AbstractDetailed spatial representation of socioeconomic exposure and the related vulnerability to natural hazards has the potential to improve the quality and reliability of risk assessment outputs. We apply a spatially weighted dasymetric approach based on multiple ancillary data to downscale important socioeconomic variables and produce a grid data set for Italy that contains multilayered information about physical exposure, population, gross domestic product, and social vulnerability. We test the performances of our dasymetric approach compared to other spatial interpolation methods. Next, we combine the grid data set with flood hazard estimates to exemplify an application for the purpose of risk assessment.
Measuring adaptive capacity as a key component of vulnerability assessments has become one of the most challenging topics in the climate change adaptation context. Numerous approaches, methodologies and conceptualizations have been proposed for analyzing adaptive capacity at different scales. Indicator-based assessments are usually applied to assess and quantify the adaptive capacity for the use of policy makers. Nevertheless, they encompass various implications regarding scale specificity and the robustness issues embedded in the choice of indicators selection, normalization and aggregation methods. We describe an adaptive capacity index developed for Italy's regional and sub-regional administrative levels, as a part of the National Climate Change Adaptation Plan, and that is further elaborated in this article. The index is built around four dimensions and ten indicators, analysed and processed by means of a principal component analysis and fuzzy logic techniques. As an innovative feature of our analysis, the sub-regional variability of the index feeds back into the regional level assessment. The results show that composite indices estimated at higher administrative or statistical levels neglect the inherent variability of performance at lower levels which may lead to suboptimal adaptation policies. By considering the intra-regional variability, different patterns of AC can be observed at regional level as a result of the aggregation choices. Trade-offs should be made explicit for choosing aggregators that reflects the intended degree of compensation. Multiple scale assessments using a range of aggregators with different compensability are preferable. Our results show that within-region variability can be better demonstrated by bottom-up aggregation methods. ; Measuring adaptive capacity as a key component of vulnerability assessments has become one of the most challenging topics in the climate change adaptation context. Numerous approaches, methodologies and conceptualizations have been proposed for analyzing adaptive capacity at different scales. Indicator-based assessments are usually applied to assess and quantify the adaptive capacity for the use of policy makers. Nevertheless, they encompass various implications regarding scale specificity and the robustness issues embedded in the choice of indicators selection, normalization and aggregation methods. We describe an adaptive capacity index developed for Italy's regional and sub-regional administrative levels, as a part of the National Climate Change Adaptation Plan, and that is further elaborated in this article. The index is built around four dimensions and ten indicators, analysed and processed by means of a principal component analysis and fuzzy logic techniques. As an innovative feature of our analysis, the sub-regional variability of the index feeds back into the regional level assessment. The results show that composite indices estimated at higher administrative or statistical levels neglect the inherent variability of performance at lower levels which may lead to suboptimal adaptation policies. By considering the intra-regional variability, different patterns of adaptive capacity can be observed at regional level as a result of the aggregation choices. Trade-offs should be made explicit for choosing aggregators that reflect the intended degree of compensation. Multiple scale assessments using a range of aggregators with different compensability are preferable. Our results show that within-region variability can be better demonstrated by bottom-up aggregation methods.
Abstract. The combined effect of global sea level rise and land subsidence phenomena poses a major threat to coastal settlements. Coastal flooding events are expected to grow in frequency and magnitude, increasing the potential economic losses and costs of adaptation. In Italy, a large share of the population and economic activities are located along the low-lying coastal plain of the North Adriatic coast, one of the most sensitive areas to relative sea level changes. Over the last half a century, this stretch of coast has experienced a significant rise in relative sea level, the main component of which was land subsidence; in the forthcoming decades, climate-induced sea level rise is expected to become the first driver of coastal inundation hazard. We propose an assessment of flood hazard and risk linked with extreme sea level scenarios, under both historical conditions and sea level rise projections in 2050 and 2100. We run a hydrodynamic inundation model on two pilot sites located along the North Adriatic coast of Emilia-Romagna: Rimini and Cesenatico. Here, we compare alternative extreme sea level scenarios accounting for the effect of planned and hypothetical seaside renovation projects against the historical baseline. We apply a flood damage model to estimate the potential economic damage linked to flood scenarios, and we calculate the change in expected annual damage according to changes in the relative sea level. Finally, damage reduction benefits are evaluated by means of cost–benefit analysis. Results suggest an overall profitability of the investigated projects over time, with increasing benefits due to increased probability of intense flooding in the near future.
We describe a climate risk index that has been developed to inform national climate adaptation planning in Italy and that is further elaborated in this paper. The index supports national authorities in designing adaptation policies and plans, guides the initial problem formulation phase, and identifies administrative areas with higher propensity to being adversely affected by climate change. The index combines (i) climate change-amplified hazards; (ii) high-resolution indicators of exposure of chosen economic, social, natural and built- or manufactured capital (MC) assets and (iii) vulnerability, which comprises both present sensitivity to climate-induced hazards and adaptive capacity. We use standardized anomalies of selected extreme climate indices derived from high-resolution regional climate model simulations of the EURO-CORDEX initiative as proxies of climate change-altered weather and climate-related hazards. The exposure and sensitivity assessment is based on indicators of manufactured, natural, social and economic capital assets exposed to and adversely affected by climate-related hazards. The MC refers to material goods or fixed assets which support the production process (e.g. industrial machines and buildings); Natural Capital comprises natural resources and processes (renewable and non-renewable) producing goods and services for well-being; Social Capital (SC) addressed factors at the individual (people's health, knowledge, skills) and collective (institutional) level (e.g. families, communities, organizations and schools); and Economic Capital (EC) includes owned and traded goods and services. The results of the climate risk analysis are used to rank the subnational administrative and statistical units according to the climate risk challenges, and possibly for financial resource allocation for climate adaptation. This article is part of the theme issue 'Advances in risk assessment for climate change adaptation policy'.
