Letter

High-resolution mapping of global surface water and its long-term changes

  • Nature volume 540, pages 418422 (15 December 2016)
  • doi:10.1038/nature20584
  • Download Citation
Received:
Accepted:
Published:

Abstract

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.

  • Subscribe to Nature for full access:

    $199

    Subscribe

Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.

References

  1. 1.

    , , & Global water resources: vulnerability from climate change and population growth. Science 289, 284–288 (2000)

  2. 2.

    , & An improved lake model for climate simulations: model structure, evaluation, and sensitivity analyses in CESM1. J. Adv. Model. Earth Syst . 4, M02001 (2012)

  3. 3.

    & Large contribution to inland water CO2 and CH4 emissions from very small ponds. Nat. Geosci. 9, 222–226 (2016)

  4. 4.

    et al. State of the World’s Wetlands and Their Services to People: A Compilation of Recent Analyses. Ramsar Briefing Note No. 7 , (Ramsar Convention Secretariat, SSRN, 2015)

  5. 5.

    et al. in Millennium Ecosystem Assessment Vol. 1 Ecosystems and Human Well-being: Current State and Trends Ch. 7, 165–207, (Island Press, 2005)

  6. 6.

    World Economic Forum. The Global Risks Report 2016 11th edn, (World Economic Forum, 2016)

  7. 7.

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

  8. 8.

    et al. The global abundance and size distribution of lakes, ponds, and impoundments. Limnol. Oceanogr. 51, 2388–2397 (2006)

  9. 9.

    , , & A global inventory of lakes based on high-resolution satellite imagery. Geophys. Res. Lett. 41, 6396–6402 (2014)

  10. 10.

    , , & 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)

  11. 11.

    , & Development of a global ~90m water body map using multi-temporal Landsat images. Remote Sens. Environ. 171, 337–351 (2015)

  12. 12.

    et al. Changes in land surface water dynamics since the 1990s and relation to population pressure. Geophys. Res. Lett. 39, L08403 (2012)

  13. 13.

    et al. The global Landsat archive: status, consolidation, and direction. Remote Sens. Environ. 185, 271–283 (2016)

  14. 14.

    , , & Consistent increase in High Asia’s runoff due to increasing glacier melt and precipitation. Nat. Clim. Chang . 4, 587–592 (2014)

  15. 15.

    The future Aral Sea: hope and despair. Environ. Earth Sci . 75, 844 (2016)

  16. 16.

    The contribution of dams to Iran’s desertification. Int. J. Environ. Stud. 66, 327–341 (2009)

  17. 17.

    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)

  18. 18.

    Water, climate change, and sustainability in the southwest. Proc. Natl Acad. Sci. USA 107, 21256–21262 (2010)

  19. 19.

    et al. Water observations from space: mapping surface water from 25 years of Landsat imagery across Australia. Remote Sens. Environ. 174, 341–352 (2016)

  20. 20.

    , , & 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)

  21. 21.

    , & Human appropriation of renewable fresh water. Science 271, 785–788 (1996)

  22. 22.

    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 , (United Nations, 2015)

  23. 23.

    & Environmental challenges in trans-boundary waters, case study: Hamoon Hirmand Wetland (Iran and Afghanistan). Int. J. Wat. Resour. Arid Environ . 1, 16–24 (2011)

  24. 24.

    International Commission on Large Dams World Register (GIGB/ICOLD, 2016)

  25. 25.

    , & Kara-Bogaz-Gol Bay: physical and chemical evolution. Aquat. Geochem. 15, 223–236 (2009)

  26. 26.

    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)

  27. 27.

    & Rapid rise in effective sea-level in southwest Bangladesh: its causes and contemporary rates. Glob. Planet. Change 111, 237–245 (2013)

  28. 28.

    , & The increasing damming of the Paraná basin and its effects on the lower reaches. Regul. Rivers Res. Manage. 4, 333–346 (1989)

  29. 29.

    et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010)

  30. 30.

    et al. Why should we care about temporary waterways? Science 343, 1080–1081 (2014)

  31. 31.

    Landsat 8 Data Users Handbook USGS Publication LSDS-1574 (US Geological Survey, 2016)

  32. 32.

    et al. Free access to Landsat imagery. Science 320, 1011 (2008)

  33. 33.

    et al. Opening the archive: how free data has enabled the science and monitoring promise of Landsat. Remote Sens. Environ. 122, 2–10 (2012)

  34. 34.

    Landsat 7 Science Data Users Handbook (NASA, accessed 16 November 2016)

  35. 35.

