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Remote sensing northern lake methane ebullition

Nature Climate Change volume 10, pages 511–517 (2020)Cite this article

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An Author Correction to this article was published on 31 July 2020

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Abstract

Northern lakes are considered a major source of atmospheric methane (CH4), a potent GHG1,2. However, large uncertainties in their emissions (7–26 Tg CH4 yr–1; ref. 2) arise from challenges in upscaling field data, including fluxes by ebullition (bubbling), the dominant emission pathway2. Remote sensing of ebullition would allow detailed mapping of regional emissions but has hitherto not been developed. Here, we show that lake ebullition can be imaged using synthetic aperture radar remote sensing during ice-cover periods by exploiting the effect of ebullition on the texture of the ice–water interface. Applying this method to five Alaska regions and combining spatial remote sensing information with year-round bubble-trap flux measurements, we create ebullition-flux maps for 5,143 Alaskan lakes. Regional lake CH4 emissions, based on satellite remote sensing analyses, were lower compared to previous estimates based on upscaling from individual lakes2,3 and were consistent with independent airborne CH4 observations. Thermokarst lakes formed by thaw of organic-rich permafrost had the highest fluxes, although lake density and lake size distributions also controlled regional emissions. This new remote sensing approach offers an opportunity to improve knowledge about Arctic CH4 fluxes and helps to explain long-standing discrepancies between estimates of CH4 emissions from atmospheric measurements and data upscaled from individual lakes.

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Fig. 1: Quantification of northern lake ebullition using L-band T11 SAR.
Fig. 2: SAR-based lake ebullition maps.
Fig. 3: Alaska regional lake sizes and CH4 fluxes.
Fig. 4: Lake-by-lake comparison of SAR-based versus airborne EC fluxes across the landscape within the SAR footprints for two geographic regions.

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Data availability

Source data for Figs. 1 and 3 and Extended Data Figs. 3–9 are presented with the paper. SAR data that support the findings of this study were used under license for this study and are available from the Alaska Satellite Facility (www.asf.alaska.edu) for non-commercial users. The SAR-based CH4 lake shapefiles generated during this study are available at NASA’s Distributed Active Archive Center for Biogeochemical Dynamics at the Oak Ridge National Laboratory (ONRL DAAC): 10.3334/ORNLDAAC/1790 (ref. 55).

Code availability

The eddy4R v0.2.0 eddy-covariance software framework used to generate airborne flux estimates is described in ref. 56 and can be freely accessed at https://github.com/NEONScience/eddy4R. The eddy4R Environmental Response Functions v0.0.5 advanced software module of ref. 49 was accessed under Terms of Use for this study (https://www.eol.ucar.edu/content/cheesehead-code-policy-appendix) and is available upon request.

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Acknowledgements

We thank S. Metzger for processing the airborne EC raw data and for advice on the machine-learning steps in creating the regional airborne EC flux map using the free eddy4R software framework. We thank the Alaska Satellite Facility for SAR data and processing tools, and the State of Alaska Division of Geological and Geophysical Surveys for high-resolution elevation data. We thank A. Bondurant, P. Anthony, D. Whiteman, L. Plug, C. Arp, B. Jones, L. Farquharson, A. Strohm, L. Oxtoby, M. Geai, L. McFadden, M. Smith and P. Lindgren for assistance in collecting field data. This study was part of the Arctic-Boreal Vulnerability Experiment and funded by NASA (grant nos. NNX15AU49A and NNN12AA01C) and National Science Foundation ARCSS grant no. 1500931. Airborne work was supported by the Helmholtz Association of German Research Centres through a Helmholtz Young Investigators Group grant to T.S. (grant no. VH-NG-821) and is a contribution to the Helmholtz Climate Initiative REKLIM (Regional Climate Change). The AirMeth campaigns were mainly funded by Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research. G.G. was additionally supported by ERC grant no. 338335.

