Coastal zones, at the interface between land and sea, face increasing challenges from erosion, sea-level rise, and anthropogenic interventions, necessitating innovative tools for effective management and protection. This study introduces COAST-PRO_SIM, a novel numerical model specifically designed to predict shoreline evolution and assess the impacts of coastal defence structures on coastal morphology. Unlike existing models that often face a trade-off between computational efficiency and physical accuracy, COAST-PRO_SIM balances these demands by integrating two-dimensional wave propagation routines with advanced shoreline evolution equations. The model evaluates the effects of interventions such as breakwaters and groynes, enabling simulations of shoreline dynamics with reduced computational effort. By using high-resolution input data, COAST-PRO_SIM captures the interplay between hydrodynamics, sediment transport, and structural impacts. Tested on real-world case studies along the coasts of San Leone, Porto Empedocle, and Villafranca Tirrena, the model demonstrates its adaptability to diverse coastal environments. The results highlight its potential as a reliable tool for sustainable coastal management, allowing stakeholders to anticipate long-term changes in coastal morphology and design targeted mitigation strategies.

Coastal zones are dynamic interfaces shaped by the interplay of Land Cover (LC) and Land Use (LU), influenced by both natural processes and anthropogenic activities. Grasping the historical shifts in land is essential for safeguarding coastal benefits such as defense mechanisms, biodiversity conservation, and recreational spaces, alongside enhancing their management. LC and LU products offer a valuable option for monitoring urban development, vegetation coverage, and dry-beach areas. Herein, we present the first study of the spatiotemporal evolution of LC specifically tailored for coastal zones, using the coast of Sicily as an illustration. We used classified satellite imagery from Landsat and Sentinel missions as input for a semantic segmentation model based on deep neural networks. We trained the model with an extensive dataset of coastal images. Our classification and analysis of coastal LC dynamics from 1988 to 2022 provide insights at a high spatiotemporal resolution. We identified key factors driving urban transformation, underscoring the impact of urban expansion on vegetated areas, and explored its correlation with economic and demographic growth. This study includes a multiscale analysis of coastal changes, encompassing long-term trends and seasonal fluctuations across Sicilian beaches. Our findings can contribute to preserve coastal areas by informing policymaking aimed at sustainable management.

Climate change affects all the components of the hydrological cycle. Starting from precipitation distribution, climate alterations have direct effects on both surface water and groundwater in terms of their quantity and quality. These effects lead to modifications in water availability for agriculture, ecology and other social uses. Change in rainfall patterns also affects the runoff of natural rivers. For this reason, studying runoff data according to classical hydrological approaches, i.e., statistical inference methods that exploit stationary probability distributions, might result in missing important information relevant to climate change. From this point of view, a new approach has to be found in the study of this type of data that allows for non-stationary analysis. In this study, the statistical framework known as Generalized Additive Models for Location, Scale and Shape (GAMLSS), which can be used to carry out non-stationary statistical analyses, was applied in a non-stationary frequency analysis of runoff data collected by four gauges widely distributed across Sicily (Italy) in the period 1916–1998. A classical stationary frequency analysis of these runoff data was followed by a different non-stationary frequency analysis; while the first was made using annual rainfall as a covariate, with the aim of understanding how certain statistical parameters of runoff distribution vary with changes in rainfall, the second derived information about the temporal variability of runoff frequencies by considering time as a covariate. A comparison between stationary and non-stationary approaches was carried out using the Akaike information criterion as a performance metric. After analyzing four different probability distributions, the non-stationary model with annual rainfall as a covariate was found to be the best among all those examined, and the three-parameter lognormal the most frequently preferred distribution.

The Mediterranean coastlines are densely populated zones which host key socio‐economic and commercial activities. For this reason, coastal areas are vulnerable sites in case of natural disasters as tsunamis that can strike coasts causing widespread damage to the population and facilities. For these reasons, several studies were performed over the last decade to study the impact of tsunami waves on the coasts. This research assessed the inundation risk due to a tsunami wave which can hit the southeastern coast of Lampedusa Island. The coastal low‐lying geomorphological setting of the southeastern part of the island led to significant socio‐economic growth, but Lampedusa falls within the Mediterranean Sea, a high‐tsunamigenic area, therefore, the need to investigate tsunami propagation and coastal flooding of this sensitive site emerged. For this scope, a calculation chain model was implemented incorporating three steps: the DELFT‐3D software for earthquake effects modelling, MIKE 21 Flow Model FM for nearshore propagation and HEC‐RAS for onshore tsunami inundation modelling. The simulations illustrate the impact of three tsunami scenarios with different magnitudes (M_w 8.5, 7.5, 6.5) generated by hypothetical earthquakes in the Hellenic Arc. In the M_w 8.5 magnitude scenario, significant flooding occurs in the harbour region, with maximum water depths reaching approximately 3.5 m. The maximum water velocity in this scenario reaches about 15 m/s in the eastern portion, adjacent to cliffs impacted by the tsunami wave. In contrast, the M_w 7.5 magnitude scenario demonstrates reduced flooded areas, with the cliffs containing the waves and preventing further flooding. Water depths and velocities in the M_w 7.5 scenario remain minimal. Changes in both propagation and flooding are not significant between scenarios M_w 7.5 and M_w 6.5. This methodology can be employed for more accurate tsunami wave simulations not only in the Mediterranean region but also in various case studies.