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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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.
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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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