Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Rapid worldwide growth of glacial lakes since 1990

Nature Climate Change volume 10, pages 939–945 (2020)Cite this article

Subjects

Abstract

Glacial lakes are rapidly growing in response to climate change and glacier retreat. The role of these lakes as terrestrial storage for glacial meltwater is currently unknown and not accounted for in global sea level assessments. Here, we map glacier lakes around the world using 254,795 satellite images and use scaling relations to estimate that global glacier lake volume increased by around 48%, to 156.5 km3, between 1990 and 2018. This methodology provides a near-global database and analysis of glacial lake extent, volume and change. Over the study period, lake numbers and total area increased by 53 and 51%, respectively. Median lake size has increased 3%; however, the 95th percentile has increased by around 9%. Currently, glacial lakes hold about 0.43 mm of sea level equivalent. As glaciers continue to retreat and feed glacial lakes, the implications for glacial lake outburst floods and water resources are of considerable societal and ecological importance.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Near-global glacial lake distribution and evolution.
Fig. 2: Glacial lake volume change, 1990–1999 to 2015–2018.
Fig. 3: Regional glacial lake volume changes, 1990–1999 to 2015–2018.
Fig. 4: Histograms of glacial lake elevation (numbers of lakes).

Similar content being viewed by others

Data availability

The complete lakes database is available at https://nsidc.org/data/HMA_GLI/versions/1 (ref. 73).

Code availability

Our Google Earth Engine script is available at https://code.earthengine.google.com/31a9acd31b65796a47f2823572c3307c. Scripts for Monte Carlo estimation of volume from lake area is available at https://github.com/mkenn/GlacialLakeMC.git (ref. 74).

References

  1. Roe, G. H., Baker, M. B. & Herla, F. Centennial glacier retreat as categorical evidence of regional climate change. Nat. Geosci. 10, 95–99 (2017).

    CAS  Google Scholar 

  2. Haritashya, U. K. et al. Evolution and controls of large glacial lakes in the Nepal Himalaya. Remote Sens. 10, 798 (2018).

    Google Scholar 

  3. Wilson, R. et al. Glacial lakes of the Central and Patagonian Andes. Glob. Planet. Change 162, 275–291 (2018).

    Google Scholar 

  4. Irvine-Fynn, T. D. L. et al. Supraglacial ponds regulate runoff from Himalayan debris-covered glaciers. Geophys. Res. Lett. 44, 11,894–11,904 (2017).

    Google Scholar 

  5. Clague, J. J. & Evans, S. G. Formation and Failure of Natural dams in the Canadian Cordillera (Geological Survey of Canada, 1994).

  6. Warren, C. R. & Kirkbride, M. P. Temperature and bathymetry of ice-contact lakes in Mount Cook National Park, New Zealand. N. Z. J. Geol. Geophys. 41, 133–143 (1998).

    Google Scholar 

  7. Shugar, D. H., Clague, J. J. & McSaveney, M. J. Late Holocene activity of Sherman and Sheridan glaciers, Prince William Sound, Alaska. Quat. Sci. Rev. 194, 116–127 (2018).

    Google Scholar 

  8. Chernos, M., Koppes, M. N. & Moore, R. D. Ablation from calving and surface melt at lake-terminating Bridge Glacier, British Columbia, 1984–2013. Cryosphere 10, 87–102 (2016).

    Google Scholar 

  9. Watson, C. S. et al. Mass loss from calving in Himalayan proglacial lakes. Front. Earth Sci. 7, 342 (2020).

    Google Scholar 

  10. Truffer, M. & Motyka, R. Where glaciers meet water: subaqueous melt and its relevance to glaciers in various settings. Rev. Geophys. 54, 220–239 (2016).

    Google Scholar 

  11. Bolch, T. et al. The state and fate of Himalayan glaciers. Science 336, 310–314 (2012).

    CAS  Google Scholar 

  12. Trüssel, B. L., Motyka, R. J., Truffer, M. & Larsen, C. F. Rapid thinning of lake-calving Yakutat Glacier and the collapse of the Yakutat Icefield, southeast Alaska, USA. J. Glaciol. 59, 149–161 (2013).

