Unravelling Spatial Variability of Sea Level Extremes in the Netherlands: Insights from Observational Data

Coastal regions in the Netherlands face persistent challenges from sea level extremes, prompting a comprehensive exploration of their spatial variability. Our study explores the nuances of extreme sea level events across the country, using the observed sea level data from the GESLA-3 (Global Extreme Sea Level Analysis) dataset. We analyse 16 stations with observational periods spanning from 38 to 68 years.

The total observed sea level is detrended and split into two components: (i) the tidal component, derived using harmonic analysis, and (ii) the non-tidal residual, calculated by subtracting the obtained tidal signal from the observed sea-level records. Extremes of both total sea level and non-tidal residual are then identified using the Peak over Threshold method, opting for a 70th percentile threshold. This choice allows us to examine less severe scenarios, suitable for risk assessments or planning purposes.

Our preliminary analysis of extreme event characteristics, such as the duration and intensity of an event, indicates significant spatial differences across stations. Correlation coefficients between stations, particularly for total extreme sea level characteristics and extreme non-tidal residual characteristics (duration and intensity), show a noticeable pattern that consistently reveals higher values between stations with similar latitudes across all variables. Moreover, the distributions of total extreme sea level characteristics exhibit noteworthy differences as well - for example, in southern regions, the distributions of intensity are more broadly dispersed and skewed to the right, signifying higher events than those in the northern counterparts. However, this distinction is less pronounced when focusing solely on the non-tidal residual, possibly since the total sea level is influenced by factors such as the river inflow, prevalent in the south, and tidal propagation behaviour in the North Sea.

As we progress with our analysis, we plan to apply a supervised learning method for classifying extreme events based on storm characteristics, and conduct a clustering analysis to reveal hidden spatial patterns of extreme events, for both total sea level and non-tidal residual. Furthermore, we aim to explore the interactions between surges and tides across different classes of extreme events, unravelling the underlying driving mechanisms of enhanced compound events.

In summary, our ongoing study of sea level extremes in the Netherlands, from spatial dynamics to event characteristics, will provide a solid foundation for understanding the driving mechanisms behind the extremes, gaining insights about their natural variability, and evaluating the impacts of changing climatic conditions.