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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 11 Oct 2019

Submitted as: research article | 11 Oct 2019

Review status
A revised version of this preprint is currently under review for the journal HESS.

An Uncertainty Partition Approach for Inferring Interactive Hydrologic Risks

Yurui Fan1, Kai Huang2, Guohe Huang3, Yongping Li4, and Feng Wang4 Yurui Fan et al.
  • 1Department of Civil and Environmental Engineering, Brunel University, London, Uxbridge, Middlesex, UB8 3PH, United Kingdom
  • 2Faculty of Engineering and Applied Sciences, University of Regina, Regina, SK, Canada, S4S0A2
  • 3Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, Saskatchewan, Canada S4S 0A2
  • 4School of Environment, Beijing Normal University, Beijing 100875, China

Abstract. Extensive uncertainties exist in hydrologic risk analysis. Particularly for interdependent hydrometeorological extremes, the random features in individual variables and their dependence structures may lead to bias and uncertainty in future risk inferences. In this study, a full-subsampling factorial copula (FSFC) approach is proposed to quantify parameter uncertainties and further reveal their contributions to predictive uncertainties in risk inferences. Specifically, a full-subsampling factorial analysis (FSFA) approach is developed to diminish the effect of the sample size and provide reliable characterization for parameters’ contributions to the resulting risk inferences. The proposed approach is applied to multivariate flood risk inference for Wei River basin to demonstrate the applicability of FSFC for tracking the major contributors to resulting uncertainty in a multivariate risk analysis framework. In detail, the multivariate risk model associated with flood peak and volume will be established and further introduced into the proposed full-subsampling factorial analysis framework to reveal the individual and interactive effects of parameter uncertainties on the predictive uncertainties in the resulting risk inferences. The results suggest that uncertainties in risk inferences would mainly be attributed to some parameters of the marginal distributions while the parameter of dependence structure (i.e. copula function) would not produce noticeable effects. Moreover, compared with traditional factorial analysis (FA), the proposed FSFA approach would produce more reliable visualization for parameters' impacts on risk inferences, while the traditional FA would remarkable overestimate contribution of parameters' interaction to the failure probability in AND, and at the same time, underestimate the contribution of parameters' interaction to the failure probabilities in OR and Kendall.

Yurui Fan et al.

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Yurui Fan et al.

Yurui Fan et al.


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