    Google Scholar 

  13. Kershaw, J. A., Clague, J. J. & Evans, S. G. Geomorphic and sedimentological signature of a two-phase outburst flood from moraine-dammed Queen Bess Lake, British Columbia, Canada. Earth Surf. Process. Landf. 30, 1–25 (2005).

    Google Scholar 

  14. Harrison, S. et al. Climate change and the global pattern of moraine-dammed glacial lake outburst floods. Cryosphere 12, 1195–1209 (2018).

    Google Scholar 

  15. Shugar, D. H. et al. River piracy and drainage basin reorganization led by climate-driven glacier retreat. Nat. Geosci. 10, 370–375 (2017).

    CAS  Google Scholar 

  16. Carrivick, J. L. & Tweed, F. S. A global assessment of the societal impacts of glacier outburst floods. Glob. Planet. Change 144, 1–16 (2016).

    Google Scholar 

  17. Schwanghart, W., Worni, R., Huggel, C., Stoffel, M. & Korup, O. Uncertainty in the Himalayan energy–water nexus: estimating regional exposure to glacial lake outburst floods. Environ. Res. Lett. 11, 074005 (2016).

    Google Scholar 

  18. Rounce, D., Watson, C. & McKinney, D. Identification of hazard and risk for glacial lakes in the Nepal Himalaya using satellite imagery from 2000–2015. Remote Sens. 9, 654 (2017).

    Google Scholar 

  19. Byers, A. et al. A rockfall-induced glacial lake outburst flood, upper Barun valley, Nepal. Landslides 16, 533–549 (2019).

    Google Scholar 

  20. Carey, M. et al. Toward hydro-social modeling: merging human variables and the social sciences with climate-glacier runoff models (Santa River, Peru). J. Hydrol. 518, 60–70 (2014).

    Google Scholar 

  21. Farinotti, D., Round, V., Huss, M., Compagno, L. & Zekollari, H. Large hydropower and water-storage potential in future glacier-free basins. Nature 575, 341–344 (2019).

    CAS  Google Scholar 

  22. Jha, L. K. & Khare, D. Detection and delineation of glacial lakes and identification of potentially dangerous lakes of Dhauliganga basin in the Himalaya by remote sensing techniques. Nat. Hazards 85, 301–327 (2016).

    Google Scholar 

  23. Prakash, C. & Nagarajan, R. Glacial lake changes and outburst flood hazard in Chandra basin, North-Western Indian Himalaya. Geomat. Nat. Hazards Risk 9, 337–355 (2018).

    Google Scholar 

  24. Post, A. & Mayo, L. R. Glacier Dammed Lakes and Outburst Floods in Alaska (US Geological Survey, 1971).

  25. Glacial Lakes and Glacial Lake Outburst Floods in Nepal (International Centre for Integrated Mountain Development, 2011).

  26. Wolfe, D. F. G., Kargel, J. S. & Leonard, G. J. in Global Land Ice Measurements from Space (eds Kargel, J. S., Leonard, G. J., Bishop, M. P., Kääb, A. & Raup, B.) 263–295 (Springer Praxis, 2014).

  27. Zhang, G., Yao, T., Xie, H., Wang, W. & Yang, W. An inventory of glacial lakes in the Third Pole region and their changes in response to global warming. Glob. Planet. Change 131, 148–157 (2015).

    Google Scholar 

  28. Chen, F., Zhang, M., Tian, B. & Li, Z. Extraction of glacial lake outlines in Tibet Plateau using Landsat 8 imagery and Google Earth Engine. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10, 4002–4009 (2017).

    Google Scholar 

  29. 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).

    CAS  Google Scholar 

  30. Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).

    Google Scholar 

  31. Scherler, D., Wulf, H. & Gorelick, N. Global assessment of supraglacial debris cover extents. Geophys. Res. Lett. 45, 11,798–11,805 (2018).

    Google Scholar 

  32. Huss, M. & Hock, R. Global scale hydrological response to future glacier mass loss. Nat. Clim. Change 8, 135–140 (2018).

    Google Scholar 

  33. IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  34. Cook, S. J. & Quincey, D. J. Estimating the volume of Alpine glacial lakes. Earth Surf. Dynam. 3, 559–575 (2015).

    Google Scholar 

  35. Cogley, J. G. et al. Glossary of Glacier Mass Balance and Related Terms IHP-VII Technical Documents in Hydrology No. 86 (UNESCO/IHP, 2011).

