The Importance of Lake Emergent Aquatic Vegetation for Estimating Arctic‐Boreal Methane Emissions

Abstract

Areas of lakes that support emergent aquatic vegetation emit disproportionately more methane than open water but are under‐represented in upscaled estimates of lake greenhouse gas emissions. These shallow areas are typically less than ∼1.5 m deep and can be detected with synthetic aperture radar (SAR). To assess the importance of lake emergent vegetation (LEV) zones to landscape‐scale methane emissions, we combine airborne SAR mapping with field measurements of vegetated and open‐water methane flux. First, we use Uninhabited Aerial Vehicle SAR data from the NASA Arctic‐Boreal Vulnerability Experiment to map LEV in 4,572 lakes across four Arctic‐boreal study areas and find it comprises ∼16% of lake area, exceeding previous estimates, and exhibiting strong regional differences (averaging 59 [50–68]%, 22 [20–25]%, 1.0 [0.8–1.2]%, and 7.0 [5.0–12]% of lake areas in the Peace‐Athabasca Delta, Yukon Flats, and northern and southern Canadian Shield, respectively). Next, we account for these vegetated areas through a simple upscaling exercise using paired methane fluxes from regions of open water and LEV. After excluding vegetated areas that could be accounted for as wetlands, we find that inclusion of LEV increases overall lake emissions by 21 [18–25]% relative to estimates that do not differentiate lake zones. While LEV zones are proportionately greater in small lakes, this relationship is weak and varies regionally, underscoring the need for methane‐relevant remote sensing measurements of lake zones and a consistent criterion for distinguishing wetlands. Finally, Arctic‐boreal lake methane upscaling estimates can be improved with more measurements from all lake zones.

High Voltage: The Molecular Properties of Redox-Active Dissolved Organic Matter in Northern High-Latitude Lakes

“…10,35,38 DOM electrochemical properties generally increased across the Arctic-Boreal region from forested catchments in the PAD and Yellowknife to Daring, which is underlain by continuous permafrost, surrounded by sparse vegetation, and lacks emergent vegetation. 43 This follows similar patterns that were observed across a broader region, where lakes from Daring had low DOC and greater aromaticity than other regions due to its greater hydrologic connectivity with the landscape and lower autochthonous input. 46 These observations differ from previous findings where DOM ambient reducing capacity was greater in lakes from forested catchments and lower in nonforested lakes.…”

“…Furthermore, EDC and the proportion of EDC originating from aromaticity, EDC:SUVA 254 , was similar between Wekweeti ànd Daring but higher than in DOM from the forested regions (Table S1) and aquatic humic substances. 27 This illustrates a greater contribution of phenolic content in Wekweeti ̀and Daring compared to the other lakes which contain more autochthonous DOM, and DOM from emergent vegetation 43,46 even though phenolic moieties typically undergo greater oxidative degradation in terrestrial DOM than aquatic DOM. 27,28 Despite their similarities, EAC was lower in Wekweeti ̀than Daring (Figure 1A), suggesting that although DOM from both these regions is mainly terrestrial, Daring receives a grater input of quinone moieties from surrounding permafrost soils, similar to organic soils containing greater EAC than mineral soils.…”

Monitoring 13 years of drastic catchment change and the hydroecological responses of a drained thermokarst lake

“…This analysis should be expanded upon using available radar data sets (e.g., Radarsat-2), which can effectively penetrate emergent vegetation (White et al 2014;Montgomery et al 2019). Products acquired during the ABoVE (2017-2020) airborne campaigns (e.g., AVIRIS-NG and Uninhabited Aerial Vehicle Synthetic Aperture Radar) are also refining the extent of inundation (Kyzivat et al 2022).…”

Using High‐Resolution Satellite Imagery and Deep Learning to Track Dynamic Seasonality in Small Water Bodies

“…Area alone is not always a good predictor of CH 4 emissions from water bodies (Kohnert et al, 2018;Rehder et al, 2021), the emission data set used in our upscaling is not necessarily representative of aquatic CH 4 emissions in our AOIs, and the PlanetScope water maps generated in this study do not identify wet vegetated areas that tend to produce higher CH 4 emissions (Kyzivat et al, 2022;Villa et al, 2021). However this example presents the first critical steps in incorporating the effects of ponds and their seasonality on regional aquatic CH 4 emissions and CH 4 hotspot occurrence.…”