Supplemental materials and tables for the Guidelines on Biophysical Modelling | System of Environmental Economic Accounting

This page shows selected tables from the Guidelines on Biophysical Modelling for Ecosystem Accounting (last update: January 2022). If you have comments or suggestions for the tables, please contact seea@un.org.

List of tables:

Table 4: Overview of modelling platforms with potential use in SEEA EA
Table 5: Key properties of freely available global land cover products
Table 13: Major categories and examples of physical state indicators for ecosystem condition accounts
Table 14: Chemical state indicators for ecosystem condition accounts
Table 15: Compositional state indicators for ecosystem condition accounts
Table 16: Structural state indicators for ecosystem condition accounts
Table 17: Functional state indicators for ecosystem condition accounts
Table 18: Landscape and seascape characteristic indicators. Here we highlight only landscape characteristics
Table 25: Ecosystem service modelling platforms and their capacity to provide estimates of ecosystem services (in physical terms) for SEEA EA supply and use tables
Table 28: Description of major data sources that can inform biophysical modelling

Table 4: Overview of modelling platforms with potential use in SEEA EA

We reviewed only tools that are accounting compatible and open source[1]

Modelling platform	Primary goal of platform	Annual time step feasible	Spatially explicit	Scalable	Economic valuation tools	Coverage
ARIES (Villa et al., 2014)	ARIES (Artificial Intelligence for Ecosystem Services). Provides easy access to data and models through a web-based explorer and using Artificial Intelligence to simplify model selection, promoting transparent reuse of data and models in accordance with the FAIR principles.	Yes	Yes	Yes	Yes	Extent, Condition, Ecosystem Services
Data4Nature[2]	Data4Nature (formerly known as EnSym - Environmental Systems Modelling Platform) is a decision support tool that is designed to answer questions about where organizations should invest in their natural resources. Data4Nature is specifically designed with SEEA EA in mind.	Yes	Yes	Yes	No	Extent, Ecosystem Services
ESTIMAP (Zulian et al., 2014)	ESTIMAP (Ecosystem Services Mapping tool) is a collection of models for mapping ecosystem services in a multi scale perspective (it can be applied at different scales) (Zulian et. al., 2018).	Yes	Yes	Yes	No	Ecosystem Services
InVEST (Sharp et al., 2018)	A compilation of open-source models for mapping and valuing ecosystem services. InVEST is the flagship tool of the Natural Capital Project and has been the most widely used ecosystem service modelling tool globally.	Yes	Yes	Yes	Yes	Ecosystem Services, Condition
i-Tree[3]	i-Tree is a tool developed by the USDA Forest Service with capabilities of modelling ecosystem services related to trees, particularly in urban settings (i.e. air filtration, carbon storage urban heat island mitigation, and rainfall interception and infiltration).	Yes	Yes	Yes	Yes	Ecosystem Services (forest related)
Nature Braid (Jackson et al., 2013)	The Nature Braid (formerly LUCI/Polyscape) provides a suite of high spatial resolution ecosystem services models designed to improve decision-making around restoration and land management. The Nature Braid is particularly well suited for mapping soil, water and chemical transport processes at high resolution.	Yes	Yes	Yes	No	Extent, Condition, Ecosystem Services (hydrological, soil)

[1] Neugarten et al. (2018) review a larger number of tools, including Tessa, MIMES, PABAT, and Co$ting Nature and WaterWorld. The latter are closed-source platforms that provide easy entry points for ecosystem services modelling, see: http://www.policysupport.org. Co$ting Nature (Mulligan et al., 2020) is a web-based tool for analysing ecosystem services, that departs from a large number of pre-loaded global data sources. The analysis is spatially explicit (1km² or 1ha²), and it has wide functionality for doing policy scenario analysis. WaterWorld has the same approach, but focuses on hydrological services. It can be used to assess water, land use and climate policies. Both platforms allow the user to upload own data sources.

