Journal article
A multivariate statistical framework for mixed storm types in compound flood analysis
Natural hazards and earth system sciences, Vol.24(11), pp.4091-4107
11/27/2024
DOI: 10.5194/nhess-24-4091-2024
Abstract
In coastal regions, compound flooding can arise from a combination of different drivers, such as storm surges, high tides, excess river discharge, and rainfall. Compound flood potential is often assessed by quantifying the dependence and joint probabilities of flood drivers using multivariate models. However, most of these studies assume that all extreme events originate from a single population. This assumption may not be valid for regions where flooding can arise from different generation processes, e.g., tropical cyclones (TCs) and extratropical cyclones (ETCs). Here we present a flexible copula-based statistical framework to assess compound flood potential from multiple flood drivers while explicitly accounting for different storm types. The proposed framework is applied to Gloucester City, New Jersey, and St. Petersburg, Florida, as case studies. Our results highlight the importance of characterizing the contributions from TCs and non-TCs separately to avoid potential underestimation of the compound flood potential. In both study regions, TCs modulate the tails of the joint distributions (events with higher return periods), while non-TC events have a strong effect on events with low to moderate joint return periods. We show that relying solely on TCs may be inadequate when estimating compound flood risk in coastal catchments that are also exposed to other storm types. We also assess the impact of non-classified storms that are not linked to either TCs or ETCs in the region (such as locally generated convective rainfall events and remotely forced storm surges). The presented study utilizes historical data and analyzes two populations, but the framework is flexible and can be extended to account for additional storm types (e.g., storms with certain tracks or other characteristics) or can be used with model output data including hindcasts or future projections.
Details
- Title: Subtitle
- A multivariate statistical framework for mixed storm types in compound flood analysis
- Creators
- Pravin Maduwantha - University of Central FloridaThomas Wahl - University of Central FloridaSara Santamaria-Aguilar - University of Central FloridaJane Robert - University of Central FloridaJames Booth - City University of New YorkHanbeen Kim - Princeton UniversityGabriele Villarini - Princeton University
- Resource Type
- Journal article
- Publication Details
- Natural hazards and earth system sciences, Vol.24(11), pp.4091-4107
- Publisher
- Copernicus GmbH
- DOI
- 10.5194/nhess-24-4091-2024
- ISSN
- 1561-8633
- eISSN
- 1684-9981
- Number of pages
- 17
- Grant note
- National Science Foundation: ICER-2103754 National Science Foundation as part of the Megalopolitan Coastal Transformation Hub (MACH) under NSF: 1854773 NSFUSACE Climate Preparedness and Resilience Community of Practice
Pravin Maduwantha, Thomas Wahl, and Sara Santamaria-Aguilar were supported by the National Science Foundation as part of the Megalopolitan Coastal Transformation Hub (MACH) under NSF award ICER-2103754. This is MACH contribution number 47. James F. Booth was supported by NSF award 1854773. Robert Jane and Thomas Wahl acknowledge financial support from the USACE Climate Preparedness and Resilience Community of Practice.
- Language
- English
- Date published
- 11/27/2024
- Academic Unit
- IIHR--Hydroscience and Engineering; Civil and Environmental Engineering
- Record Identifier
- 9984752657202771
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