The location and persistence of surface water (inland and coastal) is both affected by climate and human activity1 and affects climate2,3, biological diversity4 and human wellbeing5,6. Global data sets documenting surface water location and seasonality have been produced from inventories and national descriptions7, statistical extrapolation of regional data8 and satellite imagery9,10,11,12, but measuring long-term changes at high resolution remains a challenge. Here, using three million Landsat satellite images13, we quantify changes in global surface water over the past 32 years at 30-metre resolution. We record the months and years when water was present, where occurrence changed and what form changes took in terms of seasonality and persistence. Between 1984 and 2015 permanent surface water has disappeared from an area of almost 90,000 square kilometres, roughly equivalent to that of Lake Superior, though new permanent bodies of surface water covering 184,000 square kilometres have formed elsewhere. All continental regions show a net increase in permanent water, except Oceania, which has a fractional (one per cent) net loss. Much of the increase is from reservoir filling, although climate change14 is also implicated. Loss is more geographically concentrated than gain. Over 70 per cent of global net permanent water loss occurred in the Middle East and Central Asia, linked to drought and human actions including river diversion or damming and unregulated withdrawal15,16. Losses in Australia17 and the USA18 linked to long-term droughts are also evident. This globally consistent, validated data set shows that impacts of climate change and climate oscillations on surface water occurrence can be measured and that evidence can be gathered to show how surface water is altered by human activities. We anticipate that this freely available data will improve the modelling of surface forcing, provide evidence of state and change in wetland ecotones (the transition areas between biomes), and inform water-management decision-making.
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Vörösmarty, C. J., Green, P., Salisbury, J. & Lammers, R. B. Global water resources: vulnerability from climate change and population growth. Science 289, 284–288 (2000)
Subin, Z. M., Riley, W. J. & Mironov, D. An improved lake model for climate simulations: model structure, evaluation, and sensitivity analyses in CESM1. J. Adv. Model. Earth Syst . 4, M02001 (2012)
Holgerson, M. A. & Raymond, P. A. Large contribution to inland water CO2 and CH4 emissions from very small ponds. Nat. Geosci. 9, 222–226 (2016)
Gardner, R. C. et al. State of the World’s Wetlands and Their Services to People: A Compilation of Recent Analyses. Ramsar Briefing Note No. 7, http://dx.doi.org/10.2139/ssrn.2589447 (Ramsar Convention Secretariat, SSRN, 2015)
Vörösmarty, C. J. et al. in Millennium Ecosystem Assessment Vol. 1 Ecosystems and Human Well-being: Current State and Trends Ch. 7, 165–207, http://www.unep.org/maweb/documents/document.276.aspx.pdf (Island Press, 2005)
World Economic Forum. The Global Risks Report 2016 11th edn, http://www3.weforum.org/docs/Media/TheGlobalRisksReport2016.pdf (World Economic Forum, 2016)
Lehner, B. & Döll, P. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296, 1–22 (2004)
Downing, J. A. et al. The global abundance and size distribution of lakes, ponds, and impoundments. Limnol. Oceanogr. 51, 2388–2397 (2006)
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)
Feng, M., Sexton, J. O., Channan, S. & Townshend, J. R. A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm. Int. J. Digit. Earth 9, 113–133 (2015)
Yamazaki, D., Trigg, M. A. & Ikeshima, D. Development of a global ~90m water body map using multi-temporal Landsat images. Remote Sens. Environ. 171, 337–351 (2015)
