<?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>0188-4611</journal-id>
<journal-title><![CDATA[Investigaciones geográficas]]></journal-title>
<abbrev-journal-title><![CDATA[Invest. Geog]]></abbrev-journal-title>
<issn>0188-4611</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Instituto de Geografía]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0188-46112019000200104</article-id>
<article-id pub-id-type="doi">10.14350/rig.59757</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Análisis de la variabilidad espacial en la precipitación en la zona metropolitana de Querétaro empleando ecuaciones de anisotropía]]></article-title>
<article-title xml:lang="en"><![CDATA[Temporal and spatial variability analysis of daily precipitation in the metropolitan area of Queretaro, using an anisotropy scheme]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gutiérrez-López]]></surname>
<given-names><![CDATA[Alfonso]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Fortanell Trejo]]></surname>
<given-names><![CDATA[Mabel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Albuquerque Gonzalez]]></surname>
<given-names><![CDATA[Nadia Ingrid]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bravo Prado]]></surname>
<given-names><![CDATA[Fernando]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Querétaro Centro de Investigaciones del Agua ]]></institution>
<addr-line><![CDATA[Santiago de Querétaro Querétaro]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2019</year>
</pub-date>
<numero>99</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-46112019000200104&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-46112019000200104&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-46112019000200104&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La cartografía en hidrología es una de las formas tradicionales de representar la variabilidad espacial de eventos climáticos y ambientales. Sin embargo, pocas veces se pone atención a cómo se construyen, por ejemplo, los mapas de isoyetas. Una incorrecta selección del método de interpolación espacial puede ocasionar errores en la estimación de magnitudes. Se realiza una caracterización espacial y temporal de la variabilidad en la precipitación horaria de la zona metropolitana de Santiago de Querétaro. Se utilizan láminas de lluvia medidas a cada minuto de las diez tormentas más intensas registradas en los años 2013, 2014 y 2015. Utilizando variogramas direccionales, distancia entre estaciones y un esquema de anisotropía se obtienen los parámetros óptimos a emplear en una interpolación por Kriging. Los resultados muestran que en el mes de junio existe una mayor variabilidad de la precipitación en la zona metropolitana y que la lámina de precipitación tiene una correlación directa con la distancia entre estaciones. El análisis permite seleccionar de forma correcta los variogramas direccionales que deben emplearse para la interpolación espacial y la cartografía de campos de lluvia.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Cartography in hydrology is one of the traditional ways of representing the spatial variability of climatic and environmental events. However, attention is hardly ever focused on how, for example, isohyets maps are constructed. Incorrect selection of the spatial interpolation method can lead to errors in the estimation of magnitudes. Because the early warning systems that work in the State of Querétaro and are used by the Civil Protection authorities during the rainy season, they transmit their information every minute. It is necessary to have spatial interpolation tools that allow us to obtain values with a minimum of error. The warning systems have a spatial interpolation subroutine (cartography) incorporated, in such a way that the authorities can estimate in real time the precipitation level in a determined place, even though there is no meteorological station in situ. A spatial and temporal characterization of the variability in the hourly precipitation of the metropolitan area of Santiago de Querétaro is carried out. Rain gauges are used, measured at each minute of the ten most intense storms recorded in the years 2013, 2014 and 2015. Using directional variograms, distance between stations and an anisotropy scheme, the optimal parameters to be used in a Kriging interpolation are obtained. The comparison of the variograms, for example for the month of July, for the years 2013, 2014, 2015 made it possible to identify that the range of validity of the measurements (distance) does not change in spite of the fact that the theoretical formulation of the variogram is modified. In this way, a parameter called alpha can be introduced to explain the spatial variability of rainfall. This is useful because it shows that the precipitation record is a function of the distance at which the observations are valid, something that was already known but not verified in urban areas. The results show that in June there is a greater variability of precipitation in the metropolitan area and that the precipitation pattern has a direct correlation with the distance between rain stations. The analysis allows the correct selection of directional variograms to be used for spatial interpolation and rain field mapping. As an example, data are taken from the storm of August 18, 2014, which was a severe storm that caused severe economic and material damage in the metropolitan area of the city of Queretaro. With this scheme of variability was made the mapping of the isohyets of the storm that day, considering an interpolation with traditional Kriging and a cartography affected by a directional variogram and the alpha parameter. In order to validate the proposed methodology, a cross validation was carried out with the Historical Center rain station, as it is the area most attended by the authorities during the rainy season. The station data were omitted in the estimation of the interpolation of the grid to generate the cartography. Also, 7800 points were calculated to generate a grid interpolated with the proposed formulation and it was demonstrated that the estimated values are acceptable. The results of this work will make it possible to calibrate the spatial interpolation models that work in the hydro-informatics tools used by the Civil Protection authorities.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[precipitación]]></kwd>
<kwd lng="es"><![CDATA[anisotropía]]></kwd>
<kwd lng="es"><![CDATA[variograma]]></kwd>
<kwd lng="es"><![CDATA[geoestadística]]></kwd>
<kwd lng="es"><![CDATA[Querétaro-México]]></kwd>
<kwd lng="en"><![CDATA[precipitation]]></kwd>
<kwd lng="en"><![CDATA[anisotropy]]></kwd>
<kwd lng="en"><![CDATA[variograph]]></kwd>
<kwd lng="en"><![CDATA[geostatistics]]></kwd>
<kwd lng="en"><![CDATA[Queretaro-Mexico]]></kwd>
</kwd-group>
</article-meta>
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