1b) The method presented in this work uses the following dataset

1b). The method presented in this work uses the following datasets as the basic information necessary for emergency planning, oil spill prevention and oil spill mitigation. Bathymetric data from EMODNET were used in this work (Berthou et al., 2008) (Fig. 1b). The EMODNET Hydrography

data repository stores Digital Terrain Models (DTM) from selected maritime basins in Europe. DTMs used in this study comprise a grid size of 0.25 min. Each grid cell comprises the following data: (a) x, y coordinates, (b) minimum water depth in metres, 26s Proteasome structure (c) average water depth in metres, (d) maximum water depth in metres, (e) standard deviation of water depth in metres, (f) number of values used for interpolation over the grid cell, (g) number of elementary surfaces used to compute the average grid cell depth, (h) average water depth smoothed by means of a sp line function in metres, and (i) selleck chemicals an indicator of the offsets between the average and smoothed water depth as a percentage of water depth. Onshore topography is amongst the principal parameters used in this study to evaluate shoreline susceptibility. Onshore Digital Terrain Models (DTMs) comprise a 3D digital model of the Earth’s surface (McCullagh, 1998 and El-Sheimy et al., 2005). For this work, an onshore digital elevation model was created for Crete through the detailed digitization

of topographic map contours (1:5000 scale maps) from the Hellenic Military Geographical Service (HAGS) (Fig. 3a). The cell size of the digital elevation model was 20 m. Geological data concerning the near-shore structure and the hydrographic network of Crete were included in the database used in this work. Data sources comprise digital geological maps on the 1:50,000 scale (IGME) and local geological maps completed in the period 2005–2013 (Alves and Lourenço, 2010, Kokinou et al., 2012 and Kokinou et al., 2013). Particular care was taken in the identification Sirolimus datasheet of local structures, bed

dips, rock and soil quality in the regions where shoreline susceptibility was recognised to be high when of the geological mapping of the shoreline. Shoreline susceptibility maps were compiled based on field geological data, later complemented by morphological data acquired from Google Maps©. Our susceptibility maps are based on the application of Adler and Inbar (2007) classification, used in Israel to characterise shorelines according to their susceptibility to oil spills and natural cleaning up capacity (Table 1). The Environmental Susceptibility Index (ESI) proposed by Adler and Inbar (2007) considers a range of values between 1 and 9, with level 1 (ESI 1) representing areas of low susceptibility, impermeable to oil spilt during accidents (Table 1). Conversely, ESI 9 shorelines are highly vulnerable, often coinciding with natural reserves and special protected areas (Table 1). As ESI 9 shorelines coincide with such areas of natural importance, data from the updated NATURA 2000 database (http://cdr.eionet.europa.

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