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

Author

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  • Zhangjun Liu

    (Jiangxi Academy of Water Science and Engineering
    Jiangxi Provincial Key Laboratory of Water Resources and Environment of Poyang Lake
    Jiangxi Provincial Eco-Hydraulic Technology Innovation Center of Poyang Lake Basin)

  • Jingwen Zhang

    (Jiangxi Academy of Water Science and Engineering)

  • Tianfu Wen

    (Jiangxi Academy of Water Science and Engineering)

  • Jingqing Cheng

    (Jiangxi Academy of Water Science and Engineering)

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.

Suggested Citation

  • Zhangjun Liu & Jingwen Zhang & Tianfu Wen & Jingqing Cheng, 2022. "Uncertainty Quantification of Rainfall-runoff Simulations Using the Copula-based Bayesian Processor: Impacts of Seasonality, Copula Selection and Correlation Coefficient," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 4981-4993, October.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:13:d:10.1007_s11269-022-03287-x
    DOI: 10.1007/s11269-022-03287-x
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