Author information

Author notes

  1. A. Serafimovich

    Present address: Deutscher Wetterdienst, Offenbach, Germany

Authors and Affiliations

  1. Water and Environmental Research Center, University of Alaska Fairbanks, Fairbanks, AK, USA

    M. Engram & K. M. Walter Anthony

  2. International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK, USA

    K. M. Walter Anthony

  3. GFZ German Research Centre for Geosciences, Potsdam, Germany

    T. Sachs, K. Kohnert & A. Serafimovich

  4. Department of Experimental Limnology, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Stechlin, Germany

    K. Kohnert

  5. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Permafrost Research Center, Potsdam, Germany

    G. Grosse

  6. Institute of Geosciences, University of Potsdam, Potsdam, Germany

    G. Grosse

  7. Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA

    F. J. Meyer

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Contributions

M.J.E. and K.M.W.A. had primary responsibility for study design. K.M.W.A. and M.J.E. wrote the paper. K.M.W.A., M.J.E. and G.G. conducted field work. M.J.E. and K.M.W.A performed SAR and field-data analyses, respectively, and interpreted results. F.J.M. contributed to SAR polarimetric processing and interpretation, and helped with statistical analyses. T.S. was responsible for airborne EC measurements. A.S. performed mesoscale modelling and produced the regional airborne EC flux map. K.K. was responsible for geographic information system work involving airborne EC measurements. All authors contributed to the revision and integration of the manuscript.

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Correspondence to M. Engram.

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Extended data

Extended Data Fig. 1 Under-ice roughness is a function of prior ebullition.

Right hand panels are a focused view on areas of ice blocks shown in left hand panels. Arrows point to the top of the ice blocks. Thick black marks on the metre stick indicate 10 cm. Panels a and c show ice blocks harvested from boreal lakes with strong ebullition seeps, with close-up view of rough ice bottom (~ 4 cm deep) shown in panels b and d. Panel e and close-up f show an ice block harvested from an ebullition-free gravel pit pond where artificial ebullition was conducted as described in Engram et al.44, demonstrating cavities below gas bubbles are caused by uneven ice growth due to the thermal insulation of gas bubbles. Panel g shows a ‘wedding cake’ pattern of stacked bubbles that had partly vented to the atmosphere. In h, fingers shown for scale point to a crack in the ice, through which gas from the ice pocket periodically escaped. This wedding cake feature is depicted in Extended Data Fig. 2e.

Extended Data Fig. 2 Roughness caused by ice-trapped ebullition bubbles.

The main roughness phenomenon is caused by insulating bubbles leading to slower ice growth directly beneath the bubble column. The resulting upward cavities in the bottom of ice are filled by water (feature a), partly filled by gas (feature b), or completely filled by gas (feature c). Rough surfaces are shown by black arrows; arrow width depicts expected roughness intensity. Feature d shows a pause in ebullition (a weaker seep) that allows ice growth to ‘catch up’ with surrounding ice sheet. Feature e shows stacked bubbles from a strong seep in a ‘wedding cake’ formation. In this example, the feature has vented the gas to the atmosphere, resulting in multiple ridges and a near-vertical ice–water interface with strong SAR backscatter. Wedding cake cavities typically re-fill with gas and vent again in an alternating cycle. Real portions of dielectric constants for different media are represented by ɛ′.

Extended Data Fig. 3 L-band T11 SAR backscatter versus gas flux from ebullition as measured by field work.

Triangles are 48 training lakes from five geographic regions in Alaska, representing boreal and tundra ecosystems, yedoma and non-yedoma soils, thermokarst, kettle, and anthropogenic lakes resulting from gold or gravel extraction. The regression model for these lakes (backscatter = 1.0*10−3x + 0.019, r2 = 0.91, P < 0.01, n = 48) was inverted (Fig. 1b) to estimate ebullition in individual lakes and regional-scale lake maps. Dotted lines delineate 95% confidence interval. Circles are 16 validation lakes where additional field work was performed in 2016 and 2017 in two geographic regions in Alaska, showing good agreement with the model, but not included in the model.

Source data

Extended Data Fig. 4 Random subset results for the Alaska geographic study regions.

We show the frequency distributions of the lake-area-weighted mean ebullition fluxes of lakes randomly and repeatedly selected (with no replacement) from all lakes in the SAR scene. We iteratively selected the same number of lakes (n) that we had field data from for our study regions: (a) Barrow Peninsula, n = 5; (b) Atqasuk, n = 7; (c) Toolik n = 9; (d) northern Seward Peninsula n = 11; (e) Fairbanks Yedoma n = 13; (f) Fairbanks Anthropogenic, n = 3 from all lakes in each region, then calculated the regional flux based on the n lakes’ weighted mean SAR values. This approach revealed large variability in the regional lake ebullition estimates when using a small sample size of lakes. Data were binned in 0.5 L gas m−2 yr−1 bins. The blue star represents the field-based flux estimated through field work conducted on n specific lakes. The yellow star indicates the SAR-based flux from all lakes in the SAR scene (see Table 1). Note logarithmic scale for y-axis for the Atqasuk region.

