## 5 Discussion and Conclusions  
In the present paper, we presented the implemented methodology that is flexible, exportable, and reliable, with the aspiration of making a significant contribution to the evolution of coastal risk assessment and adaptation tools through what we hope will be an accessible climate service in the future. The projections provided by IPCC often lack the regional specificity required to accurately characterize SLR in the Mediterranean Sea. To address this gap, our research used projections from the MED16 model. This model boasts a high-resolution coverage of the Mediterranean basin, including a very high representation of the complex tidal  
Figure 13: Assets exposed to inundation associated with the relative sea level rise by the end of the current century within the five coastal areas. Terrain Under Sea Level is overlapped to CZLC classification level 1. Percentage values are reported in the bar diagrams on the left for each of the five studied areas (see legend at bottom right). (*) “Open Space with Little or No Vegetation”. dynamics at the Strait of Gibraltar which determine significant differences between the Mediterranean SLR and the global one [72]. The high resolution of the numerical model also allows researchers to capture spatial variations of SLR along the coasts of the basin. To consider the geological factors that contribute to the flooding hazard, we used the synthetic data on vertical ground motion from EGMS, which provides the state-of-the-art product for the remote characterization of the coastal environment. The high-resolution projections of SLR and vertical ground motion were then inserted in a GIS procedure that also includes very detailed information on the local topography, to produce flooding maps in the sites in consideration. Finally, an in-depth assessment of the categories of exposed assets was performed using the Corine LC data. We found, perhaps not surprisingly, that the main assets exposed to the risk of flooding are wetlands, backshore and coastal plains, and coastal infrastructures. Two of the test areas considered in the present study (Alghero-Fertilia and Marina di Campo) were already investigated by some of the authors in the past, using the same DTM, with different results concerning the flooding risk. Those previous works used SLR data from [PERSON] (2007) [117] and from the IPCC 2013 [118] projections, including the contribution of tectonics and GIA. In Fertilia, the results of potential submersion areas were, respectively, 2.29 and 1.89 km\({}^{2}\), against the 1.44 km\({}^{2}\) of the present study [25]. In Marina di Campo, the results of potential submersion areas were, respectively, 0.39 and 0.14 km\({}^{2}\), against the 0.09 km\({}^{2}\) of the present study (Table 6; Figure 14). The flooded surfaces predicted using the MED16 results are somewhat smaller than those estimated before (the bigger differences are with respect to the predictions based on [PERSON], 2007 [117]). Figure 14 shows that the changes in the SLR contribution are amplified by the local topography, determining large differences between the two sites. So, for these two tectonically stable low coastal areas, we can say that the main factor influencing the different results is the lower SLR projection simulated by MED16 compared to the IPCC 2013 and [PERSON] 2007 scenarios. For all study areas, we are already processing multisource data in order to verify the congruency between long-term geological data and short-term remote sensing data. Some limitations of the present approach should be pointed out. As already noted, the nominal vertical accuracy of the Italian DTM we used appears not negligible (\(\pm\)15 cm), even though the real one can be smaller. This aspect will soon be improved, since in 2026 a new, higher resolution LIDAR mapping of the entire Italian coast will be available thanks to the Marine Ecosystem Restoration (MER) Project which will allow for the reduction of the topographic uncertainties [119].