Environment90m layers

We recently prepared a new dataset called Environment90m!

For each of the roughly 726 million sub-catchments present in Hydrography90m, it provides detailed data on soil condition, bioclimatic variables, land cover, and many more. Just like the Hydrography90m layers, it is openly available for download, and easy to download using the dedicated functions in the R package hydrographr.

Here is an overview of all tables in the dataset. It consists of the summary statistics of 112 environmental layers in 7 categories which we calculated for each single sub-catchment of the Hydrography90m dataset (Amatulli et al. (2022), see references below).

Please see the paper by Garcia Marquez et al. (in prep) for further details (see references below).

For downloading and working with the data, we recommend using the R package hydrographr. Examples of how to do this are included below. A full vignette showing how to prepare and process the data needed to run a species distribution model to predict the habitat suitability of a fish species in a specific basin can be found at https://glowabio.github.io/hydrographr/articles/case_study_Danube.html.


Pick a tile!

… to generate download links.

If you click on any tile, the sections below will feature download links and R code for that specific tile!


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Land Cover (ESA-CCI)

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For land use data, we aggregated the consistent global land cover maps of the Land Cover European Space Agency (ESA) Climate Change Initiative (CCI) project into 22 categories from the original 37 ESA category level 2 land cover classes at a spatial resolution of 300m (CCI (2017), see references below). The annual data are available for the years 1992 to 2020. The data source is ESA (2017) (see references below).

download_landcover_tables(
base_vars=c(“c10”, “c130”), years=c(1992), tile_ids=c(“h00v04”), download_dir=”.”, download=TRUE)

  • Directory containing all the variables: public.igb-berlin.de

  • Pick a year to generate download links for all land cover variables for that year:

c10: Cropland, rainfed
Cropland, rainfed. Combined classes 10+11+12.
Unit: Proportion
Download link for selectedd tile
c20: Cropland, irrigated/post-flooding
Cropland, irrigated or post-flooding.
Unit: Proportion
Download link for selected tile
c30: Cropland/natural vegetation
Mosaic cropland (>50%) - natural vegetation (tree, shrub, herbaceous cover) (<50%).
Unit: Proportion
Download link for selected tile
c40: Natural vegetation/cropland
Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%).
Unit: Proportion
Download link for selected tile
c50: Tree cover, broadleaved, evergreen
Tree cover, broadleaved, evergreen, closed to open (>15%).
Unit: Proportion
Download link for selected tile
c60: Tree cover, broadleaved, deciduous
Tree cover, broadleaved, deciduous, closed to open (>15%). Combined classes: 60+61+62.
Unit: Proportion
Download link for selected tile
c70: Tree cover, needleleaved, evergreen
Tree cover, needleleaved, evergreen, closed to open (>15%). Combined classes 70+71+72.
Unit: Proportion
Download link for selected tile
c80: Tree cover, needleleaved, deciduous
Tree cover, needleleaved, deciduous, closed to open (>15%). Combined classes 80+81+82.
Unit: Proportion
Download link for selected tile
c90: Tree cover, mixed leaf type
Tree cover, mixed leaf type (broadleaved and needleleaved).
Unit: Proportion
Download link for selected tile
c100: Tree and shrub
Mosaic tree and shrub (>50%) / herbaceous cover (<50%).
Unit: Proportion
Download link for selected tile
c110: Herbaceous/tree and shrub
Mosaic herbaceous cover (>50%) / tree and shrub (<50%).
Unit: Proportion
Download link for selected tile
c120: Shrubland
Shrubland. Combined classes 120+121+122.
Unit: Proportion
Download link for selected tile
c130: Grassland
Grassland.
Unit: Proportion
Download link for selected tile
c140: Lichens, mosses
Lichens, mosses.
Unit: Proportion
Download link for selected tile
c150: Sparse vegetation
Sparse vegetation (tree, shrub, herbaceous cover) (<15%). Combined classes 150+151+152+153.
Unit: Proportion
Download link for selected tile
c160: Tree cover, flooded, fresh/brackish water
Tree cover, flooded, fresh or brackish water.
Unit: Proportion
Download link for selected tile
c170: Tree cover, flooded, saline water
Tree cover, flooded, saline water.
Unit: Proportion
Download link for selected tile
c180: Shrub or herbaceous
Shrub or herbaceous cover, flooded, fresh - saline - brackish water.
Unit: Proportion
Download link for selected tile
c190: Urban areas
Urban areas.
Unit: Proportion
Download link for selected tile
c200: Bare areas
Bare areas. Combined classes 200+201+202.
Unit: Proportion
Download link for selected tile
c210: Water bodies
Water bodies.
Unit: Proportion
Download link for selected tile
c220: Snow and ice
Permanent snow and ice.
Unit: Proportion
Download link for selected tile