Source data

Extended Data Fig. 5 Comparison of SAR-based (yellow) and field-based (blue) fluxes, weighted by area of each lake, alongside higher non-weighted field-based fluxes (red).

Box-plot centre line indicates median; box limits show upper and lower quartiles; whiskers show 1.5 x interquartile range, circles are outliers and asterisks are far-outliers. The scale in panel a omits four far-outliers (asterisks) for the northern Seward Peninsula (at 132, 133, 162, and 845 g CH4 m−2 yr−1) and one far-outlier for the Fairbanks Yedoma region (at 201 g CH4 m−2 yr−1); no near-outliers (open circles) were truncated. A subset of the data for Barrow Peninsula, Atqasuk and Toolik regions are re-projected on a finer scale in panel b.

Source data

Extended Data Fig. 6 Difference in airborne EC and SAR mean fluxes as a function of airborne EC coverage of a lake.

Circles represent individual lakes in the Barrow Peninsula region (a) and Atqasuk region (b). Difference values greater than zero (black line) indicate how much higher the airborne EC estimated flux for each lake was compared to the June–July SAR-estimated flux. Difference values lower than zero indicate SAR-based estimates higher than airborne EC-based estimates. All differences are plotted against the areal percentage of each lake covered with airborne EC data.

Source data

Extended Data Fig. 7 Difference in airborne EC mean flux and SAR mean flux as a function of lake size.

Circles represent individual lakes in the Barrow Peninsula region (a) and Atqasuk region (b). Difference values greater than zero (black line) indicate how much higher the airborne EC estimated flux for each lake was compared to the June/July SAR-estimated flux. Difference values lower than zero indicate SAR-based estimates higher than airborne EC-based estimates. Note different scale in x-axis for each region.

Source data

Extended Data Fig. 8 Results of airborne EC coverage test.

Three lakes (one each small, medium, large) were randomly selected for each region (Supplementary Discussion), then 25% of the pixels were randomly and stepwise removed to determine the effect of less airborne coverage on the whole-lake flux calculation using the Kruskal-Wallis test. Box-plot centre line indicates median; box limits show upper and lower quartiles; whiskers show 1.5 x interquartile range, dots are outliers. The decreased number of pixels did not lead to significantly different fluxes (alpha = 0.05) in either the Atqasuk region (a) or the Barrow Peninsula region (b).

Source data

Extended Data Fig. 9

Percent of contribution of lakes by SAR-based volumetric flux in L gas m−2 yr−1. Low emitting lakes contribute the majority of gas in all tundra regions and in Fairbanks Anthropogenic lakes, while low emitting lakes contribute < 20% in boreal lakes (Tanana River Flats and Fairbanks Yedoma).

Source data

Extended Data Fig. 10 Remote-sensing detection of within-lake variability in CH4 ebullition.

Panel a shows within-lake variability in backscatter values for a northern Seward Peninsula lakes in L-band T11 data acquired on November 14, 2009. Within-lake patterns of ebullition detected by SAR in panel a are also visible in high-resolution optical remote sensing data (Geoeye 1 10/21/2009) shown in panels b and c, in which white patches within-lake correspond to ebullition bubbles trapped in lake ice. White outlines i and iii show areas of high ebullition, both in SAR and in Geoeye; white outline ii shows area of low ebullition in both sensors16.

Supplementary information

Supplementary Information

Supplementary methods, discussion, references and Supplementary Tables 1–3.

Reporting Summary

Supplementary Data

Source data for Table 1 and Supplementary Tables 1–3.

Source data

Fig. 1

Source data for graph in Fig. 1b.

Fig. 3

Source data for graphs.

Extended Data Fig. 3

Source data for graph of empirical regression model.

Extended Data Fig. 4

Source data for histograms.

Extended Data Fig. 5

Source data for box-plots.

Extended Data Fig. 6

Source data for graphs.

Extended Data Fig. 7

Source data for graphs.

Extended Data Fig. 8

Statistical source data for box-plots.

Extended Data Fig. 9

Source data for graph.

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Engram, M., Walter Anthony, K.M., Sachs, T. et al. Remote sensing northern lake methane ebullition. Nat. Clim. Chang. 10, 511–517 (2020). https://doi.org/10.1038/s41558-020-0762-8

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