C%20data%20acquisition%20and%20field%20validation.%20%E2%80%94%20Third%20Conference%20on%20Hydrogeology%2C%20Ecology%2C%20Monitoring%2C%20and%20Management%20of%20Ground%20Water%20in%20Karst%20Terranes.%20National%20Ground%20Water%20Association%2C%20Dublin%2C%20Ohio%3A%2017-35." target=_blank rel="noopener noreferrer" data-moz-translations-id=1484><span class=anchor-text data-moz-translations-id=1485>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1486><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1487></path></svg></a></div></span><li data-moz-translations-id=1488><span class="label u-font-sans" data-moz-translations-id=1489><a class="anchor anchor-default" href=#bb0530 id=ref-id-b0530 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1490><span class=anchor-text data-moz-translations-id=1491>Thierrien y Sudicky, 1996</span></a></span><span class=reference id=h0530 data-moz-translations-id=1492><div class=contribution data-moz-translations-id=1493><div class="authors u-font-sans" data-moz-translations-id=1494>R. Thierrien, E.A. Sudicky</div><div id=ref-id-h0530 class="title text-m" data-moz-translations-id=1495>Análisis tridimensional de flujo y transporte de solutos variablemente saturados en medios porosos discretamente fracturados</div></div><div class="host u-font-sans" data-moz-translations-id=1496>J. Contam. Hydrol., 23 (1996), pp. 1 a 44</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1497><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Three-dimensional%20analysis%20of%20variably%20saturated%20flow%20and%20solute%20transport%20in%20discretely%20fractured%20porous%20media&publication_year=1996&author=R.%20Thierrien&author=E.A.%20Sudicky" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0530 data-moz-translations-id=1498><span class=anchor-text data-moz-translations-id=1499>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1500><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1501></path></svg></a></div></span><li data-moz-translations-id=1502><span class="label u-font-sans" data-moz-translations-id=1503><a class="anchor anchor-default" href=#bb0535 id=ref-id-b0535 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1504><span class=anchor-text data-moz-translations-id=1505>Tiére, 2014</span></a></span><span class=reference id=h0535 data-moz-translations-id=1506><div class=other-ref data-moz-translations-id=1507><span data-moz-translations-id=1508>Thiéry, D., 2014. Logiciel GARD-NIA, versión 8.2. Guía de la dutilización. BRGM/RP-62797-FR, 136 p. <a class="anchor u-display-inline anchor-default" href=http://infoterre.brgm.fr/rapports/RP-62797-FR.pdf target=_blank rel="noreferrer noopener" data-moz-translations-id=1509><span class=anchor-text data-moz-translations-id=1510>http://infoterre.brgm.fr/rapports/RP-62797-FR.pdf</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1511><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1512></path></svg></a>.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1513><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Thi%C3%A9ry%2C%20D.%2C%202014.%20Logiciel%20GARD%C3%89NIA%2C%20version%208.2.%20Guide%20d%E2%80%99utilisation.%20%E2%80%94%20BRGM%2FRP-62797-FR%2C%20136%20p.%20http%3A%2F%2Finfoterre.brgm.fr%2Frapports%2FRP-62797-FR.pdf." target=_blank rel="noopener noreferrer" data-moz-translations-id=1514><span class=anchor-text data-moz-translations-id=1515>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1516><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1517></path></svg></a></div></span><li data-moz-translations-id=1518><span class="label u-font-sans" data-moz-translations-id=1519><a class="anchor anchor-default" href=#bb0540 id=ref-id-b0540 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1520><span class=anchor-text data-moz-translations-id=1521>Thiéry, 2015</span></a></span><span class=reference id=h0540 data-moz-translations-id=1522><div class=other-ref data-moz-translations-id=1523><span data-moz-translations-id=1524>Thiéry D., 2015. Validación du code de calARD-NIA par modélisations fisico comparaciones. BRGM/RP-64500-FR: 48 p. <a class="anchor u-display-inline anchor-default" href=http://infoterre.brgm.fr/rapports/RP-64500-FR.pdf target=_blank rel="noreferrer noopener" data-moz-translations-id=1525><span class=anchor-text data-moz-translations-id=1526>http://infoterre.brgm.fr/rapports/RP-64500-FR.pdf</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1527><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1528></path></svg></a>.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1529><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Thi%C3%A9ry%20D.%2C%202015.%20Validation%20du%20code%20de%20calcul%20GARD%C3%89NIA%20par%20mod%C3%A9lisations%20physiques%20comparatives.%20%E2%80%94%20BRGM%2FRP-64500-FR%3A%2048%20p.%20http%3A%2F%2Finfoterre.brgm.fr%2Frapports%2FRP-64500-FR.pdf." target=_blank rel="noopener noreferrer" data-moz-translations-id=1530><span class=anchor-text data-moz-translations-id=1531>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1532><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1533></path></svg></a></div></span><li data-moz-translations-id=1534><span class="label u-font-sans" data-moz-translations-id=1535><a class="anchor anchor-default" href=#bb0545 id=ref-id-b0545 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1536><span class=anchor-text data-moz-translations-id=1537>Thornthwaite, 1948</span></a></span><span class=reference id=h0545 data-moz-translations-id=1538><div class=other-ref data-moz-translations-id=1539><span data-moz-translations-id=1540>Thornthwaite C. W., 1948. Un enfoque hacia una clasificación racional del clima. 38, no 1, 1948: 55o94.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1541><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Thornthwaite%20C.%20W.%2C%201948.%20An%20approach%20toward%20a%20rational%20classification%20of%20climate.%E2%80%94%20Geographical%20Review%2C%20vol.%2038%2C%20no%201%2C%201948%3A%2055%E2%80%9394." target=_blank rel="noopener noreferrer" data-moz-translations-id=1542><span class=anchor-text data-moz-translations-id=1543>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1544><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1545></path></svg></a></div></span><li data-moz-translations-id=1546><span class="label u-font-sans" data-moz-translations-id=1547><a class="anchor anchor-default" href=#bb0550 id=ref-id-b0550 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1548><span class=anchor-text data-moz-translations-id=1549>Trombe, 1948</span></a></span><span class=reference id=h0550 data-moz-translations-id=1550><div class=other-ref data-moz-translations-id=1551><span data-moz-translations-id=1552>Trombe F., 1948. Traité de Spéléologie. París, Payot: 376 p.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1553><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Trombe%20F.%2C%201948.%20Trait%C3%A9%20de%20Sp%C3%A9l%C3%A9ologie.%20%E2%80%94%20Paris%2C%20Payot%3A%20376%20p." target=_blank rel="noopener noreferrer" data-moz-translations-id=1554><span class=anchor-text data-moz-translations-id=1555>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1556><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1557></path></svg></a></div></span><li data-moz-translations-id=1558><span class="label u-font-sans" data-moz-translations-id=1559><a class="anchor anchor-default" href=#bb0555 id=ref-id-b0555 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1560><span class=anchor-text data-moz-translations-id=1561>Turc, 1961</span></a></span><span class=reference id=h0555 data-moz-translations-id=1562><div class=contribution data-moz-translations-id=1563><div class="authors u-font-sans" data-moz-translations-id=1564>L. Turc</div><div id=ref-id-h0555 class="title text-m" data-moz-translations-id=1565>Evaluation des besoins en eau dáirrigation, évapotranspiration potentielle</div></div><div class="host u-font-sans" data-moz-translations-id=1566>Ann. agron, 12 (1) (1961), pp. 13 a 49</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1567><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Evaluation%20des%20besoins%20en%20eau%20dirrigation%2C%20%C3%A9vapotranspiration%20potentielle&publication_year=1961&author=L.%20Turc" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0555 data-moz-translations-id=1568><span class=anchor-text data-moz-translations-id=1569>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1570><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1571></path></svg></a></div></span><li data-moz-translations-id=1572><span class="label u-font-sans" data-moz-translations-id=1573><a class="anchor anchor-default" href=#bb0560 id=ref-id-b0560 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1574><span class=anchor-text data-moz-translations-id=1575>Vuilleumier et al., 2019</span></a></span><span class=reference id=h0560 data-moz-translations-id=1576><div class=contribution data-moz-translations-id=1577><div class="authors u-font-sans" data-moz-translations-id=1578>C. Vuilleumier, P.-Y. Jeannin, P. Perrochet</div><div id=ref-id-h0560 class="title text-m" data-moz-translations-id=1579>Modelo numérico a escala fina basado en la física de un sistema kárstico (Milandre Cave Suiza)</div></div><div class="host u-font-sans" data-moz-translations-id=1580>Hidrogeol. J., 27 (7) (2019), pp. 2347-2363</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1581><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Physics-based%20fine-scale%20numerical%20model%20of%20a%20karst%20system%20&publication_year=2019&author=C.%20Vuilleumier&author=P.-Y.%20Jeannin&author=P.%20Perrochet" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0560 data-moz-translations-id=1582><span class=anchor-text data-moz-translations-id=1583>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1584><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1585></path></svg></a></div></span><li data-moz-translations-id=1586><span class="label u-font-sans" data-moz-translations-id=1587><a class="anchor anchor-default" href=#bb0565 id=ref-id-b0565 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1588><span class=anchor-text data-moz-translations-id=1589>Blanco y Blanco, 1970</span></a></span><span class=reference id=h0565 data-moz-translations-id=1590><div class=contribution data-moz-translations-id=1591><div class="authors u-font-sans" data-moz-translations-id=1592>W.B. White, E.L. Blanco</div><div id=ref-id-h0565 class="title text-m" data-moz-translations-id=1593>Canal hidráulicas de arroyos de superficie libre en cuevas</div></div><div class="host u-font-sans" data-moz-translations-id=1594>Cuevas Karst, 12 (6) (1970), pp. 41 a 48</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1595><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=Channel%20hydraulics%20of%20free-surface%20streams%20in%20caves&publication_year=1970&author=W.B.%20White&author=E.L.%20White" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0565 data-moz-translations-id=1596><span class=anchor-text data-moz-translations-id=1597>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1598><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1599></path></svg></a></div></span><li data-moz-translations-id=1600><span class="label u-font-sans" data-moz-translations-id=1601><a class="anchor anchor-default" href=#bb0570 id=ref-id-b0570 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1602><span class=anchor-text data-moz-translations-id=1603>Williams, 1983</span></a></span><span class=reference id=h0570 data-moz-translations-id=1604><div class=contribution data-moz-translations-id=1605><div class="authors u-font-sans" data-moz-translations-id=1606>P.W. Williams</div><div id=ref-id-h0570 class="title text-m" data-moz-translations-id=1607>El papel de la zona subcutánea en la hidrología kárstica</div></div><div class="host u-font-sans" data-moz-translations-id=1608>J. Hydrol., 61 (1983), pp. 45 - 67</div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1609><a class="anchor link anchor-default" href="https://scholar.google.com/scholar_lookup?title=The%20role%20of%20the%20subcutaneous%20zone%20in%20karst%20hydrology&publication_year=1983&author=P.W.%20Williams" target=_blank rel="noopener noreferrer" aria-describedby=ref-id-h0570 data-moz-translations-id=1610><span class=anchor-text data-moz-translations-id=1611>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1612><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1613></path></svg></a></div></span><li data-moz-translations-id=1614><span class="label u-font-sans" data-moz-translations-id=1615><a class="anchor anchor-default" href=#bb0575 id=ref-id-b0575 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1616><span class=anchor-text data-moz-translations-id=1617>Worthington, 1991</span></a></span><span class=reference id=h0575 data-moz-translations-id=1618><div class=other-ref data-moz-translations-id=1619><span data-moz-translations-id=1620>Worthington S. R. H., 1991. Hidrología Karst de las Montañas Rocosas Canadienses. Doctorado en Tesis, Universidad Mc-Master, Hamilton, Ontario: 370 p.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1621><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Worthington%20S.%20R.%20H.%2C%201991.%20Karst%20hydrogeology%20of%20the%20Canadian%20Rocky%20Mountains.%20%E2%80%94%20PhD-Thesis%2C%20Mc-Master%20University%2C%20Hamilton%2C%20Ontario%3A%20370%20p." target=_blank rel="noopener noreferrer" data-moz-translations-id=1622><span class=anchor-text data-moz-translations-id=1623>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1624><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1625></path></svg></a></div></span><li data-moz-translations-id=1626><span class="label u-font-sans" data-moz-translations-id=1627><a class="anchor anchor-default" href=#bb0580 id=ref-id-b0580 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name" data-moz-translations-id=1628><span class=anchor-text data-moz-translations-id=1629>Wu et al., 2008</span></a></span><span class=reference id=h0580 data-moz-translations-id=1630><div class=other-ref data-moz-translations-id=1631><span data-moz-translations-id=1632>Wu Y., Jiang Y., Yuan D., Li L., 2008. Modelando las respuestas hidrológicas de la primavera kárstica a los eventos de tormenta: ejemplo de la primavera de Shuifang (Jinfo Mt., Chongqing, China). Geología Ambiental, 55: 1545-1553.</span></div><div class="ReferenceLinks u-font-sans" data-moz-translations-id=1633><a class="anchor link anchor-default" href="https://scholar.google.com/scholar?q=Wu%20Y.%2C%20Jiang%20Y.%2C%20Yuan%20D.%2C%20Li%20L.%2C%202008.%20Modeling%20hydrological%20responses%20of%20karst%20spring%20to%20storm%20events%3A%20example%20of%20the%20Shuifang%20spring%20(Jinfo%20Mt.%2C%20Chongqing%2C%20China).%20%E2%80%94%20Environmental%20Geology%2C%2055%3A%201545%E2%80%931553." target=_blank rel="noopener noreferrer" data-moz-translations-id=1634><span class=anchor-text data-moz-translations-id=1635>Búsquetca de Google</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=1636><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=1637></path></svg></a></div></span></ol></section></section><div id=section-cited-by><section aria-label="Cited by" class="ListArticles preview"><div class=PageDivider></div><header id=citing-articles-header><h2 class="u-h4 u-margin-l-ver u-font-serif">Citado por (30)</h2></header><div aria-describedby=citing-articles-header><div class="citing-articles u-margin-l-bottom"><ul><li class="ListArticleItem u-margin-l-bottom" data-moz-translations-id=0><div class="sub-heading u-margin-xs-bottom" data-moz-translations-id=1><h3 class=u-font-serif id=citing-articles-article-0-title data-moz-translations-id=2><a class="anchor anchor-default" href=https://www.sciencedirect.com/science/article/pii/S0022169423011289 data-moz-translations-id=3><span class=anchor-text data-moz-translations-id=4>Influencia de la conductividad hidráulica de la matriz kárstica y rendimiento específico sobre la exactitud de la estimación de la variación del almacenamiento de agua kárstica</span></a></h3><div class=text-s data-moz-translations-id=5>2023, Journal of Hydrology</div></div><div class="buttons text-s" data-moz-translations-id=6><button class="button-link button-link-secondary button-link-icon-right" type=button data-aa-button="sd:product:journal:article:location=citing-articles:type=view-details" aria-describedby=citing-articles-article-0-title aria-controls=citing-articles-article-0 aria-expanded=false data-moz-translations-id=7><span class=button-link-text data-moz-translations-id=8>Mostrar resumen</span><svg focusable=false viewBox="0 0 92 128" width=17.25 height=24 class="icon icon-navigate-down" data-moz-translations-id=9><path d="m1 51l7-7 38 38 38-38 7 7-45 45z" data-moz-translations-id=10></path></svg></button></div><div class="u-display-none sf-hidden" aria-hidden=true data-moz-translations-id=11></div><li class="ListArticleItem u-margin-l-bottom" data-moz-translations-id=15><div class="sub-heading u-margin-xs-bottom" data-moz-translations-id=16><h3 class=u-font-serif id=citing-articles-article-1-title data-moz-translations-id=17><a class="anchor anchor-default" href=https://www.sciencedirect.com/science/article/pii/S0022169423007941 data-moz-translations-id=18><span class=anchor-text data-moz-translations-id=19>Factores de estructura hidrogeológica, geoquímica y geoquímicas en las características de la escorredor en las cuencas kársticas</span></a></h3><div class=text-s data-moz-translations-id=20>2023, Journal of Hydrology</div></div><div class="buttons text-s" data-moz-translations-id=21><button class="button-link button-link-secondary button-link-icon-right" type=button data-aa-button="sd:product:journal:article:location=citing-articles:type=view-details" aria-describedby=citing-articles-article-1-title aria-controls=citing-articles-article-1 aria-expanded=false data-moz-translations-id=22><span class=button-link-text data-moz-translations-id=23>Mostrar resumen</span><svg focusable=false viewBox="0 0 92 128" width=17.25 height=24 class="icon icon-navigate-down" data-moz-translations-id=24><path d="m1 51l7-7 38 38 38-38 7 7-45 45z" data-moz-translations-id=25></path></svg></button></div><div class="u-display-none sf-hidden" aria-hidden=true data-moz-translations-id=26></div><li class="ListArticleItem u-margin-l-bottom" data-moz-translations-id=30><div class="sub-heading u-margin-xs-bottom" data-moz-translations-id=31><h3 class=u-font-serif id=citing-articles-article-2-title data-moz-translations-id=32><a class="anchor anchor-default" href=https://www.sciencedirect.com/science/article/pii/S2214581823000861 data-moz-translations-id=33><span class=anchor-text data-moz-translations-id=34>Evaluación de las estimaciones de recarga basadas en la cabeza de las aguas subterráneas de diferentes modelos agrupados en Europa</span></a></h3><div class=text-s data-moz-translations-id=35>2023, Journal of Hydrology: Estudios Regionales</div></div><div class="buttons text-s" data-moz-translations-id=36><button class="button-link button-link-secondary button-link-icon-right" type=button data-aa-button="sd:product:journal:article:location=citing-articles:type=view-details" aria-describedby=citing-articles-article-2-title aria-controls=citing-articles-article-2 aria-expanded=false data-moz-translations-id=37><span class=button-link-text data-moz-translations-id=38>Mostrar resumen</span><svg focusable=false viewBox="0 0 92 128" width=17.25 height=24 class="icon icon-navigate-down" data-moz-translations-id=39><path d="m1 51l7-7 38 38 38-38 7 7-45 45z" data-moz-translations-id=40></path></svg></button></div><div class="u-display-none sf-hidden" aria-hidden=true data-moz-translations-id=41></div><li class="ListArticleItem u-margin-l-bottom" data-moz-translations-id=47><div class="sub-heading u-margin-xs-bottom" data-moz-translations-id=48><h3 class=u-font-serif id=citing-articles-article-3-title data-moz-translations-id=49><a class="anchor anchor-default" href=https://www.sciencedirect.com/science/article/pii/S0022169423003839 data-moz-translations-id=50><span class=anchor-text data-moz-translations-id=51>Hacia la estimación del coeficiente de transferencia en los sistemas kársticos: Utilizando coeficiente de recesión de flujo de base bajo régimen de flujo de flujo de la matriz</span></a></h3><div class=text-s data-moz-translations-id=52>2023, Journal of Hydrology</div></div><div class="buttons text-s" data-moz-translations-id=53><button class="button-link button-link-secondary button-link-icon-right" type=button data-aa-button="sd:product:journal:article:location=citing-articles:type=view-details" aria-describedby=citing-articles-article-3-title aria-controls=citing-articles-article-3 aria-expanded=false data-moz-translations-id=54><span class=button-link-text data-moz-translations-id=55>Mostrar resumen</span><svg focusable=false viewBox="0 0 92 128" width=17.25 height=24 class="icon icon-navigate-down" data-moz-translations-id=56><path d="m1 51l7-7 38 38 38-38 7 7-45 45z" data-moz-translations-id=57></path></svg></button></div><div class="u-display-none sf-hidden" aria-hidden=true data-moz-translations-id=58></div><li class="ListArticleItem u-margin-l-bottom" data-moz-translations-id=63><div class="sub-heading u-margin-xs-bottom" data-moz-translations-id=64><h3 class=u-font-serif id=citing-articles-article-4-title data-moz-translations-id=65><a class="anchor anchor-default" href=https://www.sciencedirect.com/science/article/pii/S0022169422010307 data-moz-translations-id=66><span class=anchor-text data-moz-translations-id=67>Cambios en la humedad del suelo causados únicamente por la restauración de la vegetación en la región kárstica del suroeste de China</span></a></h3><div class=text-s data-moz-translations-id=68>2022, Journal of Hydrology</div><div class="CitedSection u-margin-s-top" data-moz-translations-id=69><div class=u-margin-s-left data-moz-translations-id=70><div class="cite-header text-s u-text-italic u-font-sans" data-moz-translations-id=71>Extracto de citas:</div><p class="u-font-serif text-xs" data-moz-translations-id=72>Si se utilizan ciertos modelos de proceso físico, la predicción puede mejorarse generalmente mediante la calibración de los parámetros (Baudena et al., 2008; Abdelhamed et al., 2022). Sin embargo, debido a la naturaleza binaria superficial de la superficie única del sistema hidrológico en ambientes kárstico (Liang y Yang, 1995), una gran cantidad de agua superficial y del suelo se filtra en el subsuelo a través de las fisuras de la disolución, y las condiciones hidrológicas subterráneos son particularmente complejas, de modo que simular la hidrología kársmica basada en modelos de procesos físicos sigue siendo un gran desafío (Sarrazin et al., 2018; Jeannin et al., 2021). Como se mencionó anteriormente, hay desafíos en la predicción de SM durante temporadas no de crecimiento en áreas kársticas.</p></div></div></div><div class="buttons text-s" data-moz-translations-id=73><button class="button-link button-link-secondary button-link-icon-right" type=button data-aa-button="sd:product:journal:article:location=citing-articles:type=view-details" aria-describedby=citing-articles-article-4-title aria-controls=citing-articles-article-4 aria-expanded=false data-moz-translations-id=74><span class=button-link-text data-moz-translations-id=75>Mostrar resumen</span><svg focusable=false viewBox="0 0 92 128" width=17.25 height=24 class="icon icon-navigate-down" data-moz-translations-id=76><path d="m1 51l7-7 38 38 38-38 7 7-45 45z" data-moz-translations-id=77></path></svg></button></div><div class="u-display-none sf-hidden" aria-hidden=true data-moz-translations-id=78></div><li class="ListArticleItem u-margin-l-bottom" data-moz-translations-id=86><div class="sub-heading u-margin-xs-bottom" data-moz-translations-id=87><h3 class=u-font-serif id=citing-articles-article-5-title data-moz-translations-id=88><a class="anchor anchor-default" href=https://www.sciencedirect.com/science/article/pii/S2214581822001380 data-moz-translations-id=89><span class=anchor-text data-moz-translations-id=90>Modelización de precipitación subdiaria de la cuenca dominada por los kársticos utilizando un modelo hidrológico Xinanjiang distribuido a la red mejorado</span></a></h3><div class=text-s data-moz-translations-id=91>2022, Journal of Hydrology: Estudios Regionales</div><div class="CitedSection u-margin-s-top" data-moz-translations-id=92><div class=u-margin-s-left data-moz-translations-id=93><div class="cite-header text-s u-text-italic u-font-sans" data-moz-translations-id=94>Extracto de citas:</div><p class="u-font-serif text-xs" data-moz-translations-id=95>Sin embargo, las estructuras simples de los modelos agrupados implican que estos modelos generalmente no son suficientes para describir los procesos hidrológicos kárstico, ya que el rendimiento de la simulación es limitado (Hartmann et al., 2014) porque los modelos agrupados no pueden explicar tales procesos hidrológicos variables espacialmente (Knáll et al., 2020). El enfoque de modelado distribuido puede explicar mejor la variabilidad espacial de los sistemas kárstico y por lo tanto demuestra un potencial considerable para ser utilizado para impulsar la capacidad de simulación necesaria para investigar los sistemas hidrológicos kárstico (Bailly Comte et al., 2012; Bittner et al., 2018; Doummar et al., 2012; Malard et al., 2016; Ollivier et al., 2020; Pardo-Igúzquiza et al., 2018), pero la escala y las características de una captura kársmica también deben considerarse en modelización distribuida (Janeen y otros, 2021). La modelización distribuida puede ayudar a proporcionar información detallada sobre los procesos hidrológicos temporales y espaciales, pero presentes con el inconveniente del alto consumo de datos computacionales.</p></div></div></div><div class="buttons text-s" data-moz-translations-id=96><button class="button-link button-link-secondary button-link-icon-right" type=button data-aa-button="sd:product:journal:article:location=citing-articles:type=view-details" aria-describedby=citing-articles-article-5-title aria-controls=citing-articles-article-5 aria-expanded=false data-moz-translations-id=97><span class=button-link-text data-moz-translations-id=98>Mostrar resumen</span><svg focusable=false viewBox="0 0 92 128" width=17.25 height=24 class="icon icon-navigate-down" data-moz-translations-id=99><path d="m1 51l7-7 38 38 38-38 7 7-45 45z" data-moz-translations-id=100></path></svg></button></div><div class="u-display-none sf-hidden" aria-hidden=true data-moz-translations-id=101></div></ul><a class="button-alternative button-alternative-secondary button-alternative-icon-left" href="http://www.scopus.com/scopus/inward/citedby.url?partnerID=10&rel=3.0.0&eid=2-s2.0-85109396161&md5=43c86943be61dce57a8c67bd1a0d984" target=_blank id=citing-articles-view-all-btn><span class=button-alternative-icon><svg focusable=false viewBox="0 0 54 128" width=32 height=32 class="icon icon-navigate-right"><path d="m1 99l38-38-38-38 7-7 45 45-45 45z"></path></svg></span><span class=button-alternative-text>Ver todos los artículos de citas en Scopus</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link"><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z"></path></svg></a></div></div></section></div><div class="Footnotes text-xs"><dl class=footnote><dt class=footnote-label><a class="anchor u-padding-s-hor u-padding-xs u-display-inline-block anchor-default" href=#btxtfn1 data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1><sup data-moz-translations-id=2>1</sup></span></a><dd class="footnote-detail u-padding-xs-top"><p id=ntparaVKR2Iu3XRN>El reto está sucediendo: cualquier persona interesada puede utilizar los datos disponibles en el material suplementario para este artículo, y puede enviarla/su resultado a la autora de los corrs para obtener los valores de los criterios.</p></dl></div><div class=Copyright><span class=copyright-line data-moz-translations-id=0>2021 El Autor (s). Publicado por Elsevier B.V.</span></div></article><div class="u-show-from-md col-lg-6 col-md-8 pad-right u-padding-s-top"><aside class=RelatedContent aria-label="Related content"><section class="RelatedContentPanel u-margin-s-bottom"><header id=recommended-articles-header class="related-content-panel-header u-margin-s-bottom"><button class="button-link related-content-panel-toggle is-up button-link-primary button-link-icon-right" type=button aria-expanded=true data-aa-button="sd:product:journal:article:location=recommended-articles:type=close"><span class=button-link-text><h2 class="section-title u-h4"><span class=related-content-panel-title-text data-moz-translations-id=0>Artículos recomendados</span></h2></span><svg focusable=false viewBox="0 0 92 128" width=24 height=24 class="icon icon-navigate-down"><path d="m1 51l7-7 38 38 38-38 7 7-45 45z"></path></svg></button></header><div aria-hidden=false aria-describedby=recommended-articles-header><div id=recommended-articles class=text-xs><ul><li class="RelatedContentPanelItem u-display-block" data-moz-translations-id=0><div class="sub-heading u-padding-xs-bottom" data-moz-translations-id=1><h3 class="related-content-panel-list-entry-outline-padding text-s u-font-serif" id=recommended-articles-article0-title data-moz-translations-id=2><a class="anchor u-clamp-2-lines anchor-default" href=https://www.sciencedirect.com/science/article/pii/S0022169418307789 title="Modeling the hydrological impact of land use change in a dolomite-dominated karst system" data-moz-translations-id=3><span class=anchor-text data-moz-translations-id=4><span data-moz-translations-id=5>Modelando el impacto hidrológico del cambio de uso de la tierra en un sistema kárstico dominado por dolomitas</span></span></a></h3><div class="article-source u-clr-grey6" data-moz-translations-id=6><div class=source data-moz-translations-id=7>Journal of Hydrology, Volumen 567, 2018, pp. 267 a 279</div></div><div class=authors data-moz-translations-id=8><span data-moz-translations-id=9>Daniel</span> <span data-moz-translations-id=10>Bittner</span>, <span data-moz-translations-id=11>Gabriele</span> <span data-moz-translations-id=12>Chiogna</span></div></div><div class=buttons data-moz-translations-id=13></div><li class="RelatedContentPanelItem u-display-block" data-moz-translations-id=14><div class="sub-heading u-padding-xs-bottom" data-moz-translations-id=15><h3 class="related-content-panel-list-entry-outline-padding text-s u-font-serif" id=recommended-articles-article1-title data-moz-translations-id=16><a class="anchor u-clamp-2-lines anchor-default" href=https://www.sciencedirect.com/science/article/pii/S0022169414008361 title="Performance and complementarity of two systemic models (reservoir and neural networks) used to simulate spring discharge and piezometry for a karst aquifer" data-moz-translations-id=17><span class=anchor-text data-moz-translations-id=18><span data-moz-translations-id=19>Rendimiento y complementariedad de dos modelos sistémicos (redes de depósito y neurales) utilizados para simular la descarga de muelles y la piezoometría para un acuífero kárstico</span></span></a></h3><div class="article-source u-clr-grey6" data-moz-translations-id=20><div class=source data-moz-translations-id=21>Journal of Hydrology, Volume 519, Part D, 2014, pp. 3178 - 3182</div></div><div class=authors data-moz-translations-id=22><span data-moz-translations-id=23>Line</span> <span data-moz-translations-id=24>Kong-A-Siou</span>, ..., <span data-moz-translations-id=25>Nathalie</span> <span data-moz-translations-id=26>Dárfliger</span></div></div><div class=buttons data-moz-translations-id=27></div><li class="RelatedContentPanelItem u-display-block" data-moz-translations-id=28><div class="sub-heading u-padding-xs-bottom" data-moz-translations-id=29><h3 class="related-content-panel-list-entry-outline-padding text-s u-font-serif" id=recommended-articles-article2-title data-moz-translations-id=30><a class="anchor u-clamp-2-lines anchor-default" href=https://www.sciencedirect.com/science/article/pii/S0022169420311008 title="A new distributed karst-tunnel hydrological model and tunnel hydrological effect simulations" data-moz-translations-id=31><span class=anchor-text data-moz-translations-id=32><span data-moz-translations-id=33>Nuevo modelo kárstico-túnel distribuidos simulaciones de efecto hidrológico y efecto hidrológico de túneles</span></span></a></h3><div class="article-source u-clr-grey6" data-moz-translations-id=34><div class=source data-moz-translations-id=35>Journal of Hydrology, Volumen 593, 2021, Artículo 125639</div></div><div class=authors data-moz-translations-id=36><span data-moz-translations-id=37>Ji</span> <span data-moz-translations-id=38>Li</span>, <span data-moz-translations-id=39>Jiao</span> <span data-moz-translations-id=40>Liu</span></div></div><div class=buttons data-moz-translations-id=41></div><li class="RelatedContentPanelItem u-display-none sf-hidden" data-moz-translations-id=42><li class="RelatedContentPanelItem u-display-none sf-hidden" data-moz-translations-id=56><li class="RelatedContentPanelItem u-display-none sf-hidden" data-moz-translations-id=70></ul></div><button class="button-link more-recommendations-button button-link-secondary text-s u-margin-s-bottom button-link-icon-right" type=button><span class=button-link-text data-moz-translations-id=0>Mostrar 3 artículos más</span><svg focusable=false viewBox="0 0 92 128" height=20 width=17.25 class="icon icon-navigate-down" data-moz-translations-id=1><path d="m1 51l7-7 38 38 38-38 7 7-45 45z" data-moz-translations-id=2></path></svg></button></div></section><section class="RelatedContentPanel u-margin-s-bottom hidden"><header id=metrics-header class="related-content-panel-header u-margin-s-bottom"><button class="button-link related-content-panel-toggle is-up button-link-primary button-link-icon-right" type=button aria-expanded=true><span class=button-link-text><h2 class="section-title u-h4"><span class=related-content-panel-title-text data-moz-translations-id=0>Artículo Metrics</span></h2></span><svg focusable=false viewBox="0 0 92 128" width=24 height=24 class="icon icon-navigate-down"><path d="m1 51l7-7 38 38 38-38 7 7-45 45z"></path></svg></button></header><div aria-hidden=false aria-describedby=metrics-header><div class=plum-sciencedirect-theme><div class=PlumX-Summary><div class="pps-container pps-container-vertical plx-is-empty plx-no-print"><div class="pps-branding pps-branding-top sf-hidden"></div><div class=pps-cols><div class="pps-col pps-col-empty">No metrics available.</div></div><div><div class="pps-branding pps-branding-bottom" data-moz-translations-id=0><img alt="plumX logo" src=data:image/png;base64,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 class=plx-logo data-moz-translations-id=1></div><a target=_blank href="https://plu.mx/plum/a/?doi=10.1016/j.jhydrol.2021.126508&theme=plum-sciencedirect-theme&hideUsage=true" class=pps-seemore title="PlumX Metrics Detail Page" data-moz-translations-id=2>View details<svg fill=currentColor tabindex=-1 focusable=false width=16 height=16 viewBox="0 0 16 16" class=svg-arrow data-moz-translations-id=3><path d="M16 4.452l-1.26-1.26L8 9.932l-6.74-6.74L0 4.452l8 8 8-8z" data-moz-translations-id=4></path></svg></a></div></div></div></div></div></section></aside></div></div></div></div><footer role=contentinfo class="els-footer u-bg-white text-xs u-padding-s-hor u-padding-m-hor-from-sm u-padding-l-hor-from-md u-padding-l-ver u-margin-l-top u-margin-xl-top-from-sm u-margin-l-top-from-md"><div class="els-footer-elsevier u-margin-m-bottom u-margin-0-bottom-from-md u-margin-s-right u-margin-m-right-from-md u-margin-l-right-from-lg"><a class="anchor anchor-default anchor-icon-only" href=https://www.elsevier.com/ target=_blank aria-label="Elsevier home page (opens in a new tab)" rel=nofollow><img class=footer-logo src=data:image/svg+xml;base64,<svg xmlns="http://www.w3.org/2000/svg" height="64" viewBox="0 0 58 64" width="58">
    <path d="M0 0h58v64H0z" fill="none"/>
    <path d="M52.8 57v3h.8c1.1 0 1.6-.6 1.6-1.5 0-.8-.3-1.5-1.8-1.5h-.6zm3 6.8a46.3 46.3 0 01-2.2-3.3h-.8v2.7l1.2.2v.4h-3.8v-.4l1.3-.2V57l-1.3-.1v-.5H53.6c1.6 0 3 .6 3 2 0 1-.8 1.6-1.7 1.9l2.3 3h.8v.5h-2.1zM38.2 57l-1.3-.1v-.5h3.9v.5h-1.3v6.3l1.3.2v.4h-3.9v-.4l1.3-.2V57zm-3 0l-2.4 6.9h-1l-2.4-7h-1v-.5H32v.5h-1.2l1.8 5.6 1.8-5.5-1.2-.1v-.5H36v.5h-.8zM1.3 63.2V57L0 56.9v-.5h5.2l.1-.4h.5v2.1h-.5L5.1 57H2.6v2.8h1.8l.2-.8H5v2.2h-.5l-.2-.8H2.6V63.2h1.7l1-.1c.2-.2.4-.6.5-1.4h.6L6 63.8H0v-.4l1.3-.2zM19.9 62c0 1.3-1 2-2.4 2-.7 0-1.4-.2-1.9-.5l-.1.6H15v-2.5h.5l.2 1.1c.4.5 1.1.8 1.7.8.8 0 1.3-.5 1.3-1.2 0-1.7-3.6-1.7-3.6-4 0-1.2 1-2 2.3-2 .5 0 1 .2 1.4.3l.2-.5h.5v2.2H19a8.5 8.5 0 00-.2-1c-.4-.2-.8-.4-1.2-.4-.7 0-1.3.4-1.3 1.1 0 1.6 3.6 1.7 3.6 4zM9.2 63.2V57l-1.3-.1v-.5h3.9v.5h-1.3v6.3h1.3l1-.1c.1-.2.3-.6.5-1.7h.6l-.2 2.4H7.8v-.4l1.4-.2zm13.5 0V57l-1.3-.1v-.5h5.2l.1-.4h.5v2.1h-.5l-.2-1.1H24v2.8H26l.1-.8h.5v2.2H26l-.1-.8H24V63.2H26l1-.1c.2-.2.3-.6.5-1.4h.5l-.2 2.1h-6.3v-.4l1.3-.2zm21 0V57l-1.4-.1v-.5h5.2l.1-.4h.6v2.1h-.6l-.2-1.1H45v2.8h1.9l.1-.8h.5v2.2H47l-.1-.8h-1.9V63.2h1.8l1-.1.5-1.4h.5l-.2 2.1h-6.2v-.4l1.3-.2z" fill="#FF6C00"/>
    <path d="M16.2 24.2c.6-.4 1-.8 1-1.3v-.3a.8.8 0 00-.4 0c-.3.3-.8.8-1.2 1l-.6.1s-.1 0 0-.1l1-.8c.2-.2.1-.4-.2-.5-.4 0-.9.1-1.2.4-.7.6-1 1.7-1 2.5l-.3.1c-.1-.5-.2-1 0-1.6l.3-.7c0-.2 0-.4-.2-.5l-.2.1c-.3.9-.8 1.3-1.6 1.8l-.9.5c-.3-.1-.5-.3-.8-.7l-1-1.4a.5.5 0 00-.4-.2H7a2 2 0 01-1.3-.9c-.3-.4-.4-.4-.7 0l-1 .7a1.7 1.7 0 01-.2.3c-.3.1-.4 0-.5-.2-.3-.8-.4-1.6-.4-2.5v-1.3a3 3 0 00-.7-.5c-.8-.3-1.4-.9-2-1.5a.6.6 0 01-.2-.4s0-.3.3-.4c.4-.3 1-.4 1.5-.4l1.6.2c.4 0 .5-.3.4-.8-.1-1.2.3-2 1-2.6.5-.6.4-1.2.3-1.8 0-.3-.1-.4-.4-.4-.8.2-.8.2-1 1 0 .4-.2.8-.4 1.2L2 13.7c-.1.2-.3.2-.4 0a2.6 2.6 0 01-1-3.2c0-.4.3-.8.5-1v.3l-.2.7c-.3.9-.3 1.7.3 2.5l.2.1h.3l1-1c.5-.5.6-1 .5-1.6a10 10 0 00-.2-.7v-.2l-.3.1c-.4.4-.5.7-.7 1.3a.5.5 0 01-.2.3v-.4c0-.6.2-1.1.6-1.5.2-.3.2-.4 0-.5-1-.2-1.7-.5-2.3-1.3a.7.7 0 01-.2-.4c0-.2 0-.3.3-.4.7-.5 1.5-1 2.3-1.3h.6a28 28 0 01-1-3.6l.3-1h.7l1 .6c.3.2.4.2.6-.1C5 .9 5.5.4 6 0l.4-.1c.2 0 .3.1.5.4l.8 1.5c.3.5.6.5 1 .2l1.5-1c.4-.4.5-.4 1-.1.4.3.9.8 1.1 1.3 0 .1 1-.4 1.1-.7 0-.4.2-.7.3-1 .3-.5.6-.6.8-.6.3 0 .7.3 1 .5l.9.4c.3.3.4.3.6-.1.3-.4.5-.5 1-.5 1.2 0 2.2.3 3 1.2 0 .2.2.2.4 0 .3-.3.6-.4 1.1-.4h1c.3 0 .4 0 .7-.3.2-.4.6-.8 1-.8.7 0 1.7 1 2.1 1.9l-.2.1a4.5 4.5 0 00-.7-.6 6.4 6.4 0 00-1.2-.6c-.4-.2-.6 0-.6.5l.4 1.6c.1.5.6.9 1 1.1v.2H25c-.6.1-1.1.4-1.7.6-.2.1-.2.2 0 .4l1 .9c.9.7 1.8.5 3 .1l.4-.1c.5-.1 1 0 1.3.4l1.1 1c.3.3.4.6.2 1a53 53 0 01-1.1 2l-.3.3c-.1 0-.2-.3-.1-.6l.2-.2a4.2 4.2 0 00.7-1.8c0-.5-.2-.8-.7-.6l-.5.1c-.2.1-.3 0-.3-.2l-.2-.4c-.1-.3-.3-.4-.6-.2a3 3 0 00-1 1 3.7 3.7 0 00-.4 2.3l.1.2a.4.4 0 00.2-.2c.2-.3.2-.7.3-1l.1-.8h.3c.3 0 .4.1.4.3-.5.6-.7 1.4-1 2.2l-.4.2-.4-.2-.6-1.5c-.3-1.6-.4-1.7-.5-1.8A10 10 0 0022 4.8l-.2-.2.3-.1.9-.4c.4-.1.7-.4.9-.8l.3-.6c.2-.3.2-.5 0-.7-.5-.5-1-.6-1.6-.5-.6.1-1 .6-1.2 1.2v.6c0 .3 0 .4.3.2l1.2-.8.2-.1c-.2.5-.5.9-1 1.1a13.8 13.8 0 01-.9.6c-.2.1-.3 0-.4-.2V3c0-1-.6-1.8-1.4-2.1l-.8-.4a.8.8 0 00-.3.7c0 .6 0 1 .2 1.7l.1.6c.1.3.5.4.8.4.2 0 .4-.3.1-.8l-.4-.7-.1-.3V2l.2.1a5.3 5.3 0 011.1 2c.2.2 0 .3-.2.4-.7 0-1.4 0-2 .2-1 .2-1.4.6-1.8 1.3l-.2.7 1.4-.1a4 4 0 001.4-.5l1-1h.4c1.3.6 2.2 1.4 2.8 2.7l.2.3v.2h-.1l-.7-.2-.2-.3-.2-.6c-.2-.2-.2-.2-.5-.2l-.4.2c-.2.1-.3.1-.4 0l-.2-1c0-.3-.2-.3-.5-.2a3 3 0 00-1.4 1.4c-.3.6-.4 1-.1 2.5 0 .3.3.7.6.8l2.6.8h.1v.2l-1 .7c-.3 0-.3 0-.4-.2a2 2 0 00-1-1c-.3 0-.3 0-.4.2l-.4 1c0 .1-.1.2-.3 0-.7-.5-1.5-.6-2.4-.4-.5 0-.7.1-.6.6.2.7.6 1.2 1.2 1.6l1.1.6a.6.6 0 00.3-.2c.2-.2.1-.4 0-.6l-1.6-1 .3-.3h.3c.7.4 1.2 1 1.8 1.4.2.3.4.3.5-.1l.3-1.1v-.1h.2l.1 1-.2.9c0 .3-.3.4-.5.4-.7-.1-1.3.1-1.9.3a5.6 5.6 0 01-.9.1l-.6-.1-.1-.2a1 1 0 01.3 0c.3 0 .7 0 1-.2a1.4 1.4 0 00-.4-.3c-.6-.5-1.3 0-1.9.3-.3.2-.3.2-.2.6.2.5.6.6 1 .6h1.3l1-.5h.4l-.1.4-.9.5-.2.1-.3.3c.1.1.2.3.4.3l1.1.3c.7.1 1 0 1-.7l.4-1c.1-.2.3-.2.4 0l1.3 2.3c0 .2 0 .3-.2.3a63.5 63.5 0 01-2.9-.4c-.3 0-.6-.3-1-.4-.4-.2-1-.4-1.5-.4s-.8.3-.6.8l.7 2c.2.4.7.6 1.2.7.2 0 .1-.2 0-.3l-.8-1.7v-.2h.2c.4.8.9 1.4 1.3 2v.5l-.4.1c-1-.1-2-.4-2.9-.8l-.2-.4h.6c.2 0 .4 0 .3-.2a1 1 0 00-.5-.4 6 6 0 00-1.5-.6c-.3 0-.5.1-.4.4 0 .3.2.6.4 1a.5.5 0 010 .3l-1.1-.2c-.8-.4-.8-.6-.8-1.5v-.7c.1-.3.2-.4.4-.3l2.3.7.7.4c.2 0 .3 0 .3-.2a.7.7 0 00-.4-.6l-2.5-1c-.2-.1-.3-.2-.2-.4.4-.6.5-1.2.4-1.9l.1-.2.3.2a.6.6 0 010 .3c.1.6.5 1 1 1.4.7.8.7.8 1.8.5l.3-.2-.3-.3-.7-.2c-.7-.3-.9-.8-.7-1.6l.7-2 .1-1c0-.3.2-.3.3-.3l1.6-.1c.4 0 .8-.5 1-.8l.5-.8a1 1 0 00-.5-.3l-.6.1c.2-.4.3-.7.3-1.1l-.1-.8c-.1-.2-.3-.3-.5-.1-.4.3-.7.7-.8 1.2a24.9 24.9 0 00-.3 1.6 1.5 1.5 0 00.3-.2l.5-.2h.4c-.1.2-.3.2-.4.3a3.4 3.4 0 01-.8.5c-.3.1-.4 0-.3-.3l.1-.8-.1-1.2-.6-1.6a4.5 4.5 0 00-.4-.7c-.1-.2-.3-.1-.6.3-.5.5-.7 1-.9 2v.2l-.4.3-.2-.1.3-1.1c0-.5-.2-.8-.5-1l-.8.6a1 1 0 000 1.2c.4.6.4 1.3.2 2.1L11.1 9l-.5-1c-.2-.2-.3-.7-.2-1 .2-.3.9-.4 1-.8.3-.3.2-.5.5-.9.2-.2.6-.7 1-.5.8.5 1.8.6 2.8.4.4 0 .5 0 .7-.5 0-.3.1 0 .5-.2.6-.2.9-.7.9-1.4V2.1l-.3-1h-.3l-.9 1.4-.3.7c0 .1-.2.2-.3 0a3.3 3.3 0 00-.6-.5C15 2.5 15 2 15 1.5c.2 0 .3.2.4.4 0 .3.1.6.4.7.2.2.5-.4.5-.9a.8.8 0 00-.1-.3c-.3-.5-1-1-1.5-1s-.9.7-.7 1.3l.2.4c-1.2.6-1.5 1-2 1.6 0 .1-.1.3 0 .4.2.4 0 .6-.4.9a4.2 4.2 0 01-.7.3h-.2a.3.3 0 010-.2l1-2c.3-.7-.4-1.4-1-1.6-.3-.2-.7-.1-1 .2-.6 1-.6 2.1.1 3.2l.1.1c.1.2.1.3-.1.3l-1.6.1a2.1 2.1 0 00-1.6.9c-.1.2-.3.2-.4 0-.5-.2-.8-.1-1.2.1L3.7 7l-.5.1L3 7s0-.2.2-.2l.9-.2.3-.1.1-.4-.3-.2A5.3 5.3 0 003 6C2.1 6 1.5 6.4.9 7c-.3.3-.3.6 0 .8.7.5 1.4 1 2.4.8L4 8l.7-.7C5 7 5.6 6.5 6 7c.6.7.7 1.5 1.7 1.3.4 0 .5-.1.7-.5v-.4c0-.3 0-.4-.4-.3l-.5.2h-.3l.1-.4.6-.3a41.6 41.6 0 012.2-.5.6.6 0 01.2 0v.2c-.3.6-.7.8-1.2 1l-.2.4c-.4 1-.2 2 .5 2.8l.8.7c.6.3.7.8.9 1.3l.4 1c-.4.3-.8.6-1 1-.1.2-.2.1-.3 0l-.2-.7c-.3-.6-.7-1-1.4-1l-.8-.2c-.3 0-.5.1-.6.5-.2.8-.2 1.6.6 2.4l.6.4a4 4 0 00-.4.2 4 4 0 00-1 .4 2.7 2.7 0 00-.8.7c0 .2.4.4.7.5 1 .4 1.8.4 2.7 0 .5-.1.5 0 .6.4l.2 1.1c.1.3 0 .4-.3.3-1.4-.4-3-1-4.2-1.8a7 7 0 00-1.5-.5c-.2.1-.4.3-.5.2-.4 0-1.2-.3-1.5-.5a.4.4 0 01-.2-.2.4.4 0 01.2 0l1.8.1.3-.1a.5.5 0 00-.2-.2c-1-.8-2.1-.6-3.2-.4-.4 0-.5.2-.2.5.6.8 1.3 1.4 2.3 1.5h2c1.1.7 2.3 1.4 3.6 1.8v.2c-.5 0-1 0-1.4.2l-1.3.2c-.4 0-.5.4-.3.8.4.5 1 .8 1.6 1 .5.1 1 0 1.4-.1.6-.3.9-.7 1.4-1 .3-.2.8 0 1.1.1l1.4.4c.7.2 1.5.2 2.3.3h3.2a7.9 7.9 0 00-.4 1.2c-.1.2 0 .3.2.3.3 0 .7-.3.9-.6v-.8l.2-.3.5.1-.3.8-.2.4.5.1c.7 0 .8-.1.8-.8a3.9 3.9 0 00-.1-.6c0-.5 0-.9.5-1.1.4-.2.9-.2 1.3-.1 1.4.1 2.9 1 3.8 1.7.6.4 0 1 0 1.9v1.3c0 .4.2.7.3.8l1.6 1c.5.4.7.6 1.4.8h.4a1.6 1.6 0 000-.6l-1.2-.5-.3-.2.4-.2.8-.2a.5.5 0 00.2-.1l-.2-.2-1-.3c-.1 0-.2 0-.2-.2a1 1 0 01.3-.2h.9a.4.4 0 00.2-.2l-.2-.2-1-.5-.2-.3h1.1l.2-.2a.6.6 0 00-.1-.2l-.8-.5c-.2 0-.3-.2-.3-.4h.4l.7.2h.3a.3.3 0 00-.1-.3l-.4-.2-.6-.6c-.1 0 0-.2 0-.2h.1l.8.2a.7.7 0 00.3 0l-.1-.3-.7-.5v-.4l.3.1h.4l.3.1a.4.4 0 000-.2 5 5 0 00-.2-.6V20h.2l.9.3h.3L31 20l-.2-.4v-.2h.3l1 .5h.3l-.3-.6.1-.1.7.2.7.1a.5.5 0 00.2 0l-.2-.6a.5.5 0 01.3 0c.3 0 .5.3.9.3l1.2-.1c1.6-.3 2.8-.4 4.3-.8a15.5 15.5 0 001.9-.8h.2c.4 1.1 1.3 1.5 2.2 1.6h.9c.2 0 .3 0 .2-.3-.2-1-.7-1.7-1.4-2.4l-.6-.3c-.2-.3-.2-.6 0-.8l1.7-1a2 2 0 00.9-1.8c0-.2 0-.4-.3-.5a.8.8 0 00-.3.2l-.4.9-.2.2-.4.2V13c0-.2 0-.3.2-.5l.9-1v-.3h-.2a2 2 0 00-1.1.5l-.7 1-1 2.3-.2.7c-.1.4-.1.6.2.8l1.8 1 .2.3h-.3a2 2 0 01-1.5-.6l-.6-.4-1.8.5c-.4 0-.6.2-.8.5 0 .2-.3.3-.6.2l-1.2-.4c-.5-.1-1.1 0-1.7.1H36c-.2.1-.3 0-.2-.2l.2-1v-.4c-.2 0-.4 0-.5.2l-.5 1c-.2.2-.2.2-.5.1a5.1 5.1 0 01-1.1-.7l-.7-1.1c0-.2 0-.2.2-.2l1 .8.3.6h.4v-.5c.2-.4.1-1-.2-1.3l-1.4-1c-.8-.5-.9-.4-1.1 0a3 3 0 00-.4 1.5c.2 1.2.5 1.7 2 2.4a.4.4 0 01-.2.2c0 .1-.1.2-.3.2l-1.8-.1c-.2 0-.3 0-.3-.2-.5-1.8-.2-3.7.7-5.4l.3-.3a18 18 0 011.1-.6c.2 0 .3 0 .4.2l.2 1.2.1.6c.3 0 .5-.1.6-.3.4-.7.6-1.6.2-2.4 0-.1 0-.3.2-.3l3-1.3c.5 0 1.3 0 2.3.2.2 0 .5.4 1 .6-.6.2-1.2.7-1.7 1.2-.2.1-.3.3-.2.7l.9-.2c.5-.2.7-.2 1.1-.2h.3a.5.5 0 01-.1.3c-.2.1-.4 0-.5.2-.2 0-.3.2-.2.4l.4.3c.5 0 1-.1 1.4-.3.2-.2 1-1.3.6-1.4-.3 0-.9 0-1.1-.2a.5.5 0 01.3-.3l1 .1h2c.2-.2.3-.3.2-.4a3 3 0 00-1.8-1.6H39l-.3-.2.2-.1 1-.6c.4-.3.7-.7.9-1.3a.6.6 0 000-.3c-.3-1.1-1-2-2.2-2l-.6-.2c-.2 0-.3.1-.3.3v1c0 .2 0 .3.3.3l1.7.5c.3.2.4.4 0 .7l-1 .6c0 .1-.1 0-.1 0l-.1-.8a.5.5 0 00-.1-.2.6.6 0 00-.5.4 5.4 5.4 0 00-.3.9c0 .4-.3.6-.7.8-.1 0-.3 0-.3-.2l.2-.6a4 4 0 00.3-2V4.6a.4.4 0 01.2-.5l1.7-.6c.1 0 .3 0 .5.2L40.7 5c.3.4.4.4.6 0 .3-.3.5-.7 1-.9.1 0 .1-.2-.1-.3l-1.3-.4-.3-.1-.2-.2.2-.2 1.8-.7.4-.1c.2-.1.3-.3.3-.5l-.2-.7c0-.3-.2-.4-.4-.5-.5 0-1 0-1.4.2a3.8 3.8 0 00-1 .7.6.6 0 000 .6.7.7 0 00.2 0 4.4 4.4 0 001.1-.5l.4-.4.2.3a35.9 35.9 0 01-3.3 1.5l-2 .6h-.2v-.2a58 58 0 011.2-1.6V2l-.3.6v.2h.2c.6-.1 1.2-.8 1.2-1.5 0-.2-.1-.3-.4-.3-.4 0-.8 0-1.2.2-.7.2-1 .6-1.3 1.3a6.3 6.3 0 00-.2.9c0 .3-.4.4-.7.6-.4.3-.7.3-1.1.3a6.9 6.9 0 00-1 .3v-.3l.6-1.1c.4-.5.3-.8-.3-1a2 2 0 00-2.4.8l-.7 1.6c-.1.3 0 .5.3.5.5.1 1.2 0 1.6-.2v.9l-.4.6c-.2.3-.4.4-.7.3a15.8 15.8 0 01-1.6-.9c-.6-.3-1.2-.6-1.8-.6a.9.9 0 01-.3 0c-.2 0-.2-.1 0-.2v-.6L26.5 3l-.3-.7.8.3.2 1 .4.6c.1.1.3.2.4.1 0 0 .2-.1.2-.3 0-.3-.4-1 .2-2.3.3-.6 1.2-1.7 1.7-1.7.3 0 .6.1.7.6l.3.8c0 .3.2.3.4.3.5 0 1.3-.4 1.9-.4.4-.1.7.9.9 1.2.3.5.4 1 .8.4a48.5 48.5 0 011.4-2c.8-.4 1.8-.5 2.6-.4.2 0 .3.1.2.4v.7h.3c0-.2.3-.3.4-.4l.3-.4c.5-.5 1-.8 2-.8.5 0 .9.1 1.3.3.3 0 .4.1.3.4l-.3 1c-.1.4 0 .5.4.7l.8.6.4-.1c.4-.9 1.2-1.7 1.9-2.1.3-.2.4-.2.5.1l.4 1.4V3l.4-.5.7-.7a3 3 0 011.7-1 5 5 0 012 .3c.3 0 0 .4-.2.7 0 .2-.2.3-.3.4 0 .2-.1.2-.3 0l-.5-.7c-.3-.3-.9-.1-1.3.1a5.4 5.4 0 00-2 2c-.2.5-1.2 1.7-1.6 2h-.3v-.2a7.2 7.2 0 001.2-3.7c-.2 0-.4 0-.6.2a2.3 2.3 0 00-.7.7 7.2 7.2 0 00-1 3.2c0 .2 0 .5-.3.6l-1 .5h-.2c.2-.9 0-1.5-.5-2.3 0-.2-.2-.2-.4-.2l-.4.3c-.8.7-1.2 1.7-1.2 3 0 .1.2.1.4 0a1.4 1.4 0 00.3-.4l.7-1.3.3-.2a.5.5 0 010 .3l-.1.8c-.1.3-.5.8-.9 1l.1.4c.9 0 1.4 0 2.1-.5.8-.4 1.3-.7 1.7-.7l1.5.5-.3.2-.9.7c-.4.3-.5.4-.5 1.1l.2.9.2.2.2-.1 1-1.3.2-.1v.2c0 .5-.3.9-.6 1.2l-.2.3c-.3.3-.3.4 0 .8s.3.9.2 1.4a21.6 21.6 0 00-.2 1.4l-1 .4-.8.6c-.4.3-.4.5-.4.6l.2.2.4.2c.4.1 1.2 0 1.4-.5l.1-.5.3.1c.2.5.3.9.7 1.2l-.3.4c-.2.4-.3.6-.6.6h-.3c-.2 0-.4 0-.4.3V19c0 .3-.1.6-.4.5-.6 0-1-.5-1.7-.4-.5 0-.8-.1-1.1-.3l-.7-.4-2.6.5-2.8.5-2.1.4c-.2 0-.2.2-.3.4 0 .6-.3 1.1-.4 1.7l.2.3.2-.2.4-.6c.2-.3.9 0 1 .4.1.2-.2.3-.2.6 0 .2.4.4.4.7l-.2.5c1 .6 1.2 1.1 1.2 2 0 .2 0 .3.2.4l.4.5c.4.5.8 1.3 1.1 1.4h.9v.2c0 .2-.3.5-.6.5-.7.3-1-.8-1.4-1.3a5 5 0 00-.6-.8c-.3.3-.7 1.6-.7 2.3l.3.8.7 1.5c.3.4.7.5 1.1.6.4.2.7.2 1 .1h.4V31l.3-2.4.1-1.6c0-.3.3-.8.7-1.1.4-.3.6-.7.5-1.2a1 1 0 01.2-.5c0-.3-.2-.3-.5-.3-.2 0-.3-.2-.2-.5l1-1.3a3.8 3.8 0 00.4-.7c0-.4 0-1 .3-1h1.4c.4 0 1.1.4 1.3.6v.3l.7.6c.3.3.6 1.1 0 2.5.3.2.3.4.3.7 0 .3 0 .3.3.4.3.1.5.3.7 1 .2.5.9 2 1.3 6-.1 1.1-.3 2-.6 3 .3 1.9.6 4.1.8 7.3l.3 5.6c.5 0 .7.2.8-.3l.1-.4a.6.6 0 01.2-.2.5.5 0 01.2.2l.3 1 .3.6.2.2h.2a2 2 0 000-.5l.1-.8c0-.1 0-.2.2-.3l.2.2.5 1c0 .2 0 .3.2.4h.2V48l-.2-.7.1-.3a.6.6 0 01.2.1l1.5 2 .1.2h.3a.4.4 0 00.1-.3l-.1-.5v-.3l.4.2.5.6.3.5.3.2a.5.5 0 000-.4L56 49c.2 0 .4 0 .5.2l.3.4c.2.3.5.6 1.2.7v.1l-.6.3h-2.5a2.9 2.9 0 00-1.9.4c-.4.2-.8.2-1.3.2H49c-1.6-.1-2.2.1-3.8.3H44c-.7 0-2.1-.3-2.1-.7l.2-.8c0-.2 0-.2-.3-.3a5.6 5.6 0 00-.5 0c-.8-.1-1.7 0-2.5.1-.5 0-1 .2-1.4.3-1.2.1-1.8 0-2.4-.5-.4-.4-.6-.5-.9-1-.5-.7-1.4-1.5-2.4-2.2-1-.8-2.2-2.3-3-3.3-.2-.2-.5-1.2-.6-1.5a3 3 0 01-.2-1.3l.4-.2 1.1-.3c.2 0 .4-.2.4-.3v-.1l-.1-.1h-1.2L28 39c0-.2.2-.3.3-.4.7-.1 1.2-.3 1.6-.6a.4.4 0 000-.2l-1.4-.1-.4-.3c0-.2.2-.2.3-.3l.8-.4.6-.4v-.2c-.5 0-1 0-1.6-.2a1 1 0 01-.1-.1.7.7 0 01.2-.3c.3 0 1-.1 1.3-.3a.5.5 0 00.2-.2.6.6 0 00-.2-.2 7.8 7.8 0 01-1.3-.2c-.1 0-.2 0-.2-.2l.2-.2 1.2-.2a.8.8 0 00.3-.3l-.3-.2-1-.2a1.6 1.6 0 01-.3-.1V33l.3-.1.7-.2c.2 0 .4-.2.4-.3a.2.2 0 000-.2h-1.2L28 32l.3-.2c.3 0 .7-.2 1-.4l.1-.2a.7.7 0 00-.2-.1 11.3 11.3 0 01-1-.3.7.7 0 010-.3l.1-.2h.6a3.3 3.3 0 00-.3-.6.5.5 0 00-.2-.2c-.3-.2-.7-.3-1-.6-.6-.3-1.2-.7-1.7-1.2a3.9 3.9 0 01-1.3-2 4.4 4.4 0 010-1.5c0-.4.3-.8.5-1.2v-.3a6.6 6.6 0 00-.9-1 1.6 1.6 0 00-.6-.3L22 21c-.4-.1-.7-.2-1-.1a1.9 1.9 0 00-.4.7l.3 1.5c.4 0 1 .2 1.5.5.4.3.5.4.7 1 .4 1.2.5 2.7.7 4.3.3 1.6-.5 2.9-2.1 3a3 3 0 01-1.1-.2c-.3 0-.6-.2-.7-.6l-.5-1.3c0-.4-.2-.8-.3-1l-3.5 1.2-3 1.5c-2.1.8-4.4 1.4-6.5 1.4-1.1 0-2.4-.3-3.2-.6-.8-.3-1-.6-1.2-1.6a10.8 10.8 0 01.7-5.3c.3-.6.6-1 1-1.2a6.5 6.5 0 011.4-.4c.5-.2.9-.2 1.4-.2l1.1.2c.6.3.7.7.7 1.3v1.6c.8.1 2.3 0 3-.1a8 8 0 002.1-.8c.8-.3 1.8-1.1 3-1.7zM40 37.5l.2 1.2v2.1c-.2 1.2 0 2.5-.2 3.7l-.2 2.4c0 .3 0 .4.3.4.6.2 1 .2 1.3.2.9 0 1.5-.4 1.4-.9l-.2-1c-.2-1-.2-1.4-.2-2.4h.2l.7 2.8c.2.6 0 1.7.2 2.3 1.5.2 3.3 0 4 0 0-1.3 0-2.6-.5-3.1l-1.3-1a6.9 6.9 0 01-1.6-1.7c-.7-1-.8-1.4-1.4-2.4l-1.3-2.3a4.3 4.3 0 00-.5-.7l-1.4-.3c-.7 0-1.1-.1-1.5-.5-.3-.4-.4-.6-.2-1 0-.3 0-.4-.2-.6-.2-.3-.2-.5 0-.7.5-.4 1.1-.9 1.8-1a.3.3 0 00.1 0l-.1-.6-1.2.2c-.9 0-1.6-.4-1.9-1.5l-.8-2c0-.3 0-.6.2-1l-.7-1c-.5-.2-1-.4-1.3-.8-.2-.2-.3-.2-.4-.1l-.3.3c-.2.2-.5.2-.8 0-.3 0-.5-.4-.5-.7 0-.4.3-.7 0-.9-.2-.3-.5-.7-.1-1l.4-.1a1 1 0 00.5-1.1c0-.4-.2-.5-.6-.5-.5 0-.7-.3-.7-.6 0-.4.4-.6.7-.5.1 0 .3 0 .4.2 0 .1.1.3.3.3.5 0 1-.6 1-1.1 0-.2-.2-.3-.6-.2-.6 0-1.4.3-2.2.6-.4.2-.5.5-.2.9l.1.3c.1.2.1.3 0 .4l-.3.1c-.2.2-.2.4 0 .7a.9.9 0 00.1.2c.2.2.2.3 0 .5s-.2.5-.1.8l.2 1.8v2c0 .4.3.8.7 1.3l1.5 1.7a4 4 0 011.3 2.6c0 .5.2.7.7 1 1 .3 1.4.8 1.5 1.6.2 1.4-.5 2.7-1.7 2.6-.6 0-1-.5-1-.8 0-.3.2-.7.5-.8a.8.8 0 01.3 0 .6.6 0 010 .2l-.1.4v.5c.2.1.4.2.7.1.2 0 .4-.3.6-.6.5-1 .2-2.2-.9-2.5h-1.1c-.7.5-1.3.7-1.9 1.2a.4.4 0 000 .4 56 56 0 00.4 6.8l.2.8.3.3.3-.3.5-.8h.2l.1.1V46l.3.3.2-.3.1-.5a.4.4 0 01.3-.1v1l.3.2.2-.3.3-.5.7-.6a.5.5 0 01.3 0 .6.6 0 010 .2l-.5 1.2-.2.5h.3s.2 0 .2-.2v-.2l.3-.3a.5.5 0 01.2.3l.2.5.3.2.2-.1.2-.6.3.1.2.4 1.2.1v-.5-3l.3-3.2.2-3h.2zm15-20.4l-1.5-.1c-.3 0-.9 0-.9.3l.3.3c.7.5 2.8.6 3.6.5a1.6 1.6 0 000-.6c-.3-.6-1-1.2-1.7-1.4-.7-.2-1.6-.2-2.2 0l-.2-.5a7.2 7.2 0 000-2.8c0-.5-.1-.7-.4-1-.1-.2-.3-.2-.4 0a3.2 3.2 0 00-.5.4c-.7.9-.6 1.4-.5 2.7l.4.1c.3 0 .3 0 .3-.4l.1-1a.8.8 0 01.1-.2l.3.2v.4c0 .7-.3 1.2-.3 1.5 0 .1.5.3.4.5 0 .2-.8.3-1.3.4-.3-.4-.6-.8-1.2-1-.3-.1-.7-.2-.4-.4l.4-.2c.7-.3.5-.7.4-1.4 0-.2-.1-.3-.3-.3-.4 0-.6 0-1 .2l-.4.4-.8.9H47v-.2l.6-1.6c0-.3.2-.4.4-.4a3 3 0 003-.7c.2-.4.2-.5-.2-.8-.6-.5-1.4-.8-2.3-.5a1 1 0 00-.4.3l-.2.4h1.1l.3-.1.2.1-.2.2-.6.1-1.2.3c-.2.1-.3 0-.3-.2 0-.3 0-.6.3-.8.5-.4.8-.9 1-1.5 0-.4-.3-.9-.4-1.2v-.2l1.3.6c.2 1.1.9 1.9 2 2.2l1 .4c.4 0 .6 0 .6-.4v-.5l.7-.2c.6 0 1.2.3 1.8.5l.2.3h-.3c-.5 0-.9-.2-1.3-.3-.3 0-.5.1-.6.4.2.3 1 .7 1.4.8.4.1 2 .3 2-.1a5.9 5.9 0 00-.2-1.3c0-.4-.4-.6-.7-.7-.7-.2-1.2-.3-2-.3h-.3l-.8.3V9c-.2-.7-.5-.8-1-1v-.5c.5-.3 1-.7 1.2-1l-.7-.4c-.6-.2-.7-.5-1.2-.7a3 3 0 00-1-.2c-.4 0-.9 0-1 .2 0 0 0 .2.2.3.3.3 1 .4 1.3.7h.1l-.1.1a3 3 0 01-1.3-.3l-.4-.3c-.3-.1-.4 0-.6.3-.1.3 0 .4.3.5.7.4 1.2.7 2.1.8l.2.4a22.7 22.7 0 01-1.1-.1 3.4 3.4 0 01-.7-.2l-1.5-.8-1-.3c-.1-.1-.2-.2 0-.3l2.7-1.7h.5c.4 0 .6.2 1 .3.3.1.5.2.6 0a4 4 0 01.5-1.5c.4-.6 1-.9 1.7-1.4C54.1 1 55 0 55.3 0c1 0 1.8 1.6 2 2.2.1.5-.6.9-.1.9.4 0 .8 0 .8.4 0 .6-.2 1.3-.5 1.7s-.7 1-1.2 1.3v.2c.8.5 1.7 1 1.7 1.8 0 .5-.1.8-.4 1-.2.2-.4.4-.4.7v1.5c0 .4-1.2.5-1.3.7a5 5 0 01-.4 3l.3.5c1.2.9 1.4 1.7 1.5 2.3-.3.4-.9.7-1 .7-.9 0-2.6-.6-3-.7-.1.3 0 .5.1 1 .2.4.5.7.5 1.2 0 .2 0 .5-.6.6a4 4 0 01-2.7-.8l-.4-.2c-.3-.2-.6 0-.7.2l-.2.6c-.1.4-.3.4-.7.3-1.3-.3-1.9-1.6-1.9-2.8l.1-.2.2.1c0 .3 0 .5.2.7 0 .3.1.7.3 1 .3.4.6.7 1 .7.2 0 .3-.1.4-.4.3-.5.6-1 .5-1.6 0-.2-.1-.5-.3-.7l-.4-.4.3-.2c.6.3 1 .2 1.3 0 .3 0 .4-.2.5-.4a20.1 20.1 0 012-.4c.8 0 1.6.1 2.3.4v.2zM20.9 39c.4 0 1 .2 1 .7a1.4 1.4 0 01-.1.2.3.3 0 01-.1 0c-.2-.4-.5-.6-1-.5a2 2 0 00-1 .6c-.2.2.1.3.4.5l.5.3-.3.6a4 4 0 01-.6-.3l.3-.4h.6c0 .5-.7 2.2-.4 2.3.9.1 2-1 2.2-2.7 0-.6.3-1 1-1.1.7-.3 1-.7 1-1.5-.1-.5.1-.3.4-.7l.2-.3c0 .2.2.4.1.6-.2.6 0 1.2.3 1.8l.7 1.7.4.2c.5 1 1.2 2.7 2 3.4.7.6 1.4 1.6 2 2.2.5.3.7 1.2 1.2 1.4.3 0 .3.3.3.6s0 .4.2.4h.7c.2-.1.4 0 .3.2 0 .1-.1.2-.3.2h-.7l-.3.1.1.3 1.2.3.2.1-.2.3a1.2 1.2 0 01-.5 0c-.3 0-.7-.3-1-.4l-.3-.3H31v.5l.1.3-.3.1-.9-.6c-.3-.2-.6-.2-1 0l-1 .7c-.4.2-.8.7-1.3.7-.3 0-1-.5-1.2-.6l-1.4-.3c-.5-.1-1 0-1.4.3l-2 .3h-3a4 4 0 00-1.2.2H16c-.3 0-.6 0-.8-.2a1.1 1.1 0 00-.9-.1h-1c-.3-.4-1-1-1.3-.9-1.2.2-1.8 1-3 .6-.8-.2-1.3-.4-2.1-.1h-1c-.3 0-.4 0-.3-.4v-.1c0-.3-.2-.6-.5-.5-.4.1-.6.5-.9.6-.5.3-1 .2-1.7 0-.4-.2-.8-.1-1.2.1-.3.1-.7.8-1 .8L0 51v-.3c.1-.3.5-.6.7-.7.7-.3 1.4-.4 2.2-.3.4.1.8.2 1.1 0 .3-.1.6-.1.8-.3.5-.2.9-.4 1.4-.2.4-.2.5-.3.7-.6v-1.4l.5-1.2c0-.2.2-.2.3-.2.4.5.8 1 .6 1.5-.1.4.6.6.5 1-.1.2 0 .4.2.4h.2c.2 0 .2-.1.2-.4a8 8 0 00-.7-3.3l-.3-.8c-.3-.5-.2-1 0-1.5l.3-1a2 2 0 011-1.1l.3.1c.7 1 1 2.3.6 3.6l-.2 1.1v.3l.2-.2c.4-.5.8-1 1-1.6.3-.6.8-.8 1.4-1 .6 0 .8 0 .6.6l-.3 1a2 2 0 01-1 1.4c-.3 0-.6.3-.8.5a1.4 1.4 0 00-.3.2v.3h.5c.2-.3.5-.4.8-.6l1-.6c.2 0 .2 0 .2.3s0 .7-.3.8l-1.1.3a2 2 0 00-.5.4l-.4.8v.3h.3l1-1c.3-.2.4-.1.5.2a.9.9 0 00.2.4l.4-.3.2-.4c0-.3.5-1.5.8-1.9l.3.1V47c0 .5.1.6.5.4l1-.5.2-.1h.3v.3l-.4.5v.4h.3l.7-.4h1.1l1.1.4c.3 0 .7 0 1-.2a9.3 9.3 0 011.5-.6 5 5 0 002-1.2l.4-.3c.2-.1.3-.6.3-.7V43l.2-.4h.2v3.5c0 .2-.3.5-.5.5-1.2.5-2.5 1-3.8 1.3-.6.2-1.2.1-1.8.1H18c-.4 0-.9.4-1.3.4l-3 .3-2.7.7a1.6 1.6 0 00-.6.3l-.1.2.3.1h.6A33 33 0 0113 50l.4.2c.3.2.6.2 1 .2.3-.1.6 0 .9.2.5.4.6.4 1.2.1.5-.3 1-.3 1.5-.2.6 0 1.2 0 1.8.2l1.4.2c.9 0 1.6-.1 2.1-.8l.8-1.1c.2-.3.4-.4.7-.4l2-.2c.3 0 .5.4.5.6 0 .2-.1.4-.3.3h-1.5c-.5-.1-.9 0-1.2.4-.2.2-.1.3.1.4h.4c.2 0 .3.2.4.4 0 .2.4.3.9.3l1.6-.1c.2 0 .4-.4.6-.6.4-.5.6-.7 1.3-.5.5 0 .8-.4 1.2-.4.5 0 .3-.7.1-1a5.4 5.4 0 00-.7-1c-.7-.5-1.2-1.5-1.8-2.3-.5-.5-1-.8-1.4-1.4a33.4 33.4 0 01-.9-1.6l-.6-1.2-.6-.9c-.3-.3-1 0-1.4.1l-.4.3c1 .8 1 2 .7 2.8 0 .3-.2.3-.5.2l-.7-.3H22l-1.8 1c-.3-.4-.8-1.3-.8-1.7 0-.4.2-.7.3-1a7.5 7.5 0 01-1-.6v-.3c.7-.6 1.5-1.2 2.2-1.2zM2 30.2c.1.6.4 1 1 1.3.7.4 1.8.5 2.7.6l3.6-.3c1.9-.4 4.1-1.2 5.8-2a16 16 0 013.5-1.4 8 8 0 013.8 0c.6 0 .7 0 .7-.4l-.2-.8c-.1-1-.1-1.8-.5-2.7 0-.1-.2-.3-.5-.4-.3-.2-.6-.3-1-.3a6.5 6.5 0 00-1.9 0 9.7 9.7 0 00-2.9.9l-2.7 1.5a7.9 7.9 0 01-4 1.1l-2.6-.1a9 9 0 01-3.8-1c-.2-.2-.4-.1-.5.2l-.2.7c-.2.8-.4 2-.3 3.1zm44.5 10.5a17.5 17.5 0 01-1.8.1l.2-.2c.5-.2 1-.4 1.3-.7.2-.2.2-.3.2-.5a8 8 0 01-2 .6h-.2l1.7-1.2c.3-.2 0-.3 0-.4l-1.8.6H44l.3-.3 1.7-1v-.2h-.3l-2 .2h-.2a.2.2 0 010-.1 3.6 3.6 0 001.1-.8c.1-.2.1-.3 0-.4l-.3.2-1.1.1v-.2l.8-.5.5-.4v-.2a7 7 0 01-1.3.3.7.7 0 01-.3 0l.1-.2 1.4-.7c.2-.1.3-.3.3-.4h-.2l-1.3.4-.3-.2.2-.2 1.8-.8 1-.8.6-.6v-.1c-1 .5-1.3.8-1.6 1a8.7 8.7 0 01-1.6.8.4.4 0 01-.2-.1.8.8 0 010-.2c.2 0 .5-.4.7-.7l1.8-.9.9-.6V31l-2 1V32v-.3c.5-.3 1.2-.8 1.7-1.4v-.2l-.3.1-1 .4h-.1l.1-.4a6 6 0 001.3-1.3v-.4c-.4.3-.9.5-1.3.6h-.1v-.2c.5-.5.9-.8 1.2-1.3v-.2l-1.1.3h-.2v-.3l.9-1v-.2a7 7 0 01-.7.1c-.1 0-.2 0-.3-.2l.3-.1.4-.3a.6.6 0 00.2-.2l-.3-.1-.9.1c-.6.2-.6.2-.7.4l-.7 2.2c0 .2-.2.4-.4.5-.3.1-.2.3-.3.5-.1.6-.2.6-.4 1l-.6 1h-.2v-.7l-.3-.9v-.4l.1-.9.1-1.7-.1-.1a.4.4 0 00-.1.2l-.3 1.2c-.2.9 0 1.6 0 2.5-.1.7-.3 1.4-.2 2.1v.4l.7-.2c.5-.2.9-.2 1.4-.7.3-.3.3-.9.4-1.4.2-.6.4-.8.6-1l.2.1-.3.9c-.1.3-.3.8 0 .9h.3v.2c-.3.3-.6.7-2 1.1-.2 