Flood Season Staging-Based Joint Refill Optimization for Cascade Reservoirs: A Case Study of the Dadu River in Southwest China

Joint refill of cascade reservoirs must balance flood control safety against water conservation and hydropower generation, yet conventional operation applies fixed flood-limiting water levels that ignore the time-varying nature of flood risk within a flood season. Existing refill optimization models rarely embed flood season staging as time-varying operational constraints, which limits their ability to capture stage-dependent flood risk and refill opportunity, particularly in cascades headed by a large regulating reservoir. This study develops a framework that identifies flood season stages via the mean change-point method across multiple hydrological stations, transforms the stage boundaries into time-varying water-level constraints, and embeds them in a genetic algorithm-based joint refill optimization model for the Dadu River cascade in southwestern China. Applied to a 65-year inflow record, the framework consistently identifies early August as the main-to-post-flood transition and a secondary autumn flood rebound from mid-September to early October. Relative to the design scheme based on approved conventional operating rules and fixed flood-limiting water level controls, the optimized operation increases the multi-year average total generation of the five-station cascade by 10.08%, from 507.98 × 108 kWh to 559.16 × 108 kWh, while achieving a 100% filling rate at the leading Shuangjiangkou reservoir and maintaining acceptable flood storage during the post-flood period. By demonstrating that within-year inflow trend substantially modulates refill outcomes, especially when annual inflow volumes are comparable, the framework offers differentiated refill strategies and a transferable approach for cascade systems with newly commissioned leading reservoirs in monsoon-driven basins.