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Ice-rich permafrost has been subject to abrupt thaw and thermokarst formation in the past and is vulnerable to current global warming. The ice-rich permafrost domain includes Yedoma sediments that have never thawed since deposition during the late Pleistocene and Alas sediments that were formed by previous thermokarst processes during the Lateglacial and Holocene warming. Permafrost thaw unlocks organic carbon (OC) and minerals from these deposits and exposes OC to mineralization. A portion of the OC can be associated with iron (Fe), a redox-sensitive element acting as a trap for OC. Post-depositional thaw processes may have induced changes in redox conditions in these deposits and thereby affected Fe distribution and interactions between OC and Fe, with knock-on effects on the role that Fe plays in mediating present day OC mineralization. To test this hypothesis, we measured Fe concentrations and proportion of Fe oxides and Fe complexed with OC in unthawed Yedoma and previously thawed Alas deposits. Total Fe concentrations were determined on 1,292 sediment samples from the Yedoma domain using portable X-ray fluorescence; these concentrations were corrected for trueness using a calibration based on a subset of 144 samples measured by inductively coupled plasma optical emission spectrometry after alkaline fusion (R (2) = 0.95). The total Fe concentration is stable with depth in Yedoma deposits, but we observe a depletion or accumulation of total Fe in Alas deposits, which experienced previous thaw and/or flooding events. Selective Fe extractions targeting reactive forms of Fe on unthawed and previously thawed deposits highlight that about 25% of the total Fe is present as reactive species, either as crystalline or amorphous oxides, or complexed with OC, with no significant difference in proportions of reactive Fe between Yedoma and Alas deposits. These results suggest that redox driven processes during past thermokarst formation impact the present-day distribution of total Fe, and thereby the total amount of reactive Fe in Alas versus Yedoma deposits. This study highlights that ongoing thermokarst lake formation and drainage dynamics in the Arctic influences reactive Fe distribution and thereby interactions between Fe and OC, OC mineralization rates, and greenhouse gas emissions.
Water bodies are a highly abundant feature of Arctic permafrost ecosystems and strongly influence their hydrology, ecology and biogeochemical cycling. While very high resolution satellite images enable detailed mapping of these water bodies, the increasing availability and abundance of this imagery calls for fast, reliable and automatized monitoring. This technical work presents a largely automated and scalable workflow that removes image noise, detects water bodies, removes potential misclassifications from infrastructural features, derives lake shoreline geometries and retrieves their movement rate and direction on the basis of ortho-ready very high resolution satellite imagery from Arctic permafrost lowlands. We applied this workflow to typical Arctic lake areas on the Alaska North Slope and achieved a successful and fast detection of water bodies. We derived representative values for shoreline movement rates ranging from 0.40-0.56 m yr(-1) for lake sizes of 0.10 ha-23.04 ha. The approach also gives an insight into seasonal water level changes. Based on an extensive quantification of error sources, we discuss how the results of the automated workflow can be further enhanced by incorporating additional information on weather conditions and image metadata and by improving the input database. The workflow is suitable for the seasonal to annual monitoring of lake changes on a sub-meter scale in the study areas in northern Alaska and can readily be scaled for application across larger regions within certain accuracy limitations.
Arctic lakes located in permafrost regions are susceptible to catastrophic drainage. In this study, we reconstructed historical lake drainage events on the western Arctic Coastal Plain of Alaska between 1955 and 2017 using USGS topographic maps, historical aerial photography (1955), and Landsat Imagery (ca. 1975, ca. 2000, and annually since 2000). We identified 98 lakes larger than 10 ha that partially (>25% of area) or completely drained during the 62-year period. Decadal-scale lake drainage rates progressively declined from 2.0 lakes/yr (1955-1975), to 1.6 lakes/yr (1975-2000), and to 1.2 lakes/yr (2000-2017) in the ~30,000-km(2) study area. Detailed Landsat trend analysis between 2000 and 2017 identified two years, 2004 and 2006, with a cluster (five or more) of lake drainages probably associated with bank overtopping or headward erosion. To identify future potential lake drainages, we combined the historical lake drainage observations with a geospatial dataset describing lake elevation, hydrologic connectivity, and adjacent lake margin topographic gradients developed with a 5-m-resolution digital surface model. We identified ~1900 lakes likely to be prone to drainage in the future. Of the 20 lakes that drained in the most recent study period, 85% were identified in this future lake drainage potential dataset. Our assessment of historical lake drainage magnitude, mechanisms and pathways, and identification of potential future lake drainages provides insights into how arctic lowland landscapes may change and evolve in the coming decades to centuries.
