The coastal vulnerability index (CVI) is a popular index in literature to assess the coastal vulnerability of climate change. The present paper proposes a CVI formulation to make it suitable for the Mediterranean coasts; the formulation considers ten variables divided into three typological groups: geological; physical process and vegetation. In particular, the geological variables are: geomorphology; shoreline erosion/accretion rates; coastal slope; emerged beach width and dune width. The physical process variables are relative sea-level change; mean significant wave height and mean tide range. The vegetation variables are width of vegetation behind the beach and posidonia oceanica. The first application of the proposed index was carried out for a stretch of the Apulia region coast, in the south of Italy; this application allowed to (i) identify the transects most vulnerable to sea level rise, storm surges and waves action and (ii) consider the usefulness of the index as a tool for orientation in planning strategies. For the case study presented in this work, the most influential variables in determining CVI are dune width and geomorphology. The transects that present a very high vulnerability are characterized by sandy and narrow beaches (without dunes and vegetation) and by the absence of Posidonia oceanica.

Salvadori, G., Tomasicchio, G.R.., D'Alessandro, F. 2013. Multivariate approach to design coastal and off-shore structures. A frequent statistical problem in many coastal and off-shore engineering situations is to estimate the probability of structural failure expressed in terms of Return Period and Design Quantile. Usually, an univariate analysis is carried out to quantify the risk of failure. However, coastal and off-shore structures typically fail because of the occurrence of a critical combination of all the variables at play in a sea storm: thus, it may be important to consider the joint occurrence of dangerous conditions. The present manuscript outlines an original approach, exploiting Copulas, in order to provide a consistent framework for the calculation of bivariate Return Periods and Design Quantiles, which can be generalized to the multivariate case.