Mediterranean hurricanes, also known as medicanes, can cause significant damage to coastal communities. Consequently, they have been the subject of intense study in recent decades. Geographic Information Systems (GISs) and Web-GIS applications are essential for processing and analyzing geographic data from various sources, particularly in the field of spatial planning. Due to their ability to integrate heterogeneous information, these tools can provide experts with a comprehensive overview of relevant geospatial data. Here, we present ARCHIMEDE, a relational geodatabase connected to an open-source Web-GIS platform focused on Mediterranean hurricanes that contains data from recent research on these extreme weather phenomena. These datasets comprise climatic and oceanographic data obtained from remote sensing techniques as well as seismic and geomorphological data obtained from field observations. Consequently, this Web-GIS platform can enhance our understanding of Mediterranean cyclones by relating the physical properties of these natural phenomena with their impacts on coastal regions. ARCHIMEDE is an innovative tool for the dissemination of geographic information to stakeholders, researchers, and decision-makers, offering valuable support for the development of robust coastal management strategies aimed at mitigating the challenges posed by Mediterranean hurricanes.

Mediterranean cyclones induce significant biogeochemical perturbations in the Mediterranean Sea, with particularly notable effects on chlorophyll a (Chl a) dynamics. This study examines Chl a variability during twenty Mediterranean cyclones, comparing offshore and nearshore responses. Through an integrated investigation of reanalysis products, ARGO float observations, and Sentinel-2 multispectral satellite imagery, we quantified vertical and surface Chl a concentrations, while concurrently assessing nitrate distribution, currents, and mixed layer depth (MLD) variability. Our results revealed that both tropical-like cyclones and extratropical cyclones generated a pronounced uplift of the deep chlorophyll maximum (DCM) in cyclones exhibiting slow-moving phases. Notably, wind-driven upwelling and air–sea heat exchange critically govern DCM uplift for these cyclones. We demonstrated that these physical mechanisms collectively drive DCM uplift along the trajectories of intense, slow-moving Mediterranean cyclones, with significant implications for nutrient cycling and primary productivity across the Mediterranean basin.

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.

An open-source geodatabase and its associate WebGIS platform (CONNECTOSED) were developed to collect and utilize data for the Sediment Flow Connectivity Index (SfCI) for the Apulia region of southern Italy. Maps depicting sediment mobility and connectivity across the hydrographic basins of the Apulia region were generated and stored in the geodatabase. This geodatabase is organized into folders containing data in TIFF, shapefile, Jpeg and Pdf formats, including input variables (digital elevation model, land cover map, rainfall map, and soil units dataset for each hydrographic basin), classification graphs (ranking of variable values), dimensionless index maps (slope, ruggedness, rainfall, land cover, and soil stability) and key products (maps of sediment mobility, SfCI, and applied SfCI). The geodatabase maintains the mapping methodology underlying the SfCI algorithm by integrating various Earth datasets from multiple sources through ArcMap™, QGIS® and Matlab® software. This approach aligns surface characteristics with driving forces to describe the spatial variability of sediment pathways and identify hotspot areas. The availability of both input and processed data enables the computation and continuous updating of this applied geomorphological indicator, which is useful for assessing susceptibility to rapid Earth surface changes related to multi-hazard exposure. The geodatabase and the CONNECTOSED platform are valuable tools for researchers and stakeholders involved in land monitoring. The geodatabase and the CONNECTOSED platform are essential tools for researchers, policymakers, and stakeholders involved in land monitoring and environmental management. These tools provide open access to extensive datasets and detailed descriptions of surface dynamics, establishing connections between the causes and effects of extreme phenomena, such as floods, landslides, fires, soil pollution. This integration allows users to combine various forms of environmental data, a capability that is vital for enhancing scientific knowledge, supporting the development of insights, and fostering more informed, evidence-based decision-making in land use planning, conservation efforts, and sustainability initiatives.

Mediterranean cyclones induce significant biogeochemical perturbations in the Mediterranean Sea, with particularly notable effects on chlorophyll-a (Chl-a) dynamics. This study examined Chl-a variability during twenty Mediterranean cyclones, comparing offshore and nearshore responses. Through an integrated investigation of reanalysis products, ARGO float observations, and Sentinel-2 multispectral satellite imagery, we quantified vertical and surface Chl-a concentrations while concurrently assessing nitrate distribution, currents, and mixed layer depth (MLD) variability. Our results revealed that both Tropical-like Cyclones and Extratropical Cyclones generated a pronounced uplift of the deep chlorophyll maximum (DCM) in cases where cyclones exhibited slow-moving phases. Notably, wind-driven upwelling and air-sea heat exchange critically govern DCM uplift for these cyclones. We demonstrated that these physical mechanisms collectively drive DCM uplift along the trajectories of intense, slow-moving Mediterranean cyclones, with significant implications for nutrient cycling and primary productivity across the Mediterranean basin.