Memorias de investigación
Artículos en revistas:
Modelling uncertainty of flood quantile estimation at ungauged sites by Bayesian networks
Año:2013
Áreas de investigación
  • Ingenieria civil
Datos
Descripción
Prediction at ungauged sites is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. Regression models relate physiographic and climatic basin characteristics to flood quantiles, which can be estimated from observed data at gauged sites. However, these models assume linear relationships between variables Prediction intervals are estimated by the variance of the residuals in the estimated model. Furthermore, the effect of the uncertainties in the explanatory variables on the dependent variable cannot be assessed. This paper presents a methodology to propagate the uncertainties that arise in the process of predicting flood quantiles at ungauged basins by a regression model. In addition, Bayesian networks were explored as a feasible tool for predicting flood quantiles at ungauged sites. Bayesian networks benefit from taking into account uncertainties thanks to their probabilistic nature. They are able to capture non-linear relationships between variables and they give a probability distribution of discharges as result. The methodology was applied to a case study in the Tagus basin in Spain.
Internacional
Si
JCR del ISI
Si
Título de la revista
Journal of Hydroinformatics
ISSN
1464-7141
Factor de impacto JCR
1,153
Información de impacto
Datos JCR del año 2012
Volumen
DOI
10.2166/hydro.2013.065
Número de revista
Desde la página
1
Hasta la página
17
Mes
SIN MES
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Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Hidroinformática y Gestión del Agua
  • Departamento: Ingeniería Civil: Hidráulica y Energética