A probabilistic view of extreme sea level events in the Baltic Sea

Abstract. The importance of accounting for extreme sea level events is often an integrate part in any risk mitigation strategies that aims to protect present and future coastal infrastructure. The Extreme value theory (EVT) gives a probabilistic framework for studying such events. However, the conventional methods used in the application of EVT are often restrictive, since they are generally confined to location where there are sufficiently long tide gauge observation data, while simultaneously fail to obtain good estimates of lower probability events.

In this article, we use the Bayesian hierarchical modeling paradigm and the Block Maxima method in the EVT, to estimate the extreme sea level event that occur on average ones every e.g. 1000 years. Four novel models are presented in this study, and each of the models incorporates both missing values and spatial dependency structures to obtain estimates of such extreme events, with a varying complex dependency structure. In addition, two of the models (Hilbert and Latent) allow for the estimation of extreme sea level events at both gauged and ungauged locations.

The results of this study show that Hilbert and Latent obtain good estimates with a reduced uncertainty range for both higher and lower probability events. From the in and out-of-sample evaluation, it follows that the two model’s out-performance the conventional method of combining maximum likelihood estimates and bootstrapping, when comparing the uncertainty range, for the estimates of extreme sea level events that occurs on average ones every e.g. 100 years and up to 100000 years.