<?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>S2007-24222019000500075</article-id>
<article-id pub-id-type="doi">10.24850/j-tyca-2019-05-03</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Ajuste con momentos L de las distribuciones no estacionarias GVE1 y GVE2 a series de PMD]]></article-title>
<article-title xml:lang="en"><![CDATA[Fitting with L-moments of the non-stationary distributions GVE1 and GVE2 to PMD series]]></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>10</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>10</month>
<year>2019</year>
</pub-date>
<volume>10</volume>
<numero>5</numero>
<fpage>75</fpage>
<lpage>105</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-24222019000500075&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-24222019000500075&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-24222019000500075&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Las crecientes de diseño permiten el dimensionamiento hidrológico de las obras hidráulicas. Cuando no existen datos hidrométricos, las crecientes de diseño se estiman con métodos hidrológicos que se basan en las lluvias de diseño. La escasez de estaciones pluviográficas origina que los registros más comunes empleados para estimar las lluvias de diseño sean los de precipitación máxima diaria (PMD) anual. Debido a los impactos del cambio climático y/o a la alteración del entorno geográfico de las estaciones pluviométricas, los registros de PMD están mostrando tendencias y por lo tanto son no estacionarios. El análisis probabilístico de los registros de PMD no estacionarios, orientado a estimar predicciones de baja probabilidad de excedencia que puede realizar, de manera simple y sin dificultades computacionales, con base en la extensión del método de los momentos L para aplicar la distribución general de valores extremos (GVE) con su parámetro de ubicación (u) variable con el tiempo (t) en años, se introduce como covariable. Cuando la tendencia en el registro de PMD es lineal, se aplica el modelo probabilístico GVE1, en el cual u  t = &#956;0 + &#956;1·t y cuando es curva el modelo GVE2 con u  t = &#956;0 + &#956;1·t + &#956;2·t 2. Entonces, la distribución GVE1 tiene cuatro parámetros de ajuste (&#956;0, &#956;1, &#945;, k) y la GVE2 cinco (&#956;0, &#956;1, &#956;2, &#945;, k). Se describen cuatro aplicaciones numéricas, y a través del análisis de sus resultados se demuestra la sencillez de la extensión del método de los momentos L y su versatilidad para estimar predicciones dentro del registro histórico y a futuro.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Design Floods allow the hydrological sizing of hydraulic works. When hydrometric data is not available, design floods are estimated using hydrological methods that are based on Design Rainfalls. The most common records used to estimate design rainfalls are the annual maximum daily precipitations (PMD), this, due to the scarcity of rainfall recorder stations. The impacts of climate change and/or the alteration of the geographic environment of rain-gauge stations cause PMD records to show trends and therefore these records become non-stationary. In order to estimate predictions of low probability of exceedance a probabilistic analysis of the non-stationary PMD records can be performed. A simple approach without computational difficulties is based on the extension of the method of L moments applied to the General of Extreme Values (GVE) distribution with its location parameter (u) variable with time (t) in years, which is entered as a covariate. When the trend in the PMD register is linear, the probabilistic model GVE1 is applied in which u  t = &#956; 0 + &#956; 1· t and when the trend is curve the model GVE2 with u  t = &#956; 0 + &#956; 1· t + &#956; 2·t 2 is used. Thus, the GVE1 distribution has four fit parameters (&#956; 0, &#956; 1, &#945;, k) and five for the GVE2 distribution (&#956; 0, &#956; 1, &#956; 2, &#945;, k). Four numerical applications are described and the analysis of their results shows the simplicity of the extension of the L moments method and its versatility to estimate predictions within the historical record and to the future.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[momentos L]]></kwd>
<kwd lng="es"><![CDATA[distribución GVE]]></kwd>
<kwd lng="es"><![CDATA[error estándar de ajuste]]></kwd>
<kwd lng="es"><![CDATA[regresión lineal]]></kwd>
<kwd lng="es"><![CDATA[regresión parabólica]]></kwd>
<kwd lng="es"><![CDATA[determinantes]]></kwd>
<kwd lng="es"><![CDATA[regresión lineal múltiple]]></kwd>
<kwd lng="en"><![CDATA[L moments]]></kwd>
<kwd lng="en"><![CDATA[GEV distribution]]></kwd>
<kwd lng="en"><![CDATA[standard error of fit]]></kwd>
<kwd lng="en"><![CDATA[linear regression]]></kwd>
<kwd lng="en"><![CDATA[parabolic regression]]></kwd>
<kwd lng="en"><![CDATA[determinants]]></kwd>
<kwd lng="en"><![CDATA[multiple linear regression]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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