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Lakes are a ubiquitous landscape feature in northern permafrost regions. They have a strong impact on carbon, energy and water fluxes and can be quite responsive to climate change. The monitoring of lake change in northern high latitudes, at a sufficiently accurate spatial and temporal resolution, is crucial for understanding the underlying processes driving lake change. To date, lake change studies in permafrost regions were based on a variety of different sources, image acquisition periods and single snapshots, and localized analysis, which hinders the comparison of different regions. Here, we present a methodology based on machine-learning based classification of robust trends of multi-spectral indices of Landsat data (TM, ETM+, OLI) and object-based lake detection, to analyze and compare the individual, local and regional lake dynamics of four different study sites (Alaska North Slope, Western Alaska, Central Yakutia, Kolyma Lowland) in the northern permafrost zone from 1999 to 2014. Regional patterns of lake area change on the Alaska North Slope (-0.69%), Western Alaska (-2.82%), and Kolyma Lowland (-0.51%) largely include increases due to thermokarst lake expansion, but more dominant lake area losses due to catastrophic lake drainage events. In contrast, Central Yakutia showed a remarkable increase in lake area of 48.48%, likely resulting from warmer and wetter climate conditions over the latter half of the study period. Within all study regions, variability in lake dynamics was associated with differences in permafrost characteristics, landscape position (i.e., upland vs. lowland), and surface geology. With the global availability of Landsat data and a consistent methodology for processing the input data derived from robust trends of multi-spectral indices, we demonstrate a transferability, scalability and consistency of lake change analysis within the northern permafrost region.
Lakes cover large parts of the climatically sensitive Arctic landscape and respond rapidly to environmental change. Arctic lakes have different origins and include the predominant thermokarst lakes, which are small, young and highly dynamic, as well as large, old and stable glacial lakes. Freshwater diatoms dominate the primary producer community in these lakes and can be used to detect biotic responses to climate and environmental change. We used specific diatom metabarcoding on sedimentary DNA, combined with next-generation sequencing and diatom morphology, to assess diatom diversity in five glacial and 15 thermokarst lakes within the easternmost expanse of the Siberian treeline ecotone in Chukotka, Russia. We obtained 163 verified diatom sequence types and identified 176 diatom species morphologically. Although there were large differences in taxonomic assignment using the two approaches, they showed similar high abundances and diversity of Fragilariceae and Aulacoseiraceae. In particular, the genetic approach detected hidden within-lake variations of fragilarioids in glacial lakes and dominance of centric Aulacoseira species, whereas Lindavia ocellata was predominant using morphology. In thermokarst lakes, sequence types and valve counts also detected high diversity of Fragilariaceae, which followed the vegetation gradient along the treeline. Ordination analyses of the genetic data from glacial and thermokarst lakes suggest that concentrations of sulfate (SO42-), an indicator of the activity of sulfate-reducing microbes under anoxic conditions, and bicarbonate (HCO3-), which relates to surrounding vegetation, have a significant influence on diatom community composition. For thermokarst lakes, we also identified lake depth as an important variable, but SO42- best explains diatom diversity derived from genetic data, whereas HCO3- best explains the data from valve counts. Higher diatom diversity was detected in glacial lakes, most likely related to greater lake age and different edaphic settings, which gave rise to diversification and endemism. In contrast, small, dynamic thermokarst lakes are inhabited by stress-tolerant fragilarioids and are related to different vegetation types along the treeline ecotone. Our study demonstrated that genetic investigations of lake sediments can be used to interpret climate and environmental responses of diatoms. It also showed how lake type affects diatom diversity, and that such genetic analyses can be used to track diatom community changes under ongoing warming in the Arctic.
Thermokarst lakes are prevalent in Arctic coastal lowland regions and sublake permafrost degradation and talik development contributes to greenhouse gas emissions by tapping the large permafrost carbon pool. Whereas lateral thermokarst lake expansion is readily apparent through remote sensing and shoreline measurements, sublake thawed sediment conditions and talik growth are difficult to measure. Here we combine transient electromagnetic surveys with thermal modeling, backed up by measured permafrost properties and radiocarbon ages, to reveal closed-talik geometry associated with a thermokarst lake in continuous permafrost. To improve access to talik geometry data, we conducted surveys along three transient electromagnetic transects perpendicular to lakeshores with different decadal-scale expansion rates of 0.16, 0.38, and 0.58m/year. We modeled thermal development of the talik using boundary conditions based on field data from the lake, surrounding permafrost and a borehole, independent of the transient electromagnetics. A talik depth of 91m was determined from analysis of the transient electromagnetic surveys. Using a lake initiation age of 1400years before present and available subsurface properties the results from thermal modeling of the lake center arrived at a best estimate talk depth of 80m, which is on the same order of magnitude as the results from the transient electromagnetic survey. Our approach has provided a noninvasive estimate of talik geometry suitable for comparable settings throughout circum-Arctic coastal lowland regions.