On the use of discrete seasonal and directional models for the estimation of extreme wave conditions

Extreme value theory is commonly used in offshore engineering to estimate extreme significant wave height. To justify the use of extreme value models it is of critical importance either to verify that the assumptions made by the models are satisfied by the data or to examine the effect violating mod...

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Bibliographic Details
Published in:Ocean engineering 2010-05, Vol.37 (5), p.425-442
Main Authors: L. Mackay, Edward B., Challenor, Peter G., Bahaj, AbuBakr S.
Format: Article
Language:English
Subjects:
Applied sciences
Buildings. Public works
Computation methods. Tables. Charts
Derivation
Directional model
Estimates
Exact sciences and technology
Extreme values
Extremes
Generalised Pareto distribution
Hydraulic constructions
Marine
Ocean engineering
Offshore engineering
Offshore structure (platforms, tanks, etc.)
Origins
Peaks-over-threshold
Seasonal model
Seasons
Storms
Structural analysis. Stresses
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Summary:Extreme value theory is commonly used in offshore engineering to estimate extreme significant wave height. To justify the use of extreme value models it is of critical importance either to verify that the assumptions made by the models are satisfied by the data or to examine the effect violating model assumptions. An important assumption made in the derivation of extreme value models is that the data come from a stationary distribution. The distribution of significant wave height varies with both the direction of origin of a storm and the season it occurs in, violating the assumption of a stationary distribution. Extreme value models can be applied to analyse the data in discrete seasons or directional sectors over which the distribution can be considered approximately stationary. Previous studies have suggested that models which ignore seasonality or directionality are less accurate and will underestimate extremes. This study shows that in fact the opposite is true. Using realistic case studies, it is shown that estimates of extremes from non-seasonal models have a lower bias and variance than estimates from discrete seasonal models and that estimates from discrete seasonal models tend to be biased high. The results are also applicable to discrete directional models.
ISSN:0029-8018
1873-5258
1873-5258
DOI:10.1016/j.oceaneng.2010.01.017