07 Jun 2022
07 Jun 2022
Status: this preprint is open for discussion.

Importance of non-stationary analysis for assessing extreme sea levels under sea level rise

Damiano Baldan, Elisa Coraci, Franco Crosato, Maurizio Ferla, Andrea Bonometto, and Sara Morucci Damiano Baldan et al.
  • Italian Institute for Environmental Protection and Research, ISPRA, Venice, Italy

Abstract. Coastal flooding caused by extreme sea levels (ESLs) is one of the major impacts related to the climate change. It is expected to increase in the future due to sea level rise and storm surge intensification. Estimates of return levels obtained under the framework provided by extreme events theory might be biased under climatic non-stationarity. Additional uncertainty is related to the choice of the model. In this work, we fit several extreme values models to a long-term (96 years) sea level record from the city of Venice (NW Adriatic Sea, Italy): a Generalized Extreme Value distribution (GEV), a Generalized Pareto Distribution (GPD), a Point Process (PP), and the Joint Probability Method (JPM) under different detrending strategies. We model non-stationarity with a linear dependence of the model’s parameters from the mean sea level. Our results show that non-stationary GEV and PP models fit the data better than stationary models even with detrended data. The non-stationary PP model is able to reproduce the rate of extremes occurrence fairly well. Actualized 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 using stationary models. Overall, we show that non-stationary extremes analyses can provide more robust estimates of return levels to be used in coastal protection planning.

Damiano Baldan et al.

Status: open (until 19 Jul 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-347', Anonymous Referee #1, 30 Jun 2022 reply

Damiano Baldan et al.


Total article views: 164 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
121 37 6 164 16 2 2
  • HTML: 121
  • PDF: 37
  • XML: 6
  • Total: 164
  • Supplement: 16
  • BibTeX: 2
  • EndNote: 2
Views and downloads (calculated since 07 Jun 2022)
Cumulative views and downloads (calculated since 07 Jun 2022)

Viewed (geographical distribution)

Total article views: 138 (including HTML, PDF, and XML) Thereof 138 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 02 Jul 2022
Short summary
Extreme events analysis is widely used to provide information for the design of coastal protection structures. Non-stationarity due to e.g. sea level rise can affect such estimates. Using different methods on a long time series of sea level data, we show that estimates of the magnitude of extreme events in the future can be inexact due to relative sea level rise. Thus, considering non-stationarity is important when analyzing extremes sea level events.