  36. Bamber, J. L., Westaway, R. M., Marzeion, B. & Wouters, B. The land ice contribution to sea level during the satellite era. Environ. Res. Lett. 13, 063008 (2018).

    Google Scholar 

  37. Lenaerts, J. T. M., Medley, B., Broeke, M. R. & Wouters, B. Observing and modeling ice sheet surface mass balance. Rev. Geophys. 57, 376–420 (2019).

    Google Scholar 

  38. 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).

    CAS  Google Scholar 

  39. Loriaux, T. & Casassa, G. Evolution of glacial lakes from the Northern Patagonia Icefield and terrestrial water storage in a sea-level rise context. Glob. Planet. Change 102, 33–40 (2013).

    Google Scholar 

  40. Serreze, M. C. & Barry, R. G. Processes and impacts of Arctic amplification: a research synthesis. Glob. Planet. Change 77, 85–96 (2011).

    Google Scholar 

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

  42. Kirschbaum, D. et al. The state of remote sensing capabilities of cascading hazards over High Mountain Asia. Front. Earth Sci. 7, 197 (2019).

    Google Scholar 

  43. Carey, M. Living and dying with glaciers: people’s historical vulnerability to avalanches and outburst floods in Peru. Glob. Planet. Change 47, 122–134 (2005).

    Google Scholar 

  44. Kargel, J. et al. in Land Remote Sensing and Global Environmental Change: NASA’s Earth Observing System and the Science of ASTER and MODIS (eds Ramachandran, B., Justice, C. O. & Abrams, M. J.) 325–373 (Springer, 2011).

  45. Post, E. et al. The polar regions in a 2 °C warmer world. Sci. Adv. 5, eaaw9883 (2019).

    CAS  Google Scholar 

  46. Cowtan, K. & Way, R. G. Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Q. J. Roy. Meteror. Soc. 140, 1935–1944 (2014).

    Google Scholar 

  47. Lala, J. M., Rounce, D. R. & McKinney, D. C. Modeling the glacial lake outburst flood process chain in the Nepal Himalaya: reassessing Imja Tsho’s hazard. Hydrol. Earth Syst. Sci. 22, 3721–3737 (2018).

    Google Scholar 

  48. Kargel, J. S. et al. The Community Based Flood and Glacial Lake Outburst Risk Reduction Project (CFGORRP) (United Nations Development Programme, 2014); http://cfgorrp.dhm.gov.np/wp-content/uploads/2016/03/9.-2015-Feb-UNDP-Imja-FinalReport-13Feb-jsk-commented-31-march-cleaned.pdf

  49. Shrestha, A. B. et al. Glacial lake outburst flood risk assessment of Sun Koshi basin, Nepal. Geomat. Nat. Hazards Risk 1, 157–169 (2010).

    Google Scholar 

  50. Khan, Z. Shishper Glacier near Hunza turned into a glacial lake outburst flood yesterday. Mashable Pakistan https://pk.mashable.com/science/3394/shishper-glacier-near-hunza-turned-into-a-glacial-lake-outburst-flood-yesterday (2020).

  51. Palmer, J. The dangers of glacial lake floods: pioneering and capitulation. EOS https://doi.org/10.1029/2019EO116807 (2019).

  52. Final Environmental Impact Statement: Proposed Trans-Alaska Pipeline (US Department of the Interior, 1972).

  53. Ashraf, A., Naz, R. & Roohi, R. Glacial lake outburst flood hazards in Hindukush, Karakoram and Himalayan Ranges of Pakistan: implications and risk analysis. Geomat. Nat. Hazards Risk 3, 113–132 (2012).

    Google Scholar 

  54. Bhambri, R. et al. The hazardous 2017–2019 surge and river damming by Shispare Glacier, Karakoram. Sci. Rep. 10, 4685 (2020).

    CAS  Google Scholar 

  55. Frey, H. et al. Multi-source glacial lake outburst flood hazard assessment and mapping for Huaraz, Cordillera Blanca, Peru. Front. Earth Sci. 6, 210 (2018).

    Google Scholar 

  56. Cook, S. J., Kougkoulos, I., Edwards, L. A., Dortch, J. & Hoffmann, D. Glacier change and glacial lake outburst flood risk in the Bolivian Andes. Cryosphere 10, 2399–2413 (2016).

