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).

• Land Cover
• Bioclimatic variables
• Soil
• Stream Flow
• Elevation
• Aridity and Evapotranspiration
• Hydrography90m variables

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!

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).

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

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:

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:

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).

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

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

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

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).

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

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

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

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

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

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

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

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).

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

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

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

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.

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

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

• How to download the same variables as spatial data (GeoTIFF and/or geopackage files), using the function download_tiles:

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

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

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

1 base layer:

• accumulation (download link for selected tile)

4 stream slope layers:

• slope_curv_max_dw_cel (download link for selected tile)
• slope_curv_min_dw_cel (download link for selected tile)
• slope_elv_dw_cel (download link for selected tile)
• slope_grad_dw_cel (download link for selected tile)

6 stream distance layers:

• stream_dist_dw_near (download link for selected tile)
• stream_dist_proximity (download link for selected tile)
• stream_dist_up_farth (download link for selected tile)
• stream_dist_up_near (download link for selected tile)
• outlet_dist_dw_basin (download link for selected tile)
• outlet_dist_dw_scatch (download link for selected tile)

5 elevation layers:

• stream_diff_dw_near (download link for selected tile)
• stream_diff_up_farth (download link for selected tile)
• stream_diff_up_near (download link for selected tile)
• outlet_diff_dw_basin (download link for selected tile)
• outlet_diff_dw_scatch (download link for selected tile)

11 segment properties layers:

• channel_curv_cel (download link for selected tile)
• channel_dist_dw_seg (download link for selected tile)
• channel_dist_up_cel (download link for selected tile)
• channel_dist_up_seg (download link for selected tile)
• channel_elv_dw_cel (download link for selected tile)
• channel_elv_dw_seg (download link for selected tile)
• channel_elv_up_cel (download link for selected tile)
• channel_elv_up_seg (download link for selected tile)
• channel_grad_dw_seg (download link for selected tile)
• channel_grad_up_cel (download link for selected tile)
• channel_grad_up_seg (download link for selected tile)

5 stream order layers:

• order_hack (download link for selected tile)
• order_horton (download link for selected tile)
• order_shreve (download link for selected tile)
• order_strahler (download link for selected tile)
• order_topo (download link for selected tile)

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:

• ESA: Land Cover CCI Product User Guide Version 2. Tech. Rep. Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf, Tech. rep., European Space Agency, 2017.
• CCI, E. L. C.: Product user guide version 2.0, UCL-Geomatics: London, UK, 685, 2017.

Aridity and Evapotranspiration:

• Zomer, R. J. and Trabucco, A.: Version 3 of the “Global Aridity Index and Potential Evapotranspiration (ET0) Database”: Estimation of Penman-Monteith Reference Evapotranspiration. Available online from the CGIAR-CSI GeoPortal at: https://cgiarcsi.community/2019/0124/global-aridity-index-and-potential-evapotranspiration-climate-database-v3/, Tech. rep., CGIAR-320 CSI, https://cgiarcsi.community/2019/01/24/global-aridity-index-and-potential-evapotranspiration-climate-database-v3, 2022

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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