<?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>S0187-62362024000100032</article-id>
<article-id pub-id-type="doi">10.20937/atm.53334</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Enhancing geostatistical precipitation estimations for the Santiago River basin, Mexico]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ávila-Carrasco]]></surname>
<given-names><![CDATA[José Roberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Júnez-Ferreira]]></surname>
<given-names><![CDATA[Hugo Enrique]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Herrera]]></surname>
<given-names><![CDATA[Graciela del Socorro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional Autónoma de México Facultad de Ingeniería Departamento de Hidráulica]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma de Zacatecas Unidad Académica de Ciencia y Tecnología de la Luz y la Materia ]]></institution>
<addr-line><![CDATA[Zacatecas Zacatecas]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Nacional Autónoma de México Instituto de Geofísica Departamento de Recursos Naturales]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2024</year>
</pub-date>
<volume>38</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0187-62362024000100032&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0187-62362024000100032&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0187-62362024000100032&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Accurate precipitation estimation is crucial for understanding the hydrological cycle, its applications in basin-specific planning, and outliers event prediction. Multivariate geostatistics leverage correlated variables, such as terrain elevation and shoreline distance, to reduce estimation error uncertainty. However, the distinct characteristics of humid and dry seasons demand specific estimation approaches. Precise precipitation estimation poses a challenge in the vast and diverse Santiago River basin (SRB) along Mexico&#8217;s west coast. This study assessed precipitation estimates for dry and humid seasons using ordinary kriging and ordinary cokriging with altitude and shoreline distance as auxiliary variables. Evaluation of error metrics revealed superior results incorporating shoreline distance as a covariable in the wet month of July, especially after logarithmic transformation, yielding a 17% improvement in average standardized error compared to the univariate approach. Conversely, optimal results were achieved for the dry month (February) using ordinary kriging excluding outliers&#8217; values, effectively reducing the average squared error.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN La estimación precisa de la precipitación es crucial para comprender el ciclo hidrológico, sus aplicaciones en la planificación específica de cuencas y la predicción de eventos extremos. La geoestadística multivariada aprovecha las variables correlacionadas, como la elevación del terreno y la distancia a la costa, para reducir la incertidumbre de la estimación. Sin embargo, las distintas características de las estaciones húmeda y seca exigen enfoques de estimación particulares. La estimación precisa de la precipitación plantea un desafío en la vasta y diversa cuenca del río Santiago (SRB) a lo largo de la costa oeste de México. Este estudio evaluó las estimaciones de precipitación para las estaciones seca y húmeda utilizando kriging ordinario y cokriging ordinario con la altitud y la distancia a la costa como variables auxiliares. La evaluación de las métricas de error reveló resultados superiores al incorporar la distancia a la costa como una covariable en el mes húmedo de julio, especialmente después de la transformación logarítmica, lo que arrojó una mejora del 17 % en el error estandarizado promedio en comparación con el enfoque univariado. Por el contrario, se lograron resultados óptimos para el mes seco (febrero) usando kriging ordinario excluyendo valores atípicos, reduciendo efectivamente el error cuadrático promedio.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[seasonal changes]]></kwd>
<kwd lng="en"><![CDATA[kriging]]></kwd>
<kwd lng="en"><![CDATA[cokriging]]></kwd>
<kwd lng="en"><![CDATA[gridded rain]]></kwd>
<kwd lng="en"><![CDATA[relief variability]]></kwd>
<kwd lng="en"><![CDATA[shoreline distance]]></kwd>
<kwd lng="en"><![CDATA[topographic elevation]]></kwd>
</kwd-group>
</article-meta>
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