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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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Do beaver ponds increase methane emissions along Arctic tundra streams?
Abstract Beaver engineering in the Arctic tundra induces hydrologic and geomorphic changes that are favorable to methane (CH4) production. Beaver-mediated methane emissions are driven by inundation of existing vegetation, conversion from lotic to lentic systems, accumulation of organic rich sediments, elevated water tables, anaerobic conditions, and thawing permafrost. Ground-based measurements of CH4emissions from beaver ponds in permafrost landscapes are scarce, but hyperspectral remote sensing data (AVIRIS-NG) permit mapping of ‘hotspots’ thought to represent locations of high CH4emission. We surveyed a 429.5 km2area in Northwestern Alaska using hyperspectral airborne imaging spectroscopy at ∼5 m pixel resolution (14.7 million observations) to examine spatial relationships between CH4hotspots and 118 beaver ponds. AVIRIS-NG CH4hotspots covered 0.539% (2.3 km2) of the study area, and were concentrated within 30 m of waterbodies. Comparing beaver ponds to all non-beaver waterbodies (including waterbodies >450 m from beaver-affected water), we found significantly greater CH4hotspot occurrences around beaver ponds, extending to a distance of 60 m. We found a 51% greater CH4hotspot occurrence ratio around beaver ponds relative to nearby non-beaver waterbodies. Dammed lake outlets showed no significant differences in CH4hotspot ratios compared to non-beaver lakes, likely due to little change in inundation extent. The enhancement in AVIRIS-NG CH4hotspots adjacent to beaver ponds is an example of a new disturbance regime, wrought by an ecosystem engineer, accelerating the effects of climate change in the Arctic. As beavers continue to expand into the Arctic and reshape lowland ecosystems, we expect continued wetland creation, permafrost thaw and alteration of the Arctic carbon cycle, as well as myriad physical and biological changes.
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- Award ID(s):
- 2114051
- PAR ID:
- 10428746
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 18
- Issue:
- 7
- ISSN:
- 1748-9326
- Page Range / eLocation ID:
- Article No. 075004
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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