Preprints
https://doi.org/10.5194/egusphere-2024-1122
https://doi.org/10.5194/egusphere-2024-1122
13 May 2024
 | 13 May 2024
Status: this preprint is open for discussion.

A multivariate statistical framework for mixed populations in compound flood analysis

Pravin Maduwantha, Thomas Wahl, Sara Santamaria-Aguilar, Robert Andrew Jane, James F. Booth, Hanbeen Kim, and Gabriele Villarini

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 the 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 neither linked to 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.

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Pravin Maduwantha, Thomas Wahl, Sara Santamaria-Aguilar, Robert Andrew Jane, James F. Booth, Hanbeen Kim, and Gabriele Villarini

Status: open (until 24 Jun 2024)

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Pravin Maduwantha, Thomas Wahl, Sara Santamaria-Aguilar, Robert Andrew Jane, James F. Booth, Hanbeen Kim, and Gabriele Villarini
Pravin Maduwantha, Thomas Wahl, Sara Santamaria-Aguilar, Robert Andrew Jane, James F. Booth, Hanbeen Kim, and Gabriele Villarini

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Short summary
Most of the studies on compound flooding assume events that generate extreme rainfall and coastal water level responses originate from a single population, in reality, they originate from multiple populations each with unique statistical characteristics. This paper presents a flexible statistical framework for assessing the compound flood potential from multiple flood drivers that explicitly accounts for different event types.