Developing physical coastal vulnerability index for eastern coast of Istrian peninsula

Abstract

Coastal zones are dynamic and complex, usually densely populated areas with socio-economic significance for the region and country (Šimac et al., 2023). Recognising the exposure and vulnerability of coastal areas is essential to sustainable development. The Coastal Vulnerability Index is the most frequently utilised method for assessing coastal vulnerability to sea level rise. (Koroglu et al., 2019). Coastal height, coastal slope, coastal type, geology, tide range, wave height, and sea level change are most commonly used in coastal vulnerability(Gornitz, 1991; Hammar-Klose and Thieler, 1999). Investigations of algal rims on the southern and eastern coast of Istria (Faivre et al., 2019) and analyses of relative sea level changes based on tide measurements (Tsimplis et al., 2012) indicate a recent
increase in sea level average values of about 1 mm/year. This paper aims to test different calculation methods to assess coastal vulnerability and to calculate the pilot area’s physical vulnerability to the sea level rise based on four factors: coastal height, slope,
coastal type and coastal aspect concerning prevailing winds. The pilot area, with its length of 34,409 meters, stretches from Cape Baćeva near Brestova in the north to Sveta Marina Bay in the south, on the Istrian eastern coast. The analysed area was divided into different numbers of cells of different dimensions (5x5 m, 10x10 m, 25x25m, 50x50m, 100x100m, 250x250m, 500x500m and 1000x1000m) using the Create grid tool within the QGIS software. Testing results show that the 5x5 m cell size is the
most suitable dimension for calculating the physical vulnerability of the coast. Therefore, the coast was divided into 6004 cells. Coastal height, slope, type, and aspect were used to determine vulnerability classes for each cell. A digital relief model (DMR) with a resolution of 5 meters and the tools Zonal statistics, GDAL Slope and GDAL Aspect, which use the Horn calculation method, were used
for morphometric analysis. The mean value of the average height of the cells is 1,28 m. Slopes with less than 5° make up 8% of total cells. To determine the type of coast, digital orthophoto maps (DOF5) of the State Geodetic Administration, satellite images and photos from Google Maps, topographic maps 1:25000, Openstreet map and field research were utilised. Four thousand three hundred fiftysix cells (72,5%) are classified as a low-lying rocky coast. The coastal zone in 563 cells (9,4%) is exposed to the most frequent and strongest winds, northeasterly winds. The obtained qualitative and quantitative coastal vulnerability factor values were divided into five bins. Semi-empirical projection of the sea level change of the Adriatic Sea by the end of the XXI. century in the B1 climate scenario
conditions, which predicts an increase of 62 ± 14 cm (Orlić and Pasarić, 2013), was used to define the height classes. The classes and ranges of the International Geomorphological Union were used to define slope classes (Lozić, 1996). The distinction between high and low rocky shores was made using the slope of the shore (Davidson-Arnott, 2010; Faivre, 2023). Data from the Climate Atlas of Croatia
(Zaninović, 2008), specifically the Annual Wind Rose for Rijeka station, was used to define coastal aspect classes. Values for coastal height, slope and aspect are quantitative, while coastal type values are qualitative. Coastal height and slope have the bin with the lowest class vulnerability as open intervals, while the remaining four bins have closed intervals. In addition, coastal height bins with
closed intervals are the same size. The coastal aspect has five closed interval bins of the same size. Several measures of central tendency were used to calculate physical coastal vulnerability: 1) arithmetic mean, 2) geometric mean, 3) arithmetic mean square root of the self-weighted factor classes values, 4) arithmetic mean of weighted factor classes values, with weight being average class
values for each factor, 5) square root of product mean factor classes value, 6) square root of selfweighted factor classes value product divided by number of factors and 7) self-weighted factor classes values sum divided by number of factors.The testing showed the arithmetic mean square root of the self-weighted factor classes values as the most appropriate way of calculating the physical vulnerability of the pilot area. Very high and high vulnerability exhibits 803 (13,83%) 5x5m cells.