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- Title
Uncertainty Quantification of Rainfall-runoff Simulations Using the Copula-based Bayesian Processor: Impacts of Seasonality, Copula Selection and Correlation Coefficient.
- Authors
Liu, Zhangjun; Zhang, Jingwen; Wen, Tianfu; Cheng, Jingqing
- Abstract
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.
- Subjects
KENDALL (Fla.); CHINA; RANK correlation (Statistics); WATER management; STATISTICAL correlation; COPULA functions; MARGINAL distributions
- Publication
Water Resources Management, 2022, Vol 36, Issue 13, p4981
- ISSN
0920-4741
- Publication type
Article
- DOI
10.1007/s11269-022-03287-x