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Name: Eleanor D'Arcy

Talk Title: Extreme Sea Level Estimation: Accounting for Seasonality

Abstract: Storm surges pose an increasing risk to coastline communities. These events, combined with high tide, can result in coastal flooding. To reduce the impact of storm surges, an accurate estimate of coastal flood risk is necessary. Specifically, estimates are required for the return level of sea levels (still water). This estimate is used as an input to determine the height for a coastal defence, such as a sea wall. The return level estimation requires statistical analysis based on extreme value theory, as we need to know about the frequency of events that are more extreme than those previously observed.

Large storm surges exhibit seasonality, they are typically at their worst in the winter and least extreme in the summer. We focus on the skew surge: the difference between the observed and predicted high water within a tidal cycle. As well as an annual cyclic trend for seasonality, we investigate a linear trend in the mean skew surge and any residual trend in each season by sharing information spatially.

The seasonal pattern of skew surge differs from that of the tide, whose seasonality is driven astronomically, resulting in tidal peaks at the spring and autumn equinoxes. Hence, the worst levels of the two components of still water level are likely to peak at different times in the year, and so statistical methods that treat them as independent variables are likely to over-estimate return levels.

Our work aims to model how the distribution of skew surges changes over a year and we combine our results with the known seasonality of tides to derive estimates of still water level return levels.

This talk is a contributed talk at EVA 2021.