Articles | Volume 24, issue 7
https://doi.org/10.5194/nhess-24-2403-2024
https://doi.org/10.5194/nhess-24-2403-2024
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

Related authors

Automated tail-informed threshold selection for extreme coastal sea levels
Thomas P. Collings, Callum J. R. Murphy-Barltrop, Conor Murphy, Ivan D. Haigh, Paul D. Bates, and Niall D. Quinn
EGUsphere, https://doi.org/10.5194/egusphere-2025-1138,https://doi.org/10.5194/egusphere-2025-1138, 2025
Short summary

Related subject area

Sea, Ocean and Coastal Hazards
A multiscale modelling framework of coastal flooding events for global to local flood hazard assessments
Irene Benito, Jeroen C. J. H. Aerts, Philip J. Ward, Dirk Eilander, and Sanne Muis
Nat. Hazards Earth Syst. Sci., 25, 2287–2315, https://doi.org/10.5194/nhess-25-2287-2025,https://doi.org/10.5194/nhess-25-2287-2025, 2025
Short summary
Super typhoons Mangkhut (2018) and Saola (2023) during landfall: comparison and insights for wind engineering practice
Yujie Liu, Yuncheng He, Pakwai Chan, Aiming Liu, and Qijun Gao
Nat. Hazards Earth Syst. Sci., 25, 2255–2269, https://doi.org/10.5194/nhess-25-2255-2025,https://doi.org/10.5194/nhess-25-2255-2025, 2025
Short summary
Recent Baltic Sea storm surge events from a climate perspective
Nikolaus Groll, Lidia Gaslikova, and Ralf Weisse
Nat. Hazards Earth Syst. Sci., 25, 2137–2154, https://doi.org/10.5194/nhess-25-2137-2025,https://doi.org/10.5194/nhess-25-2137-2025, 2025
Short summary
Development of a wind-based storm surge model for the German Bight
Laura Schaffer, Andreas Boesch, Johanna Baehr, and Tim Kruschke
Nat. Hazards Earth Syst. Sci., 25, 2081–2096, https://doi.org/10.5194/nhess-25-2081-2025,https://doi.org/10.5194/nhess-25-2081-2025, 2025
Short summary
Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates
Naveen Ragu Ramalingam, Kendra Johnson, Marco Pagani, and Mario L. V. Martina
Nat. Hazards Earth Syst. Sci., 25, 1655–1679, https://doi.org/10.5194/nhess-25-1655-2025,https://doi.org/10.5194/nhess-25-1655-2025, 2025
Short summary

Cited articles

Amadeo, K.: Hurricane Harvey Facts, Damage and Costs, 1–5 pp., https://www.lamar.edu/_files/documents/resilience-recovery/grant/recovery-and-resiliency/hurric2.pdf (last access: December 2022), 2019. 
Andrée, E., Su, J., Larsen, M. A. D., Madsen, K. S., and Drews, M.: Simulating major storm surge events in a complex coastal region, Ocean Model., 162, 101802, https://doi.org/10.1016/j.ocemod.2021.101802, 2021. 
Andreevsky, M., Hamdi, Y., Griolet, S., Bernardara, P., and Frau, R.: Regional frequency analysis of extreme storm surges using the extremogram approach, Nat. Hazards Earth Syst. Sci., 20, 1705–1717, https://doi.org/10.5194/nhess-20-1705-2020, 2020. 
Arns, A., Wahl, T., Haigh, I. D., and Jensen, J.: Determining return water levels at ungauged coastal sites: a case study for northern Germany, Ocean Dynam., 65, 539–554, https://doi.org/10.1007/s10236-015-0814-1, 2015. 
AVISO: Combined mean dynamic topography – MDT HYBRID-CNES-CLS18-CMEMS2020, https://www.aviso.altimetry.fr/en/data/products/auxiliary-products/mdt.html [dataset], last access: May 2022. 
Download
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.
Share
Altmetrics
Final-revised paper
Preprint