Articles | Volume 24, issue 11
https://doi.org/10.5194/nhess-24-4091-2024
https://doi.org/10.5194/nhess-24-4091-2024
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

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This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Cited articles

Akaike, H.: A new look at the statistical model identification, IEEE T. Automat. Contr., 19, 716–723, https://doi.org/10.1109/TAC.1974.1100705, 1974. 
Barth, N. A., Villarini, G., and White, K.: Accounting for Mixed Populations in Flood Frequency Analysi.: Bulletin 17C Perspective, J. Hydrol. Eng., 24, 4019002, https://doi.org/10.1061/(asce)he.1943-5584.0001762, 2019. 
Bass, B. and Bedient, P.: Surrogate modeling of joint flood risk across coastal watersheds, J. Hydrol. (Amst), 558, 159–173, https://doi.org/10.1016/j.jhydrol.2018.01.014, 2018. 
Bauer, M., Tselioudis, G., and Rossow, W. B.: A new climatology for investigating storm influences in and on the extratropics, J. Appl. Meteorol. Clim., 55, 1287–1303, https://doi.org/10.1175/JAMC-D-15-0245.1, 2016. 
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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.
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