Prigent, C. et al. Changes in land surface water dynamics since the 1990s and relation to population pressure. Geophys. Res. Lett. 39, L08403 (2012)
Wulder, M. A. et al. The global Landsat archive: status, consolidation, and direction. Remote Sens. Environ. 185, 271–283 (2016)
Lutz, A. F., Immerzeel, W. W., Shrestha, A. B. & Bierkens, M. F. P. Consistent increase in High Asia’s runoff due to increasing glacier melt and precipitation. Nat. Clim. Chang . 4, 587–592 (2014)
Micklin, P. The future Aral Sea: hope and despair. Environ. Earth Sci . 75, 844 (2016)
Zafarnejad, F. The contribution of dams to Iran’s desertification. Int. J. Environ. Stud. 66, 327–341 (2009)
van Dijk, A. I. et al. The Millennium Drought in southeast Australia (2001–2009): natural and human causes and implications for water resources, ecosystems, economy, and society. Wat. Resour. Res . 49, 1040–1057 (2013)
MacDonald, G. M. Water, climate change, and sustainability in the southwest. Proc. Natl Acad. Sci. USA 107, 21256–21262 (2010)
Mueller, N. et al. Water observations from space: mapping surface water from 25 years of Landsat imagery across Australia. Remote Sens. Environ. 174, 341–352 (2016)
Tulbure, M. G., Broich, M., Stehman, S. V. & Kommareddy, A. Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sens. Environ. 178, 142–157 (2016)
Postel, S. L., Daily, G. C. & Ehrlich, P. R. Human appropriation of renewable fresh water. Science 271, 785–788 (1996)
United Nations Department of Economic and Social Affairs, Population Division. World Population Prospects: The 2015 Revision, Key Findings and Advance Tables. Working Paper No. ESA/P/WP.241, https://esa.un.org/unpd/wpp/publications/files/key_findings_wpp_2015.pdf (United Nations, 2015)
Najafi, A. & Vatanfada, J. Environmental challenges in trans-boundary waters, case study: Hamoon Hirmand Wetland (Iran and Afghanistan). Int. J. Wat. Resour. Arid Environ . 1, 16–24 (2011)
International Commission on Large Dams World Registerhttp://www.icold-cigb.org/GB/World_register/general_synthesis.asp?IDA=206 (GIGB/ICOLD, 2016)
Kosarev, A. N., Kostianoy, A. G. & Zonn, I. S. Kara-Bogaz-Gol Bay: physical and chemical evolution. Aquat. Geochem. 15, 223–236 (2009)
Liu, B. et al. Outburst flooding of the moraine-dammed Zhuonai Lake on Tibetan plateau: causes and impacts. IEEE Geosci. Remote Sens. Lett. 13, 570–574 (2016)
Pethick, J. & Orford, J. D. Rapid rise in effective sea-level in southwest Bangladesh: its causes and contemporary rates. Glob. Planet. Change 111, 237–245 (2013)
Bonetto, A. A., Wais, J. R. & Castello, H. P. The increasing damming of the Paraná basin and its effects on the lower reaches. Regul. Rivers Res. Manage. 4, 333–346 (1989)
Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010)
Acuña, V. et al. Why should we care about temporary waterways? Science 343, 1080–1081 (2014)
Landsat 8 Data Users Handbookhttp://landsat.gsfc.nasa.gov/?p=10659, USGS Publication LSDS-1574 (US Geological Survey, 2016)
Woodcock, C. E. et al. Free access to Landsat imagery. Science 320, 1011 (2008)
Wulder, M. A. et al. Opening the archive: how free data has enabled the science and monitoring promise of Landsat. Remote Sens. Environ. 122, 2–10 (2012)
Landsat 7 Science Data Users Handbookhttp://landsathandbook.gsfc.nasa.gov/orbit_coverage/prog_sect5_2.html (NASA, accessed 16 November 2016)
Markham, B. L., Storey, J. C., Williams, D. L. & Irons, J. R. Landsat sensor performance: history and current status. IEEE Trans. Geosci. Remote Sens. 42, 2691–2694 (2004)
Chen, J. et al. A simple and effective method for filling gaps in Landsat ETM+ SLC-off images. Remote Sens. Environ. 115, 1053–1064 (2011)
Goward, S. et al. Historical record of Landsat global coverage. Photogramm. Eng. Remote Sensing 72, 1155–1169 (2006)
Loveland, T. R. & Dwyer, J. L. Landsat: building a strong future. Remote Sens. Environ. 122, 22–29 (2012)
