Modelación estocástica de los caudales en la cuenca del río Santa
Authors
Manrique Díaz Salas, Abelardo
Abstract
La interpretación del comportamiento temporal y espacial de las descargas medias anuales o mensuales, se ha realizado a través de modelos estocásticos de series estacionarias, donde se ha encontrado el modelo autorregresivo de orden 1 AR(1) como adecuado para el caso de caudales medios anuales y al modelo periódico autorregresivo de orden 1 PAR(1), para el caso de caudales medios mensuales. La interpretación regional del comportamiento temporal y espacial de las descargas medias anuales se ha realizado mediante un modelo regional que está en función del área de la cuenca del porcentaje del área glaciar y del parámetro regional anual fi; la componente aleatoria que depende de la variancia del error del modelo AR(1) se estima en función al área de la cuenca, del porcentaje de área glaciar. La interpretación regional del comportamiento temporal y espacial de las descargas mensuales se ha realizado en función del área de la cuenca del porcentaje del área glaciar y del parámetro del parámetro regional mensual fi;la componente aleatoria depende de la variancia mensual del error del modelo PAR(1), se estima en función al área de la cuenca y porcentaje del área glaciar. Estas variables aleatorias tienen media cero y variancia constante. Para verificar la validación del modelamiento se ha comparado las estadísticas media y variancia de las series históricas y de las series generadas, a nivel anual y mensual, donde se ha encontrado resultados aceptables, es decir, se puede inferir que los datos provienen de una misma población.
The interpretation of temporal and spatial behavior average unloading both annual or monthly were done through stochastical models of stationary series, where we have found the autoregressive model of proportion 1 AR (1) convenient for the case of annual average water flow and the periodical autoregressive model of proportion 1 PAR (1) for the case of monthly average water flow. The regional interpretation for temporal and spatial behavior of annual average unloading were done through a regional model which is in accordance with the area of the basin, percentage of glacier area and the fi annual regional parameter, the random component is estimated based on the area of the basin, which depends on the error variance model AR (1). The regional behavior from the monthly verage unloadings were done in accordance with the area the basin, a percentage of glacier area and the monthly regional parameter fi, the random component is estimated based on the area of the basin, which depends on the monthly error variance model PAR (1). To verify the validity of the modeling we have compared average statistics and variance of the historical series and of the generated series, both at the annual and monthly level and where we have found acceptable results; that is to say, that we can infer that these data come from the same population.
The interpretation of temporal and spatial behavior average unloading both annual or monthly were done through stochastical models of stationary series, where we have found the autoregressive model of proportion 1 AR (1) convenient for the case of annual average water flow and the periodical autoregressive model of proportion 1 PAR (1) for the case of monthly average water flow. The regional interpretation for temporal and spatial behavior of annual average unloading were done through a regional model which is in accordance with the area of the basin, percentage of glacier area and the fi annual regional parameter, the random component is estimated based on the area of the basin, which depends on the error variance model AR (1). The regional behavior from the monthly verage unloadings were done in accordance with the area the basin, a percentage of glacier area and the monthly regional parameter fi, the random component is estimated based on the area of the basin, which depends on the monthly error variance model PAR (1). To verify the validity of the modeling we have compared average statistics and variance of the historical series and of the generated series, both at the annual and monthly level and where we have found acceptable results; that is to say, that we can infer that these data come from the same population.
Description
Universidad Nacional Agraria La Molina. Escuela de Posgrado. Doctorado en Recursos Hídricos
Keywords
Cursos de agua; Cuencas hidrográficas; Disponibilidad del agua; Modelos matemáticos; Modelos de simulación; Procesamiento de información; Aplicaciones del ordenador; Métodos estadísticos; Evaluación; Perú; Caudal de agua; Modelación estocástica; Cuenca del Río Santa; Ancash (Dpto); La libertad (Dpto)
Citation
Date
2017
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Licencia de uso
Excepto si se señala otra cosa, la licencia del ítem se describe como info:eu-repo/semantics/openAccess