[2] The D4N website (as of Jan. 2022) is still a draft: http://www.data4nature.com.au/resources/. For EnSYM see: https://ideeagroup.com/ensym/.

[3] “i-Tree Canopy. iTree Software Suite,” n.d., http://www.itreetools.org/

Table 5: Key properties of freely available global land cover products

Land cover data set	Resolution	Developer	Source	Accuracy	Coverage	Year	Data Access	Citation and licensing
Climate Change Initiative (CCI) Land cover v2	300m	European Space Agency	Data from the MERIS 2003 to 2012 archive, SPOT-Vegetation	70 to 74%	Global	Annually from 1992 to 2019	https://www.esa-landcover-cci.org/, https://www.esa-landcover-cci.org/?q=node/158	Official documentation: http://cci.esa.int/sites/default/files/CCI_Data_Policy_v1.1.pdf
MODIS-based Global Land Cover Climatology	500m	USGS Land Cover Institute	MODIS images from 2001 to 2010	73.6% overall	Global	Annually 2001 to 2018	https://lpdaac.usgs.gov/products/mcd12q1v006/	Official documentation: https://www.sciencedirect.com/science/article/pii/S0034425718305686
Copernicus Global Land Service Land Cover	100m	Copernicus	Proba-V	80%	Global	2015 - 2019	https://lcviewer.vito.be/download	Buchhorn et al. (2019)
GlobeLand	30m	National Geomatics Center of China (NGCC)	Landsat primarily, MODIS NDVI, global geographic information, global DEM, thematic data (global mangrove forest, wetland and glacier, etc.) and online resources (Google Earth, Bing Map, OpenStreetMap and Map World)	80.33%	Global between 80N and 80S	2000 and 2010	http://www.globallandcover.com	Chen et al. (2014)
FROM-GLC	10m	Tsinghua University, Beijing	Landsat, Sentinel 2	72.8%	Global	2017	http://data.ess.tsinghua.edu.cn	Gong et al. (2019)

Table 13: Major categories and examples of physical state indicators for ecosystem condition accounts

Indicators category	Indicator examples	Definition	Unit	Common modelling approach	Available modeles	Global data sources
Water availability	Hydrological flow	Volume of water discharged by a watershed or river over a timeframe	Volume	Process-based models	- InVEST - LUCI / Nature Braid - WaterWorld - VIC (semi-distributed macroscale model) - SWAT - WaterWorld	Global surface water explorer: https://global-surface-water.appspot.com/#features Hydrosheds: https://www.hydrosheds.org/page/overview
Water availability	Groundwater table	Upper surface of the zone of saturation	Depth	- Spatial interpolation - Numerical models	-	GGIS provides maps of aquifers across the globe, as well as other ground water data, typically at the country level: https://www.un-igrac.org/global-groundwater-information-system-ggis Global Ground Water Monitoring Network (https://ggmn.un-igrac.org/) allows for interpolation between ground stations The GRACE model detects changes in gravity which are used to assess changes in water stocks: http://www2.csr.utexas.edu/grace/gravity/
Soil	Impervious surface (soil sealing)	Paved surface areas (e.g. buildings, roads)	Area (percentage)	Earth Observation data	-	The GMIS data set available from CIESIN consists of two components: 1) global percent of impervious cover; and 2) per-pixel associated uncertainty for the global impervious cover. These layers are co-registered to the same spatial extent at a common 30m spatial resolution, see: https://sedac.ciesin.columbia.edu/data/set/ulandsat-gmis-v1 Global impervious surface area (GISA) dataset (30m) from 1972 to 2019.) based on Landsat images (Xin Huang, et al. 2021).
Soil	Soil Organic Carbon Content	The amount of carbon stored in soil	- Stock (tC/ha) or - Concentration (g/kg)	- Look-up tables - Spatial interpolation - Geostatistical models	S-world model has maps and accounts detailing soil organic carbon concentration in (%, 0-30cm) and (%, 30-100)	GSDE Global soil data set for Earth Systems Modelling http://globalchange.bnu.edu.cn/research/soilw ISRIC SoilGrids: https://soilgrids.org/ FAO Global Soil Organic Map GSOC: http://54.229.242.119/GSOCmap/

