<?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>S2007-24222020000300107</article-id>
<article-id pub-id-type="doi">10.24850/j-tyca-2020-03-04</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Detección de registros homogéneos en 16 series amplias de precipitación anual del Altiplano Potosino, México]]></article-title>
<article-title xml:lang="en"><![CDATA[Detection of homogeneous records in 16 large series of annual precipitation of the Potosino Plateau, 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[ San Luis Potosí]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2020</year>
</pub-date>
<volume>11</volume>
<numero>3</numero>
<fpage>107</fpage>
<lpage>157</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-24222020000300107&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-24222020000300107&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-24222020000300107&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Los métodos de homogenización han sido desarrollados para reducir el impacto de las alteraciones no climáticas en los registros o series de datos procedentes de las estaciones climatológicas. Tales impactos proceden de cambios en la ubicación de la estación, en el equipo utilizado y en la técnica de medición, así como por las alteraciones que sufre su entorno. Debido a que la información histórica sobre los cambios ocurridos en la estación climatológica por lo general no está disponible, se recurre a aplicar pruebas estadísticas para detectar puntos de quiebre que implican cambios en la media y, por lo tanto, pérdida de homogeneidad del registro. Existen dos enfoques de aplicación de las pruebas estadísticas: (1) de manera individual a cada registro por probar y (2) haciendo uso de series de referencia, formadas con base en estaciones circunvecinas al registro que se prueba. En este estudio se aplicó el primer enfoque, en una zona geográfica y haciendo uso de las cuatro pruebas siguientes: Von Neumann, Pettitt, Buishand y SNHT. Se procesaron 16 series de precipitación anual del Altiplano Potosino de México, en el periodo común de 1964 a 2016, cuya amplitud varió de 42 a 53 datos. Se trabajó con datos mensuales, y por ello se expone cómo se dedujeron los valores faltantes, y cómo se detectaron y corrigieron los máximos extremos. También se describe cómo se estimaron los valores anuales faltantes. Se encontró que 11 registros resultaron confiables debido a que ninguna prueba los detectó como no homogéneos. Tres registros se clasificaron como menos confiables, pues un máximo de dos pruebas los encontró no homogéneos y, por último, dos registros son no confiables, ya que un mínimo de tres pruebas los encuentra no homogéneos. El procedimiento adoptado formuló una estrategia práctica para detectar registros homogéneos de precipitación anual, cuyas ventajas se exponen en el estudio.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Homogenization methods have been developed to reduce the impact of non-climatic alterations in the records or series of data coming from climatological stations. Such impacts arise from changes either in the location of the station, in the equipment used or in the measurement technique, as well as from alterations suffered by the surroundings. Due to the fact that the historic information on the changes that occurred in the climatological station is generally not available, statistical tests are used to detect breaking points that imply changes in the mean and, therefore, loss of homogeneity of the record. There are two approaches to the application of statistical tests: (1) individually to each record to be tested and (2) using reference series based on neighboring stations. In this study, the first approach was applied to a specific geographical area and using the following four tests: Von Neumann, Pettitt, Buishand and SNHT. Sixteen annual precipitation series of the Potosino Plateau of Mexico were processed in the common period from 1964 to 2016, the amount of values varied from 42 to 53 data. Monthly data were used; hence, the procedures to deduce missing values and to detect and correct extreme maximums are exposed. A description of how the missing annual values were estimated is also included. It was found that 11 records were reliable, because no test detected them as non-homogeneous. Three records were classified as less reliable, because a maximum of two tests found them non-homogeneous and finally, two records are unreliable, since a minimum of three tests find them non-homogeneous. The adopted procedure proposes a practical strategy to detect homogeneous records of annual precipitation; its advantages are exposed at the study.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[homogenización]]></kwd>
<kwd lng="es"><![CDATA[valores máximos dispersos]]></kwd>
<kwd lng="es"><![CDATA[test de Von Neumann]]></kwd>
<kwd lng="es"><![CDATA[test del rango de Pettitt]]></kwd>
<kwd lng="es"><![CDATA[test del rango de Buishand]]></kwd>
<kwd lng="es"><![CDATA[prueba SNHT]]></kwd>
<kwd lng="es"><![CDATA[precipitación mensual y anual]]></kwd>
<kwd lng="en"><![CDATA[homogenization]]></kwd>
<kwd lng="en"><![CDATA[maximum outliers values]]></kwd>
<kwd lng="en"><![CDATA[von Neumann test]]></kwd>
<kwd lng="en"><![CDATA[Pettitt rank test]]></kwd>
<kwd lng="en"><![CDATA[Buishand rank test]]></kwd>
<kwd lng="en"><![CDATA[SNHT test]]></kwd>
<kwd lng="en"><![CDATA[monthly and annual rainfall]]></kwd>
</kwd-group>
</article-meta>
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