Bioclimatic Variables (CHELSA)

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We derived high-resolution climate information from the Chelsa v2.1 dataset available at chelsa-climate.org (Karger et al. 2017, 2021, see references below). We used 19 bioclimatic variables (bio 1 to 19) at 30-arc-sec (ca. 1 km²) resolution for 30-year averages of temperature and precipitation. We aggregated the data for three time ranges: from 1981 to 2010, corresponding to observational data, and future projections for the years 2041 to 2070, as well as 2071 to 2100, corresponding to 2050 and 2070, respectively. For each future projection, we used general circulation models (GCMs) following three shared socioeconomic pathways (SSP1-RCP2.6, SSP3-RCP7, and SSP5-RCP8.5; Ebi et al. (2014); O’Neill et al. (2017), see references below).

  • How to download observed data using the R package hydrographr:

download_observed_climate_tables(
subset = c(“bio01_1981-2010_observed”), tile_ids = c(“h00v04”), download = TRUE, download_dir = “.”)

  • How to download projected data using the R package hydrographr:

download_projected_climate_tables(
subset = c(“bio01_2071-2100_ukesm1-0-ll_ssp585_v2_1”), tile_ids = c(“h00v04”), download_dir = “.”, download = TRUE)

You can also specify the separate aspects separately (time period, scenario, …):

download_projected_climate_tables(
base_vars = c(“bio01”), time_periods = c(“2071-2100”), models=c(“ukesm1-0-ll”), scenarios=c(“ssp585”), version=c(“v2_1”), tile_ids = c(“h00v04”), download = TRUE)

  • Directory containing all the variables: public.igb-berlin.de

  • Pick a time period to generate download links for all bioclim variables for that period:

  • Pick a model to generate download links for all bioclim variables for that model:
  • Pick a scenario to generate download links for all bioclim variables for that scenario:
bio01: Annual mean temperature
Mean annual daily mean air temperatures averaged over 1 year.
Unit: Degrees Celsius
Scale: 0.1
Offset: -273.15
Download link for selected tile
bio02: Mean diurnal range
Mean diurnal range of temperatures averaged over 1 year.
Unit: Degrees Celsius
Scale: 0.1
Offset: -
Download link for selected tile
bio03: Isothermality
Ratio of diurnal variation to annual variation in temperatures.
Unit: Degrees Celsius
Scale: 0.1
Offset: -
Download link for selected tile
bio04: Temperature seasonality
Standard deviation of the monthly mean temperatures.
Unit: Degrees Celsius / 100
Scale: 0.1
Offset: None.
Download link for selected tile
bio05: Max temperature of warmest month
The highest temperature of any monthly daily mean maximum temperature.
Unit: Degrees Celsius
Scale: 0.1
Offset: -273.15
Download link for selected tile
bio06: Min temperature of coldest month
The lowest temperature of any monthly daily mean minimum temperature.
Unit: Degrees Celsius
Scale: 0.1
Offset: -273.15
Download link for selected tile
bio07: Temperature annual range
The difference between the Maximum Temperature of Warmest month and the Minimum Temperature of Coldest month.
Unit: Degrees Celsius
Scale: 0.1
Offset: -
Download link for selected tile
bio08: Mean temperature of wettest quarter
The wettest quarter of the year is determined (to the nearest month).
Unit: Degrees Celsius
Scale: 0.1
Offset: -273.15
Download link for selected tile
bio09: Mean temperature of driest quarter
The driest quarter of the year is determined (to the nearest month).
Unit: Degrees Celsius
Scale: 0.1
Offset: -273.15
Download link for selected tile
bio10: Mean temperature of warmest quarter
The warmest quarter of the year is determined (to the nearest month).
Unit: Degrees Celsius
Scale: 0.1
Offset: -273.15
Download link for selected tile
bio11: Mean temperature of coldest quarter
The coldest quarter of the year is determined (to the nearest month).
Unit: Degrees Celsius
Scale: 0.1
Offset: -273.15
Download link for selected tile
bio12: Annual precipitation
Accumulated precipitation amount over 1 year.
Unit: kg/m²
Scale: 0.1
Offset: -
Download link for selected tile
bio13: Precipitation of wettest month
The precipitation amount of the wettest month.
Unit: kg/m²
Scale: 0.1
Offset: -
Download link for selected tile
bio14: Precipitation of driest month
The precipitation amount of the driest month.
Unit: kg/m²
Scale: 0.1
Offset: -
Download link for selected tile
bio15: Precipitation seasonality
The Coefficient of Variation is the standard deviation of the monthly precipitation estimates expressed as a percentage of the mean of those estimates (i.e. the annual mean).
Unit: kg/m²
Scale: 0.1
Offset: -
Download link for selected tile
bio16: Precipitation of wettest quarter
The wettest quarter of the year is determined (to the nearest month).
Unit: kg/m²
Scale: 0.1
Offset: -
Download link for selected tile
bio17: Precipitation of driest quarter
The driest quarter of the year is determined (to the nearest month).
Unit: kg/m²
Scale: 0.1
Offset: -
Download link for selected tile
bio18: Precipitation of warmest quarter
The warmest quarter of the year is determined (to the nearest month).
Unit: kg/m²
Scale: 0.1
Offset: -
Download link for selected tile
bio19: Precipitation of coldest quarter
The coldest quarter of the year is determined (to the nearest month).
Unit: kg/m²
Scale: 0.1
Offset: -
Download link for selected tile


Soil (SoilGrids250)

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The soil dataset consists of 16 variables that represent chemical and physical soil properties globally. These variables were sourced from the global gridded soil information dataset, SoilGrids250 v2.0, which is originally provided at seven standard depths for each numerical soil property (with the exception of depth to bedrock and soil organic carbon content) and at a spatial resolution of 250 m (Hengl et al. (2017), see references below). To integrate all soil depths (up to 220 cm), we calculated the weighted average for each soil property originally measured at different depths (Hengl et al. (2017)) and finally aggregated the 16 variables across Hydrography90m sub-catchments (Amatulli et al. (2022), see references below).

download_soil_tables(
subset = c(“clyppt”), tile_ids = c(“h00v04”), download_dir = “.”, download = TRUE)

awcts: Derived saturated water content
Derived saturated water content.
Unit: -
Download link for selected tile
clyppt: Clay content
Clay content.
Unit: Percent
Download link for selected tile
sndppt: Sand content
Sand content.
Unit: Percent
Download link for selected tile
sltppt: Silt content
Silt content.
Unit: Percent
Download link for selected tile
wwp: Derived available soil water capacity
Derived available soil water capacity.
Unit: -
Download link for selected tile
texmht: Texture class
Texture class. Following the USDA classification.
Unit: -
Download link for selected tile
orcdrc: Soil organic carbon content
Soil organic carbon content.
Unit: g/kg.
Download link for selected tile
phihox: Soil pH
Soil pH x 10 in H2O.
Unit: pH
Download link for selected tile
bldfie: Bulk density
Bulk density.
Unit: Kg/m³
Download link for selected tile
cecsol: Cation exchange capacity
Cation exchange capacity.
Unit: cmolc/kg
Download link for selected tile
crfvol: Coarse fragments volumetric
Coarse fragments volumetric.
Unit: Percent
Download link for selected tile
acdwrb: Grade of a sub-soil being acid
Grade of a sub-soil being acid, e.g. having a pH < 5 and low BS.
Unit: pH.
Download link for selected tile
bdricm: Depth to bedrock
Depth to bedrock (r horizon) up to 200 cm.
Unit: cm
Download link for selected tile
bdrlog: Probability of occurence of r horizon
Probability of occurence of e horizon.
Unit: Percent
Download link for selected tile
histpr: Cumulative probability of organic soil
Cumulative probability of organic soil based on the TAXOUSDA and TAXNWRB.
Unit: -.
Download link for selected tile
slgwrb: Sodic soil grade
Sodic soil grade based on WRB soil types and soil pH.
Unit: pH
Download link for selected tile