    , , & Landsat sensor performance: history and current status. IEEE Trans. Geosci. Remote Sens. 42, 2691–2694 (2004)

  36. 36.

    et al. A simple and effective method for filling gaps in Landsat ETM+ SLC-off images. Remote Sens. Environ. 115, 1053–1064 (2011)

  37. 37.

    et al. Historical record of Landsat global coverage. Photogramm. Eng. Remote Sensing 72, 1155–1169 (2006)

  38. 38.

    & Landsat: building a strong future. Remote Sens. Environ. 122, 22–29 (2012)

  39. 39.

    et al. Assessment of the NASA–USGS global land survey (GLS) datasets. Remote Sens. Environ. 134, 249–265 (2013)

  40. 40.

    , & Landsat 7’s long-term acquisition plan—an innovative approach to building a global imagery archive. Remote Sens. Environ. 78, 13–26 (2001)

  41. 41.

    Optical Properties and Remote Sensing of Multicomponental Water Bodies Vol. XII of Marine Science and Coastal Management Ch. 1 (Springer Science Praxis, 2003)

  42. 42.

    & A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 28, 823–870 (2007)

  43. 43.

    , & An expert system for land cover classification. IEEE Trans. Geosci. Remote Sens. 33, 58–66 (1995)

  44. 44.

    Knowledge based expert systems in remote sensing task: quantifying gains from intelligent inference. Int. Soc. Photogramm. Remote Sens. Arch. XXXVII (B7) 1085–1088, (XXIst ISPRS Congress, Technical Commission VII, 2008)

  45. 45.

    et al. in Visual Data Mining 76–90, (Springer, 2008)

  46. 46.

    & On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty. IEEE Trans. Syst. Man Cybern. A 32, 289–304 (2002)

  47. 47.

    Color gamut transform pairs. Comput. Graph. 12, 12–19 (1978)

  48. 48.

    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)

  49. 49.

    in Coordinated and Multiple Views in Exploratory Visualization (CMV'07 Fifth Int. Conf.) 61–71, (IEEE, 2007)

  50. 50.

    Sur la sphere vide. Bull. Acad. Sci. USSR 7, 793–800, (1934)

  51. 51.

    et al. Randolph Glacier Inventory—A Dataset of Global Glacier Outlines: Version 5.0 (Global Land Ice Measurements from Space, Digital Media, 2015)

  52. 52.

    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)

  53. 53.

    et al. Operating Procedure for the Production of the Global Human Settlement Layer from Landsat Data of the Epochs 1975, 1990, 2000, and 2014 (Publications Office of the European Union, 2016)

  54. 54.

    Global 30-Arc Second Elevation Data Set (GTOPO30) (Department of the Interior, USGS, 1996)

  55. 55.

    & Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010) . USGS Report 2011–1073, (USGS Publications Warehouse, 2011)

  56. 56.

    , , & Hole-filled SRTM for the Globe Version 4 (CGIAR-CSI SRTM 90m Database, 2008)

  57. 57.

    Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global (Land Processes Distributed Active Archive Center (LP DAAC), USGS/EROS, accessed November 2016)

  58. 58.

    & Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 118, 83–94 (2012)

  59. 59.

    , , & 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)

  60. 60.

    Shuttle Radar Topography Mission Water Body Data (SRTM Water Body Data (SWBD), 2003)

  61. 61.

    Global Administrative Areas (GADM) version 2.6, (Univ. Berkeley, Museum of Vertebrate Zoology and the International Rice Research Institute, 2012)

  62. 62.

    , & Datasets of the boundary and area of the Tibetan Plateau. Glob. Change Res. Data Publ. Repository (2014)

  63. 63.

    et al. Interannual variability of surface water extent at the global scale, 1993–2004. J. Geophys. Res. 115, D12 (2010)

  64. 64.

    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)

  65. 65.

    & 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)

  66. 66.

    & Landsat’s role in ecological applications of remote sensing. Bioscience 54, 535–545 (2004)

Download references

Acknowledgements

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.

Author information

Affiliations

  1. European Commission, Joint Research Centre, Directorate for Sustainable Resources, 20127 Ispra, Lombardy, Italy

    • Jean-François Pekel
    • , Andrew Cottam
    •  & Alan S. Belward
  2. Google Switzerland GmbH, Brandschenkestrasse 110, 8002 Zürich, Switzerland

    • Noel Gorelick

Authors

  1. Search for Jean-François Pekel in:

  2. Search for Andrew Cottam in:

  3. Search for Noel Gorelick in:

  4. Search for Alan S. Belward in:

Contributions

Each author contributed extensively and indispensably to the work presented in this paper.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Jean-François Pekel.

Reviewer Information Nature thanks I. Klein and D. Yamazaki for their contribution to the peer review of this work.

Extended data

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains Supplementary Tables 1-2.

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.