Alvise Benetazzo

Physical Oceanography is the branch of marine sciences concerned with understanding the oceans as a geophysical fluid system, focusing on the processes that govern the spatial organisation and temporal variability of momentum, heat, and mass across a wide range of scales. Knowledge in Physical Oceanography is produced through a combination of observations, theory, numerical models, and statistical inference; yet its epistemological structure remains largely implicit. This paper develops a framework to clarify how such knowledge is generated, validated, and constrained by the ocean’s vast spatial extent, continuous variability, and multiscale dynamics. We propose a two-dimensional epistemic map that classifies knowledge claims by their aim and representational form, providing a structured vocabulary for interpreting the diversity of oceanographic research. We illustrate the map with an exploratory classification of scientific synopses, and use it to read recent tendencies in the discipline. We further examine the epistemic status of oceanic numerical models and reanalysis systems, and present a case study of wind-wave spectral representation that exposes the underlying assumptions and limits of common practices. Building on these elements, we propose a diagnostic reporting template aimed at making explicit the assumptions, validation criteria, and epistemic payoff of individual studies. By rendering the epistemic structure of a result legible, the framework supports clearer interpretation, comparison, and communication of physical-oceanographic knowledge, both within the discipline and across the wider Earth-system sciences.

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.

This study aims to contribute to the understanding of marine and coastal hazard due to cyclones in the Mediterranean region, through the reproduction of sea level and wind waves with the use of an unstructured and coupled 2D hydrodynamic-wave modelling system. The modelling system is used to produce a numerical hindcast for the period 1994–2020 which is validated against tide gauges, wave buoys and satellite-borne instruments. The sea level and wave results are analysed for 2483 Mediterranean cyclones detected through a meteorological approach based on a composite cyclone detection and tracking method. We study which portion of extreme values (i.e. values exceeding the 99^th percentile) of storm surge and significant wave height is associated to Mediterranean cyclones and we find that in the Mediterranean basin, extreme waves are more frequently associated to cyclones than extreme storm surge. Indeed, more than 50 % of extreme significant wave heights of the hindcast is associated to waves generated by cyclones in most of the Mediterranean basin. The percentage is higher than 75 % in the Western Adriatic basin and in some sub-regions of the Ionian-Meridional basin, as well as in the northwestern part of the Aegean Sea. For storm surge we find that more than 50 % of extremes values are linked to cyclones in the Western Mediterranean basin, in most of the Thyrrenian and the Ionian-Meridional basin, and in the Western Adriatic Sea. On the contrary, less than 50 % of extreme values of storm surge are associated to cyclones in the Northern and Eastern Adriatic basin, in the Ligurian Sea and in the Levantine-Aegean basin. The cyclone hazard is then evaluated through selected indicators, including peak values of storm surge and significant wave height, as well as the storm erosion potential index and the total storm wave energy which are integrated indices suitable to indicate cyclones intensity. The spatial distribution of the 99^th percentile of all hazard parameters shows that the regions characterized by high hazard values vary depending on the parameter considered. In case of maximum significant wave height and total storm wave energy, the highest values are reached in the Western Mediterranean Sea, in the open sea area between the Balearic Islands and Sardinia. In case of maximum storm surge and storm erosion power index, the highest values are observed in the Northern Adriatic basin while the Western Mediterranean is characterized by moderate values. Despite some common patterns between hazard parameters in the Mediterranean basin, the analysis of their relationships stress that there is not an high correlation among most of them. Through some case studies we show that these parameters provide complementary information enabling a more comprehensive overview of cyclone hazard.

Physical Oceanography is the branch of marine sciences concerned with understanding the oceans as a geophysical fluid system, focusing on the processes that govern the spatial organisation and temporal variability of momentum, heat, and mass across a wide range of scales. Knowledge in Physical Oceanography is produced through a combination of observations, theory, numerical models, and statistical inference; yet its epistemological structure remains largely implicit. This paper develops a framework to clarify how such knowledge is generated, validated, and constrained by the ocean’s vast spatial extent, continuous variability, and multiscale dynamics. We propose a two-dimensional epistemic map that classifies knowledge claims by their aim and representational form, providing a structured vocabulary for interpreting the diversity of oceanographic research. We illustrate the map with an exploratory classification of scientific synopses, and use it to read recent tendencies in the discipline. We further examine the epistemic status of oceanic numerical models and reanalysis systems, and present a case study of wind-wave spectral representation that exposes the underlying assumptions and limits of common practices. Building on these elements, we propose a diagnostic reporting template aimed at making explicit the assumptions, validation criteria, and epistemic payoff of individual studies. By rendering the epistemic structure of a result legible, the framework supports clearer interpretation, comparison, and communication of physical-oceanographic knowledge, both within the discipline and across the wider Earth-system sciences.

