Shiklomanov, I. A. & Rodda, J. C. World water resources at the beginning of the twenty-first century. (Cambridge University Press, 2003).
Biggs, J., von Fumetti, S. & Kelly-Quinn, M. The importance of small waterbodies for biodiversity and ecosystem services: implications for policy makers. Hydrobiologia 793, 3–39 (2017).
Google Scholar
Heino, J. et al. Lakes in the era of global change: moving beyond single-lake thinking in maintaining biodiversity and ecosystem services. Biol. Rev. 96, 89–106 (2021).
Google Scholar
Janssen, A. B. G. et al. Shifting states, shifting services: linking regime shifts to changes in ecosystem services of shallow lakes. Freshw. Biol. 66, 1–12 (2021).
Google Scholar
Knoll, L. B. et al. Consequences of lake and river ice loss on cultural ecosystem services. Limnol. Oceanogr. Lett. 4, 119–131 (2019).
Google Scholar
Sterner, R. W. et al. Ecosystem services of Earth’s largest freshwater lakes. Ecosyst. Serv. 41, 101046 (2020).
Google Scholar
Reynaud, A. & Lanzanova, D. A global meta-analysis of the value of ecosystem services provided by lakes. Ecol. Econ. 137, 184–194 (2017).
Google Scholar
Cooley, S. W., Ryan, J. C. & Smith, L. C. Human alteration of global surface water storage variability. Nature 591, 78–81 (2021).
Google Scholar
Downing, J. A. Global limnology: up-scaling aquatic services and processes to planet Earth. SIL Proceedings, 1922–2010 30, 1149–1166 (2009).
Google Scholar
Tranvik, L. J., Cole, J. J. & Prairie, Y. T. The study of carbon in inland waters—from isolated ecosystems to players in the global carbon cycle. Limnol. Oceanogr. Lett. 3, 41–48 (2018).
Google Scholar
Balsamo, G. et al. On the contribution of lakes in predicting near-surface temperature in a global weather forecasting model. Tellus A Dyn. Meteorol. Oceanogr. 64, 15829 (2012).
Google Scholar
DelSontro, T., Beaulieu, J. J. & Downing, J. A. Greenhouse gas emissions from lakes and impoundments: upscaling in the face of global change. Limnol. Oceanogr. Lett. 3, 64–75 (2018).
Google Scholar
Beaulieu, J. J. et al. Methane and carbon dioxide emissions from reservoirs: controls and upscaling. J. Geophys. Res. Biogeosciences 125, e2019JG005474 (2020).
Google Scholar
Slater, J. A. et al. The SRTM data “finishing” process and products. Photogramm. Eng. Remote Sens. 72, 237–247 (2006).
Google Scholar
Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).
Google Scholar
Verpoorter, C., Kutser, T., Seekell, D. A. & Tranvik, L. J. A global inventory of lakes based on high-resolution satellite imagery. Geophys. Res. Lett. 41, 6396–6402 (2014).
Google Scholar
Pickens, A. H. et al. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sens. Environ. 243, 111792 (2020).
Google Scholar
Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 13603 (2016).
Google Scholar
Tickner, D. et al. Bending the curve of global freshwater biodiversity loss: an emergency recovery plan. Bioscience 70, 330–342 (2020).
Google Scholar
Downing, J. A., Polasky, S., Olmstead, S. M. & Newbold, S. C. Protecting local water quality has global benefits. Nat. Commun. 12, 1–6 (2021).
Google Scholar
Hill, R. A., Weber, M. H., Debbout, R. M., Leibowitz, S. G. & Olsen, A. R. The Lake-Catchment (LakeCat) Dataset: characterizing landscape features for lake basins within the conterminous USA. Freshw. Sci. 37, 208–221 (2018).
Google Scholar
Soranno, P. A. et al. LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes. Gigascience 6, 1–22 (2017).
Google Scholar
Toptunova, O., Choulga, M. & Kurzeneva, E. Status and progress in global lake database developments. Adv. Sci. Res. 16, 57–61 (2019).
Google Scholar
Meyer, M. F., Labou, S. G., Cramer, A. N., Brousil, M. R. & Luff, B. T. The global lake area, climate, and population dataset. Sci. Data 7, 174 (2020).
