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Coupling infrastructure resilience and flood risk assessment via copulas analyses for a coastal green-grey-blue drainage system under extreme weather events

This study sheds light on the coupling of potential flood risk and drainage infrastructure resilience of low-lying areas of a coastal urban watershed to evaluate flood hazards and their possible driving forces. Copulas analyses with the aid of joint probability of simultaneous occurrence help charac...

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Bibliographic Details
Published in:Environmental modelling & software : with environment data news 2018-02, Vol.100, p.82-103
Main Authors: Joyce, Justin, Chang, Ni-Bin, Harji, Rahim, Ruppert, Thomas
Format: Article
Language:English
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Summary:This study sheds light on the coupling of potential flood risk and drainage infrastructure resilience of low-lying areas of a coastal urban watershed to evaluate flood hazards and their possible driving forces. Copulas analyses with the aid of joint probability of simultaneous occurrence help characterize the complexity for hazard classification based on subsequent exposure to inundation under varying levels of adaptive capacity. Adaptive measures of consideration include traditional flood proofing structures and low impact development facilities for a coastal urban watershed - the Cross Bayou watershed, near Tampa Bay, Florida. Findings indicate that coupling flood risk and infrastructure resilience is achievable through the careful formulation of flood risk associated with a resilience metric, which is a function of the predicted hazards, vulnerability, and adaptive capacity. The results also give insights into improving existing methodologies for municipalities in flood management practices such as incorporating a multi-criteria flood impact assessment that couples risk and resilience in a common evaluation framework. •Coupling flood risk and infrastructure resilience is critical for floodplain management.•Copulas functions can account for multiple sources of risk to help flood risk analysis.•Spatial variations of risk level are referential for the selection of adaptive measures.
ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2017.11.008