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 i
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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 practical alternative compared to conventional stationary analysis in the design and risk assessment of hydraulic infrastructure in light of climate change.

Temporal trend analysis of Hm0 in the North Sea showed that the period between 1990 and 2020 can be characterized by a decreasing trend. Between 1950 and 2020, a decrease in Hm0 intensity is observed in the Western regions and an increase is observed in the East. This is reason to believe that the variability in extreme wave climate is cyclical rather than monotonic. There is reason to believe that temporal variations of extreme U10 are responsible for the temporal variability of extreme Hm0. Initial clustering results partition the North Sea domain into 50 clusters based on characteristic values for the significant wave height (Hm0), peak period (Tp), and dominant wave directions (θ1 and θ2). After splitting clusters based on geo-location and merging clusters based on the intra-cluster statistical properties of the wave parameters, 63 clusters are obtained. The identified clusters and temporal variability are used to define NEVA models describing extreme Hm0 statistics at cluster level. Intra-cluster Hm0 observations are detrended before fitting the GEV parameters by means of Bayesian Inference. Informative priors are constructed by pooling the GEV parameter information from the intra-cluster grid points. Potential non-stationarity is accounted for by adding the Theil-Sen parameters (b and b0) to the location parameter (μ∗), making the location parameter a linear function of time. The model parameters subsequently read: Hm0 ∼ GEV (μ∗ + (b · t + b0) , σ∗, ξ∗). Using the extreme Hm0 data from the clustering centroid yields the most promising results for describing extreme Hm0 statistics at cluster level under the condition that the intra-cluster exhibits homogeneous values for b and b0.

The applied HAC analysis presented in this research is not the optimal strategy. The identified clusters exhibit heterogeneous values for b and b0 Because non-stationarity ofHm0 was not accounted for during the HAC analysis. This hinders the performance of the NEVA models at cluster level. Also, whether the derived methodology can be applied for the long-term projection of future extreme wave events in the North Sea is debatable. The non-stationary of extreme Hm0 is best described by a cyclic pattern. Without a thorough understanding of the underlying causes of the non-stationary in Hm0 and without future projections of the extreme wave climate, the applicability of NEVA for deriving extreme Hm0 design conditions in light of climate change cannot be guaranteed.