Table 14: Chemical state indicators for ecosystem condition accounts

Indicator category	Indicator example	Definition	Modelling approach	Available models	Global data sources
Air quality	Pollutant concentrations	The amount of pollutants (e.g. micrograms per cubic meter (µg/m3) parts per million (ppm)), such as particulate matter and nitrogen dioxide, that cause damage to human health or the environment.	Typically modelled with an air pollution dispersion model, photochemical modelling, and receptor modelling. These models estimate concentrations based on meteorological data, pollution sources and chemical reactions.	The EPA provides access to several air pollutant models: https://www.epa.gov/scram/modelling-applications-and-tools	World air pollution: https://waqi.info/ PM 2.5 grids: https://sedac.ciesin.columbia.edu/data/set/sdei-global-annual-gwr-pm2-5-modis-misr-seawifs-aod
Water quality (inland freshwater including lakes, rivers, and streams)	Pollutant concentrations	The amount of pollutants, such as nitrogen or other chemicals that cause damage to human health or the environment.	Process-based models are common including export coefficient approaches	InVEST, LUCI/Nature Braid, ARIES, SWAT all provide approaches for water quality modelling. Different pollutants may require different models and approaches	SDG 6.3.2 core parameters: total phosphorus, total nitrogen, pH and dissolved oxygen in rivers, lakes and reservoirs or aggregated for a particular country or catchment: https://gemstat.org/data/maps/
	Dissolved oxygen	The amount of oxygen dissolved in water, which is available for biota and enters via diffusion from the atmosphere. Rapidly moving water will typically have more dissolved oxygen than stagnant water, as will water with lower amounts of biomass.	Spatialization of point data	Spatial extrapolation; Machine learning	Ibid
Water quality (inland freshwater including lakes, rivers, and streams)	Chlorophyll-a	A photosynthetic pigment used as an indicate algal levels in water	For large water bodies, multispectral imagery, such as MERIS have been used to map chlorophyll-a concentrations in lakes		Approaches for oceans have been modified for use in large lakes, e.g., ocean data available: https://oceancolor.gsfc.nasa.gov/atbd/chlor_a/
	Turbidity			SWAT
Soil quality	Heavy metal content	The concentration of heavy metal, such as lead, which are detrimental to human health Especially relevant in urban areas	Spatial interpolation, geostatistical models

Table 15: Compositional state indicators for ecosystem condition accounts

Indicator category	Indicator example	Definition	Unit	Modelling approach	Available models	Global data sources
Species	Species diversity	The varied species on Earth	Total number of species or number of species within different taxonomic groups (e.g., birds, fishes) or guilds (e.g., soil biota) Functional Diversity	Species Area Relationships Species Distribution Models (SDMs) Macroecological models		www.iucnredlist.org (See Section 5.4.1 for more details) Global Biodiversity Information Facility (GBIF) database: https://www.gbif.org/occurrence/search
	Species abundance	The number of individuals of a single species The number of individuals belonging to the same species	Abundance (number of individuals)	Species Abundance Models	Maxent, R	www.iucnredlist.org Global Biodiversity Information Facility (GBIF) database: https://www.gbif.org/occurrence/search
	Relative species abundance	The abundance of a species relative to the total number of organisms	Shannon’s Index, Simpson’s Index	Stacked species distribution and species abundance models
	Red-list indices/conservation status	Species-level risk of extinction	Risk category	https://sis.iucnsis.org/apps/org.iucn.sis.server/SIS/index.html	https://www.ramas.com/	www.iucnredlist.org Global Biodiversity Information Facility (GBIF) database: https://www.gbif.org/occurrence/search
	Biodiversity Intactness Index; Mean species abundance	Indices to measure how much of local biodiversity remains intact		GLOBIO measures MSA by combining the impact of land use change, climate change, atmospheric N deposition, biotic exchange, atmospheric CO2 concentration, fragmentation, infrastructure, harvesting, human population density, and energy use on biodiversity loss (Alkemade et al., 2009)	GLOBIO (also available within the InVEST modelling framework)

