Wave height return periods from combined measurement–model data: a Baltic Sea case study

Abstract

This paper presents how to account for the lack of sampling variability in model data when they are combined with wave measurements. We addressed the dissimilarities between the types of data by either (i) low-pass filtering the observations or (ii) adding synthetic sampling variability to the model. Measurement–model times series combined with these methods served as the basis for return period estimates of a high wave event in January 2019. During this storm northerly wind speeds in the Baltic Sea rose to 32.5 m s−1 and an unprecedented significant wave height of 8.1 m was recorded in the Bothnian Sea sub-basin. Both methods successfully consolidated the combined time series but produced slightly different results: using low-pass-filtered observations gave lower estimates for the return period than using model data with added sampling variability. Extremes in both types of data followed the same type of theoretical distributions, and our best estimate for the return period was 104 years (95 % confidence 39–323 years). A similar wave event can potentially be more likely in the future climate, and this aspect was discussed qualitatively.