Global application of a regional frequency analysis  to extreme sea levels

Collings, Thomas P.; Quinn, Niall D.; Haigh, Ivan D.; Green, Joshua; Probyn, Izzy; Wilkinson, Hamish; Muis, Sanne; Sweet, William V.; Bates, Paul D.

doi:https://doi.org/10.5194/nhess-24-2403-2024

Articles | Volume 24, issue 7

https://doi.org/10.5194/nhess-24-2403-2024

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

https://doi.org/10.5194/nhess-24-2403-2024

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

Articles | Volume 24, issue 7

Research article

|

16 Jul 2024

Research article |

| 16 Jul 2024

Global application of a regional frequency analysis to extreme sea levels

Thomas P. Collings, Niall D. Quinn, Ivan D. Haigh, Joshua Green, Izzy Probyn, Hamish Wilkinson, Sanne Muis, William V. Sweet, and Paul D. Bates

Data sets

The Global Extreme Sea Level Analysis (GESLA) Version 3 dataset I. D. Haigh et al. https://doi.org/10.5285/d21a496a-a48f-1f21-e053-6c86abc08512

Water level change time series for the European coast from 1977 to 2100 derived from climate projections K. Yan et al. https://doi.org/10.24381/cds.8c59054f

FES2014 AVISO, NOVELTIS, LEGOS, CLS Space Oceanography Division and CNES https://www.aviso.altimetry.fr/en/data/products/auxiliary-products/global-tide-fes/description-fes2014.html

Combined mean dynamic topography – MDT HYBRID-CNES-CLS18-CMEMS2020 AVISO https://www.aviso.altimetry.fr/en/data/products/auxiliary-products/mdt.html

Copernicus Global Digital Elevation Model European Space Agency https://doi.org/10.5069/G9028PQB

COAST-RP: A global COastal dAtaset of Storm Tide Return Periods (Version 2) J. Dullaart et al. https://doi.org/10.4121/13392314.V2

Model code and software

GESLA dataset codes P. R. Thompson https://github.com/philiprt/GeslaDataset

FES2014 prediction package AVISO and CNES https://anaconda.org/fbriol/pyfes

Short summary

Coastal areas are at risk of flooding from rising sea levels and extreme weather events. This study applies a new approach to estimating the likelihood of coastal flooding around the world. The method uses data from observations and computer models to create a detailed map of where these coastal floods might occur. The approach can predict flooding in areas for which there are few or no data available. The results can be used to help prepare for and prevent this type of flooding.