Table 16: Structural state indicators for ecosystem condition accounts

Indicator category

Indicator example

Definition

Unit

Modelling approaches

Available models

Global data sources

Biomass

Density

Biomass density

Growing stock volume (m3/ha)

Above ground biomass (AGB) (ton/ha)

http://globbiomass.org/wp-content/uploads/GB_Maps/
Globbiomass_global_dataset.html

Table 17: Functional state indicators for ecosystem condition accounts

Indicator category

Indicator example

Definition

Unit

Modelling approaches

Available models

Global data sources

Processes

Net Primary Productivity (NPP);
Dry matter Productivity (DMP)

The rate at which an ecosystem accumulates biomass

cg/cm³/year

DMP uses units customized for agro-statistical purposes (kg/ha/day).

Dynamic Vegetation models

General Ecosystem Models

Leaf Area Index (LAI)

Models include variables such as solar radiation, nitrogen, CO₂, water, temperature, fraction of phosynthetically active radiation.

MODIS satellite imagery provides estimates, LPJ DGVM (Lund–Potsdam–Jena Dynamic Global Vegetation model)

NPP: https://neo.sci.gsfc.nasa.gov/view.php?
datasetId=MOD17A2_M_PSN

DMP: https://land.copernicus.eu/global/products/dmp

LAI: https://land.copernicus.eu/global/products/lai

Table 18: Landscape and seascape characteristic indicators

Here we highlight only landscape characteristics.^[4]

Indicator category	Indicator example	Definition	Unit	Modelling approaches	Available models	Global data sources
Composition	Diversity	The abundance and evenness of different soil types within a BSU. This indicator may also be aggregated for EAs, ETs, or EAAs.	Richness, Shannon’s Index, or Simpson’s Index	Metric calculated based on thematic maps	Vegan package in R LUCI / Nature Braid	Ecosystem extent accounts likely form the basis for these indicators Global Biodiversity Information Facility (GBIF) database, can also be used as input: https://www.gbif.org/occurrence/search
Connectivity/ fragmentation	Barrier density	The number of barriers, such as roads or dams, which may prevent the migration of species.	Number per area or length per area	Metric calculate based on point or line data	ArcGIS, QGIS	Dams for freshwater barriers: http://globaldamwatch.org/data/ Road maps: https://sedac.ciesin.columbia.edu/data/set/groads-global-roads-open-access-v1 https://www.openstreetmap.org/
	Patch size	Mean patch size (MPS) is the average size of all patches of all habitats over a landscape.	Area (ha, m, or km)	Calculated metric based on thematic maps	Frag stats (R stats version is available)	Ecosystem extent accounts likely form the basis for these indicators.
	Shape	Several shape indices are available, typically based on edge to area ratios.	Ratio of perimeter to edge	Calculated metric based on thematic maps	Frag stats (R stats version is available	Ecosystem extent accounts likely form the basis for these indicators.

^[4] Some approaches may be adapted for oceans as well (e.g., diversity).

Table 25: Ecosystem service modelling platforms and their capacity to provide estimates of ecosystem services (in physical terms) for SEEA EA supply and use tables

This list of modelling platforms is not comprehensive but illustrative.

