Vulnerability datasets

Vulnerability datasets#

In current pan-European CRAs, vulnerability is accounted for to a limited degree, and is often approximated with the help of damage or mortality functions. Nonetheless, we describe a range of spatial datasets that can be used to characterize vulnerability, including current as well as future vulnerability characteristics, before turning to the more widely-used vulnerability functions.

To characterize social vulnerability, demographic data are available from the Gridded Population of the World (GPW), including age and sex raster data at 30 arc seconds spatial resolution for the year 2010, as well as for the years 2000-2020 from WorldPop at 3 and 30 arc seconds spatial resolution.
The subnational Human Development Index further provides data on education levels, income and inequality at administrative unit level, roughly corresponding to NUTS 2 level. Similarly, the World Bank provides administrative unit-level data on several poverty indicators such as daily consumption and the number and ratio of poor people.
The European Commission’s Joint Research Centre Risk Data Hub (JRC RDH) includes several datasets mainly at NUTS2 level that can be used as indicators of vulnerability such as life expectancy, education levels, household income, and employment. Within CLIMAAX, we have recently developed a raster-based Social Vulnerability Index (SoVI) validated with the help of flood fatalities that combines global datasets on age and sex (GPW v4.11), education and income (SHDI), travel time to the nearest healthcare facility (Weiss et al., 2020), and settlement type (GHSL) into a composite index with a spatial resolution of 30 arc seconds (Reimann et al., 2024). The so-called GlobE-SoVI ranges from 1 to 10, with high values reflecting high vulnerability, and allows for a consistent assessment of social vulnerability across Europe.

Table 4 Social vulnerability datasets#
Variable	Dataset	Temporal resolution	Spatial resolution	References
Age & sex	GPW (v4.11)	2010	30 arcsec	(CIESIN, 2018a)
	WorldPop	2000-2020	3 arcsec, 30 arcsec	(Pezzulo et al., 2017)
Education; Income; Inequality	SHDI	~2010	Administrative units	(Smits & Permanyer, 2019)
Poverty	GSAP	~2018	Administrative units	(Rentschler et al., 2022)
Social vulnerability	GlobE-SoVI	~2010	30 arcsec	(Reimann et al., 2024)

Currently, hardly any future projections of social vulnerability are available at European scale, an exception being age and sex structure data at NUTS 2 level for a selected set of SSPs (Terama et al., 2019), which are currently not publicly available. Furthermore, several studies produced projections of Gross Domestic Product (GDP) based on spatial population projections, e.g. at 0.5 degree, 0.25 degree, and 30 arc seconds spatial resolution. We are planning to develop projections of future social vulnerability as part of CLIMAAX which will be made openly available once published.

Table 5 Vulnerability projection datasets#
Variable	Scenarios	Temporal resolution	Spatial resolution	References
Gross Domestic Product	SSPs 1-3	2010-2100	0.5 decimal degrees	(Murakami & Yamagata, 2019)
	SSPs 1-5	2030-2100	0.25 decimal degrees, 30 arcsec	(T. Wang & Sun, 2022)

In addition to the spatial dataset described above, vulnerability is often assessed with the help of damage or mortality functions. For instance, damage curves assume that structures (e.g. buildings, infrastructure) will suffer a certain damage based on hazard intensity and structure materials. Therefore, building materials data can be used from the Pager database (Jaiswal et al., 2010). For flood vulnerability, damage curves are available for different buildings and land use types. The table below provides and overview of vulnerability functions for floods and heat stress, with more detail provided on the corresponding website.

Table 6 Damage or mortality functions#
Name	Source	Hazard type	Exposure category	Model type	Scale
Global flood damage functions	(Huizinga et al., 2017)	Floods	Built-up	Damage funciton	Regional
Global flood mortality functions	(Jonkman & Vrijling, 2008)	Floods	Population	Mortality function	Global
Heat stress index classification	(Zare et al., 2019)	Heat stress	Population	Impact classification	Global