Hybrid high-dimensional vine copula–Bayesian network framework for flood risk analysis in reservoir–lake systems: Addressing multisource uncertainties

Abstract

Complex flood control systems which comprise reservoirs, lakes, and external rivers, frequently encounter multifaceted risk sources that are spatiotemporally interconnected, resulting in diverse flood risks. This study developed a comprehensive risk analysis framework integrating stochastic simulation and Bayesian networks to facilitate refined risk prediction and diagnosis. Vine copula and Monte Carlo methods were used for probabilistic modeling and simulation, while Bayesian network was used for bidirectional risk assessment. A case study of Chaohu Lake Basin (China) show that vine copula effectively elucidates both intervariable correlations and single variable characteristics. The lateral inflow volume of lake and the external river water levels are dominant risk sources. When the maximum water level of lake increases from 9.5 m to 11.5 m, the posterior probability of dominant risk sources exceeding the design value at 20 % increases by 46.12 % and 32.22 %. This study represents an innovative approach to risk analysis for complex reservoir-lake systems.