Saverio Fracaros

1. Coastal transgressive dune systems are large-scale, mobile, partially vegetated dune complexes that can decouple from the backshore and migrate inland over prior terrain, often cannibalising and...

This study presents the first fine-scale Coastal Vulnerability Index (CVI) assessment for Togo, evaluating coastal vulnerability and risk along the country’s 50 km barrier coastline in the context of accelerating erosion, rising sea level, and growing human exposure. Using remote sensing, GIS, and a CVI framework, shoreline trend rates, beach width, land use, and the role of existing coastal defences were analysed to support risk-informed decision-making. The coastline was segmented into 99 coastal units of 500 m, and shoreline trend rates were computed using the End Point Rate (EPR) method based on multi-temporal satellite-derived shorelines spanning from 1988 to 2024. Results show strong spatial contrasts in vulnerability, with the eastern sector of the Port of Lomé, particularly a 24.5 km stretch, exhibiting high vulnerability due to persistent shoreline retreat and narrow beach widths. In contrast, the western coastline displays lower vulnerability levels. Several erosion hotspots were identified, including Baguida and Dévinkemé, where recent shoreline retreat reaches up to −12.8 m/year. Existing coastal defences locally mitigate erosion impacts, reducing the extent of highly vulnerable shoreline from 23.5 km to 15 km. The integrated risk assessment identifies 6.5 km of coastline, primarily in the eastern port area, as being at high risk due to the combined effects of erosion and dense human settlement. These results provide spatially explicit information to support integrated coastal zone management, land-use planning, and adaptation strategies in Togo.

Sea level rise (SLR) and increased urbanisation of coastal areas have exacerbated coastal flood threats, making them even more severe in important cultural sites. In this context, the role of hard coastal defences such as promenades and embankments needs to be carefully assessed. Here, a thorough investigation is conducted in Grado, one of the most significant coastal and historical towns in the Friuli Venezia Giulia region of Italy. Grado is located on a barrier island of the homonymous lagoon, the northernmost of the Adriatic Sea, and is prone to flooding from both the sea and the back lagoon. The mean and maximum sea levels from the historical dataset of Venice (1950–2023) were analysed using the Gumbel-type distribution, allowing for the identification of annual extremes based on their respective return periods (RPs). Grado and Trieste sea level datasets (1991–2023) were used to calibrate the statistics of the extremes and to calculate the local component (subsidence) of relative SLR. The research examined the occurrence of annual exceedance of the minimum threshold water level of 110 cm, indicating Grado’s initial notable marine ingression. The study includes a detailed analysis of flood impacts on the urban fabric, categorised into sectors based on the promenade elevation on the lagoon side, the most vulnerable to flooding. Inundated areas were obtained using a high-resolution digital terrain model through a GIS-based technique, assessing both the magnitude and exposure of the urban environment to flood risk due to storm surges, also considering relative SLR projections for 2050 and 2100. Currently, approximately 42% of Grado’s inhabited area is inundated with a sea level threshold value of 151 cm, which occurs during surge episodes with a 30-year RP. By 2100, with an optimistic forecast (SSP1-2.6) of local SLR of around +53 cm, the same threshold will be met with a surge of ca. 100 cm, which occurs once a year. Thus, extreme levels linked with more catastrophic events with current secular RPs will be achieved with a multi-year frequency, inundating more than 60% of the urbanized area. Grado, like Venice, exemplifies trends that may impact other coastal regions and historically significant towns of national importance. As a result, the generated simulations, as well as detailed analyses of urban sectors where coastal flooding may occur, are critical for medium- to long-term urban planning aimed at adopting proper adaptation measures.

Summary: Bycatch, the incidental capture of non-target species in fishing gear, has been recognised as the most significant global conservation threat affecting seabird species. Geographically, bycatch rates vary widely, depending on local fishing efforts, environmental features, and seabird community composition. Regional and local research is essential due to the complexity of accurately extrapolating general conclusions regarding the impacts of bycatch. Existing European bycatch research predominantly focuses on northern regions, leaving a significant knowledge gap regarding bycatch in the Mediterranean Sea. This work presents findings of wintering diving seabirds as bycatch of small-scale fisheries in a coastal area of the northern Adriatic Sea, based on data collected between 2021 and 2023. Seabird distribution varied along the depth profile. The bathymetric range between 3 m and 5 m was the most exploited by fishermen. Bycatch of seabirds was confirmed in the study area, with five species recorded, i.e. Black-necked Grebe Podiceps nigricollis, Red-throated Loon Gavia stellata, Black-throated Loon Gavia arctica, Mediterranean Shag Gulosus aristotelis desmarestii, and Great Crested Grebe Podiceps cristatus. Our results suggest that bathymetry likely plays a strong influence on bycatch occurrence. Incidental captures were not widespread but appeared concentrated in the shallowest depths <5 m and the range <2.5 m was identified as particularly susceptible due to the low associated fishing effort and the majority of bycatch events recorded. We estimate that between 46 and 108 birds were incidentally captured during the research period. This study identifies key factors shaping the areas of bycatch vulnerability and risk, proposing a spatial–temporal mitigation framework within Natura 2000 sites and highlighting the value of local stakeholders’ engagement.

