We developed a storm surge ensemble prediction system (EPS) for lagoons and transitional environments. Lagoons are often threatened by storm surge events with consequent risks for human life and economic losses. The uncertainties connected with a classic deterministic forecast are many, thus, an ensemble forecast system is required to properly consider them and inform the end-user community accordingly. The technological resources now available allow us to investigate the possibility of operational ensemble forecasting systems that will become increasingly essential for coastal management. We show the advantages and limitations of an EPS applied to a lagoon, using a very high-resolution unstructured grid finite element model and 45 EPS members. For five recent storm surge events, the EPS generally improves the forecast skill on the third forecast day compared to just one deterministic forecast, while they are similar in the first two days. A weighting system is implemented to compute an improved ensemble mean. The uncertainties regarding sea level due to meteorological forcing, river runoff, initial boundaries, and lateral boundaries are evaluated for a special case in the northern Adriatic Sea, and the different forecasts are used to compose the EPS members. We conclude that the largest uncertainty is in the initial and lateral boundary fields at different time and space scales, including the tidal components. Significance Statement: Storm surges are extreme sea level events that may threaten densely populated coastal areas. The purpose of this work is to improve the extreme sea level forecast for transitional areas with the understanding of what are the most important forcing generating uncertainties and find a technique to reach a reliable sea level forecast. This is achieved by implementing an ensemble prediction system running 45 members for each event considered. Results show that initial and lateral boundary conditions provide most of the uncertainty, including the tidal components. The weighting system applied to find the ensemble mean improves the forecast skill on the third forecast day while it is comparable with the deterministic forecast in the first two days.

The adoption of Nature-Based Solutions (NBSs) represents a novel means to mitigate natural hazards. In the framework of the OPERANDUM project, this study introduces a methodology to assess the efficiency of the NBSs and a series of Open-Air Laboratories (OALs) regarded as a proof-of-concept for the wider uptake of NBSs. The OALs are located in Finland, Greece, UK, Italy, and Ireland. The methodology is based on a wide modeling activity, incorporated in the context of future climate scenarios. Herein, we present a series of models’ chains able to estimate the efficiency of the NBSs. While the presented models are mainly well-established, their coupling represents a first fundamental step in the study of the long-term efficacy and impact of the NBSs. In the selected sites, NBSs are utilized to cope with distinct natural hazards: floods, droughts, landslides, salt intrusion, and nutrient and sediment loading. The study of the efficacy of NBSs to mitigate these hazards belongs to a series of works devoted to the implementation of NBSs for environmental purposes. Our findings prove that land management plays a crucial role in the process. Specifically, the selected NBSs include intensive forestry; the conversion of urban areas to grassland; dunes; marine seagrass; water retention ponds; live cribwalls; and high-density plantations of woody vegetation and deep-rooted herbaceous vegetation. The management of natural resources should eventually consider the effect of NBSs on urban and rural areas, as their employment is becoming widespread.

A well-validated storm surge numerical model is crucial, offering precise coastal hazard information and serving as a basis for extensive databases and advanced data-driven algorithms. However, selecting the best model setup based solely on common error indicators like the root-mean-square error (RMSE) or Pearson correlation does not always yield optimal results. To illustrate this, we conducted 34-year high-resolution simulations for storm surge under barotropic (BT) and baroclinic (BC) configurations using atmospheric data from ERA5 and a high-resolution downscaling of the Climate Forecast System Reanalysis (CFSR) developed by the University of Genoa (UniGe). We combined forcing and configurations to produce three datasets: (1) BT-ERA5, (2) BC-ERA5, and (3) BC-UniGe. The model performance was assessed against nearshore station data using various statistical metrics. While RMSE and Pearson correlation suggest BT-ERA5, i.e., the coarsest and simplest setup, is the best model (followed by BC-ERA5), we demonstrate that these indicators are not always reliable for performance assessment. The most sophisticated model (BC-UniGe) shows worse values of RMSE or Pearson correlation due to the so-called “double penalty” effect. Here we propose new skill indicators that assess the ability of the model to reproduce the distribution of the observations. This, combined with an analysis of values above the 99th percentile, identifies BC-UniGe as the best model, while ERA5 simulations tend to underestimate the extremes. Although the study focuses on the accurate representation of storm surge by the numerical model, the analysis and proposed metrics can be applied to any problem involving the comparison between time series of simulation and observation.

