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Abstract Sea level rise and more frequent and larger storms will increase saltwater flooding in coastal terrestrial ecosystems, altering soil‐atmosphere CO2and CH4exchange. Understanding these impacts is particularly relevant in high‐latitude coastal soils that hold large carbon stocks but where the interaction of salinity and moisture on greenhouse gas flux remains unexplored. Here, we quantified the effects of salinity and moisture on CO2and CH4fluxes from low‐Arctic coastal soils from three landscape positions (two Wetlands and Upland Tundra) distinguished by elevation, flooding frequency, soil characteristics, and vegetation. We used a full factorial laboratory incubation experiment of three soil moisture levels (40%, 70%, or 100% saturation) and four salinity levels (freshwater, 3, 6, or 12 ppt). Salinity and soil moisture were important controls on CO2and CH4emissions across all landscape positions. In saturated soil, CO2emissions increased with salinity in the lower elevation landscape positions but not in the Upland Tundra soil. Saturated soil was necessary for large CH4emissions. CH4emissions were greatest with low salinity, or after 11 weeks of incubation when SO42−was exhausted allowing for methanogenesis as the dominant mechanism of anaerobic respiration. In partially saturated soil, greater salinity suppressed CO2production in all soils. CH4fluxes were overall quite low, but increased between 3 and 6 ppt in the Tundra. In the future, a small increase in floodwater salinity may increase CO2production while suppressing CH4production; however, where water is impounded, CH4production could become large, particularly in the landscapes most likely to flood.
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Scaling waterbody carbon dioxide and methane fluxes in the arctic using an integrated terrestrial-aquatic approach
Abstract 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.
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- Award ID(s):
- 1915307
- PAR ID:
- 10496479
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 18
- Issue:
- 6
- ISSN:
- 1748-9326
- Page Range / eLocation ID:
- 064019
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1088/1748-9326/acd467
