Chiara Favaretto

Coastal flooding poses a major threat to low-lying coastal areas, particularly under increasing storm intensity and sea-level rise. Early warning systems traditionally rely on deterministic forecasts, which provide a single possible scenario and fail to represent forecast uncertainty. In this study, we evaluate a probabilistic early warning framework for coastal flooding based on short-range ensemble forecasts and assess the feasibility of ensemble reduction to support operational applications. A Coastal Flooding Index (CFI) is introduced, linking the total sea levels at the beach, computed by an atmosphere-ocean-wave modelling chain including nearshore wave processes, to the geometry of local coastal defences. The framework is applied as a pilot case to a low-lying, urbanized coastal stretch in the northern Adriatic Sea (Italy) and tested during two severe storm events (Vaia, 2018, and Detlef, 2019). Ensemble forecasts derived from a full atmospheric ensemble (50 members) are compared with a reduced ensemble (15 members) obtained through a clustering-based selection of representative members. Results show that the reduced ensemble consistently preserves the key probabilistic properties of the full ensemble, including the spatial patterns, timing, and magnitude of ensemble mean and spread of wind speed, significant wave height, nearshore sea level, and derived flooding indicators, while considerably reducing the computational cost (30 % of numerical simulations required). Although limited to two events and a single site, this study demonstrates the potential of combining CFI with ensemble reduction to retain the benefits of probabilistic forecasting for coastal flooding early warning within operational constraints.

Coastal flooding poses a major threat to low-lying coastal areas, particularly under increasing storm intensity and sea-level rise. Early warning systems traditionally rely on deterministic forecasts, which provide a single possible scenario and fail to represent forecast uncertainty. In this study, we evaluate a probabilistic early warning framework for coastal flooding based on short-range ensemble forecasts and assess the feasibility of ensemble reduction to support operational applications. A Coastal Flooding Index (CFI) is introduced, linking the total sea levels at the beach, computed by an atmosphere-ocean-wave modelling chain including nearshore wave processes, to the geometry of local coastal defences. The framework is applied as a pilot case to a low-lying, urbanized coastal stretch in the northern Adriatic Sea (Italy) and tested during two severe storm events (Vaia, 2018, and Detlef, 2019). Ensemble forecasts derived from a full atmospheric ensemble (50 members) are compared with a reduced ensemble (15 members) obtained through a clustering-based selection of representative members. Results show that the reduced ensemble consistently preserves the key probabilistic properties of the full ensemble, including the spatial patterns, timing, and magnitude of ensemble mean and spread of wind speed, significant wave height, nearshore sea level, and derived flooding indicators, while considerably reducing the computational cost (30 % of numerical simulations required). Although limited to two events and a single site, this study demonstrates the potential of combining CFI with ensemble reduction to retain the benefits of probabilistic forecasting for coastal flooding early warning within operational constraints.

Coastal flooding poses a major threat to low-lying coastal areas, particularly under increasing storm intensity and sea-level rise. Early warning systems traditionally rely on deterministic forecasts, which provide a single possible scenario and fail to represent forecast uncertainty. In this study, we evaluate a probabilistic early warning framework for coastal flooding based on short-range ensemble forecasts and assess the feasibility of ensemble reduction to support operational applications. A Coastal Flooding Index (CFI) is introduced, linking the total sea levels at the beach, computed by an atmosphere-ocean-wave modelling chain including nearshore wave processes, to the geometry of local coastal defences. The framework is applied as a pilot case to a low-lying, urbanized coastal stretch in the northern Adriatic Sea (Italy) and tested during two severe storm events (Vaia, 2018, and Detlef, 2019). Ensemble forecasts derived from a full atmospheric ensemble (50 members) are compared with a reduced ensemble (15 members) obtained through a clustering-based selection of representative members. Results show that the reduced ensemble consistently preserves the key probabilistic properties of the full ensemble, including the spatial patterns, timing, and magnitude of ensemble mean and spread of wind speed, significant wave height, nearshore sea level, and derived flooding indicators, while considerably reducing the computational cost (30 % of numerical simulations required). Although limited to two events and a single site, this study demonstrates the potential of combining CFI with ensemble reduction to retain the benefits of probabilistic forecasting for coastal flooding early warning within operational constraints.

Understanding the likelihood of extreme sea level events intensified by climate change is vital for effective coastal management. This study focuses on the Mediterranean and part of the European Atlantic coastline. Because sea level records are often short, non-asymptotic extreme value modeling is more accurate, as it does not depend on large-sample limiting assumptions. However, selecting independent events (IEs) is critical and depends on the chosen threshold sea level (TSL) and time window width (TWW) used. Existing literature often lacks a definitive methodology for IE selection. This study aimed to fill this gap by proposing two methodologies for selecting IEs (overlapping and sorting approaches) and a methodology to find the optimal combination of TWW and TSL. The identified IEs were employed to model extreme sea level events utilizing the Peak Over Threshold-Generalized Pareto Distribution (POT-GPD) and the metastatistical extreme value distribution (MEVD) approaches. A cross-validation approach, along with statistical metrics, was used to rigorously assess performance. Findings indicate that MEVD outperforms POT-GPD and is effective in estimating extreme events with longer return periods (RPs). Overall, MEVD with the overlapping method proved more effective at predicting long RP events, although MEVD with the sorting approach had comparatively lower uncertainty.

