101. Extreme Waves in the North Sea: Deriving extreme wave conditions applying Hierarchical Clustering and Non-Stationary Extreme Value Modelling
- Author
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Smit, Michiel (author) and Smit, Michiel (author)
- Abstract
Coastal and offshore infrastructure must be designed to withstand extreme wave-induced loading conditions. Extreme Value Analysis (EVA) is often employed to infer probabilistic distributions that provide information about extreme design conditions. In traditional practices, EVA is performed under the assumption of stationarity. This means that the probability of extreme events is constant in time. However, hydraulic loading conditions are expected to exhibit temporal variability in severity and frequency as a result of climate change. Therefore, the assumption of stationarity becomes questionable. Nonstationary extreme value analysis (NEVA) for inferring extreme hydraulic loads have become more attractive in recent years. However, the applicability of NEVA models is debatable ansd differs on a case-by-case basis. Large scale oceanic bodies can be characterized by spatially and temporally varying extreme wave characteristics. Clustering analyses have proven to be successful to identify regions exhibiting similar extreme wave characteristics. Creating clusters based on similar extreme wave characteristics can potentially improve extreme value modelling because intra-cluster information can be pooled to derive more accurate extreme value models. This research presents a practical assessment of the applicability of clustering analysis and non-stationary extreme value modeling of extreme wave statistics at cluster level in the North Sea. The primary objectives of this research are: (1) Study the temporal variability extreme significant wave height (Hm0) and extreme wind speeds (U10) in the North Sea domain, (2) Investigate how hierarchical clustering analysis (HAC) can be employed to cluster grid points that exhibit similar extreme wave characteristics, (3) How the obtained clusters and temporal variability can be employed to derive extreme value models describing extreme Hm0 statistics at cluster level and (4) assess whether NEVA models at cluster level form a, Civil Engineering | Hydraulic Engineering | Hydraulic Structures and Flood Risk
- Published
- 2022