Bayesian hierarchical modelling of sea-level extremes in the Finnish coastal region

Abstract

Occurrence probabilities of extreme sea levels required in coastal planning, e.g. for calculating design floods, have been traditionally estimated individually at each tide-gauge location. However, these estimates include uncertainties, as sea-level observations typically have only a small number of extreme cases such as annual maxima. Moreover, exact information on sea-level extremes between the tide-gauge locations and incorporation of dependencies between the adjacent stations is often lacking in the analysis. In this study, we use Bayesian hierarchical modelling to estimate return levels of annual maxima of short-term sea-level variations related to storm surges in the Finnish coastal region. We use the generalised extreme value (GEV) distribution as the basis and compare three hierarchical model structures of different complexity against tide-gauge-specific fits. The hierarchical model structures allow us to share information on annual maximum sea levels between the neighbouring stations and also provide a natural way to estimate uncertainties in the theoretical estimates. The results show that compared to the tide-gauge-specific fits, the hierarchical models, which pool information across the tide gauges, provide narrower uncertainty ranges for both the posterior parameter estimates and the corresponding return levels in most locations. The estimated shape parameter of the GEV model is systematically negative for the hierarchical models, which indicates a Weibull type of behaviour for the extremes along the Finnish coast. The negative shape parameter also allows us to calculate the theoretical upper limit for the annual maximum sea levels on the Finnish coast. Depending on the tide gauge and hierarchical model considered, the median value of the theoretical upper limit was 47–73 cm higher than the highest observed sea level.