Bayesian hierarchical modeling of sea level extremes in the Finnish coastal region

Abstract

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 modeling 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 generalized 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 to share information on annual maximum sea levels between the neighboring 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 stations, provide narrower uncertainty ranges both for the posterior parameter estimates and for the corresponding return levels on most of the tide gauges. The estimated shape parameter of the GEV model is systematically negative for the hierarchical models, which indicates a Weibull-type of behavior for the extremes along the Finnish coast. This also suggests that the hierarchical models can be used to estimate theoretical upper limits of the extremes of short-term sea level variations along 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.

Should Swedish sea level planners worry more about mean sea level rise or sea level extremes?

“…Räty et al. ( 2022 ) found a large number of tide-gauges in neighbouring Finland to also have Weibull distributed yearly maxima, indicating that bounded sea level extremes are likely common in the area. Taken together this means that the range of plausible mean sea levels grows much faster with the length of the planning period than the range of plausible sea level extremes.…”

Simulating sea level extremes from synthetic low-pressure systems

“…These levels are of the same magnitude as our result for the extreme sea level at Oulu without the water balance component. Räty et al (2022) used Bayesian hierarchical modeling to analyse return level estimates and theoretical upper limits for the sea level on the Finnish coast from tide gauge data. At Oulu, the 1000-year return level is approximately 200 cm and the theoretical upper limit for the sea level using Bayesian modeling is approximately 250 cm.…”

The sea level simulator v1.0: a model for integration of mean sea level change and sea level extremes into a joint probabilistic framework

“…This range depends on the length of tide-gauge time series, and not on understanding of 220 the local oceanographic conditions. For locations, where only short time series are available, it could thus be useful to use data also from neighbouring tide gauges (Calafat and Marcos, 2020;Räty et al, 2022) or from numerical ocean models (Särkkä et al, 2017) to better constrain a plausible range of GEV parameters. Figure 6.…”