Exposure datasets

Exposure datasets#

A variety of exposure datasets are available at pan-European scale that can be selected based on the data needs in terms of spatial and temporal resolution and the underlying acquisition or modeling approach. We describe a selected set of exposure datasets, including current exposure and future projections, in more detail below.

A range of population datasets are available, with different spatial and temporal resolutions, such as the satellite-based Global Human Settlement Layer (GHSL) population data GHS-POP, available at \(100 m/3 arcsec\) and \(1 km/30 arcsec\) from 1975 to 2030 in 5-year time steps. GHS-POP spatially disaggregates census unit-level population numbers with the help of built-up land derived from satellite imagery. Further, WorldPop provides population data in annual time steps for the period 2000-2020 based on a Random Forest modelling approach. GHS-POP and WorldPop are based on the Gridded Population of the World (GPW) (version 4) that have a spatial resolution of 30 arc seconds and a temporal resolution of 5-year time steps from 2000-2020, collected from the national census and population registries. Figure 1 visualizes the three global population datasets for Central Europe.

At the European scale, several population datasets are worth mentioning. The GEOSTAT population grids have a spatial resolution of 1 km and are available for the years 2006, 2011, 2018, 2021. While the years 2011 and 2021 are based on census data, the other years use land cover data and built-up land to disaggregate the population spatially. The Historical Analysis of Natural HaZards in Europe (HANZE) database v2.0 provides population raster data at 100 m spatial resolution for the years 1870-2020, derived from the GEOSTAT data of 2011. On an administrative unit level, i.e. NUTS regions, population data are available from Eurostat and the RDH. An overview of the datasets described in this section can be found in Table 1.

Table 1 Pan-European population datasets with technical specifications and advantages and disadvantages#

Dataset

Spatial scale

Temporal resolution

Spatial resolution

Analysis type

References

Pros

Cons

GHS-POP

Global

1975-2030

100 m, 3 arcsec;
1 km, 30 arcsec

Spatial distribution based on built-up land

European Commission, 2023

Lightly modelled based on census data and Landsat imagery;
available in 5-year time steps

Overconcentration of population where built-up land undetected (less problematic in Europe)

WorldPop

Global

2000-2020

3 arcsec, 30 arcsec

Random Forest algorithm

Bondarenko et al., 2020; Stevens et al., 2015

High spatial and temporal resolution

Modelling algorithm based on several input datasets

GPW v4

Global

2000-2020

30 arcsec

National census and population registries

CIESIN, 2018b

Unmodeled

Different spatial and temporal input data

GEOSTAT

Europe

2006, 2011, 2018, 2021

1 km

Derived and modelled from census data

GEOSTAT

Based on census data of 2011 and 2021

No pan-European coverage;
2006 and 2018 modelled

HANZE 2.0

Europe

1870-2020

100 m

Modelled from GEOSTAT 2011

Paprotny & Mengel, 2023

High spatial and temporal resolution

No pan-European coverage

EUROSTAT

Europe

1960-2023

NUTS regions

National census and population registries

EUROSTAT

Consistent across EU countries

No pan-European coverage

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Fig. 10 Spatial population distribution in GHS-POP, WorldPop, and GPW v4#

Several datasets that represent settlements, buildings, infrastructure, and different land uses are available.

  • GHSL provides raster data of built-up land and volume, residential and non-residential settlement zones (Morphological Settlement Zones), settlement classes, and building height at 2 m to 1 km/ 30 arc seconds spatial resolution and a temporal resolution of five-year time steps from 1975 to 2030 for most datasets. It also provides built-up land data summarized at Local Administrative Unit Level (LAU) for 1975-2020.

  • Open Street Map (OSM) provides spatial data in vector format (i.e. points, lines, polygons) of e.g. building footprints and types, health and education facilities, energy and telecommunication towers, and roads and railway networks. This crowd-sourced data product is continuously updated by the OSM community.

  • The Critical Infrastructure Spatial Index (CISI) is based on OSM data and is available in raster format at a spatial resolution of 0.1 decimal degrees.

  • For assessing the exposure of different land uses, the Europe-wide CORINE land cover data are available for 44 land cover classes at 100 m spatial resolution for the years 1990, 2000, 2006, 2012, and 2018. The CORINE data provide the basis for the higher-detail LUISA land cover map, available for 2012 and 2018 at 50 m spatial resolution. Compared to CORINE, the LUISA Base Map delivers a higher overall spatial detail and finer thematic breakdown of artificial land use/cover categories (17 categories instead of 11 in CORINE). The LUISA Base Map can be used for multiple purposes, and it is more suitable than CORINE for applications requiring fine spatial and/or thematic detail of land use/land cover consistently across Europe, such as land use/cover accounting and modelling. Based on the LUISA land cover map of 2018 combined with OSM data, the European Settlement Map (ESM) was developed at 2 m spatial resolution, including residential versus non-residential buildings.

  • SPAM is a global crop distribution model covering 42 crops and four different technologies available for 2010 (latest) on a 5 arc-minutes crop-specific grid. The model outputs include both harvested and physical cropland. The Gridded Livestock of the World maps (GLW) show the density of eight different livestock animals in 2010 and 2015 on a 5 arc-minutes animal-specific grid and can be used to represent the exposure of animal husbandry systems. The Global Agro-Ecological Zones (GAEZ) platform provides a range of datasets for agriculture exposure and vulnerability in 2010 values. For instance, the Aggregate Crop Production Value (US$) can be the exposure term in an agricultural drought risk assessment.

