Dependence Between Extreme Rainfall Events and the Seasonality and Bivariate Properties of Floods. A Continuous Distributed Physically-Based Approach
Área de conocimiento
Ecologíal; Tecnologías del Medio AmbientePatrocinadores
The authors acknowledge the computer resources and technical assistance provided by the Centro de Supercomputacion y Visualizacion de Madrid (CeSViMa) and the funds from Universidad Politecnica de Madrid in the framework of their Program "Ayudas para contratos predoctorales para la realizacion del doctorado en sus escuelas, facultad, centro e institutos de I + D + i", "ayudas a proyectos de I + D de investigadores posdoctorales" Ref. number: VJIDOCUPM19AFSW) and "XVI Convocatoria de ayudas del consejo social para el fomento de la formacion e internacionalizacion de doctorandos". The authors also wish to gratefully acknowledge Enrique Vivoni, Giuseppe Mascaro, Ara Ko, Adil Mounir, and their teammates for the data, advice, and support provided during the stay of A.S.W. and I.G.M. in Arizona State University. The authors also gratefully acknowledge Riccardo Nalesso for his help provided during the first steps of the experiment. This research received no external fundingRealizado en/con
Universidad Politécnica de Cartagena; Universidad Politécnica de MadridFecha de publicación
2019-09-11Editorial
MDPICita bibliográfica
Gabriel-Martin I, Sordo-Ward A, Garrote L, T. García J. Dependence Between Extreme Rainfall Events and the Seasonality and Bivariate Properties of Floods. A Continuous Distributed Physically-Based Approach. Water. 2019; 11(9):1896. https://doi.org/10.3390/w11091896Revisión por pares
SiPalabras clave
Stochastic weather generationAWE-GEN
Distributed hydrological model
tRIBS
Storm identification
Bivariate flood frequency curve
Resumen
This paper focuses on proposing the minimum number of storms necessary to derive the extreme flood hydrographs accurately through event-based modelling. To do so, we analyzed the results obtained by coupling a continuous stochastic weather generator (the Advanced WEather GENerator) with a continuous distributed physically-based hydrological model (the TIN-based real-time integrated basin simulator), and by simulating 5000 years of hourly flow at the basin outlet. We modelled the outflows in a basin named Peacheater Creek located in Oklahoma, USA. Afterwards, we separated the independent rainfall events within the 5000 years of hourly weather forcing, and obtained the flood event associated to each storm from the continuous hourly flow. We ranked all the rainfall events within each year according to three criteria: Total depth, maximum intensity, and total duration. Finally, we compared the flood events obtained from the continuous simulation to those considering the N highest storm events ...
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