Advances in machine learning and deep learning approaches have drawn substantial interest across diverse research domains, including environmental studies. These innovative techniques have transformed the approaches to measuring marine parameters by facilitating automated and remote data collection. This study focuses on the implementation of a deep learning model to automatically assess tide and surge, aiming for precise outcomes through the analysis of surveillance camera imagery.Utilizing the Inception v3 structure, the deep learning model was applied to predict tide and storm surge from surveillance cameras strategically positioned in two distinct coastal regions, namely Santa Lucia in southeastern Sicily and Lignano Sabbiadoro in Friuli Venezia Giulia, Italy. The deep learning model is based on classification methods to assign a value of water level to a given frame. This approach is particularly advantageous in scenarios where traditional tide sensors face inaccessibility or are distant from measurement points, especially during extreme events demanding accurate surge measurements. The dataset used for the training and validation of the deep learning model covers the entire tide values that could be observed in the study areas. Predictions of the deep learning model were compared with tide gauge values in order to assess the system accuracy.The conducted experiments demonstrate the efficiency of the model in remotely and effectively measuring tide and surge, achieving an accuracy exceeding 90% while maintaining a loss value below 1 for the deep learning model. These findings underscore its potential to fill the data collection gap in challenging coastal environments, offering valuable insights for coastal management and hazard assessment. This study makes an important contribution to the rapidly growing field of remote sensing and machine learning applications in environmental monitoring, facilitating greater comprehension and decision-making in coastal areas.

In recent decades, the scientific community has increasingly focused on extreme events linked to climate change, which are leading to more intense and frequent natural disasters. The Mediterranean can be considered a hotspot where the effects of these changes are expected to be more intense compared to other regions of the planet. Italy is not exempt; in fact, with its extensive shoreline, it is particularly vulnerable, especially to high sea levels and coastal erosions. In this framework, from late October to early November 2023, six storm surges occurred in the Gulf of Trieste (NE Italy). These events, characterized by winds from 190°N to 220°N and the significant wave height, which reached up to 1.81 m nearshore—an uncommon meteorological condition in the northern Adriatic Sea—caused the occurrence of eight coastal sinkholes and substantial damages to man-made structures. Thanks to Unmanned Aerial Vehicles (UAVs) and their derived products (high-resolution orthomosaics, Digital Elevation Models—DEMs, and point clouds), it was possible to study these features over time, enabling long-term coastal dynamics monitoring, which can be crucial for timely and effective response and restoration efforts.

The latest progress in deep learning approaches has garnered significant attention across a variety of research fields. These techniques have revolutionized the way marine parameters are measured, enabling automated and remote data collection. This work centers on employing a deep learning model for the automated evaluation of tide and surge, aiming to deliver accurate results through the analysis of surveillance camera images. A mode of deep learning based on the Inception v3 structure was applied to predict tide and storm surges from surveillance cameras located in two different coastal areas of Italy. This approach is particularly advantageous in situations where traditional tide sensors are inaccessible or distant from the measurement point, especially during extreme events that require accurate surge measurements. The conducted experiments illustrate that the algorithm efficiently measures tide and surge remotely, achieving an accuracy surpassing 90% and maintaining a loss value below 1, evaluated through Categorical Cross-Entropy Loss functions. The findings highlight its potential to bridge the gap in data collection in challenging coastal environments, providing valuable insights for coastal management and hazard assessments. This research contributes to the emerging field of remote sensing and machine learning applications in environmental monitoring, paving the way for enhanced understanding and decision-making in coastal regions.

The advantages derived from the use of Uncrewed Aerial Vehicles (UAVs) are well-established: they are cost-effective and easy to use. There are numerous environmental applications, particularly when monitoring contexts characterized by rapid morphological changes and high rates of sediment transport, such as coastal areas. In this paper, three different case studies of survey and monitoring with high resolution and accuracy obtained through the use of UAVs are presented; these concern transgressive coastal sites. Results allow for the definition and quantification of coastal landforms and processes, including: (i) The anatomy of a parabolic dune and the rate of landward migration that could interfere with a tourist settlement; (ii) The mode and timing of morphological recovery and realignment of a barrier island overwashed by storm surge episodes; and (iii) The potential flood risk of a progradational spit that is a nesting site of a species of migratory breeding birds of conservation concern. The results demonstrate and confirm that, through a good coupling of drone-sensed quality data and accurate topographic control, quantitative estimates that are useful in assessing the impacts of natural processes involving both human and natural assets can be obtained.

While beach erosion and sand loss are typically of great concern to the tourism industry, managing rapid morphological changes linked to large amounts of moving sediments is the challenge facing Grado, an important seaside resort in the northern Adriatic, Italy. The cause of the unusual management conflict is the presence of the Mula di Muggia Bank, a nearshore depositional system made up of relict and active migrating sandbanks extending up to 2 km seawards from the touristic beachfront. A reconstruction of the morpho-sedimentary evolution of the coastal system over a 200-year period was done using a large dataset which includes historical cartography, topographic maps, aerial photos and topo-bathymetric surveys. The results show the growth of a significant urban development aimed at creating a tourist destination by occupying the waterfront along fetch-limited coastal tracts with very shallow water and scarce hydrodynamics. Furthermore, a number of sandy dynamic landforms (longshore migrating bars, a bypass corridor, an ebb-tidal delta) and accumulation zones attest to a sediment excess which can be mostly attributed to the eastern river supplies. The progressive constant migration rate of 12.6 my⁻¹ allowed the bank to induce the expansion of the low-energy silty backbarrier environment, characterised by abundant seagrass meadows a short distance directly in front of the tourist beaches of Grado. As a result of historical analysis and more current observations, areas with diverse morphosedimentary features and with varying tourist/recreational, ecological, and conservation values have been identified. These can be considered as basic units for future accurate planning and re-evaluation of coastal management choices to balance environmental protection and tourist use. A soft coastal defence approach is proposed which includes either the preservation of specific environments or the proper use of excess sand for beach nourishment via periodic dredging or sediment bypassing.