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

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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.

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Data available after request from the corresponding author.

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Acknowledgements

This research was funded by the National Natural Science Foundation of China (51909112), Major Discipline Academic and Technical Leaders Training Program of Jiangxi Province (20212BCJ23016) and Jiangxi Provincial Water Resources Science and Technology Project (202023ZDKT02).

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Authors and Affiliations

  1. Jiangxi Academy of Water Science and Engineering, Nanchang, 330029, China

    Zhangjun Liu, Jingwen Zhang, Tianfu Wen & Jingqing Cheng

  2. Jiangxi Provincial Key Laboratory of Water Resources and Environment of Poyang Lake, Nanchang, 330029, China

    Zhangjun Liu

  3. Jiangxi Provincial Eco-Hydraulic Technology Innovation Center of Poyang Lake Basin, Nanchang, 330029, China

    Zhangjun Liu

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  1. Zhangjun Liu
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  2. Jingwen Zhang
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  3. Tianfu Wen
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  4. Jingqing Cheng
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Contributions

Conceptualization, methodology, formal analysis, investigation, and writing original draft preparation, Zhangjun Liu; Data curation, Writing, review and editing, Jingwen Zhang; Resources, Supervision and updating some parts, Tianfu Wen and Jingqing Cheng.

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Correspondence to Jingqing Cheng.

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Liu, Z., Zhang, J., Wen, T. et al. Uncertainty Quantification of Rainfall-runoff Simulations Using the Copula-based Bayesian Processor: Impacts of Seasonality, Copula Selection and Correlation Coefficient. Water Resour Manage 36, 4981–4993 (2022). https://doi.org/10.1007/s11269-022-03287-x

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