Abstract

This study proposes a methodology that combines the advantages of the event-based and continuous models, for the derivation of the maximum flow and maximum hydrograph volume frequency curves, by combining a stochastic continuous weather generator (the advanced weather generator, abbreviated as AWE-GEN) with a fully distributed physically based hydrological model (the TIN-based real-time integrated basin simulator, abbreviated as tRIBS) that runs both event-based and continuous simulation. The methodology is applied to Peacheater Creek, a 64 km2 basin located in Oklahoma, United States. First, a continuous set of 5000 years’ hourly weather forcing series is generated using the stochastic weather generator AWE-GEN. Second, a hydrological continuous simulation of 50 years of the climate series is generated with the hydrological model tRIBS. Simultaneously, the separation of storm events is performed by applying the exponential method to the 5000- and 50-years climate series. From the continuous simulation of 50 years, the mean soil moisture in the top 10 cm (MSM10) of the soil layer of the basin at an hourly time step is extracted. Afterwards, from the times series of hourly MSM10, the values associated to all the storm events within the 50 years of hourly weather series are extracted. Therefore, each storm event has an initial soil moisture value associated (MSM10Event). Thus, the probability distribution of MSM10Event for each month of the year is obtained. Third, the five major events of each of the 5000 years in terms of total depth are simulated in an event-based framework in tRIBS, assigning an initial moisture state value for the basin using a Monte Carlo framework. Finally, the maximum annual hydrographs are obtained in terms of maximum peak-flow and volume, and the associated frequency curves are derived. To validate the method, the results obtained by the hybrid method are compared to those obtained by deriving the flood frequency curves from the continuous simulation of 5000 years, analyzing the maximum annual peak-flow and maximum annual volume, and the dependence between the peak-flow and volume. Independence between rainfall events and prior hydrological soil moisture conditions has been proved. The proposed hybrid method can reproduce the univariate flood frequency curves with a good agreement to those obtained by the continuous simulation. The maximum annual peak-flow frequency curve is obtained with a Nash–Sutcliffe coefficient of 0.98, whereas the maximum annual volume frequency curve is obtained with a Nash–Sutcliffe value of 0.97. The proposed hybrid method permits to generate hydrological forcing by using a fully distributed physically based model but reducing the computation times on the order from months to hours.

Highlights

  • The estimation of floods for return periods higher than the length of the observed data series and the associated peak-flow frequency curves is often needed for the design of hydraulic infrastructures, analysis of hydrological safety, urban and rural planning, estimation of flood areas, among others

  • The calibration and validation of a stochastic weather generator (AWE-GEN), applied to the basin PCH, was carried out in Gabriel-Martín et al [48]. They analyzed the ability of the AWE-GEN to reproduce rainfall extremes by comparing the stochastic rainfall frequency curves with those observed at different aggregation periods (1, 6, 12, and 24 h)

  • Gabriel-Martín et al [48] demonstrated that, for the case of PCH, by considering the five biggest storms per year classified by their total depth can result in an accurate derivation of the flood frequency curve when using continuous simulations

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Summary

Introduction

The estimation of floods for return periods higher than the length of the observed data series and the associated peak-flow frequency curves is often needed for the design of hydraulic infrastructures, analysis of hydrological safety, urban and rural planning, estimation of flood areas, among others. As far as flood estimation methods are concerned, there are numerous classification criteria based on: the origin of the information to be considered (based on rainfall or flow data), calculation methodology (deterministic or probabilistic), expected results (peak-flows or hydrographs), and temporal scope (continuous or eventbased methods), among others. They do not specify whether the return period is associated with the peak-flow, the volume of the hydrograph or the entire hydrograph. It is well-known that return periods associated to peak-flows are not the same as those associated to hydrograph volume

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