Reservoir operation optimization approach accounting for methane emission mitigation and water quality improvement targets

Mitigating reservoir methane (CH 4 ) emissions is critical for the sustainable development of water conservancy and hydropower projects. Reservoir operations influence hydrodynamic and biogeochemical processes by regulating water storage and discharge, which significantly impact reservoir carbon (C) cycling and ensuing CH 4 emissions. However, existing operation optimization approaches have rarely considered CH 4 emission mitigation, and the relationships among CH 4 emission, water quality, and socioeconomic objectives remain unclear. This study proposes a multi-objective optimization approach for reservoir operations accounting for CH 4 mitigation, water quality, and socioeconomic objectives (i.e., water supply and hydropower generation) while exploring the relationships among these targets. A reservoir hydrodynamic-biogeochemical model is used to simulate CH 4 emissions and water quality under multiple reservoir operation scenarios. Machine learning (ML) models are trained to predict CH 4 emissions and water quality responses to reservoir operations, and these models are then incorporated into an operation optimization model. Using China’s second largest reservoir (Danjiangkou Reservoir) as a case, results show that maintaining higher water levels mitigates CH 4 emissions and improves water quality. Under the optimized operation scheme, multi-year averaged annual CH 4 emission decreases by 12.2%, chlorophyll a (Chl- a ) exceeding degree declines by 13.4%, hydropower generation increases by 3%, while water supply volume is slightly reduced by 0.4%. Pareto optimal solutions reveal a synergistic relationship between CH 4 mitigation and both water quality improvements and hydropower generation, while showing a tradeoff with water supply. This study provides a cleaner strategy for sustaining reservoir services and moving towards watershed C neutrality.