Table 2 Pan-European datasets to characterize physical exposure with technical specifications and advantages and disadvantages#

Variable

Dataset

Spatial scale

Temporal resolution

Spatial resolution

References

Pros

Cons

Settlements

GHS-BUILT

Global

1975-2030

From 10 m to 1km/30 arcsec

European Commission, 2023

Global coverage; Different products (e.g. built-up land and volume, building height, residential and non-residential settlements)

Uncommon coordinate reference system: Mollweide

ESM

Europe

2018

2 m

Szabo et al., 2023

Very high resolution; Distinguishes residential and non-residential buildings

Ukraine missing

Buildings, Infrastructure

OSM

Global

Most recent

Vector data (points, lines, polygons)

OpenStreetMap contributors, 2023

High spatial detail; Good coverage in northern Europe

Working with the data can be cumbersome (e.g. download, selection); Limited coverage in southern Europe

Infrastructure

CISI

Global

2021

0.1 degree

Nirandjan et al., 2022

Input data and final index in raster format; Easy to use (compared to OSM)

Low resolution

Land cover

CORINE

Europe

1990, 2000, 2006, 2012, 2018

100 m

Copernicus Land Monitoring Service, 2018

Relatively long time series

Fewer land cover categories or less spatial detail than LUISA

LUISA

Europe

2012, 2018

50 m

Pigaiani & Batista e Silva, 2021

17 land cover categories

Ukraine missing; Mixed land use in a cell

SPAM2010

Global

2010

5 arc minutes

Yu et al., 2020

42 crops available

Low resolution

Livestock density

GLW

Global

2010, 2015

5 arc minutes

Nicolas et al., 2016

8 different animals

Low resolution

Competition on water

Aqueduct v4

Global

1979-2019

Hydrological sub-catchment scale

Kuzma et al., 2023

Global coverage

Scaled for hydrological sub-catchments

Aggregate Crop Production Value

GAEZ

Global

2010

5 arc minutes

Fischer et al., 2021

Global coverage

Low resolution

../../_images/ev_data_image6.png

Fig. 11 The CISI for Western Europe (figure adjusted from Nirandjan et al. 2022)#

Table 3 provides an overview of future projections datasets currently available. Future projections of the population until 2100 are available under a range of socioeconomic scenarios, the Shared Socioeconomic Pathways (SSPs) (O’Neill et al., 2017). Publicly available datasets include those of Merkens et al. (2016) and Wang et al. (2022), which are available at a spatial resolution of 30 arc seconds. However, they are based on different modelling approaches and underlying population data used as model input. For instance, the population projections of Merkens et al. 2016 were specifically developed to account for coastal migration processes. Further population projections are available from GHSL at 1 km spatial resolution (Directorate General for Regional and Urban Policy (DG REGIO) of the European Commission, 2020), or from IIASA Global Community Water Model (5 arc minutes) upon request. Projections of future urban land are available for the SSPs until 2100, such as the data of Gao and O’Neill (2020) at 1/8-degree spatial resolution, also downscaled to 1 km, and Chen et al. (2020), available at 30 arc seconds. Additionally, projections of different land uses are available at 30 arc seconds resolution until 2100 (Chen et al., 2022; Zhang et al., 2023), and GHSL provides projections per settlement class (GHS-SMOD) at 1 km resolution until 2070 (Kemper et al., 2022).

Table 3 Pan-European future exposure projections datasets with technical specifications and advantages and disadvantages#

Variable

Scenario

Temporal resolution

Spatial resolution

Acquisition/modelling approach

References

Pros

Cons

Population

SSPs 1-5

2010-2100

30 arcsec

Population growth in coastal, inland, urban, and rural locations

Merkens et al., 2016

Global coverage

Developed with coastal applications in mind

SSPs 1-5

2020-2100

30 arcsec

Random Forest algorithm

X. Wang et al., 2022

Global coverage

Modelling algorithm based on several input datasets

SSP-RCP combinations

2015-2100

5 arcmin

Global CWatM

Burek et al., 2020

Global coverage

Dataset not public, but available upon request

Urban land

SSPs 1-5

2015-2100

1 km

Artificial Neural Network algorithm

Chen et al., 2020

Global coverage

Modelling algorithm based on several input datasets

SSPs 1-5

2000-2100

1/8 decimal degree, 1 km

Monte Carlo simulations

Gao & O’Neill, 2020; Gao & Pesaresi, 2021

Global coverage

Produced at 1/8 decimal degrees and downscaled to 1 km

Land cover

SSP-RCP combinations

2015-2100

30 arcsec

Cellular automata

Chen et al., 2022; Zhang et al., 2023

Based on CMIP6; global coverage

Modelling algorithm based on several input datasets

Competition on water

SSP-RCP combinations

2030-2080

Hydrological sub-catchment scale

Modelled (Aqueduct v4)

Kuzma et al., 2023

Global coverage

Scaled for hydrological sub-catchments

../../_images/ev_data_image7.png

Fig. 12 Population projections by Merkens et al. 2016: Base year (2000) and SSP1, 3, 5 (2100)#

../../_images/ev_data_image8v2.png

Fig. 13 Urban land projections for North America and Europe in 2100 under SSP2, SSP1 and SSP5. Comparison of 1/8 degree spatial resolution (panel 1) to 1 km (panels 2-4) (adjusted from Gao & Pesaresi 2021)#