			ARIES	InVEST	LUCI / Nature Braid	ESTIMAP	Data4Nature	iTree
Provisioning services
	Biomass provisioning	Crop provisioning	x	x	i		x
		Grazed biomass provisioning					x
		Timber provisioning	x				x
		Non-timber forest products and other biomass provisioning	m
		Fish and other aquatic products provisioning		x
	Water supply		x		x		x
	Genetic material

Regulating and maintenance services
	Global climate regulation services		x	x	x		x	x
	Rainfall pattern regulation services						x
	Local (micro and meso) climate regulation services			i			x	x
	Air filtration services					x		x
	Soil erosion control services		x	x	x	x	x
	Water purification services			x	x	x	x
	Water flow regulation services			x	i	x	x
	Flood mitigation services (coastal or riverine)		x	i		x	x
	Storm mitigation services					x		x
	Noise attenuation services
	Pollination services		x	x		x
	Pest control services					x
	Nursery population & habitat maintenance services					x	x
	Soil waste remediation services
	Other regulating and maintenance services						x

Cultural services
	Recreation-related services		x	x		x

Table notes:

i denotes index value – this would need to be transformed in order to include in the ES SUT
m denotes that the model is only available in monetary units
x indicates covered by

Table 28: Description of major data sources that can inform biophysical modelling

This table focuses on data sets that can support SEEA EA. One feature that is important for SEEA EA, especially for land cover data is coverage over multiple years.

