Multi-Objective Joint Robust Optimization for Flood Control Operation of Reservoir Groups under Uncertaint

To address multi-objective conflicts and hydrological uncertainty in joint flood control operation of reservoir groups, this study develops an uncertainty-aware optimization framework. An improved Vine Copula method with variable selection and structural simplification (RDV-Copula) is first introduced to describe the spatiotemporal dependence of multi-site flood processes. By simplifying the dependence structure, the method alleviates the complexity of high-dimensional modeling and generates stochastic inflow scenarios for reservoir operation. On this basis, a two-layer hedging–robust optimization model (TL-HRO) is formulated, in which hedging strategies are combined with robust optimization to coordinate flood control and hydropower generation objectives across current and future operation stages. The framework is applied to the Shifengxi Basin in Zhejiang Province, China. The analysis shows pronounced spatial dependence among flood processes, with a four-site flood synchronization probability of 41.92% and an average pairwise synchronization frequency of 65.87%. Compared with conventional approaches, the RDV-Copula achieves improvements in simulation accuracy of approximately 15.0%–61.2% while reducing model complexity, providing reliable stochastic inflow inputs for reservoir operation. Using these stochastic scenarios, the TL-HRO model is evaluated against a conventional multi-objective robust optimization (MORO) model. The results indicate that TL-HRO performs better in terms of both flood risk control and hydropower generation, with an average reduction of 58.26% in the optimization reference value relative to MORO. These findings suggest that the proposed approach can improve the overall performance of reservoir group operation under flood-related uncertainty and support flood control decision-making.