In the Arctic waterbodies are abundant and rapid thaw of permafrost is destabilizing the carbon cycle and changing hydrology. It is particularly important to quantify and accurately scale aquatic carbon emissions in arctic ecosystems. Recently available high-resolution remote sensing datasets capture the physical characteristics of arctic landscapes at unprecedented spatial resolution. We demonstrate how machine learning models can capitalize on these spatial datasets to greatly improve accuracy when scaling waterbody CO2and CH4fluxes across the YK Delta of south-west AK. We found that waterbody size and contour were strong predictors for aquatic CO2emissions, attributing greater than two-thirds of the influence to the scaling model. Small ponds (<0.001 km2) were hotspots of emissions, contributing fluxes several times their relative area, but were less than 5% of the total carbon budget. Small to medium lakes (0.001–0.1 km2) contributed the majority of carbon emissions from waterbodies. Waterbody CH4emissions were predicted by a combination of wetland landcover and related drivers, as well as watershed hydrology, and waterbody surface reflectance related to chromophoric dissolved organic matter. When compared to our machine learning approach, traditional scaling methods that did not account for relevant landscape characteristics overestimated waterbody CO2and CH4emissions by 26%–79% and 8%–53% respectively. This study demonstrates the importance of an integrated terrestrial-aquatic approach to improving estimates and uncertainty when scaling C emissions in the arctic.
Arctic regions are experiencing rapid warming, leading to permafrost thaw and formation of numerous water bodies. Although small ponds in particular are considered hot spots for methane (CH4) release, long‐term studies of CH4efflux from these surfaces are rare. We have collected an extensive data set of CH4ebullition (bubbling) measurements from eight small thaw ponds (<0.001 km2) with different physical and hydrological characteristics over four summer seasons, the longest set of observations from thaw ponds to date. The measured fluxes were highly variable with an average of 20.0 mg CH4· m−2· day−1(median: 4.1 mg CH4· m−2· day−1,
- NSF-PAR ID:
- 10375267
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Biogeosciences
- Volume:
- 124
- Issue:
- 7
- ISSN:
- 2169-8953
- Page Range / eLocation ID:
- p. 2208-2221
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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