Abstract
An adequate estimation of the extreme behavior of basin response is essential both for designing river structures and for evaluating their risk. The aim of this paper is to develop a new methodology to generate extreme hydrograph series of thousands of years using an event-based model. To this end, a spatial-temporal synthetic rainfall generator (RainSimV3) is combined with a distributed physically-based rainfall–runoff event-based model (RIBS). The use of an event-based model allows simulating longer hydrograph series with less computational and data requirements but need to characterize the initial basis state, which depends on the initial basin moisture distribution. To overcome this problem, this paper proposed a probabilistic calibration–simulation approach, which considers the initial state and the model parameters as random variables characterized by a probability distribution though a Monte Carlo simulation. This approach is compared with two other approaches, the deterministic and the semi-deterministic approaches. Both approaches use a unique initial state. The deterministic approach also uses a unique value of the model parameters while the semi-deterministic approach obtains these values from its probability distribution through a Monte Carlo simulation, considering the basin variability. This methodology has been applied to the Corbès and Générargues basins, in the Southeast of France. The results show that the probabilistic approach offers the best fit. That means that the proposed methodology can be successfully used to characterize the extreme behavior of the basin considering the basin variability and overcoming the basin initial state problem.
Highlights
Characterizing the extreme behavior of basin response is necessary for hydraulic infrastructure design, territorial planning, flood management and risk analysis
The results show that, in the case of peak flows, the probabilistic calibration offers the best fit with Nash–Sutcliffe model efficiency coefficient (NSE) coefficients larger than 0.9
The cause may be that the considered impervious area by the RR model may be greater than the actual because the rainfall–runoff event-based model (RIBS) model assumes that the cells through which the river flows are impervious
Summary
Characterizing the extreme behavior of basin response is necessary for hydraulic infrastructure design, territorial planning, flood management and risk analysis. Statistical analyses are traditionally applied in engineering practice to obtain a unique design flood. The methodologies traditionally applied in engineering practice are statistical analyses with the aim of obtaining a unique design flood. Statistical analyses consist in fitting the most appropriate extreme-value distribution to the observed series of maximum annual discharges, in order to estimate the peak flow associated to a given return period (usually high, e.g., 500 to 10,000 years, in the case of dams). These studies need long and robust observed series to obtain accurate quantile estimates
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