Gutman, G. et al. Assessment of the NASA–USGS global land survey (GLS) datasets. Remote Sens. Environ. 134, 249–265 (2013)
Arvidson, T., Gasch, J. & Goward, S. N. Landsat 7’s long-term acquisition plan—an innovative approach to building a global imagery archive. Remote Sens. Environ. 78, 13–26 (2001)
Arst, H. Optical Properties and Remote Sensing of Multicomponental Water Bodies Vol. XII of Marine Science and Coastal Management Ch. 1 (Springer Science Praxis, 2003)
Lu, D. & Weng, Q. A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 28, 823–870 (2007)
Kartikeyan, B., Majumder, K. L. & Dasgupta, A. R. An expert system for land cover classification. IEEE Trans. Geosci. Remote Sens. 33, 58–66 (1995)
Shoshany, M. Knowledge based expert systems in remote sensing task: quantifying gains from intelligent inference. Int. Soc. Photogramm. Remote Sens. Arch. XXXVII (B7) 1085–1088, http://www.isprs.org/proceedings/XXXVII/congress/7_pdf/6_WG-VII-6/06.pdf (XXIst ISPRS Congress, Technical Commission VII, 2008)
Keim, D. A. et al. in Visual Data Mining 76–90, http://kops.uni-konstanz.de/bitstream/handle/123456789/5631/Visual_Analytics_Scope_and_Challenges.pdf?sequence=1&isAllowed=y (Springer, 2008)
Yang, J.-B. & Xu, D. L. On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty. IEEE Trans. Syst. Man Cybern. A 32, 289–304 (2002)
Smith, A. R. Color gamut transform pairs. Comput. Graph. 12, 12–19 (1978)
Pekel, J.-F. et al. A near real-time water surface detection method based on HSV transformation of MODIS multi-spectral time series data. Remote Sens. Environ. 140, 704–716 (2014)
Roberts, J. C. in Coordinated and Multiple Views in Exploratory Visualization (CMV'07 Fifth Int. Conf.) 61–71, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4269947&isnumber=4269933 (IEEE, 2007)
Delaunay, B. Sur la sphere vide. Bull. Acad. Sci. USSR 7, 793–800, http://www.mathnet.ru/links/bf140e013bb2829a727614ee4e41051a/im4937.pdf (1934)
Arendt, A. et al. Randolph Glacier Inventory—A Dataset of Global Glacier Outlines: Version 5.0 http://www.glims.org/RGI/ (Global Land Ice Measurements from Space, Digital Media, 2015)
Pesaresi, M. et al. A global human settlement layer from optical HR/VHR RS data: concept and first results. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6, 2102–2131 (2013)
Pesarese, M. et al. Operating Procedure for the Production of the Global Human Settlement Layer from Landsat Data of the Epochs 1975, 1990, 2000, and 2014 http://publications.jrc.ec.europa.eu/repository/handle/JRC97705 (Publications Office of the European Union, 2016)
Global 30-Arc Second Elevation Data Set (GTOPO30)https://lta.cr.usgs.gov/GTOPO30 (Department of the Interior, USGS, 1996)
Danielson, J. J. & Gesch, D. B. Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010) . USGS Report 2011–1073, https://pubs.er.usgs.gov/publication/ofr20111073 (USGS Publications Warehouse, 2011)
Jarvis, A., Reuter, H. I., Nelson, A. & Guevara, E. Hole-filled SRTM for the Globe Version 4 http://srtm.csi.cgiar.org (CGIAR-CSI SRTM 90m Database, 2008)
Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Globalhttps://lta.cr.usgs.gov/SRTM1Arc (Land Processes Distributed Active Archive Center (LP DAAC), USGS/EROS, accessed November 2016)
Zhu, Z. & Woodcock, C. E. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 118, 83–94 (2012)
Seipp, K., Ochoa, X., Gutiérrez, F. & Verbert, K. A research agenda for managing uncertainty in visual analytics. Gesellsch. Inform. 1–10 (Human Factors in Information Visualization and Decision Support Systems (HFIDSS), Mensch und Computer Workshopband, 2016)
Shuttle Radar Topography Mission Water Body Datahttps://lta.cr.usgs.gov/srtm_water_body_dataset (SRTM Water Body Data (SWBD), 2003)
