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.
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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).
Roe, G. H., Baker, M. B. & Herla, F. Centennial glacier retreat as categorical evidence of regional climate change. Nat. Geosci. 10, 95–99 (2017).
Haritashya, U. K. et al. Evolution and controls of large glacial lakes in the Nepal Himalaya. Remote Sens. 10, 798 (2018).
Wilson, R. et al. Glacial lakes of the Central and Patagonian Andes. Glob. Planet. Change 162, 275–291 (2018).
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).
Clague, J. J. & Evans, S. G. Formation and Failure of Natural dams in the Canadian Cordillera (Geological Survey of Canada, 1994).
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).
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).
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).
Watson, C. S. et al. Mass loss from calving in Himalayan proglacial lakes. Front. Earth Sci. 7, 342 (2020).
Truffer, M. & Motyka, R. Where glaciers meet water: subaqueous melt and its relevance to glaciers in various settings. Rev. Geophys. 54, 220–239 (2016).
Bolch, T. et al. The state and fate of Himalayan glaciers. Science 336, 310–314 (2012).
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).
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).
Harrison, S. et al. Climate change and the global pattern of moraine-dammed glacial lake outburst floods. Cryosphere 12, 1195–1209 (2018).
Shugar, D. H. et al. River piracy and drainage basin reorganization led by climate-driven glacier retreat. Nat. Geosci. 10, 370–375 (2017).
Carrivick, J. L. & Tweed, F. S. A global assessment of the societal impacts of glacier outburst floods. Glob. Planet. Change 144, 1–16 (2016).
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).
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).
Byers, A. et al. A rockfall-induced glacial lake outburst flood, upper Barun valley, Nepal. Landslides 16, 533–549 (2019).
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).
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).
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).
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).
Post, A. & Mayo, L. R. Glacier Dammed Lakes and Outburst Floods in Alaska (US Geological Survey, 1971).
Glacial Lakes and Glacial Lake Outburst Floods in Nepal (International Centre for Integrated Mountain Development, 2011).
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).
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).
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).
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).
Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
Scherler, D., Wulf, H. & Gorelick, N. Global assessment of supraglacial debris cover extents. Geophys. Res. Lett. 45, 11,798–11,805 (2018).
Huss, M. & Hock, R. Global scale hydrological response to future glacier mass loss. Nat. Clim. Change 8, 135–140 (2018).
IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).
Cook, S. J. & Quincey, D. J. Estimating the volume of Alpine glacial lakes. Earth Surf. Dynam. 3, 559–575 (2015).
Cogley, J. G. et al. Glossary of Glacier Mass Balance and Related Terms IHP-VII Technical Documents in Hydrology No. 86 (UNESCO/IHP, 2011).
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).
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).
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).
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).
Serreze, M. C. & Barry, R. G. Processes and impacts of Arctic amplification: a research synthesis. Glob. Planet. Change 77, 85–96 (2011).
Arendt, A. et al. Randolph Glacier Inventory—A Dataset of Global Glacier Outlines: Version 5.0 (Global Land Ice Measurements from Space, 2015).
Kirschbaum, D. et al. The state of remote sensing capabilities of cascading hazards over High Mountain Asia. Front. Earth Sci. 7, 197 (2019).
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).
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).
Post, E. et al. The polar regions in a 2 °C warmer world. Sci. Adv. 5, eaaw9883 (2019).
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).
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).
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
Shrestha, A. B. et al. Glacial lake outburst flood risk assessment of Sun Koshi basin, Nepal. Geomat. Nat. Hazards Risk 1, 157–169 (2010).
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).
Palmer, J. The dangers of glacial lake floods: pioneering and capitulation. EOS https://doi.org/10.1029/2019EO116807 (2019).
Final Environmental Impact Statement: Proposed Trans-Alaska Pipeline (US Department of the Interior, 1972).
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).
Bhambri, R. et al. The hazardous 2017–2019 surge and river damming by Shispare Glacier, Karakoram. Sci. Rep. 10, 4685 (2020).
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).
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).
Cook, S. J. & Swift, D. A. Subglacial basins: their origin and importance in glacial systems and landscapes. Earth Sci. Rev. 115, 332–372 (2012).
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
ArcGIS Pro Version 2.4.2 (Esri Inc., 2019); https://www.esri.com/en-us/arcgis/products/arcgis-pro/
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).
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).
Nie, Y., Liu, Q. & Liu, S. Glacial lake expansion in the central Himalayas by Landsat images, 1990–2010. PLoS ONE 8, e83973 (2013).
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).
Zhu, Z. et al. Benefits of the free and open Landsat data policy. Remote Sens. Environ. 224, 382–385 (2019).
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).
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).
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
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).
Global Land Ice Measurements from Space Glacier Database (NSIDC, 2005); https://doi.org/10.7265/N5V98602
Korzeniowska, K. & Korup, O. Object-based detection of lakes prone to seasonal ice cover on the Tibetan Plateau. Remote Sens. 9, 339 (2017).
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).
R Core Team. R: A Language and Environmental for Statistical Computing v3.5.1 (2018); www.R-project.org
Shugar, D. H. High Mountain Asia Near-Global Multi-Decadal Glacial Lake Inventory (2020); https://doi.org/10.5067/UO20NYM3YQB4
Kennedy, M. C. mkenn/GlacialLakeMC: GlacialLakeMC (Zenodo, 2020); https://doi.org/10.5281/zenodo.3890307
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).
The authors declare no competing interests.
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.
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).
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.
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.
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.
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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
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