    Google Scholar 

  57. Cook, S. J. & Swift, D. A. Subglacial basins: their origin and importance in glacial systems and landscapes. Earth Sci. Rev. 115, 332–372 (2012).

    Google Scholar 

  58. National Academies of Sciences, Engineering, and Medicine. Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space (The National Academies Press, 2018); https://doi.org/10.17226/24938

  59. ArcGIS Pro Version 2.4.2 (Esri Inc., 2019); https://www.esri.com/en-us/arcgis/products/arcgis-pro/

  60. McFeeters, S. K. The use of the normalized difference water index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 17, 1425–1432 (1996).

    Google Scholar 

  61. Bhardwaj, A. et al. A lake detection algorithm (LDA) using Landsat 8 data: a comparative approach in glacial environment. Int. J. Appl. Earth Obs. Geoinf. 38, 150–163 (2015).

    Google Scholar 

  62. Nie, Y., Liu, Q. & Liu, S. Glacial lake expansion in the central Himalayas by Landsat images, 1990–2010. PLoS ONE 8, e83973 (2013).

    Google Scholar 

  63. Rahaman, K. R., Hassan, Q. K. & Ahmed, M. R. Pan-sharpening of Landsat-8 images and its application in calculating vegetation greenness and canopy water contents. ISPRS Int. J. Geoinf. 6, 168 (2017).

    Google Scholar 

  64. Zhu, Z. et al. Benefits of the free and open Landsat data policy. Remote Sens. Environ. 224, 382–385 (2019).

    Google Scholar 

  65. Cooley, S. W., Smith, L. C., Ryan, J. C., Pitcher, L. H. & Pavelsky, T. M. Arctic-Boreal lake dynamics revealed using CubeSat imagery. Geophys. Res. Lett. 46, 2111–2120 (2019).

    Google Scholar 

  66. Stokes, C. R., Sanderson, J. E., Miles, B. W. J., Jamieson, S. S. R. & Leeson, A. A.Widespread distribution of supraglacial lakes around the margin of the East Antarctic Ice Sheet. Sci. Rep. 9, 13823 (2019).

    Google Scholar 

  67. Lea, J. & Brough, S. Greenland’s supraglacial lakes increase by a quarter in the last 20 years. In EGU General Assembly Conference Abstracts 17968 (2020); https://meetingorganizer.copernicus.org/EGU2020/EGU2020-17968.html

  68. Chander, G., Markham, B. L. & Helder, D. L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 113, 893–903 (2009).

    Google Scholar 

  69. Global Land Ice Measurements from Space Glacier Database (NSIDC, 2005); https://doi.org/10.7265/N5V98602

  70. Korzeniowska, K. & Korup, O. Object-based detection of lakes prone to seasonal ice cover on the Tibetan Plateau. Remote Sens. 9, 339 (2017).

    Google Scholar 

  71. Luoto, T. P., Rantala, M. V., Kivilä, E. H., Nevalainen, L. & Ojala, A. E. K. Biogeochemical cycling and ecological thresholds in a High Arctic lake (Svalbard). Aquat. Sci. 81, 34 (2019).

    Google Scholar 

  72. R Core Team. R: A Language and Environmental for Statistical Computing v3.5.1 (2018); www.R-project.org

  73. Shugar, D. H. High Mountain Asia Near-Global Multi-Decadal Glacial Lake Inventory (2020); https://doi.org/10.5067/UO20NYM3YQB4

  74. Kennedy, M. C. mkenn/GlacialLakeMC: GlacialLakeMC (Zenodo, 2020); https://doi.org/10.5281/zenodo.3890307

Download references

Acknowledgements

Support for this work was provided by NASA (NNX16AQ62G and 80NSSC19K0653) to J.S.K., U.K.H. and D.H.S., and by NSERC (Discovery Grant 2020-04207 and Discovery Accelerator Supplement 2020-00066) to D.H.S. Without free access of the Landsat data archive, this and many other scientific efforts would not have been possible. We thank NASA and the USGS for their continued dedication to catalysing science. The work of R.A.B. forms part of the UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101).

