<?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>2007-2422</journal-id>
<journal-title><![CDATA[Tecnología y ciencias del agua]]></journal-title>
<abbrev-journal-title><![CDATA[Tecnol. cienc. agua]]></abbrev-journal-title>
<issn>2007-2422</issn>
<publisher>
<publisher-name><![CDATA[Instituto Mexicano de Tecnología del Agua, Coordinación de Comunicación, Participación e Información]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-24222021000400163</article-id>
<article-id pub-id-type="doi">10.24850/j-tyca-2021-04-04</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Análisis de frecuencias de crecientes con la distribución GVE para r eventos anuales]]></article-title>
<article-title xml:lang="en"><![CDATA[Flood frequency analysis with the GEV distribution for r-annual events]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Campos-Aranda]]></surname>
<given-names><![CDATA[Daniel Francisco]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de San Luis Potosí  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>08</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>08</month>
<year>2021</year>
</pub-date>
<volume>12</volume>
<numero>4</numero>
<fpage>163</fpage>
<lpage>218</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-24222021000400163&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-24222021000400163&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-24222021000400163&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Las crecientes son los gastos máximos anuales de un río y su análisis de frecuencias son técnicas estadísticas que permiten estimar valores asociados con bajas probabilidades de ser excedidos. Tales predicciones permiten el diseño y la revisión hidrológica de las obras hidráulicas. La etapa fundamental del análisis de frecuencias consiste en seleccionar y ajustar, a los datos o muestra disponible, una función de distribución de probabilidades para realizar las predicciones. La distribución general de valores extremos (GVE) tiene bases teóricas que la convierten en un modelo probabilístico adecuado a crecientes, niveles máximos del mar, velocidades extremas de viento, etcétera. Debido a su génesis, la GVE procesa un dato por año, el máximo. Esta desventaja fue superada con el método de los r eventos máximos anuales. Tal procedimiento se describe y aplica al ajuste de la función GVE, a través del método de máxima verosimilitud, utilizando al algoritmo Complex de múltiples variables restringidas, para la maximización numérica. Se integraron cinco registros de crecientes con cinco eventos por año, en estaciones hidrométricas de la Región Hidrológica No. 10 (Sinaloa), México. Además, se procesaron dos registros de niveles máximos del mar con r = 5, tomados de la literatura especializada; uno de ellos es no estacionario al presentar tendencia. Se analizaron los resultados y se contrastaron las predicciones del ajuste de la GVE con r eventos anuales contra las de los métodos clásicos de momentos L y LH. Por último, se formulan las conclusiones, las cuales destacan la conveniencia del método descrito y señalan las ventajas del uso del algoritmo Complex como técnica numérica; debido a lo anterior, se recomienda la aplicación sistemática del procedimiento descrito.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Floods are the maximum annual flows of a river and their frequency analysis are statistical techniques that allow estimating values associated with low probabilities of being exceeded. Such Predictions allow the hydrological design and review of hydraulic works. The fundamental stage of frequency analysis is the selection and fit, to the available data or sample, of a probability distribution function to make predictions. The Generalized Extreme Value (GEV) distribution has theoretical bases that make it a probabilistic model suitable for floods, maximum sea levels, extreme wind speeds, etc. Due to its genesis, the GEV processes one data per year, the maximum. This disadvantage has been overcome with the method of the r annual maximum events. Such procedure is described and applied to the adjustment of the GEV function, through the maximum likelihood method, using the Complex algorithm of multiple restricted variables, for numerical maximization. Five records of floods with five events per year were integrated from hydrometric stations of the Hydrological Region No. 10 (Sinaloa), Mexico. Also, two records of maximum sea levels with r = 5 were processed, taken from specialized literature, one of them is non- stationary since it shows a trend. Results were analyzed and the predictions of the GEV adjustment with r annual events, were compared against those of the classical L- and LH-moment methods. Finally, Conclusions are formulated, which highlight the convenience of the described method and point out the advantages of using the Complex algorithm as a numerical technique; due to the above, the systematic application of the described procedure is recommended.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[distribución GVE]]></kwd>
<kwd lng="es"><![CDATA[método de máxima verosimilitud]]></kwd>
<kwd lng="es"><![CDATA[algoritmo Complex]]></kwd>
<kwd lng="es"><![CDATA[variables de decisión y dependientes]]></kwd>
<kwd lng="es"><![CDATA[test de Wald-Wolfowitz]]></kwd>
<kwd lng="es"><![CDATA[error estándar de ajuste]]></kwd>
<kwd lng="es"><![CDATA[covariables]]></kwd>
<kwd lng="es"><![CDATA[predicciones]]></kwd>
<kwd lng="en"><![CDATA[GEV distribution]]></kwd>
<kwd lng="en"><![CDATA[maximum likelihood method]]></kwd>
<kwd lng="en"><![CDATA[Complex algorithm]]></kwd>
<kwd lng="en"><![CDATA[decision and dependent variables]]></kwd>
<kwd lng="en"><![CDATA[Wald-Wolfowitz test]]></kwd>
<kwd lng="en"><![CDATA[standard error of fit]]></kwd>
<kwd lng="en"><![CDATA[covariates]]></kwd>
<kwd lng="en"><![CDATA[predictions]]></kwd>
</kwd-group>
</article-meta>
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