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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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Water temperature and catchment characteristics drive variation in carbon dioxide and methane emissions from small ponds in a peatland‐rich, high‐altitude tropical ecosystem
Abstract Inland waters release significant amounts of carbon into the atmosphere, with small ponds acting as hot spots. High variability and limited research make emissions from small waterbodies a major source of uncertainty, especially in underrepresented tropical ecosystems where unique drivers remain poorly understood. We evaluated the magnitude and sources of variability in emissions from small waterbodies of the páramo—a tropical ecoregion in the Andes mountains, characterized by carbon‐rich soils. We measured partial pressure of carbon dioxide (pCO2), methane (pCH4) and CO2emissions from small (< 5000 m2) waterbodies, 11 ponds and 1 wetland, 3 times in the wet season and returned to 8 sites in the dry season. Sites were always supersaturated inpCH4(1096 ± 1482μatm), but occasionally undersaturated inpCO2(1224 ± 1585μatm). Variability between ponds was high and primarily driven by elevation and water temperature. Catchment soil‐water connectivity was also predictive ofpCO2. Mean wet‐season emission rates were 0.34 ± 0.54 g CO2‐C m−2d−1and 0.012 ± 0.018 g CH4‐C m−2d−1and surface area fluctuations were a large source of seasonal variability in some ponds. Though an open‐water transect of the wetland site was similar to ponds, we measured very highpCH4(1678 ± 2629μatm) andpCO2(5162 ± 3207μatm) along the wetland perimeter. Our findings provide essential insights for incorporating a significant yet understudied tropical ecosystem into the global carbon budget by confirming previous observations that small ponds can emit a disproportionately large amount of carbon to the atmosphere, but also highlighting the importance of variables other than pond size in controlling emission hot spots.
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- Award ID(s):
- 1847331
- PAR ID:
- 10677442
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Limnology and Oceanography
- Volume:
- 70
- Issue:
- 12
- ISSN:
- 0024-3590
- Page Range / eLocation ID:
- 3832 to 3849
- 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/10.1002/lno.70261
