Key Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education, Chongqing Jiaotong University, Chongqing, 400074, China; Cooperative Institute for Great Lakes Research, School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, 48109, United States.
College of Environmental Engineering and Science, Suzhou University of Science and Technology, Suzhou, 215009, China.
J Environ Manage. 2023 Oct 15;344:118436. doi: 10.1016/j.jenvman.2023.118436. Epub 2023 Jun 22.
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 m s (19%), the total power generation (TPG) could be increased from 6.6 × 10 to 7.1 × 10 kW h (8%), and the peak of chlorophyll a concentration (PCC) could be decreased from 42.7 to 27.2 μg L (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.
水库运行策略（ROS）被认为是控制藻类水华的一种有效且低成本的方法。然而，水库运行必须考虑常规目标，包括防洪和发电。为了解决这个多目标优化问题，我们将非支配排序遗传算法-II（NSGA-II）模型和通用湖泊模型-水生生态动力学库（GLM-AED）模型相结合，以优化水库运行。以紫坪铺水库为例，我们发现出库流量峰值（POD）可从 1059.5 减少到 861.4 m3/s（19%），总发电量（TPG）可从 6.6×10 增加到 7.1×10kW·h（8%），叶绿素 a 浓度峰值（PCC）可从 42.7 减少到 27.2μg/L（36%），与早期洪水期的原水库运行相比。得到的 Pareto 前沿揭示了控制藻类水华、防洪和发电之间的权衡。实现低 PCC 的水库运行方案通常与大 POD 和中等 TPG 相关。特别是，在固定的起讫水位下，在 5 月和 6 月保持较高的平均水位可以导致更大的流出量，有效抑制藻类积累和水华发展，从而导致较低的 PCC。在最低 PCC 方案、最大 TPG 方案和最小 POD 方案之间发现平均水龄略有变化，表明水交换变化不大，并不是 PCC 差异的原因。总体而言，增加流出量被确定为降低 PCC 的关键因素，特别是在受约束规则下运行时。这些发现为考虑藻类水华控制的最优水库运行提供了新的见解，并强调了增强流出量作为一种治理机制的重要性。此外，耦合模型为水库管理者通过 ROS 减轻富营养化提供了一个可转移的技术框架。
