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Permafrost thaw drives surface water decline across lake-rich regions of the Arctic

Matters Arising to this article was published on 05 October 2023

Abstract

Lakes constitute 20–40% of Arctic lowlands, the largest surface water fraction of any terrestrial biome. These lakes provide crucial habitat for wildlife, supply water for remote Arctic communities and play an important role in carbon cycling and the regional energy balance. Recent evidence suggests that climate change is shifting these systems towards long-term wetting (lake formation or expansion) or drying. The net direction and cause of these shifts, however, are not well understood. Here, we present evidence for large-scale drying across lake-rich regions of the Arctic over the past two decades (2000–2021), a trend that is correlated with increases in annual air temperature and autumn rain. Given that increasing air temperatures and autumn rain promote permafrost thaw, our results indicate that permafrost thaw is leading to widespread surface water decline, challenging models that do not predict a net decrease in lake area until the mid-twenty-first or twenty-second centuries.

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Fig. 1: Change in the average July superfine water index (yr−1) from 2000 to 2021.
Fig. 2: Pixel-wise surface water trends (change in the July SWI (yr−1) from 2000 to 2021).
Fig. 3: Relative importance of predictor variables in explaining surface water trends across the study region.
Fig. 4: Predictor variables contributing the most to surface water trends in 12 km pixels, as determined using Shapley values.

Data availability

The analysis in this study relied on datasets from the following sources, all of which are freely available to the public. The climate trends were generated using Copernicus Climate Change Service Information [2022] ERA5-Land hourly data (2 m temperature, total evaporation, snowmelt, snowfall and total precipitation) (https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.e2161bac?tab=overview). The surface water trends were generated using the MODIS MCD43A4.006 product (https://doi.org/10.5067/MODIS/MCD43A4.006). Snow cover trends were generated using the MODIS MOD10A1.006 product (https://doi.org/10.5067/MODIS/MOD10A1.006). Fire masking was based on MODIS MCD64A1.006 (https://doi.org/10.5067/MODIS/MCD64A1.006), fire perimeters for eastern Siberia taiga and tundra (https://arcticdata.io/catalog/view/doi%3A10.18739%2FA2N87311N), the Canadian National Forest Service National Fire Database fire perimeters (http://cwfis.cfs.nrcan.gc.ca/datamart/metadata/nfdbpoly) and the Alaska Interagency Coordination Center Wildland Fire Maps (https://fire.ak.blm.gov/predsvcs/maps.php). Land cover masking was based on the ESA CCI land cover 2015 product (https://www.esa-landcover-cci.org/?q=node/164). Permafrost extent, ground cover content and overburden thickness data were from the Circum-Arctic Map of Permafrost and Ground-Ice Conditions v.2 (https://doi.org/10.7265/skbg-kf16). Lake cover percentage was from the Boreal-Arctic Wetland and Lake dataset (https://arcticdata.io/catalog/view/doi:10.18739/A2C824F9X). Thermokarst lake and thermokarst wetland coverage was from the Arctic Circumpolar Distribution and Soil Carbon of Thermokarst Landscapes dataset (https://doi.org/10.3334/ORNLDAAC/1332). Surface water trends generated for this study are archived through the Arctic Data Center (https://doi.org/10.18739/A2037V). Source data are provided with this paper.

Code availability

Google Earth Engine code used to calculate surface water and climate variable trends and Python code used to perform machine learning analysis are available on GitHub (https://github.com/webb-e/Pan-Arctic-SWchange).

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Acknowledgements

This work was supported by NASA FINESST Award 80NSSC19K134 to E.W. and J.L., NSF Awards OPP-1722572/OPP-2051888 to A.L. and NSF awards RISE-1927872/ICER-1927723/ICER-2052107 to A.L. and C.W.

Author information

Authors and Affiliations

Authors

Contributions

E.W. developed the concept and designed the analysis with assistance from A.L., M.L. and J.L. E.W. performed the main-text analyses. J.C. and C.W. assisted with validation analyses. E.W. wrote the manuscript with assistance from J.L. and all authors edited the manuscript.

Corresponding author

Correspondence to Elizabeth E. Webb.

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Nature Climate Change thanks Rebecca Finger-Higgens, J. (Ko) van Huissteden and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Trends in annual air temperature and fall rain across the study region.

Trends are derived from the ERA5-Land reanalysis dataset30 for 2000–2021 (annual air temperature) and 1999–2020 (autumn rain). Generated using Copernicus Climate Change Service Information [2022].

Source data

Extended Data Fig. 2 Partial dependence plots for the effect of annual temperature trends and autumn rain trends on surface water trends.

These plots show that after controlling for variation in other predictors, increasing annual temperature and increasing autumn rain both lead to decreases in surface water at a given location. The line shows the partial dependence relationships, and the histogram shows the frequency distribution of 12 km pixels.

Source data

Extended Data Fig. 3 Predictor variables, other than changes in annual air temperature and autumn rain, contributing the most to surface water trends, as determined using Shapley values.

Pixels in this figure are marked as ‘other’ in Fig. 4.

Source data

Extended Data Fig. 4 Circumpolar distribution of continuous and discontinuous permafrost extent, ground ice content, thermokarst lake coverage, and thermokarst wetland coverage.

‘High to very high’ and ‘low to moderate’ indicate fractional coverage of wetlands and lakes (high to very high: 30–100%; low to moderate: 1–30%). Permafrost extent and ground ice content is from ref. 67 and thermokarst lake and wetland coverage is from ref. 66.

Source data

Extended Data Fig. 5 Change in the average July superfine water index (yr-1) from 2000 to 2021 across the entire northern permafrost zone.

As in Fig. 1, but trends are calculated over the entire northern permafrost region (lake-rich and non-lake-rich areas of the continuous and discontinuous permafrost north of 50° N) rather than only in lake-rich regions.

Source data

Extended Data Fig. 6 Relative importance of predictor variables in explaining surface water trends across the entire northern permafrost zone.

As in Fig. 3, but relative importance is calculated based on trends and geospatial data from across the entire northern permafrost region (lake-rich and non-lake-rich areas of the continuous and discontinuous permafrost north of 50° N) rather than only in lake-rich regions.

Source data

Supplementary information

Supplementary Information

SWI validation analysis, Supplementary Figs. 1–3 and Tables 1–3.

Source data

Source Data Fig. 1

Graphical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Graphical source data.

Source Data Fig. 4

Shapley values for each variable at each pixel.

Source Data Extended Data Fig. 1

Graphical source data (band 1, autumn temperature; band 2, annual temperature).

Source Data Extended Data Fig. 2

Graphical source data.

Source Data Extended Data Fig. 3

Shapley values for each variable at each pixel.

Source Data Extended Data Fig. 4

Graphical source data.

Source Data Extended Data Fig. 5

Graphical source data.

Source Data Extended Data Fig. 6

Graphical source data.

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Webb, E.E., Liljedahl, A.K., Cordeiro, J.A. et al. Permafrost thaw drives surface water decline across lake-rich regions of the Arctic. Nat. Clim. Chang. 12, 841–846 (2022). https://doi.org/10.1038/s41558-022-01455-w

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