Google Scholar
Kling, G. W., Kipphut, G. W., Miller, M. M. & O’Brien, W. J. Integration of lakes and streams in a landscape perspective: the importance of material processing on spatial patterns and temporal coherence. Freshw. Biol. 43, 477–497 (2000).
Google Scholar
Fergus, C. E. et al. The freshwater landscape: lake, wetland, and stream abundance and connectivity at macroscales. Ecosphere 8, e01911 (2017).
Google Scholar
Lehner, B., Messager, ML., Korver, MC. & Linke, S. LakeATLAS Version 1.0, figshare, https://doi.org/10.6084/m9.figshare.19312001 (2022).
Linke, S. et al. Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution. Sci. data 6, 283 (2019).
Google Scholar
Fergus, C. E. et al. National framework for ranking lakes by potential for anthropogenic hydro-alteration. Ecol. Indic. 122, 107241 (2021).
Google Scholar
Bracht-Flyr, B., Istanbulluoglu, E. & Fritz, S. A hydro-climatological lake classification model and its evaluation using global data. J. Hydrol. 486, 376–383 (2013).
Google Scholar
Soranno, P. A. et al. Using landscape limnology to classify freshwater ecosystems for multi-ecosystem management and conservation. Bioscience 60, 440–454 (2010).
Google Scholar
McCullough, I. M., Skaff, N. K., Soranno, P. A. & Cheruvelil, K. S. No lake left behind: how well do U.S. protected areas meet lake conservation targets? Limnol. Oceanogr. Lett. 4, 183–192 (2019).
Google Scholar
Stanley, E. H. et al. Biases in lake water quality sampling and implications for macroscale research. Limnol. Oceanogr. 64, 1572–1585 (2019).
Google Scholar
Hanson, P. C., Weathers, K. C. & Kratz, T. K. Networked lake science: how the Global Lake Ecological Observatory Network (GLEON) works to understand, predict, and communicate lake ecosystem response to global change. Inl. Waters 6, 543–554 (2016).
Google Scholar
Lottig, N. R. & Carpenter, S. R. Interpolating and forecasting lake characteristics using long-term monitoring data. Limnol. Oceanogr. 57, 1113–1125 (2012).
Google Scholar
Filazzola, A. et al. A database of chlorophyll and water chemistry in freshwater lakes. Sci. Data 2020 71 7, 1–10 (2020).
Lehner, B. & Messager, M. L. HydroLAKES – Technical Documentation Version 1.0. https://data.hydrosheds.org/file/technical-documentation/HydroLAKES_TechDoc_v10.pdf (2016).
Natural Resources Canada. CanVec Hydrography: Waterbody Features. Version 12.0. https://ftp.maps.canada.ca/pub/nrcan_rncan/vector/canvec (2013).
Lehner, B., Verdin, K. & Jarvis, A. New global hydrography derived from spaceborne elevation data. Eos, Trans. AGU 89, 93–94 (2008).
Google Scholar
Farr, T. G. & Kobrick, M. Shuttle radar topography mission produces a wealth of data. Eos, Trans. AGU 81, 583–585 (2000).
Google Scholar
Müller Schmied, H. et al. The global water resources and use model WaterGAP v2.2d: model description and evaluation. Geosci. Model Dev. 14, 1037–1079 (2021).
Google Scholar
Beck, H. E. et al. Global evaluation of runoff from 10 state-of-the-art hydrological models. Hydrol. Earth Syst. Sci. 21, 2881–2903 (2017).
Google Scholar
Alcamo, J. et al. Development and testing of the WaterGAP 2 global model of water use and availability. Hydrol. Sci. J. 48, 317–338 (2003).
Google Scholar
Döll, P., Kaspar, F. & Lehner, B. A global hydrological model for deriving water availability indicators: model tuning and validation. J. Hydrol. 270, 105–134 (2003).
Google Scholar
Lehner, B. & Grill, G. Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrol. Process. 27, 2171–2186 (2013).
Google Scholar
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar
Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS One 12, e0169748 (2017).
Google Scholar
Zhang, X. et al. GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery. Earth Syst. Sci. Data 13, 2753–2776 (2021).
