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Spatial Bayesian hierarchical modelling of extreme sea states

Authors: 

Colm Clancy, John O'Sullivan, Conor Sweeney, Frédéric Dias, Andrew Parnell

Publication Type: 
Refereed Original Article
Abstract: 
A Bayesian hierarchical framework is used to model extreme sea states, incorporating a latent spatial process to more e ectively capture the spatial variation of the extremes. The model is applied to a 34-year hindcast of signi cant wave height o the west coast of Ireland. The generalised Pareto distribution is tted to declustered peaks over a threshold given by the 99.8th percentile of the data. Return levels of signi cant wave height are computed and compared against those from a model based on the commonly-used maximum likelihood inference method. The Bayesian spatial model produces smoother maps of return levels. Furthermore, this approach greatly reduces the uncertainty in the estimates, thus providing information on extremes which is more useful for practical applications.
Digital Object Identifer (DOI): 
10.1016/j.ocemod.2016.09.015
Publication Status: 
Published
Date Accepted for Publication: 
Thursday, 15 September, 2016
Publication Date: 
16/09/2016
Journal: 
Ocean Modelling 2016
Volume: 
107
Issue: 
Jan-13 Elsevier
Research Group: 
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
No