Non-Stationary Flood Characteristics and Joint Risk Analysis in Inland China with Uncertainty Considerations

Under global climate change, flood processes exhibit significant non-stationarity due to multiple driving factors, rendering traditional frequency analysis methods based on stationarity assumptions inadequate for accurate risk assessment. This study, focusing on the Kuitun River Basin and utilizing observed data from the Jiangjunmiao Hydrological Station (1959–2014), develops a joint design approach that addresses both non-stationarity and multivariate dependence. The approach integrates the Generalized Additive Model for Location, Scale, and Shape (GAMLSS) with copula functions and employs a parametric bootstrap to quantify the impacts of marginal parameter estimation and sample size uncertainty on design floods. The results indicate that flooding in the Kuitun River is influenced by precipitation, temperature, and snowmelt, with summer precipitation having the greatest impact. Marginal parameter uncertainty is significantly amplified at high return periods, and the confidence intervals of design values expand as the return period increases. In the joint framework, the OR criterion is more sensitive to parameter perturbations, with the 100-year flood peak and flood volume design values approximately 24.2% and 19.8% higher than those of the AND criterion, respectively. Increasing the sample size significantly reduces uncertainty; when the sample size increases from 56 to 500, the HDR area and confidence interval width decrease by approximately 60–70%, and the stability of joint flood design estimates improves significantly. The research findings can provide a scientific basis and technical support for flood analysis and risk management in the Kuitun River Basin under changing environmental conditions.