<?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>1405-7743</journal-id>
<journal-title><![CDATA[Ingeniería, investigación y tecnología]]></journal-title>
<abbrev-journal-title><![CDATA[Ing. invest. y tecnol.]]></abbrev-journal-title>
<issn>1405-7743</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Facultad de Ingeniería]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-77432017000200199</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Uncertainty Related to Processed Gridded Meteorological Data: Implications for Hydrological Modelling]]></article-title>
<article-title xml:lang="es"><![CDATA[Incertidumbre relacionada con datos meteorológicos procesados en malla: implicaciones en la modelación hidrológica]]></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 contrib-type="author">
<name>
<surname><![CDATA[Dávila-Ortiz]]></surname>
<given-names><![CDATA[Rodrigo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Consejo Nacional de Ciencia y Tecnología  ]]></institution>
<addr-line><![CDATA[San Luis Potosí ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma de San Luís Potosí Facultad de Ingeniería ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2017</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2017</year>
</pub-date>
<volume>18</volume>
<numero>2</numero>
<fpage>199</fpage>
<lpage>208</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-77432017000200199&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-77432017000200199&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-77432017000200199&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Spatial interpolation is a procedure for estimating the value of a variable of interest at unsampled sites within an area covered by existing observations. The output of spatial interpolation is an integrated data set in which meteorological data are arranged along an evenly spaced matrix (gridded data). This work evaluates the uncertainty related to meteorological gridded data in the simulation of daily streamflow over two Mexican basins. The use of gridded data is an alternative to direct observations in those Mexican regions with low density of gauging stations. First, two meteorological data sets (observed and processed gridded data) were compared. Results show that gridded data underestimate precipitation, maximum and minimum temperature, despite the relative good agreement in the annual cycle for the latter variable. Second, the lumped conceptual rainfall-runoff model GR4J was fed with meteorological data from both data sets in order to evaluate the error that gridded data translate to simulated daily streamflow. Results show that the hydrological model can be calibrated with both data sets, leading to a good performance for medium and high flows in terms of the Nash- Sutcliffe efficiency coefficient; nevertheless, low flows are overestimated when gridded meteorological data are used. The analysis of the GR4J optimized parameters shows that the hydrological model increases the contribution of groundwater exchange to compensate for the underestimated precipitation, leading to a misrepresentation of the hydrological response of the study basins. All in all, gridded processed meteorological data should be evaluated before its use on hydrological risk assessment and climate change impact studies on water resources.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La interpolación espacial es un procedimiento estadístico para estimar los valores de las variables meteorológicas en sitios sin medición que se encuentran en un área con registros de observaciones. El resultado de esta interpolación espacial es una base de datos ordenada en una matriz (datos en malla). Este trabajo evalúa la incertidumbre relacionada con los datos meteorológicos procesados en malla en la modelación hidrológica del caudal diario en dos cuencas localizadas en México. El uso de datos meteorológicos en malla es una alternativa a las observaciones directas en aquellas regiones en México con una baja densidad de estaciones meteorológicas. En un primer paso, dos conjuntos de datos (observaciones y datos en malla) se compararon. Los resultados muestran que los datos en malla subestiman la precipitación y las temperaturas mínimas y máximas en las cuencas de estudio, aunque en el último caso muestran cierta correspondencia en el ciclo anual. En un segundo paso, los dos tipos de datos meteorológicos se emplearon como datos de entrada en el modelo hidrológico conceptual global GR4J para evaluar el error causado cuando se simula el caudal con los datos en malla. Los resultados muestran que GR4J se puede calibrar con ambos conjuntos de datos obteniéndose un buen desempeño en la simulación de caudales medios y altos en términos del coeficiente de eficiencia Nash-Sutcliffe. Sin embargo, los caudales bajos son sobre estimados cuando se simulan con los datos en malla. El análisis de los parámetros optimizados de GR4J muestra que el modelo hidrológico aumenta el intercambio del agua subterránea para compensar por la subestimación de la precipitación, lo que lleva a una interpretación errónea de la respuesta hidrológica de la cuenca. Por esta razón se debe evaluar la calidad de los datos meteorológicos en malla antes de su uso en la evaluación de riesgos hidrológicos y en los estudios de impacto del cambio climático en los recursos hídricos.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[uncertainty]]></kwd>
<kwd lng="en"><![CDATA[precipitation]]></kwd>
<kwd lng="en"><![CDATA[hydrological modelling]]></kwd>
<kwd lng="en"><![CDATA[GR4J]]></kwd>
<kwd lng="es"><![CDATA[incertidumbre]]></kwd>
<kwd lng="es"><![CDATA[precipitación]]></kwd>
<kwd lng="es"><![CDATA[modelación hidrológica]]></kwd>
<kwd lng="es"><![CDATA[GR4J]]></kwd>
</kwd-group>
</article-meta>
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