State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China.
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China; College of Civil Engineering and Architecture, Guangxi University, Nanning, 530004, China.
J Environ Manage. 2022 Nov 15;322:116036. doi: 10.1016/j.jenvman.2022.116036. Epub 2022 Aug 29.
Multi-factor risk assessment is an important prerequisite for water quality protection and the safe operation of mega hydro-projects. As the largest long-distance inter-basin water diversion project in the world, the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC) has been in operation for 8 years and has benefited 79 million people along the canal. However, concerns have been raised in recent years about the potential negative effects of abnormal algal proliferation in the MRSNWDPC. It is very important for the safety of water supply to carry out relevant risk analysis and formulate regulatory management. In order to quantitatively evaluate the risk of algal proliferation in the MRSNWDPC under the influence of multiple factors, a multivariate risk assessment method based on Vine Copula theory and Monte Carlo simulation was proposed. Five key factors (water temperature, flow velocity, flow rate, algal cell density, and dissolved oxygen) were used and multiple dependency models in each section of the MRSNWDPC from January 2016 to January 2019 were established to study the risk of algal proliferation under multiple scenarios. The results demonstrate that water temperature can be used as an appropriate early-warning indicator of algal proliferation. The early-warning interval (unit: °C) of water temperature in the upper, middle, and lower reaches are 26-29°C, 23-26°C, and 21-23°C, respectively. Unlike bivariate analysis, the multiple dependency model describes the relationship between variables more accurately and enriches the scenarios of multiple conditional probabilities. When the water temperature fluctuates in the early-warning interval, regulating the upstream, midstream, and downstream flow velocity to be higher than 0.6 m/s, 0.5 m/s, and 0.6 m/s, respectively, can effectively reduce the risk of algal proliferation. This research not only provides a reference for the ecological control of algae in the MRSNWDPC and similar mega hydro-projects but also enriches the application of the Vine Copula theory coupled with the random sampling method for multi-variable risk analysis.
多因素风险评估是水质保护和大型水利工程安全运行的重要前提。作为世界上最大的远距离跨流域调水工程，中国南水北调中线工程（MRSNWDPC）已运行 8 年，沿运河受益人口达 7900 万。然而，近年来人们对南水北调中线工程异常藻类增殖的潜在负面影响表示担忧。开展相关风险分析和制定监管管理措施对供水安全非常重要。为了定量评估多因素影响下南水北调中线工程藻类增殖的风险，提出了一种基于 Vine Copula 理论和蒙特卡罗模拟的多元风险评估方法。该方法选取了水温、流速、流量、藻细胞密度和溶解氧等 5 个关键因素，并建立了 2016 年 1 月至 2019 年 1 月南水北调中线工程各段的多元依赖模型，研究了多情景下藻类增殖的风险。结果表明，水温可作为藻类增殖的适当预警指标。上游、中游和下游水温的预警间隔（单位：°C）分别为 26-29°C、23-26°C 和 21-23°C。与二元分析不同，多元依赖模型更准确地描述了变量之间的关系，并丰富了多个条件概率的情景。当水温在预警区间波动时，分别将上游、中游和下游的流速调节至高于 0.6 m/s、0.5 m/s 和 0.6 m/s，可有效降低藻类增殖的风险。本研究不仅为南水北调中线工程及类似大型水利工程的藻类生态控制提供了参考，也丰富了 Vine Copula 理论与随机抽样法相结合的多变量风险分析的应用。
