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

Abstract

Abstract. Increased coastal flooding caused by extreme sea levels (ESLs) is one of the major hazards related to sea level rise. Estimates of return levels obtained under the framework provided by extreme-event 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-value models to two long-term sea level records from Venice (96 years) and Marseille (65 years): a generalized extreme-value (GEV) distribution, a generalized Pareto distribution (GPD), a point process (PP), the joint probability method (JPM), and the revised joint probability method (RJPM) under different detrending strategies. We model non-stationarity with a linear dependence of the model's parameters on the mean sea level. Our results show that non-stationary GEV and PP models fit the data better than stationary models. The non-stationary PP model is also able to reproduce the rate of extremes occurrence fairly well. Estimates of the return levels for non-stationary and detrended models are consistently more conservative than estimates from stationary, non-detrended models. Different models were selected as being more conservative or having lower uncertainties for the two datasets. Even though the best model is case-specific, we show that non-stationary extremes analyses can provide more robust estimates of return levels to be used in coastal protection planning.

Dynamical diagnostic of extreme events in Venice lagoon and their mitigation with the MoSE

“…The aim of this contribution is to propose a novel approach in diagnosing extreme events in Venice lagoon and in also proposing a way to inform on the role of the MoSE in a dynamical approach by using recent developments of extreme value theory (EVT). Indeed, while EVT provides a suitable theoretical background to estimate the probability of returns of extreme events (i.e., events that are large or small relative to some specific threshold), widely used in several contexts 11 including ESLs in Venice 12 , it is not suitable to deal with persistent or rare phenomena as spatially extended patterns triggering extreme events. This has motivated the introduction of a new mathematical formalism based on defining extreme events as rare recurrences in the state-space of a high-dimensional system 13 bridging together the statistics (i.e., the traditional extreme value theory) and the dynamics (dynamical systems theory) of extreme events.…”

“…This method is particularly suitable for extreme events that are triggered by spatially extended patterns as the response of the Venice lagoon to atmospheric patterns related to depressions and strong winds. Indeed, our two metrics are able to instantaneously capture and characterize the different features of the system, instead of providing a probability of return of a specific event above a selected threshold 11 , 12 . Thus, they provide a time-dependent picture of the system, thus yielding a dynamical diagnostic of the lagoon conditions based on the value of the instantaneous dimensions d and the inverse persistence .…”

Modeling Extreme Values of Non-Stationary Precipitation Data with Effects of Covariates

“…Kumar et al (2021) (2) investigated the extreme climatic elements such as extreme air temperature, low relative humidity, and severe winds as covariates, for daily, monthly, and annual rainfall values reported at the Narora site. Baldan et al (2022) (3) investigated the importance of nonstationary with linear dependence in parameters of generalized extreme value (GEV) distribution and point process model for assessing extreme sea levels. Rohmer et al (2021) (4) discussed that non-stationarity in heavy rainfall time series is often apparent in the form of trends because of long-term climate changes.…”

Improving Coastal Storm Surge Monitoring Through Joint Modeling Based on Permanent and Temporary Tide Gauges

“…To date, tide gauge (TG) stations are the only means to monitor high-frequency and continuous in situ ESLs. Their records are used to estimate the return levels of extreme SSs (Baldan et al, 2022;Boumis et al, 2023;Buchanan et al, 2017;Fang et al, 2021;X. Feng & Tsimplis, 2014;Wahl et al, 2017) for coastal adaptation measures or to analyze the characteristics of different components of ESLs (J.…”

“…To date, tide gauge (TG) stations are the only means to monitor high‐frequency and continuous in situ ESLs. Their records are used to estimate the return levels of extreme SSs (Baldan et al., 2022; Boumis et al., 2023; Buchanan et al., 2017; Fang et al., 2021; X. Feng & Tsimplis, 2014; Wahl et al., 2017) for coastal adaptation measures or to analyze the characteristics of different components of ESLs (J. Feng et al., 2023; Lowe et al., 2021; Menéndez & Woodworth, 2010; Pineau‐Guillou et al., 2023). However, all these studies are limited to a station scale due to the sparseness and uneven distribution of TGs.…”