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The rapid climate warming is affecting the Arctic which is rich in aquatic systems. As a result of permafrost thaw, thermokarst lakes and ponds are either shrinking due to lake drainage or expanding due to lake shore erosion. This process in turn mobilizes organic carbon, which is released by permafrost deposits and active layer material that slips into the lake. In this study, we combine hydrochemical measurements and remote sensing data to analyze the influence of lake change processes, especially lake growth, on lake hydrochemical parameters such as DOC, EC, pH as well as stable oxygen and hydrogen isotopes in the Arctic Coastal Plain. For our entire dataset of 97 water samples from 82 water bodies, we found significantly higher CH4 concentrations in lakes with a floating-ice regime and significantly higher DOC concentrations in lakes with a bedfast-ice regime. We show significantly lower CH4 concentrations in lagoons compared to lakes as a result of an effective CH4 oxidation that increased with a seawater connection. For our detailed lake sampling of two thermokarst lakes, we found a significant positive correlation for lake shore erosion and DOC for one of the lakes. Our detailed lake sampling approach indicates that the generally shallow thermokarst lakes are overall well mixed and that single hydrochemical samples are representative for the entire lake. Finally, our study confirms that DOC concentrations correlates with lake size, ecoregion type and underlying deposits. 

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.