Author(s): Damiano Baldan; Franco Crosato; Elisa Coraci; Andrea Bonometto; Maurizio Ferla; Sara Morucci

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Abstract: Coastal flooding caused by extreme sea levels is one of the major impacts of climate change. Extreme sea levels are expected to increase in the future due to sea level rise and storm surge intensification. Estimating return levels while assuming stationarity might lead to the underestimation of return levels for the future. Additional uncertainty is related to the choice of the model. In this work, we fit extreme values models to longterm (96 years) sea level record from the city of Venice (NW Adriatic Sea, Italy): a Generalized Extreme Value distribution (GEV), a Generalized Pareto distribution (GP), and a Point Process (PP). We model nonstationarity with a linear dependence of the model’s parameters from the mean sea level. Our results show that the inclusion of non-stationarity significantly improves the fit of the GEV and the PP models, but not the GP. The non-stationary PP models the rate of extremes occurrence fairly well. Estimates of the return levels for non-stationary models are generally higher than estimates from stationary models. Thus, projections of return levels in the future might be significantly different from those calculated in the past using stationary models.

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Year: 2022