Abstract
Abstract Design flood quantiles are crucial for hydraulic structures design, water resources planning and management, whereas previous multivariate hydrological quantile estimation methods usually do not consider historical flood information. To overcome such limitations, a meta-heuristic inference function for margins (MHIFM) approach, coupling meta-heuristic algorithm with a modified inference function for margins (IFM) method, is developed for modeling the joint distributions of flood peak and volumes with incorporation of historical flood information. Then, the most likely realization (MLR) and equivalent frequency combination (EFC) methods are employed for selecting multivariate design floods on a quantile iso-surface. The Danjiangkou reservoir located in Hanjiang River basin, the first pilot basin of most regulated water resources management policy in China, is selected as a case study. Application results indicate that the MHIFM approach shows good performance for estimating the parameters of marginal and joint distributions; moreover, the MLR method yields safer design flood quantiles than the EFC method in terms of highest routed reservoir water levels. The proposed MHIFM approach associated with the MLR method is safer and more rational for reservoir design, which would provide rich information as the reference for flood risk assessment, reservoir operation and management.
Highlights
Flood frequency analysis is performed to quantify the risk of flooding at different spatial locations and to provide guidelines for determining the design capacity of flood control structures
This study developed an meta-heuristic inference function for margins (MHIFM) approach to model the joint distribution of flood peak and volumes with incorporation of historical information
(1) The investigated data series consist of systematic record and six historical flood events, and the fitting results indicate that the developed MHIFM approach that incorporates historical information into multivariate distribution has a good performance for estimating the parameters of marginal and joint distributions
Summary
Flood frequency analysis is performed to quantify the risk of flooding at different spatial locations and to provide guidelines for determining the design capacity of flood control structures. The copula function, enabling the integration of heterogeneous dependence structures and marginal distributions, has been widely applied in constructing flexible joint distributions of interrelated hydrological variables since the introduction of copulas in hydrology and geosciences by De Michele & Salvadori ( ). This method has been applied mainly to the analysis of rainfall storms
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