<?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>0187-5779</journal-id>
<journal-title><![CDATA[Terra Latinoamericana]]></journal-title>
<abbrev-journal-title><![CDATA[Terra Latinoam]]></abbrev-journal-title>
<issn>0187-5779</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Mexicana de la Ciencia del Suelo A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0187-57792025000100107</article-id>
<article-id pub-id-type="doi">10.28940/terra.v43i.1953</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Evaluación de Datos de Precipitación de Imágenes CHIRPS en Cuencas de Clima Seco y Tropical (San Luis Potosi) y Templado (Estado de México), México]]></article-title>
<article-title xml:lang="en"><![CDATA[Evaluation of precipitation data from CHIRPS images in dry and tropical (San Luis Potosi) and temperate (Mexico State) basins in Mexico]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rodríguez-Herrera]]></surname>
<given-names><![CDATA[Jorge Guillermo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Amante-Orozco]]></surname>
<given-names><![CDATA[Alejandro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Muñoz-Robles]]></surname>
<given-names><![CDATA[Carlos Alfonso]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pimentel-López]]></surname>
<given-names><![CDATA[José]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ruiz-Vera]]></surname>
<given-names><![CDATA[Víctor M.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Salvador-Osuna]]></surname>
<given-names><![CDATA[Esteban]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Colegio de Postgraduados Posgrado en Innovación en Manejo de Recursos Naturales Campus San Luis Potosí]]></institution>
<addr-line><![CDATA[Salinas de Hidalgo San Luis Potosí]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma de San Luis Potosí Instituto de Investigación de Zonas Desérticas ]]></institution>
<addr-line><![CDATA[ San Luis Potosí]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,INIFAP  ]]></institution>
<addr-line><![CDATA[Pabellón de Arteaga Aguascalientes]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<volume>43</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0187-57792025000100107&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0187-57792025000100107&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0187-57792025000100107&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: La modelación hidrológica requiere datos de precipitación como insumo básico, ya que representa la principal entrada de agua en el sistema hidrológico. Estos datos se obtienen principalmente de estaciones climatológicas, pero en muchos casos la cantidad de estaciones es insuficiente, además de que pueden tener datos faltantes, lo que ocasiona errores en la modelación. Con el avance tecnológico en la teledetección, los datos de precipitación satelitales de libre acceso están tomando importancia como insumo potencial para la modelación hidrológica. El objetivo de esta investigación es evaluar dos fuentes de datos de precipitación: estaciones meteorológicas e imágenes CHIRPS (Climate Hazards Center InfraRed Precipitaion Station). Se compararon ambas fuentes en tres cuencas con climas contrastantes (seco, templado y tropical). El análisis se basó en medidas estadísticas como el coeficiente de correlación de Spearman &#961; (rho), el error medio cuadrático (RMSE) y la prueba de Wilcoxon para detectar correlaciones y diferencias estadísticas. Los resultados revelaron una correlación positiva y significativa (P &lt; 0.05) entre ambas fuentes de datos en las tres cuencas, aunque con variación según el tipo de clima. Los errores de precipitación más bajos (RMSE &lt; 21 mm) se registraron en las zonas templada y seca, mientras que, en la zona tropical, las imágenes CHIRPS subestimaron la precipitación, mientras que la mayor similitud (P &gt; 0.05) entre ambas fuentes se encontró en la zona templada. Por lo tanto, las imágenes CHIRPS son una alternativa viable que puede sustituir a las estaciones meteorológicas en la modelación hidrológica, especialmente en cuencas de climas templados.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Summary: Hydrological modeling requires precipitation data as a basic input since it represents the primary water source entering the hydrological system. These data are mainly obtained from weather stations, but in many cases, the number of stations is low, and may have missing data, leading to errors in modeling. With technological advances in remote sensing, freely accessible satellite precipitation data are gaining importance as a potential input for hydrological modeling. Thus, the objective of the present research is to evaluate two sources of precipitation data: weather stations and CHIRPS (Climate Hazards Center InfraRed Precipitaion Station) imagery. Both sources were compared across three basins with contrasting climates (arid, temperate, and tropical). The analysis was based on statistical measures, such as Spearman&#8217;s correlation coefficient &#961; (rho), Root Mean Square Error (RMSE), and the Wilcoxon test to detect correlations and statistical differences. The results revealed a positive and significant correlation (P &lt; 0.05) between both data sources across the three basins, though with variations depending on the climate type. The RMSE (&lt; 21 mm) were recorded in temperate and arid regions, while in the tropical region, CHIRPS imagery underestimated precipitation. The greatest similarity (P &gt; 0.05) between the two sources was found in the temperate region, which suggests that CHIRPS imagery is a viable alternative to weather stations for hydrological modeling, especially in temperate climate basins.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[estaciones climatológicas]]></kwd>
<kwd lng="es"><![CDATA[gasto]]></kwd>
<kwd lng="es"><![CDATA[imágenes de satélite]]></kwd>
<kwd lng="es"><![CDATA[modelación hidrológica]]></kwd>
<kwd lng="es"><![CDATA[SWAT]]></kwd>
<kwd lng="en"><![CDATA[climate stations]]></kwd>
<kwd lng="en"><![CDATA[runoff]]></kwd>
<kwd lng="en"><![CDATA[satellite images]]></kwd>
<kwd lng="en"><![CDATA[hydrological modeling]]></kwd>
<kwd lng="en"><![CDATA[SWAT]]></kwd>
</kwd-group>
</article-meta>
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