<?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>0187-6236</journal-id>
<journal-title><![CDATA[Atmósfera]]></journal-title>
<abbrev-journal-title><![CDATA[Atmósfera]]></abbrev-journal-title>
<issn>0187-6236</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Instituto de Ciencias de la Atmósfera y Cambio Climático]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0187-62362023000300114</article-id>
<article-id pub-id-type="doi">10.20937/atm.53103</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Application of geostatistical models for aridity scenarios in northern Mexico]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Correa-Islas]]></surname>
<given-names><![CDATA[Javier de Jesus]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Romero-Padilla]]></surname>
<given-names><![CDATA[Juan Manuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez-Rodríguez]]></surname>
<given-names><![CDATA[Paulino]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vázquez-Alarcón]]></surname>
<given-names><![CDATA[Antonio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Colegio de Postgraduados PSEI-Estadística ]]></institution>
<addr-line><![CDATA[ Estado de México]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma Chapingo Departamento de Suelos ]]></institution>
<addr-line><![CDATA[ Estado de México]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<volume>37</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0187-62362023000300114&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0187-62362023000300114&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0187-62362023000300114&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT An annual mean temperature map was calculated using the Kriging interpolation method for the north-central zone of Mexico to obtain the current aridity, as well as possible scenarios for the near and distant future. The altitudinal gradient was estimated by linear regression, and it was used to estimate the mean temperature. Climate Influence Areas (CIA) were obtained by superimposing the official precipitation layer and the annual mean temperature layer using Geographic Information Systems tools. Monthly databases of climatic variables were generated for each CIA and potential evapotranspiration was estimated using the Thorthwaite methodology. The Aridity Index (AI) was calculated and mapped for a base scenario (1970-2000). Subsequently, the aridity behavior of some scenarios was projected and mapped using the global climate models HADGEM 2.0, GFDLCM 3.0, MIP_ESM, and CRNMCM5. Under the best scenario projected, aridity will weaken the humid ecosystems and in the worst scenario, hyper-arid climates will appear in the study region.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Se calculó un mapa de temperatura media anual mediante el método de interpolación de Kriging para la zona centro-norte de México con la finalidad de obtener la condición actual de aridez, así como posibles escenarios en el futuro cercano y lejano. El gradiente altitudinal se estimó mediante regresión lineal y se usó en la estimación de la temperatura media. Las Áreas de Influencia Climática (CIA) se obtuvieron superponiendo la capa de precipitación oficial y la capa de temperatura media anual con la ayuda de herramientas de Sistemas de Información Geográfica. Se generaron bases de datos mensuales de variables climáticas para cada CIA y se estimó la evapotranspiración potencial utilizando la metodología de Thorthwaite. El Índice de Aridez (IA) se calculó y mapeo para un escenario base (1970-2000). Posteriormente, se proyectó y mapeó el comportamiento de aridez para algunos escenarios, utilizando los modelos de clima global HADGEM 2.0, GFDLCM 3.0, MIP_ESM y CRNMCM5. Se pronosticaron algunos escenarios, en el mejor escenario la aridez debilitará los ecosistemas húmedos y en el peor escenario aparecerán climas hiper áridos en el área de estudio.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Kriging]]></kwd>
<kwd lng="en"><![CDATA[Climate Change]]></kwd>
<kwd lng="en"><![CDATA[Potential Evapotranspiration]]></kwd>
<kwd lng="en"><![CDATA[Linear Regression]]></kwd>
</kwd-group>
</article-meta>
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