Google Scholar
Buchhorn, M. et al. Copernicus Global Land Service: Land Cover 100m: Collection 3: epoch 2019: Globe, Zenodo, https://doi.org/10.5281/zenodo.3939050 (2020).
ESRI. ArcGIS Desktop: Release 10.4.1 (Environmental Systems Research Institute, Redlands, CA, USA, 2016).
Soranno, P. A., Cheruvelil, K. S., Wagner, T., Webster, K. E. & Bremigan, M. T. Effects of land use on lake nutrients: the importance of scale, hydrologic connectivity, and region. PLoS One 10, e0135454 (2015).
Google Scholar
Su, Z. H., Lin, C., Ma, R. H., Luo, J. H. & Liang, Q. O. Effect of land use change on lake water quality in different buffer zones. Appl. Ecol. Environ. Res. 13, 639–653 (2015).
Brakebill, J. W., Schwarz, G. E. & Wieczorek, M. E. An enhanced hydrologic stream network based on the NHDPlus medium resolution dataset. Scientific Investigations Report https://doi.org/10.3133/sir20195127 (2020).
Carroll, M., Townshend, J., DiMiceli, C., Noojipady, P. & Sohlberg, R. Global raster water mask at 250 meter spatial resolution, Collection 5: MOD44W MODIS Water Mask. College Park, Maryland: University of Maryland (2009).
Carroll, M. L., Townshend, J. R., DiMiceli, C. M., Noojipady, P. & Sohlberg, R. A. A new global raster water mask at 250 m resolution. Int. J. Digit. Earth 2, 291–308 (2009).
Google Scholar
European Environment Agency (EEA). European Catchments and Rivers Network System (ECRINS), https://www.eea.europa.eu/data-and-maps/data/european-catchments-and-rivers-network (2012).
Ouellet Dallaire, C., Lehner, B., Sayre, R. & Thieme, M. A multidisciplinary framework to derive global river reach classifications at high spatial resolution. Environ. Res. Lett. 14, 024003 (2019).
Google Scholar
Global Runoff Data Centre (GRDC). River discharge data. Federal Institute of Hydrology, 56068 Koblenz, Germany, https://www.bafg.de/GRDC (2014).
Openshaw, S. The modifiable areal unit problem. In Quantitative Geography: A British View (eds. Wrigley, N. & Bennett, R.) 60–69 (Routledge and Kegan Paul, Andover, 1981).
United States Census Bureau. 2010 Census. ftp://ftp2.census.gov/geo/tiger (2010).
Center for International Earth Science Information Network (CIESIN) & NASA Socioeconomic Data and Applications Center (SEDAC). Gridded Population of the World, Version 4 (GPWv4): Population Count and Density. https://doi.org/10.7927/H4JW8BX5 (2016).
Grill, G. et al. Mapping the world’s free-flowing rivers. Nature 569, 215–221 (2019).
Google Scholar
Allen, D. J. et al. The Diversity of Life in African Freshwaters: Under Water, Under Threat: an Analysis of the Status and Distribution of Freshwater Species Throughout Mainland Africa. (IUCN, 2011).
Markovic, D. et al. Europe’s freshwater biodiversity under climate change: distribution shifts and conservation needs. Divers. Distrib. 20, 1097–1107 (2014).
Google Scholar
Fluet-Chouinard, E., Lehner, B., Rebelo, L.-M., Papa, F. & Hamilton, S. K. Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sens. Environ. 158, 348–361 (2015).
Google Scholar
Lehner, B. et al. High‐resolution mapping of the world’s reservoirs and dams for sustainable river‐flow management. Front. Ecol. Environ. 9, 494–502 (2011).
Google Scholar
Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science 339, 940–943 (2013).
Google Scholar
Robinson, N., Regetz, J. & Guralnick, R. P. EarthEnv-DEM90: A nearly-global, void-free, multi-scale smoothed, 90m digital elevation model from fused ASTER and SRTM data. ISPRS J. Photogramm. Remote Sens. 87, 57–67 (2014).
Google Scholar
Metzger, M. J. et al. A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. Glob. Ecol. Biogeogr. 22, 630–638 (2013).