Arctic lakes located in permafrost regions are susceptible to catastrophic drainage. In this study, we reconstructed historical lake drainage events on the western Arctic Coastal Plain of Alaska between 1955 and 2017 using USGS topographic maps, historical aerial photography (1955), and Landsat Imagery (ca. 1975, ca. 2000, and annually since 2000). We identified 98 lakes larger than 10 ha that partially (>25% of area) or completely drained during the 62-year period. Decadal-scale lake drainage rates progressively declined from 2.0 lakes/yr (1955-1975), to 1.6 lakes/yr (1975-2000), and to 1.2 lakes/yr (2000-2017) in the ~30,000-km(2) study area. Detailed Landsat trend analysis between 2000 and 2017 identified two years, 2004 and 2006, with a cluster (five or more) of lake drainages probably associated with bank overtopping or headward erosion. To identify future potential lake drainages, we combined the historical lake drainage observations with a geospatial dataset describing lake elevation, hydrologic connectivity, and adjacent lake margin topographic gradients developed with a 5-m-resolution digital surface model. We identified ~1900 lakes likely to be prone to drainage in the future. Of the 20 lakes that drained in the most recent study period, 85% were identified in this future lake drainage potential dataset. Our assessment of historical lake drainage magnitude, mechanisms and pathways, and identification of potential future lake drainages provides insights into how arctic lowland landscapes may change and evolve in the coming decades to centuries.
Permafrost is warming globally which leads to widespread permafrost thaw. Particularly ice-rich permafrost is vulnerable to rapid thaw and erosion, impacting whole landscapes and ecosystems. Retrogressive thaw slumps (RTS) are abrupt permafrost disturbances that expand by several meters each year and lead to an increased soil organic carbon release. Local Remote Sensing studies identified increasing RTS activity in the last two decades by increasing number of RTS or heightened RTS growth rates. However, a large-scale assessment across diverse permafrost regions and at high temporal resolution allowing to further determine RTS thaw dynamics and its main drivers is still lacking. In this study we apply the disturbance detection algorithm LandTrendr for automated large-scale RTS mapping and high temporal thaw dynamic assessment to North Siberia (8.1 x 106km2). We adapted and parametrised the temporal segmentation algorithm for abrupt disturbance detection to incorporate Landsat+Sentinel-2 mosaics, conducted spectral filtering, spatial masking and filtering, and a binary machine-learning object classification of the disturbance output to separate between RTS and false positives (F1 score: 0.609). Ground truth data for calibration and validation of the workflow was collected from 9 known RTS cluster sites using very highresolution RapidEye and PlanetScope imagery. Our study presents the first automated detection and assessment of RTS and their temporal dynamics at largescale for 2001-2019. We identified 50,895 RTS and a steady increase in RTS-affected area from 2001 to 2019 across North Siberia, with a more abrupt increase from 2016 onward. Overall the RTS-affected area increased by 331% compared to 2000 (2000: 20,158 ha, 2001-2019: 66,699 ha). Contrary to this, 5 focus sites show spatiotemporal variability in their annual RTS dynamics, with alternating periods of increased and decreased RTS development, indicating a close relationship to thaw drivers. The majority of identified RTS was active from 2000 onward and only a small proportion initiated during the assessment period, indicating that the increase in RTS-affected area was mainly caused by enlarging existing RTS and not by new RTS. The detected increase in RTS dynamics suggests advancing permafrost thaw and underlines the importance of assessing abrupt permafrost disturbances with high spatial and temporal resolution at large-scales. Obtaining such consistent disturbance products will help to parametrise regional and global climate change models.
Thermal erosion is a major mechanism of permafrost degradation, resulting in characteristic landforms. We inventory thermo-erosional valleys in ice-rich coastal lowlands adjacent to the Siberian Laptev Sea based on remote sensing, Geographic Information System (GIS), and field investigations for a first regional assessment of their spatial distribution and characteristics. Three study areas with similar geological (Yedoma Ice Complex) but diverse geomorphological conditions vary in valley areal extent, incision depth, and branching geometry. The most extensive valley networks are incised deeply (up to 35 m) into the broad inclined lowland around Mamontov Klyk. The flat, low-lying plain forming the Buor Khaya Peninsula is more degraded by thermokarst and characterized by long valleys of lower depth with short tributaries. Small, isolated Yedoma Ice Complex remnants in the Lena River Delta predominantly exhibit shorter but deep valleys. Based on these hydrographical network and topography assessments, we discuss geomorphological and hydrological connections to erosion processes. Relative catchment size along with regional slope interact with other Holocene relief-forming processes such as thermokarst and neotectonics. Our findings suggest that thermo-erosional valleys are prominent, hitherto overlooked permafrost degradation landforms that add to impacts on biogeochemical cycling, sediment transport, and hydrology in the degrading Siberian Yedoma Ice Complex.