Author information

Author notes

  1. Dan H. Shugar

    Present address: Water, Sediment, Hazards and Earth-surface Dynamics (WaterSHED) Laboratory, Department of Geoscience, University of Calgary, Calgary, Alberta, Canada

  2. Katherine Strattman

    Present address: Department of Atmospheric Science, University of Alabama in Huntsville, Huntsville, AL, USA

Authors and Affiliations

  1. School of Interdisciplinary Arts and Sciences, University of Washington Tacoma, Tacoma, WA, USA

    Dan H. Shugar, Aaron Burr & Maureen C. Kennedy

  2. Department of Geology, University of Dayton, Dayton, OH, USA

    Umesh K. Haritashya & Katherine Strattman

  3. Planetary Science Institute, Tucson, AZ, USA

    Jeffrey S. Kargel

  4. COMET, School of Earth & Environment, University of Leeds, Leeds, UK

    C. Scott Watson

  5. British Columbia Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Government of British Columbia, Prince George, British Columbia, Canada

    Alexandre R. Bevington

  6. Global Systems Institute, University of Exeter, Exeter, UK

    Richard A. Betts

  7. Met Office Hadley Centre, Exeter, UK

    Richard A. Betts

  8. College of Life and Environmental Sciences, University of Exeter, Penryn, UK

    Stephan Harrison

Authors
  1. Dan H. Shugar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aaron Burr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Umesh K. Haritashya
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jeffrey S. Kargel
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. C. Scott Watson
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Maureen C. Kennedy
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Alexandre R. Bevington
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Richard A. Betts
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Stephan Harrison
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Katherine Strattman
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.H.S., J.S.K. and U.K.H. designed the study and are co-investigators on the NASA grant that funded the work. D.H.S. and A.B. designed and wrote the Google Earth Engine model with input from A.R.B., and performed the subsequent data analysis in ArcGIS Pro. C.S.W. provided expert opinion on glacial lake mapping. D.H.S. and A.B. performed the error analysis on the lake digitizing, while M.K. performed the volume–area scaling analysis and error assessment. R.B. and S.H. contributed interpretations of the data. K.S. provided manually digitized lake outlines against which to test the method. D.H.S. wrote the paper with input and editing from all authors. All authors, especially J.S.K., contributed substantially to the Discussion.

Corresponding author

Correspondence to Dan H. Shugar.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Simon Cook and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Point and interval estimates of total global glacial lake volume.

Note that the prediction intervals (vertical error bars) have some overlap, with a consistent positive trend. Vertical lines extend to the lower and upper bounds of the 95% Monte Carlo prediction interval. Horizontal dashed lines indicate the time span of each time step in the series.

Extended Data Fig. 2 Example of lake shrinkage with retreat of Barnes Ice Cap, Baffin Island, Canada.

Lakes visible in 1990–99 are in yellow (or dashed yellow in panel b), while lakes in 2015–18 are in white. In the 1990-99 mosaic shown in panel a, three large lakes (and one smaller) are visible, which by 2015-18 (mosaic in panel b) have changed markedly. The two larger northern lakes shrunk due to terminus retreat exposing an outlet, while the southern lake grew due to terminus retreat. Note that background images are multiyear mosaics constructed from Landsat imagery from 1990-99 (a) and 2015-18 (b).

Extended Data Fig. 3 Total glacial lake volume for all affected countries, for all years of record.

Vertical dashed line indicates a total volume of 1 km3, while dash-dot line indicates a total volume of 10 km3. Thirty-one countries have contained at least one glacial lake over our study period, but twenty-two country totals contain <1 km3. Volumetrically, the top five countries (Greenland/Denmark, Canada, Chile, United States, Argentina) contained 84% of the world’s glacial lake volume (135.5 km3), and each country held more than 10 km3 in 2015-18. With 42.7 km3 in 2015-18, Greenland/Denmark had more glacial lake storage than any other country, with just over a quarter of the world’s 2015-18 total (Fig. 3). Canadian lakes contained slightly less, with 36.9 km3; Chilean lakes contain 16% of the total (25.3 km3); while US lakes (mostly in Alaska) contain ~12% (18.8 km3). Argentina has the fifth highest-ranked glacial lake volume in the world, holding ~8% (11.9 km3) of the 2015-18 total, though if we include the three largest lakes, Argentina would likely be the top ranked country. Generally, lake volume by country increases with time, although there are exceptions.

Extended Data Fig. 4 Flowchart of processing steps for automated delineation of glacial lakes.

