A multivariate statistical framework for mixed storm types in compound flood analysis

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

doi:https://doi.org/10.5194/nhess-24-4091-2024

Articles | Volume 24, issue 11

https://doi.org/10.5194/nhess-24-4091-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-24-4091-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 24, issue 11

Research article

|

27 Nov 2024

Research article |

| 27 Nov 2024

A multivariate statistical framework for mixed storm types in compound flood analysis

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

Supplement

https://doi.org/10.5194/nhess-24-4091-2024-supplement

Data sets

Analysis of Record for Calibration (AORC) Gridded Data National Oceanic and Atmospheric Administration, National Weather Service https://hydrology.nws.noaa.gov/pub/AORC/V1.1/

Tides and Currents Data National Oceanic and Atmospheric Administration, Center for Operational Oceanographic Products and Services https://tidesandcurrents.noaa.gov/

Model code and software

Compound flood potential Pravin Maduwantha https://doi.org/10.5281/zenodo.13755288

MultiHazard R package R. Jane https://doi.org/10.5281/zenodo.6772478

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Short summary

When assessing the likelihood of compound flooding, most studies ignore that it can arise from different storm types with distinct statistical characteristics. Here, we present a new statistical framework that accounts for these differences and shows how neglecting these can impact the likelihood of compound flood potential.