Stream Flow (FLO1K)

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For stream flow we used the FLO1K dataset which comprises the mean, maximum and minimum annual flow for each year in the period 1960–2015, provided as spatially continuous gridded layers at a high spatial resolution of 30 arc-seconds (ca. 1 km²) (Barbarossa et al. (2018), see references below). From this dataset, we only used the data from 1980 - 2010 and averaged it across this time to match the CHELSA observed climate dataset (see above).

download_flo1k_tables(
subset = c(“flo1k”), tile_ids = c(“h00v04”), download_dir = “.”, download = TRUE)

flo1k: Stream flow
The long-term mean annual flow represents the average of the year-specific FLO1K maps for mean Annual Flow over the period 1980 - 2010.
Unit: m³/s.
Scale: -
Offset: -
Download link for selected tile


Elevation (MERIT-DEM)

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To represent the elevation variable we used the 90 m resolution Multi-Error-Removed Improved-Terrain Digital Elevation Model (MERIT DEM) v1.0.3 (Yamazaki et al. (2017), see references below). The error removal procedures have improved the vertical accuracy of this product.

Spatial Resolution: 90m²

download_merit_dem_tables(
subset = c(“elev”), tile_ids = c(“h00v04”), download_dir = “.”, download = TRUE)

elev: Mean elevation
The MERIT DEM represents elevation in meters (mean elevation of the sub-catchment).
Unit: m
Download link for selected tile


Aridity and Evapotranspiration (cgiar csi v.3)

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This dataset provides global raster data on evapotranspiration processes and rainfall deficit for potential vegetation growth, at a high spatial resolution of 30 arc-seconds (ca. 1 km²). Global Aridity and Potential Evapotranspiration are both modeled using data available from WorldClim Global Climate Data. The data is available for the 1970 - 2000 period (Zomer and Trabucco (2022), see references below).

download_cgiar_tables(
subset = c(“garid”), tile_ids = c(“h00v04”), download_dir = “.”, download = TRUE)

gevapt: Evapotranspiration
Potential Evapo-Transpiration (ET0) based upon implementation of the FAO-56 Penman-Monteith Reference Evapotranspiration (ET0) equation.
Unit: mm
Scale: -
Offset: -
Download link for selected tile
garid: Aridity Index
Ratio between precipitation and ET0. Values reported have been multiplied by a factor of 10.000
Unit: -
Scale: -
Offset: (Values reported have been multiplied by a factor of 10.000)
Download link for selected tile


Hydrography90m

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The Hydrography90m is a high-resolution (∼90 m) dataset delineating a global stream channel network (Amatulli et al. (2022), see references below) and serves as the base of Environment90m. The calculation of Hydrography90m used the MERIT Hydro Digital Elevation Model at 3 arcsec (∼90 m at the Equator) (Yamazaki et al. (2017), see references below). The main feature of Hydrography90m is the delineation of small headwater streams. In addition, this dataset includes a number of stream topographic and topological properties. The summary statistics of the Environment90m dataset presented here are calculated for each of the 726 million unique sub-catchments of the Hydrography90m, which also serve as the spatial units in Environment90m.

download_hydrography90m_tables(
subset = c(“cti”), tile_ids = c(“h00v04”), download_dir = “.”, download = TRUE)

download_tiles(
variable = “cti”, file_format = “tif”, tile_id = c(“h00v04”), download_dir = “.”)

