<?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>2007-4026</journal-id>
<journal-title><![CDATA[Ingeniería agrícola y biosistemas]]></journal-title>
<abbrev-journal-title><![CDATA[Ing. agric. biosist.]]></abbrev-journal-title>
<issn>2007-4026</issn>
<publisher>
<publisher-name><![CDATA[Universidad Autónoma Chapingo]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-40262023000200023</article-id>
<article-id pub-id-type="doi">10.5154/r.inagbi.2023.06.030</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Evaluation of the SPI with high-resolution datasets in historical and future climate in central Mexico]]></article-title>
<article-title xml:lang="es"><![CDATA[Evaluación del índice de precipitación estandarizado (SPI) con datos de alta resolución en clima histórico y futuro en el centro de México]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Velázquez-Zapata]]></surname>
<given-names><![CDATA[Juan Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,El Colegio de San Luis A.C  ]]></institution>
<addr-line><![CDATA[ San Luis Potosí]]></addr-line>
<country>MÉXICO</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<volume>15</volume>
<numero>2</numero>
<fpage>23</fpage>
<lpage>43</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-40262023000200023&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-40262023000200023&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-40262023000200023&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Introduction:  Drought monitoring needs good quality meteorological data, but records frequently face problems. Therefore, satellite precipitation datasets are an alternative.  Objective:  Evaluation of three high-resolution datasets (compared with observed precipitation) to capture wet and dry periods. In addition, the influence of the selection of the dataset to downscale future climate simulations is evaluated in the estimation of drought indicators.  Methodology:  Precipitation products (NOAA, CHIRPS and PERSIANN-CDR) were compared with observations from seven meteorological stations (1983-2013). The 12-month standard precipitation index (SPI) was selected to evaluate drought conditions. Also, precipitation estimates from CanESM2 were downscaled and bias-corrected with each dataset.  Results:  The annual precipitation cycle is well capture, but underestimation is noted. The datasets have a good correlation, but less variability. Regarding the SPI, results show good correlation, but extremely dry events are generally underestimated with CHIRPS and PERSIANN-CDR. The NOAA dataset performs better in terms of categorical scores, especially for wet events. Similar median values were found in the drought indicators in future; however, the datasets lead to less variability than observations, especially in the drought frequency indicator.  Study limitations:  Limited number of good quality meteorological records.  Originality:  Selected precipitation datasets are tested under different climatic conditions for the first time in Mexico. Also, the influence of the reference precipitation dataset for bias correction is evaluated on future drought projections.  Conclusions: Precipitation products should be tested before their use in monitoring droughts in the historical period, as well as in estimating droughts with future projections.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Introducción:  El monitoreo de la sequía necesita datos meteorológicos de buena calidad, pero los registros suelen presentar problemas. Por ello, las bases de datos de precipitación estimadas con percepción satelital son una alternativa.  Objetivo:  Evaluar tres bases de datos de precipitación de alta resolución para identificar los periodos húmedos y secos, y determinar la influencia de la selección de datos en la reducción de la escala de simulaciones climáticas en el futuro para la estimación de indicadores de sequía.  Metodología:  Los productos de precipitación (NOAA, CHIRPS y PERSIANN-CDR) se compararon con las observaciones de siete estaciones meteorológicas (1983-2013). Se seleccionó el índice de precipitación estandarizado (SPI) de 12 meses para evaluar las condiciones de sequía. Las estimaciones de precipitación de CanESM2 se redujeron de escala y el sesgo se corrigió con cada base de datos.  Resultados:  El ciclo anual de precipitaciones se simuló de manera adecuada, aunque con subestimación. Las bases de datos tuvieron buena correlación y poca variabilidad. En cuanto al SPI, los resultados mostraron buena correlación, pero los fenómenos extremadamente secos se subestimaron con CHIRPS y PERSIANN-CDR. NOAA presentó mejores resultados, especialmente para los periodos húmedos. Se encontraron valores medianos similares en los indicadores de sequía a futuro; sin embargo, las bases de datos mostraron menor variabilidad que las observaciones, especialmente en el indicador de frecuencia de sequía.  Limitaciones del estudio:  Limitados registros meteorológicos de buena calidad.  Originalidad:  Se evaluaron las bases de datos de precipitación seleccionadas en distintas condiciones climáticas, y se determinó su influencia al ser usadas como precipitación de referencia para la corrección de sesgos en las proyecciones futuras de sequía.  Conclusiones: Los productos de precipitación se deben probar antes de su uso en el monitoreo de sequías en el periodo histórico, así como en la estimación de sequías con proyecciones futuras.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[drought]]></kwd>
<kwd lng="en"><![CDATA[standard precipitation index]]></kwd>
<kwd lng="en"><![CDATA[CHIRPS]]></kwd>
<kwd lng="en"><![CDATA[PERSIANN-CDR]]></kwd>
<kwd lng="en"><![CDATA[climate change]]></kwd>
<kwd lng="es"><![CDATA[sequía]]></kwd>
<kwd lng="es"><![CDATA[índice de precipitación estandarizado]]></kwd>
<kwd lng="es"><![CDATA[CHIRPS]]></kwd>
<kwd lng="es"><![CDATA[PERSIANN-CDR]]></kwd>
<kwd lng="es"><![CDATA[cambio climático]]></kwd>
</kwd-group>
</article-meta>
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