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Coastal freshwater resources are commonly under high risk of being contaminated from seawater. The main processes that affect seawater intrusion are groundwater overexploitation, land use change, and climate change effects. In this context coastal lagoons represent the more sensitive environments prone to seawater intrusion. Numerical modelling is a useful tool to understand and predict seawater intrusion. In this study, a three-dimensional SEAWAT model is employed to simulate the seawater intrusion to coastal aquifers of Variconi Oasis (Italy). The present simulation was divided into a calibration and a validation model, then the model was used to predict the salinization trend up to 2050. Results show the role of the sea in salinizing the beach front, while the retrodunal environment is characterized by transitional environments. Future seawater intrusion scenarios considering only climate data showed no significative differences in respect to the actual situation. The same happens considering also a low sea level rise prediction. On the contrary, the worst scenario (high sea level rise prediction), depicts a quite different situation, with a saline intrusion in the Variconi oasis that will severely affect the fragile transitional ecosystem. This modelling framework can be used to quantify the effects of climate changes in similar coastal environments.

Forest wildfires usually occur due to natural processes such as lightning and volcanic eruptions, but at the same time they are also an effect of uncontrolled and illegal anthropogenic activities. Different factors can influence forest wildfires, like the type of vegetation, morphology, climate, and proximity to human activities. A precise evaluation of forest fire issues and of the countermeasures needed to limit their impact could be satisfactory especially when forest fire risk (FFR) mapping is available. Here, we proposed an FFR evaluation methodology based on Geographic Information System (GIS) and the analytic hierarchy process (AHP). The study area is the Campania region (Southern Italy) that, for the last 30 years, has been affected by numerous wildfires. The proposed methodology analyzed 12 factors, and AHP was used for weight assignment, offering a new approach to some parameters. The method divided the study area into five risk classes, from very low to very high. Validation with fire alerts showed a good correlation between observed and predicted fires (0.79 R2). Analyzing the climate projections, a future FFR for 2040 was also assessed. The proposed methodology represents a reliable screening tool to identify areas under forest fire risk, and can help authorities to direct preventive actions.

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