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Abstract The dynamics of water and solutes were investigated in two northern bog ponds using sensor networks and discrete water samples. Embedded sensors monitored water level (S), precipitation (P), evaporation (E), water temperature (T) and specific conductivity (SC) in the peatlands and encircled ponds at 30 min time intervals from 2009 to 2015. Pond water chemistry was monitored seasonally from 2000 to 2020. Daily hydrographs and water budgets indicated that both bogs are ombrotrophic systems, perched above the local water table. Although the predominant flowpath for liquid water was precipitation → pond → peatland → underlying glacial deposits, evaporation accounted for 70% to 90% of water losses. High dissolved organic matter (DOM) in the ponds resulted from transient reversals of flowpath and from molecular diffusion across the peatland/pond interface (a tea bag effect). DOM of peatland origin dominated pond water chemistry, regulating the concentration of important metals, major nutrients and the acid–base status of both bog ponds. Elevated concentrations of Fe, Hg and MeHg in the ponds reflected ligand binding by DOM. The formation of DOM‐Fe‐PO4complexes likely accounted for >3‐fold higher P concentration relative to nearby clearwater lakes. Linear regression of dissolved organic carbon (DOC) against the anion deficit indicated that DOM contributed up to 6.6 mEq of strong acid per gramme carbon in pond waters. Winter maxima in the seasonal cycles of DOC, Ca, Mg, N, P, Hg and MeHg in both bog ponds were attributable, in large part, to salting out during ice formation. We conclude that multiple methods are needed to understand the dynamics of water and solutes in bog ecosystems.
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Post-drainage vegetation, microtopography and organic matter in Arctic drained lake basins
Abstract Wetlands in Arctic drained lake basins (DLBs) have a high potential for carbon storage in vegetation and peat as well as for elevated greenhouse gas emissions. However, the evolution of vegetation and organic matter is rarely studied in DLBs, making these abundant wetlands especially uncertain elements of the permafrost carbon budget. We surveyed multiple DLB generations in northern Alaska with the goal to assess vegetation, microtopography, and organic matter in surface sediment and pond water in DLBs and to provide the first high-resolution land cover classification for a DLB system focussing on moisture-related vegetation classes for the Teshekpuk Lake region. We associated sediment properties and methane concentrations along a post-drainage succession gradient with remote sensing-derived land cover classes. Our study distinguished five eco-hydrological classes using statistical clustering of vegetation data, which corresponded to the land cover classes. We identified surface wetness and time since drainage as predictors of vegetation composition. Microtopographic complexity increased after drainage. Organic carbon and nitrogen contents in sediment, and dissolved organic carbon (DOC) and dissolved nitrogen (DN) in ponds were high throughout, indicating high organic matter availability and decomposition. We confirmed wetness as a predictor of sediment methane concentrations. Our findings suggest moderate to high methane concentrations independent of drainage age, with particularly high concentrations beneath submerged patches (up to 200μmol l−1) and in pond water (up to 22μmol l−1). In our DLB system, wet and shallow submerged patches with high methane concentrations occupied 54% of the area, and ponds with high DOC, DN and methane occupied another 11%. In conclusion, we demonstrate that DLB wetlands are highly productive regarding organic matter decomposition and methane production. Machine learning-aided land cover classification using high-resolution multispectral satellite imagery provides a useful tool for future upscaling of sediment properties and methane emission potentials from Arctic DLBs.
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- PAR ID:
- 10495626
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 19
- Issue:
- 4
- ISSN:
- 1748-9326
- Format(s):
- Medium: X Size: Article No. 045001
- Size(s):
- Article No. 045001
- Sponsoring Org:
- National Science Foundation
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