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A factorial Bayesian copula framework for partitioning uncertainties in multivariate risk inference.

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  • 1Department of Civil and Environmental Engineering, Brunel University, London, Uxbridge, Middlesex, UB8 3PH, United Kingdom.

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This study introduces a factorial Bayesian copula (FBC) method to assess how parameter uncertainties impact hydrologic risk. The FBC method quantifies these uncertainties and their effects on risk predictions in complex systems.

Keywords:
CopulaFactorial analysisFlood riskMarkov chain Monte CarloUncertainty

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Area of Science:

  • Environmental Hydrology
  • Statistical Modeling
  • Risk Analysis

Background:

  • Copula-based models are crucial for multivariate hydrologic risk assessment.
  • Quantifying parameter uncertainties is essential for reliable risk inferences.
  • Understanding parameter interactions is key to improving model accuracy.

Purpose of the Study:

  • To propose a factorial Bayesian copula (FBC) method for quantifying parameter uncertainties.
  • To reveal the individual and interactive effects of parameter uncertainties on hydrologic risk.
  • To identify the contributions of parameters to uncertain risk inferences in multivariate contexts.

Main Methods:

  • Integration of Bayesian inference and factorial analysis into copula models.
  • Application of the FBC method to streamflow data from Chinese river basins.
  • Multilevel factorial analysis to dissect parameter effects.

Main Results:

  • Imprecise parameters in marginal distributions and dependence structures cause significant uncertainties in risk predictions.
  • The FBC method effectively reveals individual and interactive parameter effects.
  • Detailed contributions of parameters to failure probabilities under various scenarios were identified.

Conclusions:

  • The proposed FBC method enhances the understanding of parameter uncertainty in hydrologic risk assessment.
  • Accurate quantification of parameter uncertainties is vital for robust hydrologic risk management.
  • The FBC approach provides a framework for dissecting complex parameter influences in hydrological modeling.