Tracing Uncertainty Contributors in the Multi‐Hazard Risk Analysis for Compound Extremes. Issue 12 (15th December 2021)

• Record Type: Journal Article
• Authors: Fan, Y. R.; Yu, L.; Shi, X.; Duan, Q. Y.
• Is Part Of: Earth's future. Volume 9:Issue 12(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-15
• Subjects: Environmental sciences -- Periodicals; Environmental sciences; Periodicals; 550
• DOI: 10.1029/2021EF002280
• Languages: English
• ISSNs: 2328-4277

Abstract

Abstract: In this study, an iterative factorial multimodel Bayesian copula (IFMBC) framework was developed for revealing uncertainties in risk inferences of compound extremes under the consideration of diverse model structures and parameter sets. Particularly, an iterative factorial analysis (IFA) method would be advanced in IFMBC to track the dominant contributors to the imprecise predictions of multi‐hazard risks. The developed IFMBC framework was applied for the risk assessment of compound floods at two estuarine systems (i.e., Washington and Philadelphia) in US. The results indicate that the most likely compound events, under predefined return periods, exhibit noticeable uncertainties. Those uncertainties also present multiple hotspots which may be attributed to different impacts from different factors. By applying the IFA method, the results suggest the copula structure would likely be ranked as one of the top 2 impact factors for predictions of failure probabilities (FPs) in the scenarios of AND, and Kendall, with its contributions higher than 30% for FP in Kendall (more than 40% at Washington) and more than 25% for FP in Kendall (larger than 40% at Philadelphia). In comparison, the copula structure may not pose a visible effect on the predictive uncertainty for FP in OR, with its contribution possibly less than 5% under long‐term service time periods. However, the marginal distributions would have higher effects on FP in OR than the effects on the other two FPs. Particularly, the marginal distribution for the extreme variable with high skewness and kurtosis values tends to be ranked as one of the most significant impact factors for FP in OR. Also, the overall impacts from parametric uncertainties in both marginal and dependence models cannot be neglected for the predictions of all three FPs with their contributions probably larger than 20% under a short service time period. Compared with the traditional multilevel factorial analysis, the IFA method can provide more reliable characterization for uncertainty contributors in multi‐hazard risk analyses, since the traditional method seems to significantly overestimate the contributions from parameter uncertainties. Plain Language Summary: The risk analysis for compound extremes, consisting of concurrent or consecutive hazard drivers, is of great importance for disaster resilience and infrastructure designs, in which extensive uncertainties are inevitable issues embedded in various components such as model structure and parameters. Overlook of these uncertainties may cause undesired resilience strategies for compound extremes and further lead to unpredictable damages or fatalities. This study developed an innovative framework to generate the critical thresholds for compound floods as well as their predictive regions under consideration of uncertainties in model structures and parameters. Moreover, the dominant contributors to the predictive uncertainties in multi‐hazard risk inferences were revealed through a reliable analysis technique in the developed framework. Such a framework can help generate desired design thresholds with their predictive confidence for compound extreme events, and also direct the most efficient pathway to enhance/improve the risk inferences. Moreover, the developed framework can be extended to high‐dimensional compound extremes and have a wide application potential. Key Points: An iterative factorial multimodel Bayesian copula framework was developed for tracing uncertainty contributors in multi‐hazard risk analysis The contributions of marginals, copulas, and parameters to predictive risks were quantified through an iterative factorial analysis Results suggested marginal and dependence structures, and parameter uncertainties would have different impacts on different risk indices