Memorias de investigación
Artículos en revistas:
Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming.
Año:2012
Áreas de investigación
  • Hidrología
Datos
Descripción
Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS) model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time.
Internacional
Si
JCR del ISI
Si
Título de la revista
Natural Hazards And Earth System Sciences
ISSN
1561-8633
Factor de impacto JCR
1,792
Información de impacto
Datos JCR del año 2010
Volumen
12
DOI
Número de revista
Desde la página
1
Hasta la página
10
Mes
SIN MES
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  • Creador: Grupo de Investigación: Hidroinformática y Gestión del Agua