Physical Oceanography is the branch of marine sciences concerned with understanding the oceans as a geophysical fluid system, focusing on the processes that govern the spatial organisation and temporal variability of momentum, heat, and mass across a wide range of scales. Knowledge in Physical Oceanography is produced through a combination of observations, theory, numerical models, and statistical inference; yet its epistemological structure remains largely implicit. This paper develops a framework to clarify how such knowledge is generated, validated, and constrained by the ocean’s vast spatial extent, continuous variability, and multiscale dynamics. We propose a two-dimensional epistemic map that classifies knowledge claims by their aim and representational form, providing a structured vocabulary for interpreting the diversity of oceanographic research. We illustrate the map with an exploratory classification of scientific synopses, and use it to read recent tendencies in the discipline. We further examine the epistemic status of oceanic numerical models and reanalysis systems, and present a case study of wind-wave spectral representation that exposes the underlying assumptions and limits of common practices. Building on these elements, we propose a diagnostic reporting template aimed at making explicit the assumptions, validation criteria, and epistemic payoff of individual studies. By rendering the epistemic structure of a result legible, the framework supports clearer interpretation, comparison, and communication of physical-oceanographic knowledge, both within the discipline and across the wider Earth-system sciences.

Propagating waves on the ocean surface can be represented as a stochastic process whose statistics are characterized by a spectrum. This paper reviews methods for measuring the wave spectrum and related quantities. Observations begin by sensing fluid dynamical properties of the sea surface over space and/or time. Visual observations, collected routinely since the mid‐18th century, comprise the longest‐running wave record. Nearshore measurement methods continue to advance, including traditional pressure and acoustic sensing as well as newer technologies like distributed acoustic sensing and LiDAR. Detailed small‐scale wave physics can now be explored with measurement techniques using light, including stereo‐imaging and polarimetry. Reductions in the size, cost, and power consumption of microelectronics have propagated through ocean wave instrumentation, most notably in wave buoys. Global networks of freely drifting miniature wave buoys offer novel observational capabilities. Remote sensing techniques based on radar and LiDAR continue to evolve and are widely deployed from land, ships, aircraft, autonomous vehicles, and satellites. Spaceborne altimeters form one of the most important records of wave height, and new spaceborne sensors now observe directional spectra globally with sampling akin to traditional altimetry. Aircraft and autonomous systems provide strategic sampling capabilities for detailed process studies and access to extreme storm environments. The quality and quantity of ocean wave measurements have never been greater. This review aims to help make sense of it all.

Coastal flooding represents a major hazard for low-lying coastal environments, arising from the combined effects of atmospheric forcing, sea-level fluctuations, and wave-induced processes, which can jointly produce rapid and severe coastal impacts. In a complex context of climate change, including sea-level rise and potential shifts in storm characteristics, both the frequency and intensity of such events are expected to increase. This context calls for the development of robust operational early warning systems capable of explicitly resolving atmosphere-ocean interactions and their role in driving coastal extremes. In this work, we introduce and evaluate a Coastal Flooding Index (CFI), conceived as an impact-oriented Early Warning Index that links meteo-marine conditions with local coastal characteristics. The index combines total water level at the shoreline – accounting for tides, storm surge, and wave contributions – with the geometry of coastal defences, allowing a classification of flooding conditions from no impact to inland inundation.The proposed framework is based on a fully coupled modelling chain, including atmospheric, hydrodynamic, wave, and nearshore components. Atmospheric forcing is provided by WRF, while sea level and offshore wave conditions are simulated through SHYFEM and WAVEWATCH III, and nearshore wave processes are resolved with XBeach. This setup enables a consistent representation of air–sea interactions across scales, from synoptic forcing to local coastal impacts.To support operational applications, a reduced-ensemble approach is adopted. Starting from a larger ensemble, a subset of representative members is selected through clustering techniques applied to key atmospheric variables. This underline to retain the essential spread and extreme scenarios while reducing computational cost, making the system suitable for real-time forecasting. The methodology is tested over a vulnerable sector of the Northern Adriatic coast (Jesolo, Italy) during two severe storm events (Vaia 2018 and Detlef 2019), both characterized by strong surge-wave interactions. Results suggest that the reduced ensemble is able to preserve the probabilistic structure of the full system and provides meaningful estimates of both exceedance probability and timing of coastal flooding. Overall, the proposed approach suggests a practical way to integrated, probabilistic early warning systems, bridging weather forecasting and coastal impact assessment, and supporting emerging applications in climate services and coastal risk management.

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.