Zhu Di, Zhou Yanlai, Guo Shenglian, Chang Fi-John, Lin Kangling, Deng Zhimin
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, 430072, China; Bureau of Hydrology, Changjiang Water Resources Commission, Wuhan, 430010, China.
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, 430072, China.
J Environ Manage. 2023 Nov 1;345:118673. doi: 10.1016/j.jenvman.2023.118673. Epub 2023 Jul 26.
Due to excessive nutrient enrichment and rapidly increasing water demand, the occurrence of riverine environment deterioration events such as algal blooms in rivers of China has become more frequent and severe since the 1990s, which has imposed harmful consequences on riverine ecosystems. However, tackling river algal blooms as an important issue of restoring riverine environment is very challenging because the complex interaction mechanisms between the causes are impacted by multiple factors. The contributions of our study consist of: (1) optimizing joint operation of water projects for boosting synergies of water quality and quantity, and hydroelectricity; and (2) preventing algal bloom from perspectives of hydrological and water-quality conditions by regulating water releases of water projects. This study proposed a multi-objective optimization methodology grounded on the Non-dominated Sorting Genetic Algorithm to simultaneously minimize the excess values of algal bloom indicators (water quality, O1), minimize the used reservoir capacity for water supply (water quantity, O2), and maximize the hydropower generation (hydroelectricity, O3). The proposed methodology was applied to several catastrophic algal bloom events that took place between 2017 and 2021 and thirteen water projects in the Hanjiang River of China. The results indicated that the proposed methodology largely stimulated the synergistic benefits of the three objectives by reaching a 36.7% reduction in total nitrogen and phosphorus concentrations, a 33.1% improvement in the remaining reservoir capacity, and a 41.0% improvement in hydropower output, as compared with those of the standard operation policy (SOP). In addition, the optimal water release schemes of water projects would increase the minimum streamflow velocity of downstream algal bloom control stations by 8.6%-9.4%. This study provides a new perspective on water project operation in the environmental improvement in big river systems while boosting multi-objectives synergies to support environmentalists and decision-makers with scientific guidance on sustainable water resources management.
由于营养物质过度富集以及用水需求迅速增长，自20世纪90年代以来，中国河流中诸如藻类大量繁殖等河流环境恶化事件的发生愈发频繁且严重，给河流生态系统带来了有害影响。然而，将治理河流藻类大量繁殖作为恢复河流环境的一个重要问题极具挑战性，因为其成因之间复杂的相互作用机制受到多种因素影响。我们研究的贡献包括：（1）优化水利工程联合调度，以提升水质、水量及水电的协同效益；（2）通过调控水利工程的放水来从水文和水质条件角度预防藻类大量繁殖。本研究提出了一种基于非支配排序遗传算法的多目标优化方法，以同时最小化藻类大量繁殖指标的超标值（水质，O1），最小化供水所需的水库库容（水量，O2），并最大化水力发电量（水电，O3）。所提出的方法应用于2017年至2021年间发生的几起灾难性藻类大量繁殖事件以及中国汉江的13个水利工程。结果表明，与标准运行策略（SOP）相比，所提出的方法在很大程度上激发了三个目标的协同效益，总氮和磷浓度降低了36.7%，剩余水库库容提高了33.1%，水电产量提高了41.0%。此外，水利工程的最优放水方案将使下游藻类大量繁殖控制站的最小河道流速提高8.6% - 9.4%。本研究为大型河流系统环境改善中的水利工程运行提供了新视角，同时提升多目标协同效益，为环保人士和决策者在可持续水资源管理方面提供科学指导。