The large presence of human infrastructure, protected natural environments, cultural heritage and major economic activities makes the coast one of the most vulnerable areas to climate change-related problems such as sea level rise and an increasing number of extreme events. Storm surges are the main cause of flooding and coastal erosion due to the combined effects of waves and currents that resuspend and transport sediments. In addition to the classic "gray" solutions, including engineered structures such as seawalls and groins, nature-based solutions have emerged in the last decade, based on the idea of finding solutions that are both effective and environmentally sustainable. It is well known that seagrass meadows can provide critical ecosystem services. Among them, coastal protection is one of the most important. The aim of this work is to investigate the effects of seagrass meadows on physical ocean variables relevant for sediment transport and sea level using a digital twin of the ocean. The seagrass is implemented in a three-dimensional unstructured grid ocean model (SHYFEM-MPI) as a form drag in the momentum equations, considering the flexibility of the plants. The implementation was verified with idealized test cases and three focus areas were selected along the Italian coast. The three areas are the Venice lagoon, the Emilia-Romagna coast (northern Adriatic Sea) and the Civitavecchia coast (Tyrrhenian Sea) and differ in morphology and seagrass species (Zostera marina, Posidonia oceanica). These areas are representative of a wide range of coastal environments. Interestingly, the results show different behavior depending on the geomorphology of the area. The lagoon environment, when exposed to extreme storm surge events, shows an alternating pattern of sea level variation with a reduction that can reach 5-10%, while in the other focus area the effect of seagrass on sea level is negligible. In all three areas, seagrass is very effective in reducing bottom current velocity by up to 50-60%, suggesting a possible important role against coastal erosion.

Digitalization, particularly through the utilization of digital twins of the ocean, can play a significant role in advancing the sustainable development of the marine environment. The Digital Twin (DT) creates a digital replica of the ocean, enabling testing of various What-If scenarios, such as the impacts of sea level rise, Nature-Based Solutions (NBS), as well as the effectiveness of mitigation and adaptation plans. DTs provide insights into ocean conditions, ecosystems, and human effects, guiding decisions for sustainable resource use. DT-based What-If scenarios in NBS foster cooperation among stakeholders in shared oceanic spaces, enabling data-driven decisions and collaboration. This platform serves for decision-making and management strategies aimed at fostering the sustainable utilization of ocean resources. Such an application is a Digital Twin strategy in designed experiments for nature-based solutions. It can be employed to evaluate the effects of sea level rise and wave actions on seagrass meadows; and evaluate different management approaches to enhance resilience, while assessing diverse management tactics for bolstering resilience. We demonstrate how Digital twins of the coastal ocean can contribute by aiding decision-making through the use of Whar-If scenarios for coastal protection against erosion and sediment transport by sea level rise, while also ensuring the preservation of coastal biodiversity. By monitoring and optimizing solutions through digital twins, effectiveness and long-term sustainability are heightened, necessitating collaborative efforts for coastline protection and ecosystem preservation.To further support the coastal restoration (e.g. of seagrass meadows), digital twin technology can be utilized to monitor and model the climate (e.g. sea level rise) and human induced effects on coastal ecosystems. The collaborative efforts for nature based solution as coastline protection and ecosystem preservation are demonstrated in various coastal areas around the Global coast (e.g., in the North Atlantic, Wadden Sea coast, Danube-western Black Sea, Mediterranean coast, Eastern coast of Ghana) In the context of nature-based solutions, a digital twin help identifying areas where seagrass is most vulnerable to the impacts of sea level rise and evaluate different management strategies to promote resilience. In addition to seagrass restoration, other nature-based solutions can effectively address the impacts of sea level rise. These solutions include the restoration of wetlands, dunes, and mangroves, as well as the implementation of green infrastructure such as bioswales and green roofs. Coastal communities can play a critical role in implementing and supporting nature-based solutions. This includes engaging in community-based monitoring and restoration efforts, as well as advocating for policies that prioritize nature-based solutions for coastal protection. The integration of digital twin technology with community efforts can foster a collaborative and data-driven approach to sustainable coastal management and resilience.

This study investigates wave attenuation induced by the non-linear interactions between wave dynamics and marine vegetation, focusing on Posidonia oceanica meadows along the Civitavecchia coastal zone (north-eastern Tyrrhenian Sea, Italy). To overcome the limitations of conventional models, which often oversimplify canopy–wave dynamics and lack the integration with observational data, this research employs a digital twin approach that enhances model fidelity by coupling numerical simulations with long-term in situ measurements. We refine the seagrass parameterization in a spectral wave model by incorporating an enhanced representation into the bottom dissipation source term, explicitly accounting for the effects of plant flexibility, seasonal growth patterns, and phenotypic traits, all informed by site-specific observations. Application to the Civitavecchia site demonstrates that the seasonal variability of the meadows drives significant temporal fluctuations in wave damping capacity, with a monthly variation of up to 10 %. Spatial analysis revealed wave height reductions of 10 %–40 %, averaging 18 % across Sites of Community Importance and 24 % over rocky substrates colonized by seagrass. These results highlight the necessity of resolving seasonal cycles and the biomechanical flexibility of aquatic vegetation.