The NDWI and NDSI thresholds for each RGI region are described in Supplementary Data 1. Other thresholds applied in Google Earth Engine included surface temperature (>-1°C), slope (<40°), elevation (>5 m ASL), for each pixel. Any deviations from these values are reported in Supplementary Data 1. In the ArcGIS Pro processing chain, we used the ‘Eliminate Polygon Part’ donut-filling tool, and thresholds for area (0.05-200 km2), slope (<10°), and distance-to-glacier (<1 km) for each polygon.

Extended Data Fig. 5 Pixelwise Landsat mosaic (SWIR1-NIR-R) of the test area in Nepal/Tibet (2016-17).

Red dashed box in inset map shows approximate extent of main map, and black dashed box in main map shows extent of panels in Extended Data Fig. 6.

Extended Data Fig. 6 Results from steps in our processing chain for area outlined with black box in Extended Data Fig. 4.

Panel (a) shows all ‘lake’ polygons from the threshold NDWI/NDSI image (n = 5648 in full extent of Extended Data Fig. 4); (b) shows only those polygons with median slope <10° (n = 1930); (c) shows those polygons >0.05 km2 (n = 144); (d) compares the final lake polygons after being filtered for proximity to a glacier (n = 130) (in green) with manually digitized lake polygons (pink) (n = 140). Note the false positives in the northern part of the image. These were removed manually in the analyses presented in the Results but were included for the error analyses in Supplementary Data 1. Well-studied Imja Lake and Lower Barun Lake are labelled for reference. Background image is the RGB mosaic for 2015-2016 produced for the error analysis.

Extended Data Fig. 7 Pixelwise Landsat mosaic (SWIR1-NIR-R) of the test area in Greenland (2016-17).

Red dashed box in inset map shows approximate extent of main map, and black dashed box in main map shows extent of panels in Extended Data Fig. 5. Kangerlussuatsiaq Fjord and Maniitsoq ice cap are labelled for reference.

Extended Data Figure 8 Results from steps in our processing chain for area outlined with black box in Extended Data Fig. 7.

Panel (a) shows all ‘lake’ polygons from the threshold NDWI/NDSI image (n = 2112 in full extent of Extended Data Fig. 7); (b) compares the final lake polygons after being filtered for median slope <10°, area >0.05 km2 and proximity to a glacier (n = 36) with manually digitized lake polygons (pink) (n = 35), and RGI/IMBIE glacier outlines in white. Note the false positives preserved after filtering in Kangerlussuatsiaq Fjord, described in the text. These were removed manually in the analyses presented in the Results but were included for the error analyses in Supplementary Data 1. Background image is the RGB mosaic for 2016-2017 produced for the error analysis.

Extended Data Fig. 9 Summary of results for the demonstration regions (see Extended Data Fig. 4, 6).

a, Histogram of total lake count per area bin from automated optical (blue) and manual (red) methods for the HMA test region; (b) Histogram of total lake count per area bin from automated optical (blue) and manual (red) methods for the Greenland test region; (c) Comparison of lake area (km2) from automated optical against manual methods for both study areas. Vertical and horizontal error bars in (c) are per Haritashya et al.2. Note that the error analysis shown here (and in Supplementary Data 1) was performed prior to any manual modifications to the automatically mapped polygons. In other words, the raw but filtered output from the model was used. Data points on the X and Y axes represent lake polygons that either changed sufficiently to have different centroid coordinates, or else were not mapped in either the manual or automated procedures.

Extended Data Fig. 10

Training and test observed lake area and volume scaling for (a) lakes <0.50 km2 in area, and (b) lakes >0.50 km2 in area. Estimated models for Equation. 1 (log-log) and Equation. 2 (nls) are overlain on the points. Note that the models were estimated for the training data only. The log-log model better predicts volume of small lakes, but over predicts large lakes. The non-linear scaling model under predicts small lakes, but better predicts volume of large lakes. 95% confidence intervals for the final chosen model for each lake size are shown with dashed lines.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shugar, D.H., Burr, A., Haritashya, U.K. et al. Rapid worldwide growth of glacial lakes since 1990. Nat. Clim. Chang. 10, 939–945 (2020). https://doi.org/10.1038/s41558-020-0855-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-020-0855-4

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing