<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0188-6266</journal-id>
<journal-title><![CDATA[Acta universitaria]]></journal-title>
<abbrev-journal-title><![CDATA[Acta univ]]></abbrev-journal-title>
<issn>0188-6266</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Guanajuato, Dirección de Investigación y Posgrado]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0188-62662022000100150</article-id>
<article-id pub-id-type="doi">10.15174/au.2022.3426</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Metodología para evaluar la vulnerabilidad de sistemas hidrológicos ante los potenciales efectos del cambio climático]]></article-title>
<article-title xml:lang="en"><![CDATA[Methodology to assess the vulnerability of hydrological systems to the potential effects of climate change]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martínez Bárcenas]]></surname>
<given-names><![CDATA[Adrián]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Orozco Medina]]></surname>
<given-names><![CDATA[Ismael]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Guanajuato  ]]></institution>
<addr-line><![CDATA[Guanajuato ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Guanajuato  ]]></institution>
<addr-line><![CDATA[Guanajuato ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2022</year>
</pub-date>
<volume>32</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-62662022000100150&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-62662022000100150&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-62662022000100150&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Existe una gran incertidumbre sobre el grado de vulnerabilidad de un sistema hidrológico ante los potenciales efectos del cambio climático, principalmente la posible alteración del régimen pluvial, además de las prácticas poco eficientes, la sobreexplotación de acuíferos y el tratamiento inadecuado de aguas. Es por ello que esta investigación se ha enfocado a determinar la vulnerabilidad actual y futura de los sistemas hidrológicos a través de una metodología que acopla un modelo hidrológico distribuido, un modelo de circulación general, un modelo de crecimiento poblacional, redes neuronales artificiales (ANN, por sus siglas en inglés) y una serie de indicadores que describen de forma integral el sistema. Esta metodología ha permitido proyectar la vulnerabilidad del sistema al futuro cercano (año 2035) para los escenarios RCP4.5, RCP6.0 y RCP8.5. Los resultados obtenidos muestran que el sistema ya es vulnerable y que el escenario RCP4.5 presenta las mayores vulnerabilidades con una alta variabilidad espacial y temporal.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract There is uncertainty about the degree of vulnerability of a hydrological system to the effects of climate change, especially the possible alteration of the pluvial regime along with inefficient practices, overexploitation of aquifers, and inadequate water treatment. For this reason, this research focuses on determining current and future vulnerability of hydrological systems through a methodology that combines a distributed hydrological model, a general circulation model, a population growth model, artificial neural networks (ANN), and a series of indicators that describe the system comprehensively. This methodology has made it possible to project the system's vulnerability in the near future (the year 2035) for the RCP4.5, RCP6.0, and RCP8.5 scenarios. The results show that the system is already vulnerable and that the RCP4.5 scenario presents the highest vulnerabilities with high spatial and temporal variability.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Vulnerabilidad]]></kwd>
<kwd lng="es"><![CDATA[cambio climático]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales artificiales]]></kwd>
<kwd lng="en"><![CDATA[Vulnerability]]></kwd>
<kwd lng="en"><![CDATA[climate change]]></kwd>
<kwd lng="en"><![CDATA[artificial neural network]]></kwd>
</kwd-group>
</article-meta>
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