Coastal areas are dynamic and complex systems exposed to waves, high tides, and storm surges. Often, these areas are densely populated and have essential socio-economic values for the region and country. Any changes or disruptions can cause a tremendous social burden. Coastal Vulnerability Index (CVI) is one of the most used and straightforward methods to assess coastal vulnerability. This paper aims to analyse and summarise the current state of published coastal vulnerability indices. The analysis seeks to develop a regional vulnerability index for the eastern Adriatic coast, specifically for the Istrian peninsula. A total of 18 published papers were reviewed. A detailed survey was performed on three groups of variables that represent (a) the physical features of the coast, (b) the amount of influence of wave energy on the coast, and (c) exposed socio-economic factors. While choosing Physical and ecological variables is relatively straightforward, choosing Socio-economic variables is particularly challenging. The number of variables differs significantly from one author to another. As a result of the huge variety of global coastal characteristics and different research approaches, there is no universal CVI. Therefore, analysed indices are not suited for the calculation of the vulnerability of the Istrian coast without modification. A 5 × 5 m cell dimension was proposed as the most suitable for analysing the physical vulnerability of the Croatian coast of Istria.

We present the first stable isotope (δ¹³C and δ¹⁸O) speleothem record from continental Croatia retrieved from two coeval stalagmites from Nova Grgosova Cave. U-Th dates constrain the stalagmite growth history from 10 ka to the present, revealing coeval growth between 7.8 and 5.6 ka. We interpret δ¹⁸O as an autumn/winter hydrological proxy related to changes of vapor source, precipitation amount, and/or seasonal rainfall distribution, while δ¹³C predominantly responds to spring/summer vegetation status and soil microbial activity. We identify several centennial to millennial-scale hydroclimate oscillations during this period that result from multiple forcing factors. Along with amount and source effect, it appears that some centennial variations were governed also by seasonal moisture balance. From 9.2 to 8.8 ka BP, the local environmental setting was characterized by enhanced vegetation activity, while during the 8.2 ka event the main feature was a change in precipitation seasonality. The most prominent change, identified in both δ¹³C records, is a sudden decline of vegetation and soil biological activity around 7.4 ka, indicating a precipitation decrease at a time of maximum plant growth in spring and summer and likely also reduced precipitation in autumn and winter. Although small in magnitude in these speleothems, a peak in δ¹⁸O and δ¹³C values at 4.3–4.1 ka suggests that both summer and winter conditions were substantially drier during the 4.2 ka event, in accordance with increased Mediterranean aridity and consistent with other global climate changes reported at this time. Compared to the present North Atlantic Oscillation (NAO) influence, we assume that millennial Holocene NAO-like variations were persistent through the Holocene via their effect on modifying local/regional air temperature, vapor origin, and inter- and intrannual precipitation distribution. Anthropogenic deforestation, which was the first major human impact on the environment during the Neolithic agricultural revolution, is excluded as a leading factor in δ¹³C variability since the first sedentary settlements were established further to the east in more arable locations along river valleys. However, the impact of intensive mining around the cave site during the last millennium is evident, with substantial deforestation driving an increase in δ¹³C.

Developed GIS automated multicriteria analysis (GAMA) method presented in this article allows automation and simplification of multicriteria GIS decision analysis (GIS-MCDA) susceptibility modelling. Traditional GIS-MCDA susceptibility modelling still represents time and labor demanding process, whose success is highly influenced by the user's experience and knowledge. In order to make overall GIS-MCDA susceptibility modelling process more straightforward and practical, GAMA method is designed as easy to use three step process, which allows automation of following GIS-MCDA steps: (1) standardization of criteria, (2) criteria grouping and weight assignment and (3) susceptibility model aggregation. GAMA method is developed within ArcGIS 10.4 ModelBuilder application, as a toolset that can be easily shared and incorporated within default ESRI's ArcGIS toolbox. GAMA was successfully applied for gully erosion susceptibility modelling on example of Pag island, Croatia, whose results are published in separate article. Since GAMA method is applicable to various GIS-MCDA susceptibility modelling purposes we are encouraging its future use and therefore open-source GAMA method toolset can be acquired for research purposes (htps://gal.unizd.hr). •GAMA method simplifies overall GIS-MCDA susceptibility modelling process.•GAMA allows automated standardization, grouping, weight coefficient assignment and aggregation of GIS-MCDA's criteria.•GAMA method has broad application in various different GIS-MCDA susceptibility modelling purposes.