Spatiotemporal patterns of wet–dry encounters between water source and receiving areas in the South-to-North water transfer project - Yuping Han; Jinhang Li; Mengdie Zhao (Scientific Reports)
Abstract As global water scarcity becomes more acute, cross-basin water diversion projects, such as the South-to-North Water Diversion Project in China, need scientific risk assessment methods to ensure sustainable water allocation. This study proposes a coupling analysis framework based on Copula-Bayesian, which fills the gap in the systematic risk research of large-scale water transfer projects. By constructing a joint annual runoff distribution model for the Yangtze River and Yellow River, combined with mutation point detection, periodicity analysis (Morlet wavelet) and frequency statistics of boom-bust encounter, the characteristics of hydrological variability under climate change were systematically evaluated. The risk of water diversion under different scenarios is quantified by using Bayesian network for probabilistic reasoning. The results showed that: (1) During 1959–2023, three abrupt runoff points were detected in both the Yangtze River basin (YTB) and the Yellow River basin (YRB), with periodicism of 27/17/11 years and 12/27/29 years, respectively. The runoff of the lower Yellow River showed a significant downward trend (− 1.2%/ decade, p < 0.05). (2) The occurrence probability of extreme flood and ebb synchronous events is less than 5%, while the overall distribution of asynchronous and synchronous events is balanced (45–55%), indicating that the risk of water transfer is generally controllable; (3) The four-level water diversion risk scenario (optimal to most unfavorable) is identified through probability simulation, showing that the low-risk operation probability of the project under current conditions is 68%. This study innovatively combines statistical hydrological models with risk simulation to provide decision support for inter-basin collaborative water resources management in the context of climate change.