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The outputs of Rainfall-runoff models are inherently uncertain and quantifying the associated uncertainty is crucial for water resources management activities. This study presents the uncertainty quantification of rainfall-runoff simulations using the copula-based Bayesian processor (CBP) in Danjiangkou Reservoir basin, China. The seasonality of uncertainty in rainfall-runoff modeling is explored, and impacts of copula selection and correlation coefficient on uncertainty quantification results are investigated. Results show that the overall performance of the CBP is satisfactory, which provides a useful tool for estimating the uncertainty of rainfall-runoff simulations. It is also demonstrated that the dry season has higher reliability and greater resolution compared with wet season, which illustrates that the CBP captures the actual uncertainty of rainfall-runoff simulations more accurately in dry season. Moreover, the performance the CBP highly depends on the selected Copula function and considered Kendall tau correlation coefficient. As a result, great attention should be paid to selecting the appropriate Copula function and effectively capturing the actual dependence between observed and simulated flows in the CBP-based uncertainty quantification of rainfall-runoff simulations practice. [ABSTRACT FROM AUTHOR]

Hydrological risk analysis is essential and provides useful information for dam safety management and decision-making. This study presents the application of bivariate flood frequency analysis to risk analysis of dam overtopping for Geheyan Reservoir in China. The dependence between the flood peak and volume is modelled with the copula function. A Monte Carlo procedure is conducted to generate 100,000 random flood peak-volume pairs, which are subsequently transformed to corresponding design flood hydrographs (DFHs) by amplifying the selected annual maximum flood hydrographs (AMFHs). These synthetic DFHs are routed through the reservoir to obtain the frequency curve of maximum water level and assess the risk of dam overtopping. Sensitive analysis is performed to investigate the influence of different AMFH shapes and correlation coefficients of flood peak and volume on estimated overtopping risks. The results show that synthetic DFH with AMFH shape characterized by a delayed time to peak results in higher risk, and therefore highlight the importance of including a range of possible AMFH shapes in the dam risk analysis. It is also demonstrated that the overtopping risk is increased as the correlation coefficient of flood peak and volume increases and underestimated in the independence case (i.e. traditional univariate approach), while overestimated in the full dependence case. The bivariate statistical approach based on copulas can effectively capture the actual dependence between flood peak and volume, which should be preferred in the dam risk analysis practice. [ABSTRACT FROM AUTHOR]