National scale multivariate extreme value modelling of waves, winds and sea levels

National scale multivariate extreme value modelling of waves, winds and sea levels.
Gouldby, B.P.and Wyncoll, D. and Panzeri, M. and Franklin, M. and Hunt, T. and Tozer, N.P. and Dornbusch, U. and Hames, D. and Pullen, T.A. and Hawkes, P.J.
In: FLOODrisk 2016, 18-20 October 2016, Lyon, France. (2016)

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Abstract:It has long been recognised that extreme coastal flooding can arise from the joint occurrence of extreme waves, winds and sea levels. There has been significant development in statistical methods that are capable of appropriately accounting for the dependence between the variables. The standard simplified approach used in England and Wales can result in an underestimation of flood risk unless correction factors are applied. This paper describes the application of a state-of-the-art multivariate extreme value model, Heffernan and Tawn (2004), to offshore winds, waves and sea levels around the coast of England. The methodology overcomes the limitations of the standard method. The output of the statistical analysis is a Monte-Carlo simulation of offshore extreme events and it is necessary to translate these into peak overtopping rates for use as input to flood risk assessments. Due to non-linearities inherent in wave transformation and overtopping processes, it is not possible to identify the events which give the most extreme overtopping rates; indeed the same overtopping rate can be achieved from a wide range of offshore wave, wind and water level combinations. It was computationally impractical to transform all of these MC events from the offshore to the nearshore and through to wave overtopping rates using numerical models. A series of emulators of the SWAN wave transformation model have therefore been constructed. The emulators translate the offshore extreme events through to the nearshore in a computationally efficient manner. The outcome of the process is a national set of extreme events at a 1km resolution in the nearshore region. These events can subsquently be processed into overtopping rates for coastal flood risk assessment. The SWAN 1D model has been used for the nearshore transformation and a neural network model, BAYONET, used to calculate wave overtopping rates. Whilst the methodology has been applied for national flood risk assessment, it has the potential to be implemented for wider use, including climate change impact assessment, nearshore wave climates for detailed local assessments and coastal flood forecasting. This paper explains the limitations of the existing approach and shows outputs from the new methods, demonstrating the improvement in the results that are possible.
Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Multivariate; extreme; coastal; flood risk
Subjects:Maritime > General
Floods > General
Coasts > General
ID Code:1299
Deposited On:17 May 2016 15:22
Last Modified:18 Jan 2017 13:12

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