Coastal flooding poses a major threat to low-lying coastal areas, particularly under increasing storm intensity and sea-level rise. Early warning systems traditionally rely on deterministic forecasts, which provide a single possible scenario and fail to represent forecast uncertainty. In this study, we evaluate a probabilistic early warning framework for coastal flooding based on short-range ensemble forecasts and assess the feasibility of ensemble reduction to support operational applications. A Coastal Flooding Index (CFI) is introduced, linking the total sea levels at the beach, computed by an atmosphere-ocean-wave modelling chain including nearshore wave processes, to the geometry of local coastal defences. The framework is applied as a pilot case to a low-lying, urbanized coastal stretch in the northern Adriatic Sea (Italy) and tested during two severe storm events (Vaia, 2018, and Detlef, 2019). Ensemble forecasts derived from a full atmospheric ensemble (50 members) are compared with a reduced ensemble (15 members) obtained through a clustering-based selection of representative members. Results show that the reduced ensemble consistently preserves the key probabilistic properties of the full ensemble, including the spatial patterns, timing, and magnitude of ensemble mean and spread of wind speed, significant wave height, nearshore sea level, and derived flooding indicators, while considerably reducing the computational cost (30 % of numerical simulations required). Although limited to two events and a single site, this study demonstrates the potential of combining CFI with ensemble reduction to retain the benefits of probabilistic forecasting for coastal flooding early warning within operational constraints.

Understanding the likelihood of extreme sea level events intensified by climate change is vital for effective coastal management. This study focuses on the Mediterranean and part of the European Atlantic coastline. Because sea level records are often short, non-asymptotic extreme value modeling is more accurate, as it does not depend on large-sample limiting assumptions. However, selecting independent events (IEs) is critical and depends on the chosen threshold sea level (TSL) and time window width (TWW) used. Existing literature often lacks a definitive methodology for IE selection. This study aimed to fill this gap by proposing two methodologies for selecting IEs (overlapping and sorting approaches) and a methodology to find the optimal combination of TWW and TSL. The identified IEs were employed to model extreme sea level events utilizing the Peak Over Threshold-Generalized Pareto Distribution (POT-GPD) and the metastatistical extreme value distribution (MEVD) approaches. A cross-validation approach, along with statistical metrics, was used to rigorously assess performance. Findings indicate that MEVD outperforms POT-GPD and is effective in estimating extreme events with longer return periods (RPs). Overall, MEVD with the overlapping method proved more effective at predicting long RP events, although MEVD with the sorting approach had comparatively lower uncertainty.

Coastal flooding poses a major threat to low-lying coastal areas, particularly under increasing storm intensity and sea-level rise. Early warning systems traditionally rely on deterministic forecasts, which provide a single possible scenario and fail to represent forecast uncertainty. In this study, we evaluate a probabilistic early warning framework for coastal flooding based on short-range ensemble forecasts and assess the feasibility of ensemble reduction to support operational applications. A Coastal Flooding Index (CFI) is introduced, linking the total sea levels at the beach, computed by an atmosphere-ocean-wave modelling chain including nearshore wave processes, to the geometry of local coastal defences. The framework is applied as a pilot case to a low-lying, urbanized coastal stretch in the northern Adriatic Sea (Italy) and tested during two severe storm events (Vaia, 2018, and Detlef, 2019). Ensemble forecasts derived from a full atmospheric ensemble (50 members) are compared with a reduced ensemble (15 members) obtained through a clustering-based selection of representative members. Results show that the reduced ensemble consistently preserves the key probabilistic properties of the full ensemble, including the spatial patterns, timing, and magnitude of ensemble mean and spread of wind speed, significant wave height, nearshore sea level, and derived flooding indicators, while considerably reducing the computational cost (30 % of numerical simulations required). Although limited to two events and a single site, this study demonstrates the potential of combining CFI with ensemble reduction to retain the benefits of probabilistic forecasting for coastal flooding early warning within operational constraints.

Understanding the likelihood of extreme sea level events intensified by climate change is vital for effective coastal management. This study focuses on the Mediterranean and part of the European Atlantic coastline. Because sea level records are often short, non-asymptotic extreme value modeling is more accurate, as it does not depend on large-sample limiting assumptions. However, selecting independent events (IEs) is critical and depends on the chosen threshold sea level (TSL) and time window width (TWW) used. Existing literature often lacks a definitive methodology for IE selection. This study aimed to fill this gap by proposing two methodologies for selecting IEs (overlapping and sorting approaches) and a methodology to find the optimal combination of TWW and TSL. The identified IEs were employed to model extreme sea level events utilizing the Peak Over Threshold-Generalized Pareto Distribution (POT-GPD) and the metastatistical extreme value distribution (MEVD) approaches. A cross-validation approach, along with statistical metrics, was used to rigorously assess performance. Findings indicate that MEVD outperforms POT-GPD and is effective in estimating extreme events with longer return periods (RPs). Overall, MEVD with the overlapping method proved more effective at predicting long RP events, although MEVD with the sorting approach had comparatively lower uncertainty.

The Lagoon of Venice is the largest of the Mediterranean area with a unique and fragile ecosystem. It is the result of its long-term management, that dates back to the fourteenth century, when the Venetians decided to divert the major tributaries (Adige, Bacchiglione, Brenta, Sile, Piave rivers) out of the lagoon, so that they discharge their fresh water and sediments directly into the sea, reducing the siltation of the lagoon (Gatto and L. Carbognin 1981). These monumental hydraulic works, decisive for the splendor of Venice, gave rise to several other variations: subsidence was not compensated for by the alluvial deposit, deepening of the lagoon increased the erosion processes, diversion of freshwater caused an increment of the salinization, etc. As a result, a unique lagoon environment developed, that however cannot survive without constant management. Today, at the three inlets that connect the lagoon with the Adriatic Sea, impressive hydraulic works, storm surge barriers named Mo.S.E. (from the Italian acronym for “Experimental Electromechanical Module”, www.mosevenezia.eu) are present. They are crucial to prevent coastal flooding caused by high water levels during extreme storm events. The system started to be operative in October 2020 (Mel et al. 2021) and since then the barriers were activated more than 50 times.