Multivariate extreme value modelling of sea conditions around the coast of England

Gouldby, Ben; Wyncoll, David; Panzeri, Mike; Franklin, Mark; Hunt, Tim; Hames, Dominic; Tozer, Nigel; Hawkes, Peter; Dornsbusch, Uwe; Pullen, Tim

Abstract

It is widely recognised that coastal flood events can arise from combinations of extreme waves and sea levels. For flood risk analysis and the design of coastal structures it is therefore necessary to assess the joint probability of the occurrence of these variables. Traditional methods have involved the application of joint probability contours, defined in terms of extremes of sea conditions that can, if applied without correction factors, lead to the underestimation of flood risk and under-design of coastal structures. This paper describes the application of a robust multivariate statistical model to analyse extreme offshore waves, wind and sea levels around the coast of England. The approach described here is risk based in that it seeks to define extremes of response variables directly, rather than the joint extremes of sea conditions. The output of the statistical model comprises a Monte Carlo simulation of extreme events. These distributions of extreme events have been transformed from offshore to nearshore using a statistical emulator of a wave transformation model. The resulting nearshore extreme sea condition distributions have the potential to be applied for a range of purposes. The application is demonstrated using two structures located on the south coast of England.

• Journal Article Type: Article
• Acceptance Date: May 2, 2017
• Online Publication Date: Jul 12, 2017
• Publication Date: Mar 1, 2017
• Deposit Date: Sep 12, 2023
• Journal: Proceedings of the Institution of Civil Engineers: Maritime Engineering
• Print ISSN: 1741-7597
• Electronic ISSN: 1751-7737
• Publisher: Thomas Telford
• Peer Reviewed: Peer Reviewed
• Volume: 170
• Issue: 1
• Pages: 3-20
• Series ISSN: 1741-7597
• DOI: https://doi.org/10.1680/jmaen.2016.16
• Keywords: floods & floodworks, maritime engineering, risk & probability analysis
• Public URL: https://uwe-repository.worktribe.com/output/11096838
• Additional Information: This paper won the 2018 ICE Bill Curtin Medal.