0-.6.5-.6.7a3.6 3.6 0 000 .9c.2 1.4.9 2.8 1.7 4.2.7 1.1 1.2 2 2 2.8.3.3.7.5 1.3.3l.8-.5v-.3zm-20-21.1c0 .5 0 .7-.8.5L25 20c-.3-.1-.6-.2-.8-.5-.3-.4-.7-.4-1 0l-.3.2-.2.2v-.3c0-.4.2-.6.5-.8l.8-.3c.7-.2 1-.6 1-1.4l.1-1.5c0-.3 0-.4-.4-.3L23 16a.7.7 0 00-.4.4l-.4.9-.1.1-.2-.1-.4-.6a10.1 10.1 0 00-.4-.7c-.3-.3-.3-.4 0-.6l1-.8c.4-.4.6-1 .6-1.6V12c0-.4 0-.5.5-.4.2 0 .5 0 .7.2l1.3.6c.2.2.4.4.4.6 0 .4-.2.6-.5.5a9.2 9.2 0 00-1.4-.5c-.3 0-.5 0-.6.3 0 .5.4.5.4.7-.2.4-.8.5-.9.9 0 .3 0 .5.3.4l.9-.3 1.2-.6c.5-.2.8-.1.9.5l.7 4.1v.6zm12-4.3a2.2 2.2 0 01.3-1.1c.2 0 .4 0 .5.2.3.7.9 1.4 1.7 1.8.2.2.5.2.7.2l.2-1.3c0-.5-.2-1-.6-1.3-.3-.4-.8-.7-1.2-1l-.5.1v.4c.2.3.7.6.8.9v.3a.5.5 0 01-.2-.1 8.2 8.2 0 01-.6-.5 5 5 0 01-.8-.8c-.2-.2-.4-.2-.5 0-.4.7-.7 1.4-.7 2.2-.2 1 .3 1.9 1.1 2.3.4.2.7.1.9-.2l.4-1V16l-.7-.7a1 1 0 00-.3-.2.4.4 0 000 .2v1a.6.6 0 010 .2.6.6 0 01-.4-.4l-.1-.7zM4.4 3.4l.5.7c.3.4.4.8.5 1.2.1.3.3.3.5.2a.8.8 0 00.4-.7A2.3 2.3 0 006 4c-.2-.7 0-1.4.6-1.9.3-.1.4-.4.4-.7L6.7.4c0-.2-.3-.2-.4-.1-.8.4-1.7 1.5-1 2.5V3A1.7 1.7 0 015 3L3.6 1.6a1.9 1.9 0 00-.6-.3c0 .1-.2.5 0 1.3 0 .7.2 1.4.7 2.4l.5.6.6.2v-.6a16.3 16.3 0 00-.4-1.4l-.1-.4zm50.5 2.9l1.1-.4c.9-.3 1.4-.9 1.6-1.8.1-.3 0-.4-.3-.5l-.6-.1c-.5-.2-.6 0-.8.6-.2.4-.4.9-.8 1.2-.1.1-.6-.2-.7-.3.8-.5 1.4-1.3 1.8-2.3 0-.1 0-.3-.2-.4a2 2 0 00-.9 0c-1 0-2.5 1-3 2.2-.1.1 0 .4 0 .4h.6l.1-.2c.4-.7 1-1 1.7-1.3h.2a.3.3 0 010 .2 1 1 0 01-.3.2l-.7.5-.5.4V5l.3.1h.4l.4.7c.1.2.3.4.6.4zM36.4 6c0 .3-.2.5-.4.4-.6-.2-1-.8-1.6-1-.2-.1-.3 0-.4.3l-.2 1.4c0 1 .5 1.4 1 1.9v.3l-2 1c-.1.1-.4.2-.6 0-.2-.1-.2-.3-.2-.6l.7-2.9c.4-1.5 1-2 2.1-2.1h1c.3 0 .5 0 .5.3v1zm1.4 6.7c0-.7-.6-1-.8-1.6-.3-.6-.5-.6-1-.1s-.9 1-1 1.7c-.3.7-.2 1.4 0 2.1 0 .2.1.5.4.4a.4.4 0 00.2-.4c0-.6.1-1.2.4-1.6l.3-.3v.4l-.2 1.3v.8c.3.3.4.3.5.1.6-.9 1.2-2.2 1.2-2.8zm-31.2 2c0-.7-.2-1.2-.4-1.8-.2-.6-.6-.9-.8-.9-.3 0-.8.6-1 1a3 3 0 00-.1 1.3c0 .8.2 1.5.7 2.2 0 .2.3.4.5.2l.7-.7c.2-.4.4-.9.4-1.3zm27 19.3c-.2-1.2-1-2-1.8-2.6l-.4-.3H31v.3l.2 2.4.2 1.7c0 .3.2.4.5.3l1.1-.6c.3-.2.6-.4.4-1.2zm11.7-11.7c-.2-.5-.5-.7-1-.8h-.6l-.9-.2c-.3 0-.5.3-.6.7v.2l.6.2.3.4c0 .1-.2.3-.5.3s-.3.3-.2.6c0 .3.1.6.4.9l.4-.3c.2-.2.2-.4.2-.6 0-.7.3-1.1.8-1.6l-.2 1.1c-.1.8-.4 1.4-1.1 2h.3a.8.8 0 00.7-.2c.3-.3.5-.6.6-1l.2-.4.1.3v1l.4-.3c.3-.7.4-1.7.1-2.3zM54.7 7H54s-.2.4 0 .4c0 .1.1.2.3.2.4 0 .7 0 1.2.2l.3.1-.2.1c-.7.1-1.2.2-1.8-.2l-.2-.1c-.2-.1-.3 0-.2.2 0 .3.3.6.9.8.4.2.8.4 1.2.4L57 9c.3 0 .6-.2.6-.4l-.1-.6-.4-.4c-.6-.6-1-1-2-1 0 0-.2.4-.4.4zm.6 6.7l-.1-1c-.2-.7-.8-1.2-1.5-1.2-.4 0-.5 0-.4.4l.5.7c.2.4.4.7.4 1a1 1 0 01-.1.4c-.3-.1-.4-.4-.4-.5-.1-.3-.1-.5-.3-.7 0-.2-.1-.2-.2-.2s-.2 0-.2.2c0 .8.1 1.8 1 2.5l.6.4c.2.1.3 0 .4-.2a6 6 0 00.3-1.8zM6 9.7L5.8 11c0 .4.2.7.6.8 1 .5 2.2.6 3.3.6l.2-.2-.1-.2h-.3L8 11.3a1 1 0 01-.4-.2c-.1-.2 0-.4.2-.4l.5.1 1 .5c0 .1.1.2.2 0 .1-.1.2-.2 0-.4l-.7-.6C8 10 7.2 9.7 6 9.8zm23.1 5.8c0 .8 0 1.7-.2 2.6l-.3.3-.3-.3c-.4-1.1-.8-2.2-1-3.4L27 14c0-.3 0-.4.3-.3.6 0 1 .3 1.6.5l.1.4v.8zM4.2 20.4v-.6l.2-1a1.4 1.4 0 000-.4l-.7.1c-.2.1-.3.3-.3.7v1l.2 1.6c0 .3.2.4.4.2l.5-.5 1-1.2c.4-.4.4-.8.3-1.2-.1-.3-.3-.4-.5-.4-.3 0-.4.2-.5.4 0 .3-.2.5-.3.8a3 3 0 01-.3.5zm46.2-2h1c.5.2.7.5.8.7l.1.3a1 1 0 01-.9-.1 2.9 2.9 0 00-.5-.4c-.3-.1-.7-.2-1 0 0 .2.3.6.5.8.7.5 1 .6 1.6.6h1l.1-.5c0-.6 0-1.2-.5-1.7-.6-.6-1.5-.5-2-.3a1.2 1.2 0 00-.4.2.6.6 0 00.2.3zm-20.8-16h.1v.3c-.1.5-.4.9-.8 1.3-.2.1-.2.4 0 .5.2.3.5 0 .7-.1.4-.5.6-1 .8-1.6.3-.6.3-1.5 0-2-.2-.2-.5-.3-.7-.1-.6.6-1.2 1.5-1.2 2.4l.1.4.5-.4.5-.6zm12.2 24.3h.2l.1 1.1v.3l.3-.2.1-.7c.4-1 .1-1.8-.3-2.8 0-.2-.2-.2-.4-.1-.1 0-.2.2-.1.3v.4l-.4 1-.1.5-.1 3.6v.1a.3.3 0 000 .2.6.6 0 00.2-.1l.2-.6c0-.6.3-.6.3-1.2v-1.8zM8 3.8a.5.5 0 010-.2h.6c.3.1.4.1.5-.1.2-.5.1-.9-.3-1a2.3 2.3 0 00-.8 0l-1.2.3c-.4.1-.5.5-.2.8.4.4.8.9 1.3 1.2.4.4.8.4 1.3.3.2 0 .3-.1.3-.3a.5.5 0 00-.1-.4 1.3 1.3 0 00-.3-.2L8 3.8zm-.7 27c0 .2-.1.3-.4.3a4.1 4.1 0 01-1-.1c-.3-.1-.3-.2-.4-.4l.1-2 .5-.3 1.1.2c.2 0 .4.4.4.5v.6c0 .5-.2.9-.3 1.1zm33.4 18l.7.2c.4.1.7.2 1 .1.2 0 .4-.2.4-.5 0-.2-.2-.4-.4-.5l-1.9-.4c-.3-.1-.6-.1-.9.1-.4.4-.9.6-1.4.7l-.4.1v.3l.1.2h1.4l1.4-.3zm3.3 2c.5 0 .9-.1 1.2-.3l.8-.5.6-.2c.3 0 .5-.3.5-.4 0-.3-.2-.3-.4-.4l-1.5-.2h-.5l-1 .8c-.1.1-.6.3-.7.6-.2.1-.2.3.2.4l.8.2zM26.9 11.7c0-.3.4-.7.7-.6a.3.3 0 01.1 0l.1.6.2.4c.6 0 1.2-.3 1.4-.8.4-.5.6-1 .8-1.5.1-.2.2-.4.4-.3.1 0 .2.2.1.4l-1 3c-.1.3-.2.3-.5.2l-1.7-.6a.9.9 0 01-.6-.8zm12.6 24.7h.3a1 1 0 00.3-.2l.4-.5-.1-2.2-.2-.2h-.9l-1.2 1s-.1.2 0 .2l.2.1.6-.3.4-.2a.3.3 0 010 .2c-.4.4-.7.9-1.2 1.1v.2l.1.2.6-.3.7-.6c.1.1.2.3 0 .4l-.2.2-.6.5v.2h.1c.2 0 .4 0 .6-.2l.5-.5.2.2-.6.7zM14 11.2c0 .9-.2 1.7-.6 2.5a1.7 1.7 0 01-.3.5l-.3-.6-.6-1.7a2 2 0 01-.2-.4l.7-1.1c0-.2.2-.2.3 0l1 .8zm1.5 17.2c0 .3-.2.5-.6.7-.2.1-.5.3-1 .3 0 0-.2 0-.3-.2 0-.6-.2-1.1-.3-1.6l.2-.4c.4-.4.8-.5 1.3-.5l.5.2.2 1.5zM11 22c-.3 0-1 .4-1 .7v.2c0 .5 0 1 .3 1.4.2.2.3.3.5.1.5-.3 1-.6 1.3-1 .3-.2.5-.6.7-1 0-.2-.2-.3-.4-.4-.3-.1-.7-.1-.8 0-.2 0-.3.2-.3.4a3.6 3.6 0 01-.2.6l-.3.4a2 2 0 010-.6c0-.2 0-.5.2-.8zm4.8 27.5a3.8 3.8 0 01-.5 0c0-.2.1-.2.2-.2h.4c.6.1 1-.3 1.6-.4h2.1c.6.3 1.2.5 1.7.1a8.8 8.8 0 011-.6.6.6 0 01.5 0s.2.3.1.4a.5.5 0 01-.3.3c-.9.3-1.8.5-2.8.6-1 0-1.9 0-2.9-.2h-.5-.6zm-12.4-20c0 .2 0 .6.3.8v.2l-1-.1s.3-.3.3-.5l.2-1.9-.2-.3a.4.4 0 01.1-.2c.7.2.8.2 1 .5l.4 1.2a.4.4 0 00.2-.1v-1c0-.2 0-.3.3-.3h.6V28.1c-.4 0-.4.1-.6 1l-.1 1-.1.4h-.3a12.5 12.5 0 00-.8-1.7h-.1l-.1.4a.9.9 0 000 .2zm22 4.8a2.7 2.7 0 000-.6l-.2-.7v-.2c.2-.6.3-1.1.2-1.6a12.6 12.6 0 010-1.9v-.1c.3 0 .4.1.5.3v.6c.1.5 0 1 0 1.6l-.1 3.2.1.5c.1.3 0 .7-.2 1h-.2c0-.5-.4-1-.5-1.6l.1-.1c.2 0 .3-.2.3-.4zm10.4-8c-.4 0-.8-.3-.8-1l-.7.7c0 .2.6.6 1 .7l.7.8.4-1c0-.8-.1-1.4-.3-1.7-.3-.4-.9-.7-1.4-1-.2.2.1.8.7 1.2-.1.4-.1 1 .5 1.3zM6 25.4l1-.1c.3 0 .6-.1.6-.4 0-.3-.3-.4-.5-.5-.8-.2-2.2-.2-3.2.3l-.6.5c-.3.2-.2.4 0 .5l.8.4.2-.8.5-.4c.4-.2.9-.2 1.4-.2h.2l-.1.2-.4.2-.3.1c0 .2.2.2.4.2zm2.2 5.8l.1-.3v-.6-1.5l.2-.2.2.1.9 1V29v-.6c-.1-.3-.3-.5-.1-.5h.8l-.1.5-.2 1 .2 1.3H10a.8.8 0 01-.2 0l-1.2-1.3a.4.4 0 00-.1.2c0 .3.2.7.4 1 .2.1.2.2-.1.3l-.7.1zm13.2.5c.5 0 1 0 1.4-.4.5-.3.7-1.2.5-1.7a.6.6 0 00-.1-.3c-.2-.2-.3-.2-.4.1l-.1.7-.1.4a3.5 3.5 0 00-1 0c-.4 0-1.1.3-1.1.7 0 .4.6.5 1 .5zm4.2-5.2l.2.5 1.4 1.2 1 .5a2.5 2.5 0 011.3 1l.4.4c.2.3.5.5 1 .6h.2c0-.3 0-.3-.3-.5a20 20 0 00-2.4-2 29 29 0 01-1.5-1l-.7-.4-.6-.3zm-15.4 17a63.2 63.2 0 00-.5-1.9l-.2-.1-.1.1-.5 1.4a.7.7 0 000 .4l.7 2 .3.2.1-.3.2-1.8zm2.6-15.7l.1.4v.2h-.2c-.3-.1-.6-.3-.9-.1v.3l1 .4c.4.1.5.3.4.8 0 .4-.4.6-.8.7a1.6 1.6 0 01-.6 0v.4h-.2a4 4 0 01-.2-.3l-.1-.7v-.2c.2 0 .3.3.5.4l.7.1.2-.3-.3-.2-.8-.4c-.3-.1-.3-.4-.3-.6 0-.3.1-.6.5-.7l.5-.2.1-.4h.2l.2.4zM6.4 8.9c-.6 0-1-.2-1.5-.1-.5 0-.9.2-1.3.3l-.2.2.2.2.6.1L8 9.2l.2-.3-.4-.2-1.3-.2-.2.4zm14.3 18.5s-.2 0-.3.2a.8.8 0 01-.3.2l-.1-.1.2-.6c.1-.3.2-.3.5-.2l.2.2c.2.2.4.3.6.2.3-.1.1-.4 0-.5l-1-.5c-.3-.2-.4-.4-.4-.7 0-.2.2-.5.5-.6h.5c.2 0 .3 0 .4-.2.1-.1.3-.2.3 0v.9h-.3c-.2-.3-.4-.5-.6-.4-.2 0-.3.1-.2.3l.4.3.6.3c.5.3.4.6.4.9 0 .3-.3.5-.7.5a1.3 1.3 0 01-.7-.2zm-1-1v.5c-.2.4-.6.6-1 .6s-.6-.1-.7-.5l-.2-1.3-.5-.1-.1-.2.6-.2c.4 0 .4 0 .5.3l.2 1.4c0 .2.1.3.4.3.2 0 .3 0 .3-.3v-1.1l-.4-.5c-.1-.1-.2-.2 0-.3.7-.2.7-.4.8.4l.2 1zM20 29c0 .3.2 1.2.4 1.4l1.4-.3c.4-.1.6-.5.5-.9 0-.3-.2-.4-.4-.5a1.6 1.6 0 00-.3 0c-.6 0-1 .1-1.5.3zM7.4 25.4l-.6.1-.8.1-.8-.2c-.2 0-.4.2-.5.5 0 .3.2.5.5.6a9 9 0 002 .2c.2 0 .2 0 .2-.2v-1.1zm16-14.3l-.8-.3-.2-.3V9.4c0-.2.1-.3.3-.2.3 0 .5 0 .8.2.3.1.8 1 .7 1.4-.2.3-.4.3-.8.3zm-5.7 16.4l-.3.3c-.3.2-1 .3-1.4.6-.2.1 0-.3 0-.5l-.3-1.1-.3-.6c-.2-.2.2-.2.3-.3l.7-.2h.2v.2c-.3.2-.3.3-.2.6v.9l.2.4c.2 0 .5-.2.5-.3V27l.2-.2.1.2.3.6zm1.2-6.9c-.1 0-.2 0-.3-.2l-.3-1.3c0-.2 0-.3.3-.3l1.7.5c.2 0 .2.2.1.3l-1.3 1h-.2zm25.7-16c0-.5-.5-1.4-.7-1.7-.3-.3-.6-.4-1 0a2.6 2.6 0 00-.2.2c-.3.4-.3.5.2.7l.9.5.7.7v-.4zM12.8 43.3a2.9 2.9 0 00-1 .8l-.5 1a5 5 0 00-.3 1h.1c.3 0 .9-.6 1-.6.4-.5.6-1 .8-2l-.1-.2zm44-41.1c-.2-.6-.7-1.3-1-1.6-.2-.3-.3-.4-.6 0a2.5 2.5 0 00-.3.2c-.3.4-.5.9 0 1l1.3.1.5.4.2-.1zM6.2 29v1.3c0 .3.1.3.4.3.2 0 .3-.2.3-.5l.2-1c0-.3 0-.5-.2-.5-.3 0-.6.1-.7.4zm4-13.5a4 4 0 01-1-.3c-.5-.3-1-.8-.9-1.3 0-.2.2-.3.4-.2l.1.1.9 1c.2.1.4.4.5.7zm3.7 12l.2 1c0 .2.1.4.4.3.3 0 .4-.2.4-.4l-.2-1c0-.3-.2-.5-.5-.4-.2 0-.3 0-.3.4zm14.9-19L28 9.7l-.7.7H27v-.2L28 9l.4-.5h.2l.2.1zm20 10.4l-.1.7-.3-.5c0-.3-.4-.8-.7-1.4v-.2c0-.1.1-.2.2-.1h.3l.6 1.5zM51 2.4s.2 0 .3.2v.3l-1.9.8s-.2.1-.1 0c0-.4 1.3-1.3 1.7-1.3zM33.8 24.2c-.3 0-.6.2-.6.5s.4.6.7.6c.3 0 .5-.2.5-.5 0-.4-.2-.6-.6-.6zM7.4 49.4l-.3.2c-.4 0-.5.1-.5.3 0 .2.1.3.3.3.5-.2.8-.2 1.4-.1h.1l.3-.4-.3-.2h-1zm35.2-28.8c0 .1-.2.2-.1.3v.3h.8l1.2.2.2-.1V21c-.4-.2-.9-.4-1.4-.4h-.7zm-22-10a15 15 0 01-.8-.4v-.3l.5-.7h.3l.4 1c0 .2 0 .3-.4.3zM10 20.5c.2 0 .3 0 .2.2 0 .1 0 .2-.2.2a3.5 3.5 0 01-.8.2 4.3 4.3 0 01-1 0l-.4-.2a.4.4 0 01.3-.2l1.9-.2zm5.7-16h-.2l-1.7-1-.1-.2h.2c.4 0 1 .3 1.4.4.3.2.4.4.4.7zm8.1 11.6c.1 0 .2.2 0 .4-.3.5-.3.9-.5 1.5a1.8 1.8 0 01-.2.3l-.2.1v-.2l.2-1 .3-.7c0-.3.2-.4.4-.4zm19.6-6c-.7 0-1.4-.3-2.1-.6a.4.4 0 01-.2-.2.5.5 0 01.3 0l2 .5.2.2a.6.6 0 01-.2.2zM35 22.9a.7.7 0 00-.3-.3c0-.1-.2 0-.4 0a1.8 1.8 0 00-.3.3c-.1.2 0 .4.2.6h.6l.2-.6zM22 13.2l-.2.4a25.5 25.5 0 01-.9 1h-.2v-.3a5.4 5.4 0 01.4-.7l.5-.6h.3v.2zm-11.3-2.8l-.3-.2-.8-1.3-.2-.4.3-.3a.5.5 0 01.1.2l.5 1 .4.7v.3zm-.5 6.6l-.4-.2-1 .2a1.4 1.4 0 01-.4 0l-.3-.1.6-.3c.2 0 1-.1 1.6 0v.4zm40.5-8.5h.2l1.2 1v.1a.5.5 0 01.1.2 1.3 1.3 0 01-.2 0c-.5-.3-1-.5-1.4-1v-.2-.1zM21.5 10v.2l-.2-.2-.2-.6c0-.5 0-.8.3-1 .1-.2.2-.2.2 0V10zm-7.4-1.8v.2l.4 1.7V10.7l-.2-.2-.2-.8-.2-1c0-.2 0-.4.2-.5zm34 7.2l.5.5.7.6v.3H49l-1.2-.9v-.4a.4.4 0 01.1 0zM25.3 5.1h.8l.1.2-.1.2h-1.5a1 1 0 01-.2-.3l.2-.1h.7zM35 7c.4.5.5 1 .6 1.5v.1h-.1c-.4-.5-.7-.9-.8-1.4l.1-.3h.2zM29 41.3l.9-.6c.1-.1 0-.1 0-.2 0-.2-.1-.2-.3-.1-.4 0-.6.2-1 .3-.1 0-.2.2 0 .3l.4.3zM17.2 5.8l.7-.3.8-.4a.4.4 0 01.3.1l-.2.2-1.2.7a2.4 2.4 0 01-.4 0 .3.3 0 010-.3zm-12 9.8l-.1-1a2.6 2.6 0 010-.7l.2-.2.2.2v1.7h-.2zm26.1-12a8 8 0 011.4-.8 5 5 0 01-1.4 1.4v-.6zM25 24.4c0 .3.1.5.3.7l.2-.3v-1.3c0-.2-.2-.2-.2 0-.1.3-.3.6-.3 1zm22 17.4l-1.9.6c.4.5 1.7 0 1.9-.6zM19.6 8c0 .5 0 1.1-.3 1.6l-.2.2v-.3a61 61 0 01.4-1.6l.1.1zm26 35.4h1a.7.7 0 00.2-.3l.1-.5-.5.3a8.1 8.1 0 01-.9.2l-.1.2.1.1zm-3.8-19.8l.4-.8-.2-.3a.8.8 0 00-.2 0c-.1.2-.7.9-.6 1h.6zm-31-7.8l.4-1h.2v1a.7.7 0 01-.2.4.5.5 0 01-.2 0L11 16zm22.6 7.3c-.2 0-.4.2-.5.4 0 .2.2.4.5.4.2 0 .4-.2.4-.4 0-.3-.1-.4-.4-.4zm-.2 2.5c0-.3-.2-.5-.5-.5-.2 0-.4.2-.4.4s.3.4.5.4c.3 0 .4-.1.4-.3zm-1-2.6c0 .3-.2.4-.4.4s-.5-.2-.5-.4.2-.4.4-.4c.3 0 .5.1.5.4zm.2 1c-.3 0-.4.2-.4.4s.2.4.4.4.4-.2.4-.4-.2-.4-.4-.4zm-2.7 18c-.3 0-.6 0-.5.2 0 .2.2.4.4.4s.3-.2.3-.4 0-.3-.2-.3zm-19-38c-.2-.5-.1-1 0-1.7.3.6.3 1.4 0 1.8zm22.5 17.6c-.2 0-.3.3-.3.3l.2.4a.6.6 0 00.3-.3l-.2-.4z" fill="#53565A"/>
