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.

The Italian Institute for Environmental Protection and Research (ISPRA) manages the national sea state real time monitoring system for Italy, which consists of the National Sea Level Network (RMN), the North Adriatic and Venice Lagoon Sea Level Network (RMLV), the National Wave Networks (RON), and a marine weather forecasting system. These systems are particularly deployed to monitor and predict storm surges that affect the northern part of the Adriatic Sea and the Venice Lagoon, usually causing damages and morphological impacts over the highly anthropized coastal areas. On 22 November 2022, an extreme storm surge event occurred in the northern Adriatic Sea, producing severe damages on its coastline. Venice and the surrounding urban settlements have been protected from flooding thanks to the operation of the Mo.S.E. (Modulo Sperimentale Elettromeccanico) system, a set of artificial barriers built to isolate the lagoon from the sea in case of extreme high tides. Coastal flooding prevention measures, such as storm-surge barriers, are indeed being widely adopted globally because of the accelerating rise in sea levels. An analysis of this extreme event is presented here to highlight the functionality and the usefulness of the ISPRA sea state monitoring system. In particular, the analysis of the as-if scenario reproducing the natural tide propagation within the lagoon, neglecting the operation of the Mo.S.E. system, can only be pursued by using hydrodynamic models forced using extensive observed data. Results highlight that the “not-regulated” sea level would have exceeded 200 cm above the reference datum at Chioggia, a threshold never recorded in the Venice Lagoon since sea level monitoring systems have been operational.

Meteo-marine data are necessary and useful for all tasks and scientific activities of national interest involving the protection, enhancement and improvement of the marine environment. The Institute for Environmental Protection and Research (ISPRA) maintains and manages two monitoring networks in order to gather all physical parameters needed in defining the state of Central Mediterranean Sea: the Sea Level Measurement Network (Rete Mareografica Nazionale, RMN) and the Sea Waves Measurement Network (Rete Ondametrica Nazionale, RON). The RMN includes 36 coastal measurement stations; real-time series of sea-level and meteorological parameters are collected every minute. Tide gauges mainly collect sea level, air and water temperatures, wind speed and direction and relative humidity. The RON consists of 15 real-time directional buoys uniformly distributed along the Italian coastline. They collect the main physical parameters such as significant and maximum wave height, peak and mean wave period, wave direction, sea surface temperature (SST), air temperature, wind speed and direction, atmospheric pressure and relative humidity. In this paper attention is focused on statistical analysis in order to describe wave climates, extreme events, tsunami, sea storms and storm surges and related meteorological information. The statistics taken into consideration are mainly the joint frequency distribution of significant wave heights with respect to direction and peak and mean wave periods. Extreme-event analysis is made through the peak over threshold (POT) method and the generalised Pareto distribution (GPD). Finally, sea-level time series analyses are used to highlight examples of meteorological events (such as tsunami) through harmonic and residual analysis.

Long-term sea level variations can affect specific ocean basins, potentially leading to significant conse-quences for coastal areas, especially where changes in mean sea level are combined with subsidence or negative vertical land motion (VLM). This phenomenon is particularly noticeable along the northern Italian Adriatic coast, which are becoming increasingly vulnerable to flooding. To monitor sea level rise and develop detailed, site-specific adaptation strategies, regional and national institutions manage integrated tide gauge networks. The integration and enhancement of monitoring systems along the Adriatic Sea have been supported by several EU programs, including the Interreg Italy-Croatia AdriaClim project. This has led to the installation of GNSS instruments co-localised with tide gauges, allowing continuous monitoring of the vertical motion of sea level references. Furthermore a levelling activity was carried out in the Emilia-Romagna coastal areas to address the lack in updated vertical reference systems. The vertical reference is crucial for ensuring comparability of data from different stations and for correctly applying forecasting outputs to local conditions. Sea level data from three referenced tide gauge stations were validated and analysed to assess climate change indicators. The mean sea level trend in the northern Adriatic was evaluated using the Seasonal Trend decomposition based on Locally weighted regression smoothing using Loess (STL) method, applied to long-term historical time series from Venezia and Trieste. The Porto Garibaldi time series, however, only became available in 2009, so that a linear regression had to be used to analyse its trend. For comparison, the Venezia and Trieste time series for the same period were also analysed using the linear regression. The main results show site-specific behaviour and emphasise the importance of stable and correctly referenced stations. In fact, it is thanks to the information on VLM computed using the so-called GPS Imaging that we have been able to distinguish between the Relative Sea Level (RSL) component and the Absolute Sea Level (ASL) rise.