<?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>S2007-24222019000500034</article-id>
<article-id pub-id-type="doi">10.24850/j-tyca-2019-05-02</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Mejores FDP en 19 series amplias de PMD anual del estado de San Luis Potosí, México]]></article-title>
<article-title xml:lang="en"><![CDATA[Best PDF in 19 large annual series of MDP from the San Luis Potosi state, Mexico.]]></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>34</fpage>
<lpage>74</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-24222019000500034&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-24222019000500034&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-24222019000500034&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Todas las obras hidráulicas se planean y diseñan con base en las crecientes de diseño. Sin información hidrométrica, estas predicciones se estiman con métodos hidrológicos que transforman lluvias de diseño en los gastos buscados. La escasez de pluviógrafos origina que las lluvias de diseño se estimen a partir de los registros de precipitación máxima diaria (PMD) anual de los pluviómetros. El análisis probabilístico de las series de PMD anual es idéntico al de las crecientes; pero aún no se han definido funciones de distribución de probabilidades (FDP) adecuadas o que se deban aplicar bajo precepto, por lo cual es necesario probar varias. Primero se buscó la mejor FDP en el diagrama de cocientes L, que incluye seis modelos de tres parámetros de ajuste. Se realiza una selección objetiva al emplear la distancia absoluta ponderada en los 19 registros de PMD anual con más de 50 datos del estado de San Luis Potosí, México. Después se describen y aplican ocho índices de habilidad descriptiva (HD) a las ocho FDP que fueron contrastadas en cada uno de los 19 registros de PMD. Los resultados se concentran y analizan por áreas geográficas del estado: Altiplano Potosino y Zona Media. Se obtuvo que la FDP Wakeby es un modelo de gran HD y por ello se sugiere que su aplicación se realice bajo precepto. También se definen las dos mejores opciones de FDP en cada uno de los 19 registros procesados de acuerdo con los ocho índices de HD. Por último, se realiza un contraste de predicciones con periodos de retorno de 50, 100, 500 y 1000 años, para explorar de manera somera la habilidad predictiva de las FDP encontradas como mejores opciones. En cada registro se aplican cuatro FDP: la obtenida según el diagrama de cocientes L, las dos mejores FDP según los ocho índices de HD y la distribución Wakeby. Se concluye que el uso del diagrama de cocientes L y la aplicación de los ocho índices de HD son adecuados y conducen a una buena aproximación, pues no se tuvo dificultad para seleccionar las predicciones adoptadas y la similitud que mostraron estas estimaciones en cada registro genera confianza en tales estimaciones.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract All hydraulic works are planned and designed based on Floods Design. Without hydrometric information, these predictions are estimated using hydrological methods that yield the sought flows by means of design rainfalls. Design rainfall is estimated based on pluviometer records of annual maximum daily precipitation (MDP) due to the shortage of pluviographs. The probabilistic analysis of the annual MDP series is identical to that of the floods; however, neither adequate probability distribution functions (PDFs) nor those that should be applied by precept have been defined so far, hence the need to try several. First, the best PDF was searched for using the L-ratio diagram, which includes six models with three fit parameters. An objective selection is made by using the weighted absolute distance, in the 19 annual MDP records with more than 50 data from the state of San Luis Potosi, Mexico. Then eight descriptive ability (DA) indexes are described and applied to the eight PDFs that were compared, in each of the 19 PMD records. The results are concentrated and analyzed for geographic areas of the state: Potosino Plateau and Middle Zone. Results show that Wakeby PDF is a model having high DA and for that reason, its application is suggested as precept. The two best PDF options are also highlighted in each of the 19 records processed, according to the eight DA indexes. Finally, a comparison of predictions with periods of return of 50, 100, 500 and 1000 years is carried out to explore shallowly the predictive ability of the PDFs found as best options. In each registry four PDFs are applied, the one obtained according to the L-ratio diagram; the two best PDFs according to the eight DA indices and the Wakeby distribution. It is concluded that the use of the L-ratio diagram and the application of the eight DA indexes are adequate and lead to a good approximation, since it was not difficult to select the adopted predictions, besides the similarity of the predictions calculated in each register promotes confidence in such estimations.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[diagrama de cocientes L]]></kwd>
<kwd lng="es"><![CDATA[error estándar de ajuste]]></kwd>
<kwd lng="es"><![CDATA[error relativo estándar de ajuste]]></kwd>
<kwd lng="es"><![CDATA[error absoluto medio]]></kwd>
<kwd lng="es"><![CDATA[error absoluto máximo]]></kwd>
<kwd lng="es"><![CDATA[criterio de información de Akaike]]></kwd>
<kwd lng="es"><![CDATA[coeficiente de correlación de Q-Q]]></kwd>
<kwd lng="es"><![CDATA[índices de concordancia y habilidad predictiva]]></kwd>
<kwd lng="en"><![CDATA[L-ratio diagram]]></kwd>
<kwd lng="en"><![CDATA[standard error of fit]]></kwd>
<kwd lng="en"><![CDATA[relative standard error of fit]]></kwd>
<kwd lng="en"><![CDATA[mean absolute error]]></kwd>
<kwd lng="en"><![CDATA[maximum absolute error]]></kwd>
<kwd lng="en"><![CDATA[Akaike information criterion]]></kwd>
<kwd lng="en"><![CDATA[Q-Q correlation coefficient]]></kwd>
<kwd lng="en"><![CDATA[concordance indexes and predictive ability]]></kwd>
</kwd-group>
</article-meta>
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