A multivariate extreme value analysis for the design of coastal structures in England.

A multivariate extreme value analysis for the design of coastal structures in England.
Tozer, N.P.
In: The Second meeting of the Challenger Society Special Interest Group on Ocean Wind Waves, 19-20 October 2016, HR Wallingford, Oxfordshire. (2016)

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Abstract:This paper describes the application of a state-of-the-art multivariate extreme value statistical model to offshore winds, waves and sea levels around the coast of England and Wales. The output of the extreme value statistical model is a Monte-Carlo (MC) simulation of extreme offshore events. To undertake robust risk-based design of coastal structures, it is necessary to assess the performance of existing and proposed new structures against all of these events. A series of SWAN wave transformation models of the coastline have been established, Figure 1. It is, however, computationally impractical to transform all of these MC events from the offshore to the nearshore, particularly when covering the coastline of England and Wales. A computationally efficient statistical method has therefore been employed. The statistical method, known as an emulator, has been used to replicate the behaviour of the SWAN wave transformation model. The emulators translate the thousands of MC events from offshore to the nearshore. The nearshore results have been stored on a 1km mesh. This nearshore dataset has the potential to overcome many of the limitations of the existing joint probability methods based on exceedences. The method can be implemented for wide range of uses, including the robust, risk-based, design of coastal structures, climate change impact assessment, nearshore wave climates for detailed local flood risk assessments and coastal flood forecasting. This paper describes the practical application of the data as developed and applied in the National Flood Risk Assessment – State of the Nation project.
Item Type:Conference or Workshop Item (Paper)
Subjects:Coasts > General
ID Code:1383
Deposited On:26 Jan 2017 13:40
Last Modified:06 Feb 2018 13:25

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