Zeng-Chuan Dong’s scientific contributions

Accurate capture and presentation of the interactive feedback relationships among various objectives in multi-objective reservoir operation is essential for maximizing operational benefits. In this study, the niche theory of ecology was innovatively applied to the field of reservoir operation, and a novel stateerelationship (SeR) measurement analysis method was developed for multi-objective reservoir operation. This method enables the study of interaction among multiple objectives. This method was used to investigate the relationship among the objectives of power generation, water supply, and ecological protection for cascade reservoir operation in the Wujiang River Basin in China. The results indicated that the ecological protection objective was the most competitive in acquiring and capturing resources like flow and water level, while the water supply objective was the weakest. Power generation competed most strongly with ecological protection and relatively weakly with water supply. These findings facilitate decision-making throughout the reservoir operation process in the region. The SeR method based on the niche theory is convenient, efficient, and intuitive, allowing for the quantification of feedback relationships among objectives without requiring the solution of the Pareto frontier of a multi-objective problem in advance. This method provides a novel and feasible idea for studying multi-objective interactions.

Copula functions have been widely used in stochastic simulation and prediction of streamflow. However, existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months. To address this limitation, this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations. This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas. The up-to-down sequential method, which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach, was used to determine the structures of multivariate D-vine copulas. The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station, the inflow control station of the Longyangxia Reservoir in the Yellow River Basin. The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow. This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.