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: degrees C) of water temperature in the upper, middle, and lower reaches are 26-29 degrees C, 23-26 degrees C, and 21-23 degrees 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.
