A multivariate statistical framework for mixed storm types in compound flood analysis
<p>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 multivaria...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2024-11-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://nhess.copernicus.org/articles/24/4091/2024/nhess-24-4091-2024.pdf |
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| author | P. Maduwantha P. Maduwantha T. Wahl T. Wahl S. Santamaria-Aguilar S. Santamaria-Aguilar R. Jane R. Jane J. F. Booth H. Kim H. Kim G. Villarini G. Villarini |
| author_facet | P. Maduwantha P. Maduwantha T. Wahl T. Wahl S. Santamaria-Aguilar S. Santamaria-Aguilar R. Jane R. Jane J. F. Booth H. Kim H. Kim G. Villarini G. Villarini |
| author_sort | P. Maduwantha |
| collection | DOAJ |
| description | <p>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.</p> |
| format | Article |
| id | doaj-art-a98cd8f890c04f1ea0e22572142ce1f4 |
| institution | OA Journals |
| issn | 1561-8633 1684-9981 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Natural Hazards and Earth System Sciences |
| spelling | doaj-art-a98cd8f890c04f1ea0e22572142ce1f42025-08-20T02:28:27ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812024-11-01244091410710.5194/nhess-24-4091-2024A multivariate statistical framework for mixed storm types in compound flood analysisP. Maduwantha0P. Maduwantha1T. Wahl2T. Wahl3S. Santamaria-Aguilar4S. Santamaria-Aguilar5R. Jane6R. Jane7J. F. Booth8H. Kim9H. Kim10G. Villarini11G. Villarini12Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USANational Center for Integrated Coastal Research, University of Central Florida, Orlando, FL 32816, USADepartment of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USANational Center for Integrated Coastal Research, University of Central Florida, Orlando, FL 32816, USADepartment of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USANational Center for Integrated Coastal Research, University of Central Florida, Orlando, FL 32816, USADepartment of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USANational Center for Integrated Coastal Research, University of Central Florida, Orlando, FL 32816, USADepartment of Earth and Atmospheric Sciences, City University of New York, City College, New York, NY 10031, USADepartment of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USAHigh Meadows Environmental Institute, Princeton University, Princeton, NJ 08544, USADepartment of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USAHigh Meadows Environmental Institute, Princeton University, Princeton, NJ 08544, USA<p>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.</p>https://nhess.copernicus.org/articles/24/4091/2024/nhess-24-4091-2024.pdf |
| spellingShingle | P. Maduwantha P. Maduwantha T. Wahl T. Wahl S. Santamaria-Aguilar S. Santamaria-Aguilar R. Jane R. Jane J. F. Booth H. Kim H. Kim G. Villarini G. Villarini A multivariate statistical framework for mixed storm types in compound flood analysis Natural Hazards and Earth System Sciences |
| title | A multivariate statistical framework for mixed storm types in compound flood analysis |
| title_full | A multivariate statistical framework for mixed storm types in compound flood analysis |
| title_fullStr | A multivariate statistical framework for mixed storm types in compound flood analysis |
| title_full_unstemmed | A multivariate statistical framework for mixed storm types in compound flood analysis |
| title_short | A multivariate statistical framework for mixed storm types in compound flood analysis |
| title_sort | multivariate statistical framework for mixed storm types in compound flood analysis |
| url | https://nhess.copernicus.org/articles/24/4091/2024/nhess-24-4091-2024.pdf |
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