Inference of nonstationary design floods in the Yellow River source: a mechanism-based reconstruction method

ABSTRACT Climate change intensifies and human activities escalate, making hydrological data nonstationary. The current nonstationary flood design methods have low practicality in engineering applications due to high uncertainty, lack of causal mechanisms, or complex model structures. The study focused on the upper Yellow River region in China, which houses cascade hydropower stations, and introduced the Mechanism-based Reconstruction (Me-RS) method to solve nonstationary flood design problems. It evaluates this method against the traditional stationary method, time series decomposition synthesis (TS-DS) method, and generalized additive models for location, scale, and shape (GAMLSS). The design flood values indicate that the calculation results of the Me-RS method are significantly reduced, with the 100-year design flood estimates being 2435.53 m3/s for Me-RS, 2447.53 m3/s for GAMLSS, 3522.52 m3/s for TS-DS, and 4753.76 m3/s for the traditional stationary method. Moreover, Bootstrap uncertainty analysis demonstrates that Me-RS and GAMLSS, which consider physical factors, reduce uncertainty by about 40% compared to TS-DS. It is indicated that the Me-RS method provides a more accurate and less complicated alternative for calculating nonstationary design flood. This study advances the practical application of nonstationary flood frequency analysis methods in the domain of engineering hydrology.