Small waterbodies have potentially high greenhouse gas emissions relative to their small footprint on the landscape, although there is high uncertainty in model estimates. Scaling their carbon dioxide (CO2) and methane (CH4) exchange with the atmosphere remains challenging due to an incomplete understanding and characterization of spatial and temporal variability in CO2and CH4. Here, we measured partial pressures of CO2(
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.
more » « less- Award ID(s):
- 1915307
- NSF-PAR ID:
- 10414558
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 18
- Issue:
- 6
- ISSN:
- 1748-9326
- Page Range / eLocation ID:
- Article No. 064019
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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