Global Administrative Areas (GADM) version 2.6, https://uwaterloo.ca/library/geospatial/collections/us-and-world-geospatial-data-resources/global-administrative-areas-gadm (Univ. Berkeley, Museum of Vertebrate Zoology and the International Rice Research Institute, 2012)
Zhang, Y., Li, B. & Zheng, D. Datasets of the boundary and area of the Tibetan Plateau. Glob. Change Res. Data Publ. Repository http://www.geodoi.ac.cn/weben/doi.aspx?Id=135 (2014)
Papa, F. et al. Interannual variability of surface water extent at the global scale, 1993–2004. J. Geophys. Res. 115, D12 (2010)
Klein, I. et al. Results of the Global WaterPack: a novel product to assess inland water body dynamics on a daily basis. Remote Sens. Lett . 6, 78–87 (2015)
Belward, A. S. & Skøien, J. O. Who launched what, when and why; trends in Global Land-Cover Observation capacity from civilian Earth Observation satellites. ISPRS J. Photogramm. Remote Sens . 103, 115–128 (2015)
Cohen, W. B. & Goward, S. N. Landsat’s role in ecological applications of remote sensing. Bioscience 54, 535–545 (2004)
The USGS and NASA provided the Landsat imagery. R. Moore and her team provided the Google Earth Engine. R. Sargent and P. Dille from Carnegie Mellon University built the web interface to the global surface water occurrence maps, and M. Clerici and J. van ‘t Klooster built the web processing interface.
The authors declare no competing financial interests.
Reviewer Information Nature thanks I. Klein and D. Yamazaki for their contribution to the peer review of this work.
Extended data figures and tables
Extended Data Figure 1 Geographic and temporal coverage of the Landsat 5, 7 and 8 L1T archive between 16 March 1984 and 10 October 2015.
a, Total number of unique views. b, First year of imaging. c, Number of scenes per month and year.
a, Hue (from SWIR2, NIR, red) versus NDVI. b, Hue versus Value (both from SWIR2, NIR, red). c, Hue versus Saturation (both from SWIR2, NIR, red). d, Hue versus Value (both from NIR, green, blue).
a, Omission error protocol. b, Commission error protocol.
a, Examples of increasing surface water occurrence in Myanmar (see inset for regional context). b, Pixel-based temporal profiles showing recurrence by month over 32 years (top), water history by seasonality class and by year over 32 years (middle) and monthly water presence for each year in the water seasonality record, in this case 2011 (bottom). Collectively, the graphs show that at this location (latitude 18.3928°, longitude 96.2633°) there are no valid observations available for the period 1984–1986, in 1993, 1997 or 1998 (the gaps in the middle graph), that before 2011 this was dry land, that the dam formed in 2011 and this point was flooded sometime between April and September (bottom), but since then it has been permanent water (centre), and that in the 32 years of observation water has not been detected in June (no observations have been made in June since the dam filled (top)).
a, Surface water seasonality between October 2014 and October 2015 in the Sundarbans in Bangladesh (see inset). b, Changes in inter-annual persistence between 1984 and 2015. The increase in permanent surface water at the expense of seasonal is indicative of changes in land use from seasonally flooded paddy fields to permanently flooded fishponds.
The regional context is shown in the insets. River channel migration, changes to seasonal water across the floodplain and transitions from permanent to seasonal water (‘New seasonal’) and seasonal to permanent water (‘New permanent’), visible in the figure, are symptomatic of habitat fragmentation and changing ecosystem service delivery.
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Pekel, JF., Cottam, A., Gorelick, N. et al. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016). https://doi.org/10.1038/nature20584
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