Data domain	Data sources	Description	Resolution	Spatial Coverage	Source	Temporal coverage	Website
Land cover	Global Land Cover Share Database	Based on contributions from various institutions by a combination of “best available” high resolution national, regional and/or sub-national land cover databases. Provides 11 major thematic.	30 arc-seconds	Global	Latham et al. (2014)	1998-2012	http://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036355/
Land cover	See Section 4.4 for an overview of land cover data sources
Forest cover	See Table 6 for an overview of Hansen forest cover data
Soil	S-world	Combines Harmonized World Soil database and ISRIC‐WISE 3.1 soil profile database along with auxiliary data summaries at 30-arc second spatial scale, such as temperature, precipitation, and topography.	30-arc second spatial scale	Global	Stoorvogel et al. (2017)	2016-static	http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/
	ISRIC	Provides global prediction of soil properties including, organic carbon, bulk density, Cation Exchange Capacity, pH, soil texture fractions and coarse fractions. These predictions are based on remote sensing-based soil covariates primarily derived from MODIS.	250 m grid	Global	Hengl et al. (2017)	2016-static	https://soilgrids.org/#!/?layer=ORCDRC_M_sl2_250m&vector=1 or ftp://ftp.soilgrids.org/data/
	Harmonised World Soil Database (HSWD) v1.2	The HWSD used the spatial information provided by the FAO-UNESCO Digital Soil Map of the World (DSMW) and national/regional maps, along with soil profile information to create a global-scale map of soils. The database uses FAO classifications at the soil unit level (FAO-74, FAO-85, FAO-90). Aside from mapping information (classification, ID, etc.), a further range of characteristics are included at the topsoil (0 to 30cm) and the subsoil (30 to 100cm) level.	30 arc-second	Global	FAO (2009)	2009	http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/
	Global Soil Data set for use in Earth System Models (GSDE)	The GSDE is based on the Digital Soil Map of the World similar to the HWSD but uses additional databases to help improve the accuracy of the updated map. Uses mainly the FAO classification, but includes local soil classification.The data set gives information on a suite of soil properties at eight depths up to 2.3 m.	1km and 10km	Global	Shangguan et al. (2014)	2014-static	http://globalchange.bnu.edu.cn/research/soilw
	Global Soil Salinity Map	This data set contains maps for multiple years of soil salinity, with a validation accuracy in the range of 67–70%.	250m	Global	Ivushkin et al. (2019)	1986, 1992, 2000, 2002, 2005, 2009 and 2016	https://data.isric.org/geonetwork/srv/api/records/c59d0162-a258-4210-af80-777d7929c512
Soil water properties	Global High-Resolution Soil-Water Balance	This data set used input variables from the WorldClim and Global-PET gridded data sets to calculate the soil water balance at the monthly and annual scales. Available data include Mean annual AET, Monthly AET, Monthly Soil Water Stress, Priestley-Taylor Alpha coefficient.	30 arc-seconds (1km at equator)	Global	Trabucco and Zomer (2019)	2010	https://cgiarcsi.community/data/global-high-resolution-soil-water-balance/
Soil water properties	HiHydroSoil	Provides information about soil hydraulic properties is important for hydrological modelling and crop yield modelling. HiHydroSoil is a global data set with information about hydraulic properties.	250m	Global	De Boer (2016)	2015	https://www.futurewater.eu/2015/07/soil-hydraulic-properties/
Digital Elevation Models	Shuttle Radar Topography Mission (SRTM)	Consistently created global digital elevation model produced using. STRM does not cover latitudes north of 60°. EarthEnv has STRM-like DEM to 83°N.	30 m 1 arc-second by 1 arc-second ~30m	~80% of the globe	Farr et al. (2007)	Available since 2002-static	https://www2.jpl.nasa.gov/srtm/dataprod.htm
	ASTER Global Digital Elevation Model	Generated using stereo-pair images collected by the ASTER instrument onboard Terra	90 meters with a resolution of 30 meters in the United States	~99% of the globe	NASA/METI/AIST/Japan Spacesystems, and U.S./Japan ASTER Science Team (2001)	released in June 2009-static	https://ssl.jspacesystems.or.jp/ersdac/GDEM/E/
	EarthEnv	Generated by fusing ASTER GDEM2 and CGIAR-CSI v4.1	90 meters	~91% global coverage	Robinson et al. (2014)	2014-static	https://www.earthenv.org/DEM.html
	MERIT 10m DEM	High accuracy global DEM at 3 arcsecond resolution (~90 m at the equator), which eliminates major error components from existing DEMs (NASA SRTM3 DEM, JAXA AW3D DEM, Viewfinder Panoramas' DEM), several other data sets were also used as supplementary data including - NASA-NSIDC ICESat/GLAS GLA14, U-Maryland Landsat forest cover, NASA Global Forest Height, JAMSTEC/U-Tokyo G3WBM water body.	3″ resolution (~90 meters at equator)	Global coverage	Yamazaki et al. (2017)	2017	http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/
