AI-Assisted Physical Modeling of Sun Glint to Improve Inter-Sensor Consistency of Remote Sensing Reflectance in Coastal Waters

Coastal seas are strongly shaped by complex coastlines, shallow depths, and inputs from rivers, which influence marine life and water quality. This study introduces a new high-resolution modelling system that combines ocean circulation and marine ecosystem processes on unstructured grids. Applied to the northern Adriatic Sea, the model realistically captures seasonal changes in key biogeochemical variables, offering improved tools to support coastal environmental management.

We improved a computer model to simulate coastal flooding by including temporary barriers like sand dunes. We tested it where sand dunes are built seasonally to protect the shoreline for two real storms: one that broke through the dunes and another where dunes held strong. Our model showed how important it is to design these defenses carefully since even if a small part of a dune fails, a major flooding can happen. Overall, our work helps create better tools to manage and protect coastal areas.

Using the same sea surface temperature and the same bulk formula but different atmospheric reanalysis and analysis surface-variable datasets, we show that a higher-resolution dataset is crucial for evaluating the heat-budget closure hypothesis in the Mediterranean Sea. For the first time, we investigate the impact of extreme heat-loss events in the Mediterranean Sea by computing the long-term, basin-averaged mean heat budget. 

This research investigates how seagrass meadows attenuate coastal waves. Our methodology integrates site measurements with numerical simulations, revealing that plant flexibility and seasonal growth cycles are crucial factors that enhance model fidelity for predicting wave damping. These insights aid ecosystem-based coastal protection and conservation of these vital habitats. Future work should address current–sediment–vegetation interactions for a more complete hydrodynamic understanding.

This study examines how waves and ocean currents interact during severe weather, focusing on Medicane Ianos, one of the strongest storms in the Mediterranean. Using advanced modeling, we created a unique system to simulate these interactions, capturing effects like wave-induced water levels and wave-induced effects on the vertical structure of the ocean. We validated our approach with ideal tests and real data from the storm.

Here we show the development of high-resolution simulations of storm surge in the northern Adriatic Sea employing different atmospheric forcing data and physical configurations. Traditional metrics favor a simulation forced by a coarser database and employing a less sophisticated setup. Closer examination allows us to identify a baroclinic model forced by a high-resolution dataset as being better able to capture the variability and peak values of the storm surge.

Coastal wetlands are important for their ability to store carbon, but they also emit methane, a potent greenhouse gas. This study conducted in four wetlands in Ravenna, Italy, aims at understanding how environmental factors affect greenhouse gas emissions. Temperature and irradiance increased emissions from water and soil, while water column depth and salinity limited them. Understanding environmental factors is crucial for mitigating climate change in wetland ecosystems.

A dune restoration project on the northern Adriatic coast (Ravenna, Italy) was assessed using UAV monitoring. Structure-from-motion photogrammetry, elevation differencing, and statistical analysis were used to quantify dune development in terms of sand volume and vegetation cover change. Results show that the installed fence has been effective as there was significant sand accumulation, embryo dune development, and a decrease in blowout features due to increased vegetation colonization.

Seagrass meadows are an emblematic coastal habitat. Their sensitivity to environmental change means that it is essential to monitor their evolution closely. However, high costs make this endeavor a technical challenge. Here, we used machine learning to map seagrass meadows in 148 satellite images in the Venice Lagoon, Italy. We found that adding information such as depth of the seabed and known seagrass location improved our capacity to map temporal change in seagrass habitat.