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Open AccessArticle

Dependence Between Extreme Rainfall Events and the Seasonality and Bivariate Properties of Floods. A Continuous Distributed Physically-Based Approach

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Department of Civil Engineering, Hydraulics, Energy and Environment, Universidad Politécnica de Madrid, 28040 Madrid, Spain
2
Department of Mining and Civil Engineering, Technical University of Cartagena, Paseo Alfonso XIII 52, 30203 Cartagena, Spain
*
Author to whom correspondence should be addressed.
Water 2019, 11(9), 1896; https://doi.org/10.3390/w11091896
Received: 28 July 2019 / Revised: 5 September 2019 / Accepted: 6 September 2019 / Published: 11 September 2019
(This article belongs to the Special Issue Management of Hydrological Extremes: Floods and Droughts)
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 per year according to the three criteria and by focusing on four different aspects: Magnitude and recurrence of the maximum annual peak-flow and volume, seasonality of floods, dependence among maximum peak-flows and volumes, and bivariate return periods. The main results are: (a) Considering the five largest total depth storms per year generates the maximum annual peak-flow and volume, with a probability of 94% and 99%, respectively and, for return periods higher than 50 years, the probability increases to 99% in both cases; (b) considering the five largest total depth storms per year the seasonality of flood is reproduced with an error of less than 4% and (c) bivariate properties between the peak-flow and volume are preserved, with an error on the estimation of the copula fitted of less than 2%.
Keywords: stochastic weather generation; AWE-GEN; distributed hydrological model; tRIBS; storm identification; bivariate flood frequency curve stochastic weather generation; AWE-GEN; distributed hydrological model; tRIBS; storm identification; bivariate flood frequency curve
MDPI and ACS Style

Gabriel-Martin, I.; Sordo-Ward, A.; Garrote, L.; García, J.T. Dependence Between Extreme Rainfall Events and the Seasonality and Bivariate Properties of Floods. A Continuous Distributed Physically-Based Approach. Water 2019, 11, 1896.

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