Google Scholar
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Google Scholar
Zomer, R. J., Trabucco, A., Bossio, D. A. & Verchot, L. V. Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agric. Ecosyst. Environ. 126, 67–80 (2008).
Google Scholar
Trabucco, A., Zomer, R. J., Bossio, D. A., van Straaten, O. & Verchot, L. V. Climate change mitigation through afforestation/reforestation: a global analysis of hydrologic impacts with four case studies. Agric. Ecosyst. Environ. 126, 81–97 (2008).
Google Scholar
Trabucco, A. & Zomer, R. J. Global soil water balance geospatial database. CGIAR Consortium for Spatial Information, https://cgiarcsi.community/data/global-high-resolution-soil-water-balance (2010).
Hall, D. K., Riggs, G. A. & Salomonson, V. MODIS/Terra snow cover daily L3 global 500m grid, version 5, 2002–2015, https://doi.org/10.5067/MODIS/MOD10A1.006 (2016).
Bartholomé, E. & Belward, A. S. GLC2000: a new approach to global land cover mapping from Earth observation data. Int. J. Remote Sens. 26, 1959–1977 (2005).
Google Scholar
Ramankutty, N. & Foley, J. A. Estimating historical changes in global land cover: Croplands from 1700 to 1992. Global Biogeochem. Cycles 13, 997–1027 (1999).
Google Scholar
Lehner, B. & Döll, P. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296, 1–22 (2004).
Google Scholar
Ramankutty, N., Evan, A. T., Monfreda, C. & Foley, J. A. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochem. Cycles 22, (2008).
Siebert, S. et al. A global data set of the extent of irrigated land from 1900 to 2005. Hydrol. Earth Syst. Sci. 19, 1521–1545 (2015).
Google Scholar
GLIMS & NSIDC. Global land ice measurements from space (GLIMS) glacier database, v1. National Snow and Ice Data Center (NSIDC), https://doi.org/10.7265/N5V98602 (2012).
Gruber, S. Derivation and analysis of a high-resolution estimate of global permafrost zonation. Cryosphere 6, 221–233 (2012).
Google Scholar
UNEP-WCMC & IUCN. The World Database on Protected Areas, http://www.protectedplanet.net (2014).
Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).
Google Scholar
Abell, R. et al. Freshwater ecoregions of the world: a new map of biogeographic units for freshwater biodiversity conservation. Bioscience 58, 403–414 (2008).
Google Scholar
Hengl, T. et al. SoilGrids1km—global soil information based on automated mapping. PLoS One 9, e105992 (2014).
Google Scholar
Hartmann, J. & Moosdorf, N. The new global lithological map database GLiM: a representation of rock properties at the Earth surface. Geochem. Geophys. Geosyst. 13, Q12004 (2012).
Google Scholar
Williams, P. W. & Ford, D. C. Global distribution of carbonate rocks. Zeitschrift für Geomorphologie Suppl. 147, 1–2 (2006).
Borrelli, P. et al. An assessment of the global impact of 21st century land use change on soil erosion. Nat. Commun. 8, 1–13 (2017).
Google Scholar
Pesaresi, M. & Freire, S. GHS Settlement grid following the REGIO model 2014 in application to GHSL Landsat and CIESIN GPW v4-multitemporal (1975-1990-2000-2015). European Commission, Joint Research Centre (JRC), https://data.europa.eu/data/datasets/jrc-ghsl-ghs_smod_pop_globe_r2016a (2016).
Doll, C. N. H. CIESIN thematic guide to night-time light remote sensing and its applications. CIESIN http://sedac.ciesin.columbia.edu/binaries/web/sedac/thematic-guides/ciesin_nl_tg.pdf (2008).
Meijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J. & Schipper, A. M. Global patterns of current and future road infrastructure. Environ. Res. Lett. 13, 64006 (2018).
Google Scholar
Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. data 3, 160067 (2016).
Google Scholar
University of Berkeley. Database of global administrative areas (GADM). University of Berkeley, Museum of Vertebrate Zoology and the International Rice Research Institute, http://www.gadm.org (2012).
Kummu, M., Taka, M. & Guillaume, J. H. A. Gridded global datasets for gross domestic product and Human Development Index over 1990–2015. Sci. data 5, 180004 (2018).
Google Scholar
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