Uncertainty Quantification of Rainfall-runoff Simulations Using the Copula-based Bayesian Processor: Impacts of Seasonality, Copula Selection and Correlation Coefficient

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