Sea level rise and coastal erosion have inundated large areas of Arctic permafrost. Submergence by warm and saline waters increases the rate of inundated permafrost thaw compared to sub-aerial thawing on land. Studying the contact between the unfrozen and frozen sediments below the seabed, also known as the ice-bearing permafrost table (IBPT), provides valuable information to understand the evolution of sub-aquatic permafrost, which is key to improving and understanding coastal erosion prediction models and potential greenhouse gas emissions. In this study, we use data from 2D electrical resistivity tomography (ERT) collected in the nearshore coastal zone of two Arctic regions that differ in their environmental conditions (e.g., seawater depth and resistivity) to image and study the subsea permafrost. The inversion of 2D ERT data sets is commonly performed using deterministic approaches that favor smoothed solutions, which are typically interpreted using a user-specified resistivity threshold to identify the IBPT position. In contrast, to target the IBPT position directly during inversion, we use a layer-based model parameterization and a global optimization approach to invert our ERT data. This approach results in ensembles of layered 2D model solutions, which we use to identify the IBPT and estimate the resistivity of the unfrozen and frozen sediments, including estimates of uncertainties. Additionally, we globally invert 1D synthetic resistivity data and perform sensitivity analyses to study, in a simpler way, the correlations and influences of our model parameters. The set of methods provided in this study may help to further exploit ERT data collected in such permafrost environments as well as for the design of future field experiments.
Sea level rise and coastal erosion have inundated large areas of Arctic permafrost. Submergence by warm and saline waters increases the rate of inundated permafrost thaw compared to sub-aerial thawing on land. Studying the contact between the unfrozen and frozen sediments below the seabed, also known as the ice-bearing permafrost table (IBPT), provides valuable information to understand the evolution of sub-aquatic permafrost, which is key to improving and understanding coastal erosion prediction models and potential greenhouse gas emissions. In this study, we use data from 2D electrical resistivity tomography (ERT) collected in the nearshore coastal zone of two Arctic regions that differ in their environmental conditions (e.g., seawater depth and resistivity) to image and study the subsea permafrost. The inversion of 2D ERT data sets is commonly performed using deterministic approaches that favor smoothed solutions, which are typically interpreted using a user-specified resistivity threshold to identify the IBPT position. In contrast, to target the IBPT position directly during inversion, we use a layer-based model parameterization and a global optimization approach to invert our ERT data. This approach results in ensembles of layered 2D model solutions, which we use to identify the IBPT and estimate the resistivity of the unfrozen and frozen sediments, including estimates of uncertainties. Additionally, we globally invert 1D synthetic resistivity data and perform sensitivity analyses to study, in a simpler way, the correlations and influences of our model parameters. The set of methods provided in this study may help to further exploit ERT data collected in such permafrost environments as well as for the design of future field experiments.
As the Arctic coast erodes, it drains thermokarst lakes, transforming them into lagoons, and, eventually, integrates them into subsea permafrost. Lagoons represent the first stage of a thermokarst lake transition to a marine setting and possibly more saline and colder upper boundary conditions. In this research, borehole data, electrical resistivity surveying, and modeling of heat and salt diffusion were carried out at Polar Fox Lagoon on the Bykovsky Peninsula, Siberia. Polar Fox Lagoon is a seasonally isolated water body connected to Tiksi Bay through a channel, leading to hypersaline waters under the ice cover. The boreholes in the center of the lagoon revealed floating ice and a saline cryotic bed underlain by a saline cryotic talik, a thin ice-bearing permafrost layer, and unfrozen ground. The bathymetry showed that most of the lagoon had bedfast ice in spring. In bedfast ice areas, the electrical resistivity profiles suggested that an unfrozen saline layer was underlain by a thick layer of refrozen talik. The modeling showed that thermokarst lake taliks can refreeze when submerged in saltwater with mean annual bottom water temperatures below or slightly above 0 degrees C. This occurs, because the top-down chemical degradation of newly formed ice-bearing permafrost is slower than the refreezing of the talik. Hence, lagoons may precondition taliks with a layer of ice-bearing permafrost before encroachment by the sea, and this frozen layer may act as a cap on gas migration out of the underlying talik.
Arctic tundra ecosystems have experienced unprecedented change associated with climate warming over recent decades. Across the Pan-Arctic, vegetation productivity and surface greenness have trended positively over the period of satellite observation. However, since 2011 these trends have slowed considerably, showing signs of browning in many regions. It is unclear what factors are driving this change and which regions/landforms will be most sensitive to future browning. Here we provide evidence linking decadal patterns in arctic greening and browning with regional climate change and local permafrost-driven landscape heterogeneity. We analyzed the spatial variability of decadal-scale trends in surface greenness across the Arctic Coastal Plain of northern Alaska (similar to 60,000 km(2)) using the Landsat archive (1999-2014), in combination with novel 30 m classifications of polygonal tundra and regional watersheds, finding landscape heterogeneity and regional climate change to be the most important factors controlling historical greenness trends. Browning was linked to increased temperature and precipitation, with the exception of young landforms (developed following lake drainage), which will likely continue to green. Spatiotemporal model forecasting suggests carbon uptake potential to be reduced in response to warmer and/or wetter climatic conditions, potentially increasing the net loss of carbon to the atmosphere, at a greater degree than previously expected.