Integrated optimization and coordination of cascaded reservoir operations: Balancing flood control, sediment transport and ecosystem service

Abstract Exploring optimal operational schemes for synergistic development is crucial for sustainable management in river basins. This study introduces a multi‐objective synergistic optimization framework aimed at analyzing the interplay among flood control, ecological integrity, and desilting objectives under varying water‐sediment conditions. The framework encompasses multi‐objective reservoir optimal operation, scheme decision, and trade‐off analysis among competing objectives. To address the optimization model, an elite mutation‐based multi‐objective particle swarm optimization (MOPSO) algorithm that integrates genetic algorithms (GA) is developed. The coupling coordination degree is employed for optimal scheme decision‐making, allowing for the adjustment of weight ratios to investigate the trade‐offs between objectives. This research focuses on the Sanmenxia and Xiaolangdi cascade reservoirs in the Yellow River, utilizing three representative hydrological years: 1967, 1969, and 2002. The findings reveal that: (1) the proposed model effectively generates Pareto fronts for multi‐objective operations, facilitating the recommendation of optimal schemes based on coupling coordination degrees; (2) as water‐sediment conditions shift from flooding to drought, competition intensifies between the flood control and desilting objectives. While flood control and ecological objectives compete during flood and dry years, they demonstrate synergies in normal years ( r = 0.22); conversely, ecological and desilting objectives are consistently competitive across all three typical years, with the strongest competition observed in the normal year ( r = −0.95); (3) the advantages conferred to ecological objectives increase as water‐sediment conditions shift from flooding to drought. However, the promotion of the desilting objective requires more complex trade‐offs. This study provides a model and methodological approach for the multi‐objective optimization of flood control, sediment management, and ecological considerations in reservoir clusters. Moreover, the methodologies presented herein can be extended to other water resource systems for multi‐objective optimization and decision‐making.