28ec58b6-690a-4879-9d2a-80b7e41d4fb1 Environment90m Leibniz Institute of Freshwater Ecology and Inland Fisheries, Germany Jaime Ricardo Garcia Marquez Research Technician marquez@igb-berlin.de https://orcid.org/0000-0002-1998-5669 Leibniz Institute of Freshwater Ecology and Inland Fisheries, Germany Afroditi Grigoropoulou Postdoc afroditi.grigoropoulou@igb-berlin.de Leibniz Institute of Freshwater Ecology and Inland Fisheries, Germany Thomas Tomiczek PhD Student thomas.tomiczek@igb-berlin.de Leibniz Institute of Freshwater Ecology and Inland Fisheries, Germany | Freie Universität Berlin, Germany Marlene Schürz Doctoral Candidate marlene.schuerz@igb-berlin.de https://orcid.org/0009-0007-2072-9754 Leibniz Institute of Freshwater Ecology and Inland Fisheries, Germany Vanessa Bremerich Research Technician vanessa.bremerich@igb-berlin.de https://orcid.org/0000-0002-7657-1534 Leibniz Institute of Freshwater Ecology and Inland Fisheries, Germany Yusdiel Torres-Cambas Postdoc yusdiel.torres-cambas@igb-berlin.de Leibniz Institute of Freshwater Ecology and Inland Fisheries, Germany Merret Buurman Scientific Staff merret.buurman@igb-berlin.de https://orcid.org/0000-0001-6945-456X Leibniz Institute of Freshwater Ecology and Inland Fisheries, Germany Kristi Bego Doctoral Candidate kristi.bego@igb-berlin.de Yale University, United States of America | Spatial Ecology, United Kingdom Giuseppe Amatulli Research Scientist giuseppe.amatulli@yale.edu https://orcid.org/0000-0002-8341-2830 Leibniz Institute of Freshwater Ecology and Inland Fisheries, Germany Sami Domisch Research Group Leader domisch@igb-berlin.de https://orcid.org/0000-0002-8127-9335 SIB Swiss Institute of Bioinformatics Chiara Bortoluzzi Data Manager chiara.bortoluzzi@sib.swiss https://orcid.org/0000-0001-6589-6635 2025-08-05 English This dataset aggregates a large number of environmental layers into each of the 726 million sub-catchments of the Hydrography90m dataset, corresponding to single stream segments. Specifically, Environment90m includes 45 variables related to topography and hydrography, 19 climate variables for the observation period of 1981–2010, as well as projections for 2041–2070 and 2071–2100 under the Shared Socioeconomic Pathways (SSPs) 1.26, 3.70 and 5.85, and three global circulation models (UKESM, MPI and IPSL). Moreover, Environment90m includes 22 land cover categories for the annual time-series data from 1992–2020. In addition, the dataset provides 15 soil variables and information on aridity and modelled streamflow. Summary statistics (i.e., mean, min, max, range, sd) are provided for all continuous variables while for categorical data, the proportion of each category is calculated within each of the sub-catchments. Land cover Bioclimatic variables Soil Stream flow Elevation Aridity Evapotranspiration Hydrography90 The dataset is protected by the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) http://creativecommons.org/licenses/by-nc/4.0 Environment90m dataset (2025) https://public.igb-berlin.de/index.php/s/zw56kEd25NsQqcQ?path=%2F R package https://hydrography.org/hydrographr/ Global -180 180 90 -90 Leibniz Institute of Freshwater Ecology and Inland Fisheries, Germany Jaime Ricardo Garcia Marquez Research Technician marquez@igb-berlin.de https://orcid.org/0000-0002-1998-5669 Leibniz Institute of Freshwater Ecology and Inland Fisheries, Germany Sami Domisch Research Group Leader sami.domisch@igb-berlin.de https://orcid.org/0000-0002-8127-9335 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. The annual data are available for the years 1992 to 2020. The data source is ESA (2017). 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. We derived high-resolution climate information from the Chelsa v2.1 dataset. 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). 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. 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. To integrate all soil depths (up to 220 cm), we calculated the weighted average for each soil property originally measured at different depths and finally aggregated the 16 variables across Hydrography90m sub-catchment. 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. 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. From this dataset, we only used the data from 1980 - 2010 and averaged it across this time to match the CHELSA observed climate dataset. 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. To represent the elevation variable we used the 90 m resolution Multi-Error-Removed Improved-Terrain Digital Elevation Model (MERIT DEM) v1.0.3. The error removal procedures have improved the vertical accuracy of this product. 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. The dataset of Aridity and Evapotranspiration provides global raster data on evapotranspiration processes and rainfall deficit for potential vegetation growth, at a high spatial resolution of 30 arc-seconds. Global Aridity and Potential Evapotranspiration are both modelled using data available from WorldClim Global Climate Data. The data is available for the 1970 - 2000 period. 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. The Hydrography90m is a high-resolution (∼90 m) dataset delineating a global stream channel network and serves as the base of Environment90m. The calculation of Hydrography90m used the MERIT Hydro Digital Elevation Model at 3 arcsec (approx. 90 m at the Equator). 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. 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.