For detailed explanations of the variables, please refer to the neighbour page hydrography90m_layers:

1 base layer:

4 stream slope layers:

6 stream distance layers:

5 elevation layers:

11 segment properties layers:

5 stream order layers:

3 flow index layers:

  • cti (download link for selected tile)
  • spi (download link for selected tile)
  • sti (download link for selected tile)

Some variables are not included in Hydrography90m, so we explain them here:

10 stream reach layers:

  • length: Length of the stream reach (download link for selected tile)
  • stright: Straight length / Length of the stream as straight line (download link for selected tile)
  • sinosoid: Sinusoid of the stream reach / Fractal dimension: Stream length/straight stream length (download link for selected tile)
  • cum_length: Accumulated length / Length of stream from source (download link for selected tile)
  • out_dist: Distance to outlet / Distance of current stream init from outlet (download link for selected tile)
  • source_elev: Source elevation / Elevation of stream init (download link for selected tile)
  • outlet_elev: Outlet elevation / Elevation of stream outlet (download link for selected tile)
  • elev_drop: Elevation drop / Difference between source_elev and outlet_elev + drop outlet (download link for selected tile)
  • out_drop: Outlet drop / Drop at the outlet of the stream (download link for selected tile)
  • gradient: Gradient / Mean gradient of the sub-catchment (downstream elevation difference divided by distance) (download link for selected tile)

1 stream connectivity layer:

  • connections: TODO add description (download link for selected tile)

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References

Environment90m:

  • García Márquez, J. R., Grigoropoulou, A., Tomiczek, T., Schürz, M., Bremerich, V., Torres-Cambas, Y., … Domisch, S. (2025). Environment90m: Globally standardized environmental variables for spatial freshwater biodiversity science at high spatial resolution [Manuscript in preparation].

Hydrography90m:

  • Amatulli, G., Garcia Marquez, J.R., Sethi, T., Kiesel, J., Grigoropoulou, A., Üblacker, M., Shen, L., Domisch, S.: Hydrography90m: A new high-resolution global hydrographic dataset. Earth System Science Data, 14, 4525–4550, doi:10.5194/essd-14-4525-2022, 2022.

Bioclimatic variables:

  • Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., and Kessler, M.: Climatologies at high resolution for the earth’s land surface areas, Scientific data, 4, 1–20, 2017.
  • Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., and Kessler, M.: Climatologies at high resolution for the earth’s land surface areas, Scientific Data, 4, 170 122, doi:10.1038/sdata.2017.122, 2017.
  • Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., and Kessler, M.: Climatologies at high resolution for the earth’s land surface areas, doi:10.16904/envidat.228, 2021.
  • Ebi, K. L., Hallegatte, S., Kram, T., Arnell, N. W., Carter, T. R., Edmonds, J., Kriegler, E., Mathur, R., O’Neill, B. C., Riahi, K., et al.: A new scenario framework for climate change research: background, process, and future directions, Climatic Change, 122, 363–372, 2014.
  • O’Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, D. S., Van Ruijven, B. J., Van Vuuren, D. P., Birkmann, J., Kok, K., et al.: The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century, Global environmental change, 42, 169–180, 2017

Soil:

  • Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B.: SoilGrids250m: Global gridded soil information based on machine learning, PLOS ONE, 12, e0169 748, doi:10.1371/journal.pone.0169748, 2017.

Land Cover:

Aridity and Evapotranspiration:

Stream flow:

  • Barbarossa, V., Huijbregts, M. A. J., Beusen, A. H. W., Beck, H. E., King, H., and Schipper, A. M.: FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015, Scientific Data, 5, 180 052, doi:10.1038/sdata.2018.52, 2018.

Elevation

  • Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O’Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy map of global terrain elevations: Accurate Global Terrain Elevation map, Geophysical Research Letters, 44, 5844–5853, doi:10.1002/2017GL072874, 2017



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