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Global hydro-environmental lake characteristics at high spatial resolution

  • 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).

    Article 

    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).

    PubMed 
    Article 

    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).

    Article 

    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).

    Article 

    Google Scholar 

  • Sterner, R. W. et al. Ecosystem services of Earth’s largest freshwater lakes. Ecosyst. Serv. 41, 101046 (2020).

    Article 

    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).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Cooley, S. W., Ryan, J. C. & Smith, L. C. Human alteration of global surface water storage variability. Nature 591, 78–81 (2021).

    ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Downing, J. A. Global limnology: up-scaling aquatic services and processes to planet Earth. SIL Proceedings, 1922–2010 30, 1149–1166 (2009).

    Article 

    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).

    Article 

    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).

    Article 

    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).

    CAS 
    Article 

    Google Scholar 

  • Beaulieu, J. J. et al. Methane and carbon dioxide emissions from reservoirs: controls and upscaling. J. Geophys. Res. Biogeosciences 125, e2019JG005474 (2020).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • Slater, J. A. et al. The SRTM data “finishing” process and products. Photogramm. Eng. Remote Sens. 72, 237–247 (2006).

    Article 

    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).

    ADS 
    CAS 
    PubMed 
    Article 

    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).

    ADS 
    Article 

    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).

    ADS 
    Article 

    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).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Tickner, D. et al. Bending the curve of global freshwater biodiversity loss: an emergency recovery plan. Bioscience 70, 330–342 (2020).

    PubMed 
    PubMed Central 
    Article 

    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).

    Article 
    CAS 

    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).

    PubMed 
    PubMed Central 
    Article 

    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).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Toptunova, O., Choulga, M. & Kurzeneva, E. Status and progress in global lake database developments. Adv. Sci. Res. 16, 57–61 (2019).

    Article 

    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).

    PubMed 
    PubMed Central 
    Article 

    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).

    Article 

    Google Scholar 

  • Fergus, C. E. et al. The freshwater landscape: lake, wetland, and stream abundance and connectivity at macroscales. Ecosphere 8, e01911 (2017).

    Article 

    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).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Fergus, C. E. et al. National framework for ranking lakes by potential for anthropogenic hydro-alteration. Ecol. Indic. 122, 107241 (2021).

    Article 

    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).

    ADS 
    Article 

    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).

    Article 

    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).

    Article 

    Google Scholar 

  • Stanley, E. H. et al. Biases in lake water quality sampling and implications for macroscale research. Limnol. Oceanogr. 64, 1572–1585 (2019).

    ADS 
    CAS 
    Article 

    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).

    Article 

    Google Scholar 

  • Lottig, N. R. & Carpenter, S. R. Interpolating and forecasting lake characteristics using long-term monitoring data. Limnol. Oceanogr. 57, 1113–1125 (2012).

    ADS 
    Article 

    Google Scholar 

  • Filazzola, A. et al. A database of chlorophyll and water chemistry in freshwater lakes. Sci. Data 2020 71 7, 1–10 (2020).

    Google Scholar 

  • 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).

    ADS 
    Article 

    Google Scholar 

  • Farr, T. G. & Kobrick, M. Shuttle radar topography mission produces a wealth of data. Eos, Trans. AGU 81, 583–585 (2000).

    ADS 
    Article 

    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).

    ADS 
    Article 

    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).

    ADS 
    Article 

    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).

    Article 

    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).

    ADS 
    Article 

    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).

    ADS 
    Article 

    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).

    Article 

    Google Scholar 

  • Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS One 12, e0169748 (2017).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    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).

    ADS 
    Article 

    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).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    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).

    Google Scholar 

  • 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).

    ADS 
    Article 

    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).

    ADS 
    Article 

    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).

    ADS 
    CAS 
    PubMed 
    Article 

    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).

    Article 

    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).

    ADS 
    Article 

    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).

    Article 

    Google Scholar 

  • Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science 339, 940–943 (2013).

    ADS 
    CAS 
    PubMed 
    Article 

    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).

    ADS 
    Article 

    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).

    Article 

    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).

    Article 

    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).

    Article 

    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).

    Article 

    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).

    Article 

    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).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • Lehner, B. & Döll, P. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296, 1–22 (2004).

    ADS 
    Article 

    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).

    ADS 
    Article 

    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).

    ADS 
    Article 

    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).

    PubMed 
    PubMed Central 
    Article 

    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).

    Article 

    Google Scholar 

  • Hengl, T. et al. SoilGrids1km—global soil information based on automated mapping. PLoS One 9, e105992 (2014).

    ADS 
    PubMed 
    PubMed Central 
    Article 

    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).

    ADS 
    Article 

    Google Scholar 

  • Williams, P. W. & Ford, D. C. Global distribution of carbonate rocks. Zeitschrift für Geomorphologie Suppl. 147, 1–2 (2006).

    Google Scholar 

  • 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).

    ADS 
    CAS 
    Article 

    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).

    Article 

    Google Scholar 

  • Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. data 3, 160067 (2016).

    PubMed 
    PubMed Central 
    Article 

    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).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 


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