Rivers and watersheds and water	Hydrosheds	Includes a suite of information on river networks, watershed boundaries, drainage directions, and flow accumulations. Hydrosheds are a derivative of STRM data.	~3 arc-seconds (~90 m at equator) best available for some data sets, otherwise, 15 arc-second, and 30 arc-second resolutions-Derived from SRTM data	Near global coverage	Lehner et al. (2008)	Available since 2008-static	https://www.hydrosheds.org/
	Hydro 1k	Produced using the USGS’s 30-arc second DEM, and includes hydrologically corrected DEM’s stream basins.
	GRACE satellite data	The twin GRACE-FO satellites follow each other in orbit around the Earth, separated by about 137 miles (220 km). From distance measurements between the two satellites, GRACE data can be used to estimate Earth's gravity field. These data are then used to monitor changes in underground water storage, the amount of water in large lakes and rivers, soil moisture, ice sheets and glaciers, and sea level caused by the addition of water to the ocean.	Base products are 1 degree, updated monthly. Approaches to disaggregate to finer spatial resolution exist.	Global	NASA https://grace.jpl.nasa.gov/about/how-to-cite/	Ongoing monthly updates	https://grace.jpl.nasa.gov/data/get-data/
	Aquastat and Aquamaps	The AQUASTAT core database provides the platform for organizing and presenting over 180 variables and indicators on water resources and their use which include water withdrawal, wastewater, pressure on water resources, irrigation and drainage, and a few components on environment and health. They can be searched and extracted, along with their metadata, for 200+ countries and for different regions over an extensive time period (from 1960 to 2017). AquaMaps is complementary to AQUASTAT, FAO's Information System on Water and Agriculture. While AQUASTAT focuses on collecting mainly statistical data and qualitative information on (sub)country level, AquaMaps concentrates on geographical information.	Variable	Global	[5]	Geography and population: Every year water resources: these are long-term average annual values and therefore remain the same over the years. Updates of data for some specific sub-categories are done in collaboration, when data become available.	http://www.fao.org/nr/water/aquamaps/
	Google’s / JRC Global surface water	See Table 6 for an overview of Global surface water data set
Precipitation	There are 30 globally available precipitation data sets collected at different spatial and temporal scales with some focusing on ground-based measurements and others using satellite observations (Sun et al., 2018). Examples are: TRMM (http://www.ambiotek.com/1kmrainfall/ and https://gpm.nasa.gov/) and CHIRPS (http://chg.geog.ucsb.edu/data/chirps/index.html).
Climate	WorldClim v1 and v2: global climate data	The “current/observational” layers were created through spatially interpolating climate data from a large database of climate stations, while the future climate change conditions under the Representative Concentration Pathways were created through downscaled GCM data.	30 arc-sections to 10 minutes	Global	Fick and Hijmans (2017); Hijmans et al. (2005)	2005, 2017	https://www.worldclim.org/
	CHELSA	A high-resolution climate data set for land surface areas. It includes temperature and precipitation patterns for various time periods. CHELSA is based on a quasi-mechanistical statistical downscaling global reanalysis and global circulation model output.	30 arc-seconds	Global	Karger et al. (2017)	Multiple time series; V1.2 released 2019	https://chelsa-climate.org
	Global Potential Evapotranspiration (Global-PET) and Global Aridity Index (Global-Aridity)	The Global-PET and Global-Aridity data sets were modelled from the WorldClim data set using the Hargreaves method for PET and the Aridity Index.	30 arc-seconds	Global	Zomer et al. (2008)	V2 released 2019	https://cgiarcsi.community/data/global-aridity-and-pet-database/
	GloREDa: Global Rainfall Erosivity Database & R-factor map	This data set used information from a large database of rainfall data and covariates from the WorldClim data set to create a spatially interpolated global map of rainfall erosivity. This map can be used as input to global studies of soil erosion using the Revised Universal Soil Loss Equation (RUSLE).	30 arc-seconds	Global	Panagos et al. (2017)	2017	https://esdac.jrc.ec.europa.eu/content/global-rainfall-erosivity
Biodiversity	IUCN Red List of threatened species	Compiled polygon data for red listed species considered to be from comprehensively assessed taxonomic groups and selected freshwater groups. Freshwater species are mapped to pre-defined river/lake catchment units. Contains spatial data for about two-thirds of the 96,500 species that they have assessed. The maps are developed as part of a comprehensive assessment of global biodiversity in order to highlight taxa threatened with extinction, and thereby promote their conservation.	30 arc-seconds	Global	http://www.iucnredlist.org	2019	https://www.iucnredlist.org/resources/spatial-data-download
