Abstract

Reservoir operation strategies (ROSs) are considered an efficient and low-cost method to control algal blooms. However, reservoir operations must consider regular objectives, including flood prevention and power generation. To address this multi-objective optimization problem, we coupled the non-dominated sorting genetic algorithm-II (NSGA-II) model and the General Lake Model-Aquatic EcoDynamics library (GLM-AED) model to optimize reservoir operations. Taking the Zipingpu Reservoir as a case study, we found the peak of outflow discharge (POD) could be reduced from 1059.5 to 861.4 m3 s−1 (19%), the total power generation (TPG) could be increased from 6.6 × 108 to 7.1 × 108 kW h (8%), and the peak of chlorophyll a concentration (PCC) could be decreased from 42.7 to 27.2 μg L−1 (36%) compared with the original reservoir operation in the early flood period. The obtained Pareto frontier revealed the tradeoffs between algal bloom control, flood prevention, and power generation. Reservoir operation schemes that achieved low PCC were typically associated with large POD and moderate TPG. In particular, under fixed start and end water levels, maintaining a higher average water level during May and June could result in larger outflows, effectively inhibiting algal accumulation and bloom development, thereby leading to a lower PCC. Slight variations in average water age were found among the minimum PCC scheme, maximum TPG scheme, and minimum POD scheme, indicating that water exchange varied little and has not been responsible for the differences in PCC. Collectively, enhancing outflow was determined to play a vital role in reducing PCC, particularly when operating under constrained rules. These findings contribute new insights into optimal reservoir operations considering algal bloom control and emphasize the importance of enhancing outflow as a governing mechanism. Furthermore, the coupled model offers a transferable technical framework for reservoir managers to mitigate eutrophication through ROSs.

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