</svg>
 alt="Elsevier logo with wordmark" height=64 width=58 loading=lazy></a></div><div class=els-footer-content><div class=u-remove-if-print><ul class="els-footer-links u-margin-xs-bottom" style=list-style:none><li data-moz-translations-id=0><a class="anchor u-display-block u-clr-grey8 u-margin-s-bottom u-margin-0-bottom-from-sm u-margin-m-right-from-sm u-margin-l-right-from-md anchor-default" href=https://www.elsevier.com/solutions/sciencedirect target=_blank id=els-footer-about-science-direct rel=nofollow data-moz-translations-id=1><span class=anchor-text data-moz-translations-id=2>Acerca de ScienceDirect</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=3><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=4></path></svg></a><li data-moz-translations-id=5><a class="anchor u-display-block u-clr-grey8 u-margin-s-bottom u-margin-0-bottom-from-sm u-margin-m-right-from-sm u-margin-l-right-from-md anchor-default" href="https://www.sciencedirect.com/user/institution/login?targetURL=%2Fscience%2Farticle%2Fpii%2FS0022169421005552" id=els-footer-remote-access rel=nofollow data-moz-translations-id=6><span class=anchor-text data-moz-translations-id=7>Acceso a distancia</span></a><li data-moz-translations-id=8><a class="anchor u-display-block u-clr-grey8 u-margin-s-bottom u-margin-0-bottom-from-sm u-margin-m-right-from-sm u-margin-l-right-from-md anchor-default" href=https://sd-cart.elsevier.com/? target=_blank id=els-footer-shopping-cart rel=nofollow data-moz-translations-id=9><span class=anchor-text data-moz-translations-id=10>Carrito de compras</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=11><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=12></path></svg></a><li data-moz-translations-id=13><a class="anchor u-display-block u-clr-grey8 u-margin-s-bottom u-margin-0-bottom-from-sm u-margin-m-right-from-sm u-margin-l-right-from-md anchor-default" href=http://elsmediakits.com/ target=_blank id=els-footer-advertise rel=nofollow data-moz-translations-id=14><span class=anchor-text data-moz-translations-id=15>Anunciación</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=16><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=17></path></svg></a><li data-moz-translations-id=18><a class="anchor u-display-block u-clr-grey8 u-margin-s-bottom u-margin-0-bottom-from-sm u-margin-m-right-from-sm u-margin-l-right-from-md anchor-default" href=https://service.elsevier.com/app/contact/supporthub/sciencedirect/ target=_blank id=els-footer-contact-support rel=nofollow data-moz-translations-id=19><span class=anchor-text data-moz-translations-id=20>Contacto y apoyo</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=21><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=22></path></svg></a><li data-moz-translations-id=23><a class="anchor u-display-block u-clr-grey8 u-margin-s-bottom u-margin-0-bottom-from-sm u-margin-m-right-from-sm u-margin-l-right-from-md anchor-default" href=https://www.elsevier.com/legal/elsevier-website-terms-and-conditions target=_blank id=els-footer-terms-condition rel=nofollow data-moz-translations-id=24><span class=anchor-text data-moz-translations-id=25>Términos y condiciones</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=26><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=27></path></svg></a><li data-moz-translations-id=28><a class="anchor u-display-block u-clr-grey8 u-margin-s-bottom u-margin-0-bottom-from-sm u-margin-m-right-from-sm u-margin-l-right-from-md anchor-default" href=https://www.elsevier.com/legal/privacy-policy target=_blank id=els-footer-privacy-policy rel=nofollow data-moz-translations-id=29><span class=anchor-text data-moz-translations-id=30>Política de privacidad</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link" data-moz-translations-id=31><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z" data-moz-translations-id=32></path></svg></a></ul></div><p id=els-footer-cookie-message class=u-remove-if-print>Utilizamos cookies para ayudar a proporcionar y mejorar nuestro servicio y adaptar contenido y anuncios. Al continuar acepta el <a class="anchor u-clr-grey8 u-margin-0-right anchor-default" href=https://www.sciencedirect.com/legal/use-of-cookies target=_blank rel=nofollow data-moz-translations-id=0><span class=anchor-text data-moz-translations-id=1><strong data-moz-translations-id=2>uso de cookies</strong></span></a>.<p id=els-footer-copyright>Todo el contenido en este sitio web: Derechos de autor de 2023 Elsevier B.V., sus licenciantes y colaboradores. Todos los derechos están reservados, incluidos los destinados a la minería de textos y datos, capacitación en IA y tecnologías similares. Para todos los contenidos de acceso abierto, se aplican los términos de licencia de Creative Commons.</p></div><div class="els-footer-relx u-margin-0-top u-margin-m-top-from-xs u-margin-0-top-from-md"><a class="anchor anchor-default anchor-icon-only" href=https://www.relx.com/ target=_blank aria-label="RELX home page (opens in a new tab)" id=els-footer-relx rel=nofollow><img loading=lazy src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSI5MyIgaGVpZ2h0PSIyMCIgdmVyc2lvbj0iMSIgdmlld0JveD0iMCAwIDI4MCA2NSI+PHBhdGggZmlsbD0iI2ZmODIwMCIgZD0iTTIwNyAyNTFjMTEwIDAgMjQyIDI2IDI0MiAxMzEgMCA3NS03NyAxMDMtMTQ2IDEwM0MxOTYgNDg1IDAgNDI1IDAgMjE1IDAgMTEwIDgwIDQ5IDIwOCA0OWMxMzUgMCAyMzIgODcgMjYyIDIwNUMzOTEgMTE4IDI5MSA3NSAyMDkgNzUgMTA3IDc1IDY4IDE1MSA2OCAyMTZjMCAxODUgMTQ0IDI0OCAyMzUgMjQ4IDYxIDAgOTctNDAgOTctODMgMC0xMTktMTcyLTExNi0yNTAtMTE2aC0zOGwtMTYtMTdjMzYtNSA4NS0xNSAxMzktNDJDMzQ3IDE0OSA0NTMgMCA1OTIgMGM1MSAwIDY0IDE1IDc0IDI5LTY5LTQ0LTE4MyAzMi0yNDUgOTAtNTEgNDgtOTYgMTAzLTIxNCAxMzIiIHRyYW5zZm9ybT0ibWF0cml4KC4xMzMzMyAwIDAgLS4xMzMzMyAwIDY1KSIvPjxwYXRoIGZpbGw9IiM2NjYiIGQ9Ik04ODYgMzEwYzAgMzMtMjIgNTMtNTUgNTNoLTc3Yy0yIDAtMy0xLTMtM3YtOTljMC0yIDEtMyAzLTNoNzdjMzMgMCA1NSAyMCA1NSA1Mm03LTI2MWMtNSAwLTcgMi04IDZsLTY5IDE0NWgtNjJjLTIgMC0zLTEtMy00VjU1YzAtNC0yLTYtNi02aC01MWMtNCAwLTYgMi02IDZ2MzYxYzAgMyAyIDUgNiA1aDEzOWM2NiAwIDExNS00NSAxMTUtMTExIDAtNDktMjctODYtNjktMTAybDc2LTE1MmMyLTQgMC03LTQtN2gtNThNMTAxMCA0MTZjMCAzIDMgNSA2IDVoMjE4YzQgMCA2LTIgNi01di00N2MwLTMtMi02LTYtNmgtMTU4Yy0yIDAtMy0xLTMtM3YtOTFjMC0yIDEtMyAzLTNoMTI3YzQgMCA2LTIgNi02di00N2MwLTMtMi01LTYtNWgtMTI3Yy0yIDAtMy0xLTMtM3YtOTVjMC0yIDEtMyAzLTNoMTU4YzQgMCA2LTIgNi01VjU1YzAtNC0yLTYtNi02aC0yMThjLTMgMC02IDItNiA2djM2MU0xMjk4IDQxNmMwIDMgMiA1IDUgNWg1MmMzIDAgNi0yIDYtNVYxMTBjMC0yIDEtMyAzLTNoMTQ3YzMgMCA1LTIgNS01VjU1YzAtNC0yLTYtNS02aC0yMDhjLTMgMC01IDItNSA2djM2MU0xNzc1IDQ5Yy00IDAtNiAxLTggNWwtNzMgMTI3aC0xbC03My0xMjdjLTItNC00LTUtOC01aC01OGMtMyAwLTUgMy0zIDdsMTA4IDE4NS0xMDAgMTczYy0yIDQgMCA3IDMgN2g1OGM0IDAgNi0yIDgtNmw2NS0xMTJoMWw2NSAxMTJjMiA0IDUgNiA5IDZoNTdjNCAwIDUtMyAzLTdsLTk5LTE3MyAxMDctMTg1YzItNCAxLTctMy03aC01OE0xOTEyIDI5NWwtMiAxdjEwNGwtMSAxaC0zMmwtMiAydjE2bDIgMmg4N2wyLTJ2LTE2bC0yLTJoLTMybC0xLTFWMjk2bC0yLTFoLTE3TTE5ODggNDE5bDIgMmgxNmMxIDAgMiAwIDMtMmwzMy03OGgxbDMzIDc4YzEgMiAyIDIgMyAyaDE2bDItMlYyOTZsLTItMWgtMTZsLTEgMXY3OGgtMWwtMjYtNjAtMy0yaC0xMWwtMyAyLTI2IDYwaC0xdi03OGwtMi0xaC0xNWwtMiAxdjEyMyIgdHJhbnNmb3JtPSJtYXRyaXgoLjEzMzMzIDAgMCAtLjEzMzMzIDAgNjUpIi8+PC9zdmc+Cg==" width=93 height=20 alt="RELX group home page"></a></div></footer></div></div></div></div>
|
||
<div class=js-react-modal></div><div class=js-react-modal></div><div class=js-react-modal></div><div class=js-react-modal></div><div class=js-react-modal></div><script data-template-shadow-root>(()=>{document.currentScript.remove();processNode(document);function processNode(node){node.querySelectorAll("template[shadowroot]").forEach(element=>{let shadowRoot = element.parentElement.shadowRoot;if (!shadowRoot) {try {shadowRoot=element.parentElement.attachShadow({mode:element.getAttribute("shadowroot")});shadowRoot.innerHTML=element.innerHTML;element.remove()} catch (error) {} if (shadowRoot) {processNode(shadowRoot)}}})}})()</script> |