	Terrestrial Biodiversity Indicators	Biodiversity indicator values (scores) for grid cells at 1-kilometer resolution, based on several pieces of information including total counts (presence) of mammals, birds, amphibians and reptiles from IUCN and Birdlife International, total counts or presence, of critically endangered and endangered mammals, birds, amphibians and reptiles, the presence of endemic species/ species unique to the region, extinction risks for species over 50, 100 and 500 years, and biome vulnerability, identified from the WWF ecoregions.	1 km	Global	IUCN (2016)	2019	https://datacatalog.worldbank.org/data set/terrestrial-biodiversity-indicators
	Protected areas	WCMC & IUCN World Database on Protected Areas (WDPA)	Not specified	Global	UNEP-WCMC and IUCN	Updated regularly	https://www.protectedplanet.net/
	Global Biodiversity Information Facility	GBIF is an international network and data infrastructure funded by the world's governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth	Varies with data set	Global		Updated regularly	https://www.gbif.org/occurrence/search
Socio-economic	Global Roads Open Access Data Set (gROADS)	A global compilation of road maps with positional accuracy of 50 m (NASA Socioeconomic Data and Applications Center (SEDAC), 2009).	Not specified	Global	CIESIN and ITOS (2013)	Ranges from 1980s to 2010 on the country (most countries have no confirmed date)	https://sedac.ciesin.columbia.edu/data/set/groads-global-roads-open-access-v1
Socio-economic	GDP	Gap-filled multiannual data sets in gridded form for Gross Domestic Product and Human Development Index. Sub-national data were only used indirectly, scaling the reported national value and thus, remaining representative of the official statistics.	5 arc-min resolution	Global	Kummu et al. (2020)	1990-2015	https://datadryad.org/stash/dataset/doi:10.5061/dryad.dk1j0
Population (Leyk et al. 2019)	Urban TEP	Web-based platform that uses Earth Observations and auxiliary information to assess the urban environment and monitor and predict settlement development. Includes global urban footprint data set.	Varies with product. Highest resolution is 12 m	Global	Leyk et al. (2019)	1985-2015	https://urban-tep.eu/#!
	WorldPop	Global population data are available through WorldPop, which uses a combination of census, survey, satellite and cell phone data to produce gridded outputs (Tatem 2017).	1 km for the globe and 100 m for individual country data	Global	Tatem (2017)	2000-2020 for global data	https://www.worldpop.org/
	High Resolution Settlement Layer (HRSL)	Based on recent census data and high-resolution (0.5m) satellite imagery from DigitalGlobe. Population grids are available for both urban and rural areas	1 arc-sec	140 countries	Facebook Connectivity Lab and CIESIN	2015	https://ciesin.columbia.edu/data/hrsl/#data
Pollution	Socioeconomic Data and Applications Center (sedac)- Air polution	Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, v1 (1998 – 2016).	0.01 degrees	70 degrees north to 55 degrees south	Van Donkelaar et al. (2018)	1998-2016	https://sedac.ciesin.columbia.edu/data/set/sdei-global-annual-gwr-pm2-5-modis-misr-seawifs-aod
	World's Air Pollution: Real-time Air Quality Index	Global data on air quality. Only stations with particulate matter (PM2.5/PM10) are published.	Point data from ~12,000 stations	1000 major cities from 100 countries		Real time with variable length of availability	https://aqicn.org/sources
	Instream pollution	Provides a global overview of the water quality of ground and surface waters of water bodies and the trends at global, regional and local levels. ~250 variables are available including instream pollution.	Point data from approximately 4000 stations. Million entries for rivers, lakes, reservoirs, wetlands and groundwater systems	75 countries		1965 to 2019	https://gemstat.org/about/
Stream flow calibration	Stream flow	Provides a global overview of the water quality of ground and surface waters of water bodies and the trends at global, regional and local levels. ~250 variables are available including streamflow.	Point data from approximately 4000 stations. Million entries for rivers, lakes, reservoirs, wetlands and groundwater systems	75 countries		1965 to 2019	https://www.bafg.de/GRDC/EN/01_GRDC/13_dtbse/database_node.html
Crops	Global croplands (GFSAD30 project)	Provides cropland products (e.g. croplands with rainfed agriculture) across the world at a 30 m resolution.	30 m	Global	Thenkabail et al. (2012); Teleguntla et al. (2015)	2015	https://croplands.org/home
	Earthstat	A wide range of data on the global food system, including crop and pastureland fraction from 2000, and harvested area and yield for 175 crops.	Resolution varies with data set	Global	Citation varies with data set	variable	http://www.earthstat.org/
	FAOSTAT	Food and agricultural data for 245 countries	Tabular data	Global tabular		1961 to present	http://www.fao.org/faostat/en/#home
Other		Global Visible Infrared Imaging Radiometer Suite (VIIRS) Night-Time Lights produced by The Earth Observations Group (EOG).	15 arc second	Global	NASA	2012-YTD, Daily	https://blackmarble.gsfc.nasa.gov/

[5] AQUASTAT Core Database. Food and Agriculture Organization of the United Nations. http://www.fao.org/nr/water/aquastat/data/query/index.html?lang=en.