Retrogressive Thaw Slumps Research Articles

Recent Intensification (2004–2020) of Permafrost Mass‐Wasting in the Central Mackenzie Valley Foothills Is a Legacy of Past Forest Fire Disturbances

AbstractThe effects of recent climate change are accelerating permafrost thaw, including ice‐rich landscapes of the western Canadian Arctic. However, regional drivers of permafrost slope failure in hillslopes with warm, thin permafrost remain poorly understood. Repeat satellite imagery (1984–2020) indicates rapid increases in retrogressive thaw slumps (RTSs) and deep‐seated permafrost landslides (DSPLs) since 2004, indicating a change in slope stability thresholds in an area that otherwise appeared thaw stable. The widespread occurrence of DSPL represents a contrasting geomorphic response to the RTS‐dominated ice‐rich permafrost landscapes. In this study area, RTS and DSPL occur predominantly in areas that were burned by forest fires in the 1990s, indicating a legacy thermal disturbance that preconditioned permafrost hillslopes for failure. The relations between historic fires and the later development of widespread permafrost slope failures represent an outstanding example of the complex interactions between inherited landscape sensitivity in ice‐rich terrain and ongoing climate change.

Inventory and Frequency of Retrogressive Thaw Slumps in Permafrost Region of the Qinghai–Tibet Plateau

AbstractDue to the increasing air temperatures, retrogressive thaw slumps (RTSs) have become more common on the Qinghai–Tibet Plateau (QTP), but their inventory, frequency, and initiation have not been fully investigated. In satellite imagery for the period 2018–2020, 2669 active RTSs were identified in permafrost regions of the QTP. Most of the RTSs occur in regions with elevations ranging from 4,600 to 5,100 m along north‐facing slopes varying from 3° to 8°. The number of RTSs and their affected areas increased by 4 and 6 times, respectively, in three densely distributed RTS regions from 2008/2013 to 2021. This increase in the RTS occurrence mainly occurred during the warm season in 2010 and 2016 and is likely due to high air temperatures during the thawing season, which induced the massive occurrence of active‐layer detachment slides. Another intensification of RTS occurrences may appear when higher anomaly air temperatures are observed.

Rapid Permafrost Thaw Removes Nitrogen Limitation and Rises the Potential for N2O Emissions

Ice–rich Pleistocene permafrost deposits (Yedoma) store large amounts of nitrogen (N) and are susceptible to rapid thaw. In this study, we assess whether eroding Yedoma deposits are potential sources of N and gaseous carbon (C) losses. Therefore, we determined aerobic net ammonification and nitrification, as well as anaerobic production of nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4) in laboratory incubations. Samples were collected from non-vegetated and revegetated slump floor (SF) and thaw mound (TM) soils of a retrogressive thaw slump in the Lena River Delta of Eastern Siberia. We found high nitrate concentrations (up to 110 µg N (g DW)−1) within the growing season, a faster transformation of organic N to nitrate, and high N2O production (up to 217 ng N2O-N (g DW)−1 day−1) in revegetated thaw mounds. The slump floor was low in nitrate and did not produce N2O under anaerobic conditions, but produced the most CO2 (up to 7 µg CO2-C (g DW)−1 day−1) and CH4 (up to 65 ng CH4-C (g DW)−1 day−1). Nitrate additions showed that denitrification was substrate limited in the slump floor. Nitrate limitation was rather caused by field conditions (moisture, pH) than by microbial functional limitation since nitrification rates were positive under laboratory conditions. Our results emphasize the relevance of considering landscape processes, geomorphology, and soil origin in order to identify hotspots of high N availability, as well as C and N losses. High N availability is likely to have an impact on carbon cycling, but to what extent needs further investigation.

Deformation and Volumetric Change in a Typical Retrogressive Thaw Slump in Permafrost Regions of the Central Tibetan Plateau, China

Ice-rich permafrost in the Qinghai–Tibet Plateau (QTP), China, is becoming susceptible to thermokarst landforms, and the most dramatic among these terrain-altering landforms is retrogressive thaw slump (RTS). Concurrently, RTS development can in turn affect the eco-environment, and especially soil erosion and carbon emission, during their evolution. However, there are still a lack of quantitative methods and comprehensive studies on the deformation and volumetric change in RTS. The purpose of this study is to quantitatively assess the RTS evolution through a novel and feasible simulation framework of the GPU-based discrete element method (DEM) coupled with the finite difference method (FDM). Additionally, the simulation results were calibrated using the time series observation results from September 2021 to August 2022, using the combined methods of terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV). The results reveal that, over this time, thaw slump mobilized a total volume of 1335 m3 and approximately 1050 m3 moved to a displaced area. Additionally, the estimated soil erosion was about 211 m3. Meanwhile, the corresponding maximum ground subsidence and headwall retrogression were 1.9 m and 3.2 m, respectively. We also found that the amount of mass wasting in RTS development is highly related to the ground ice content. When the volumetric ice content exceeds 10%, there will be obvious mass wasting in the thaw slump development area. Furthermore, this work proposed that the coupled DEM-FDM method and field survey method of TLS-UAV can provide an effective pathway to simulate thaw-induced slope failure problems and complement the research limitations of small-scale RTSs using remote sensing methods. The results are meaningful for assessing the eco-environmental impacts on the QTP.

The Role of Massive Ice and Exposed Headwall Properties on Retrogressive Thaw Slump Activity

AbstractRetrogressive Thaw Slumps (RTSs), a highly dynamic form of mass wasting, are accelerating geomorphic change across ice‐cored permafrost terrain, yet the main controls on their activity are poorly constrained. Questions over the spatial variability of environmentally sensitive massive ice bodies and a paucity of high‐spatial and temporal resolution topographic data have limited our ability to project their development and wider impacts. This research addresses these key problems by investigating RTS processes on Peninsula Point—a well‐studied site for intra‐sedimental massive ice in the Western Canadian Arctic. Utilizing high‐resolution topographic data from drone surveys in 2016, 2017 and 2018 we (a) measure the temporal and spatial variations in headwall properties and retreat rates, (b) determine the spatial pattern of subsurface layering using passive seismic monitoring and (c) combine these to analyze and contextualize the factors controlling headwall retreat (HWR) rates. We find that headwall properties, namely massive ice and overburden thickness, are significant controls over rates of HWR. Where persistent massive ice exposures are present inland of the headwall, regardless of thickness, and overburden thickness remains <4 m, HWR is typically more than double that of other headwalls. Furthermore, a 3D site model was created by combining photogrammetric and passive seismic data, highlighting internal layering variability and demonstrating the limitations of extrapolations of internal layering based on headwall exposures. These results provide fresh insights into the in situ controls on HWR rates and new approaches to understanding their variability.

Retrogressive thaw slumps along the Qinghai–Tibet Engineering Corridor: a comprehensive inventory and their distribution characteristics

Abstract. The important Qinghai–Tibet Engineering Corridor (QTEC) covers the part of the Highway and Railway underlain by permafrost. The permafrost on the QTEC is sensitive to climate warming and human disturbance and suffers accelerating degradation. Retrogressive thaw slumps (RTSs) are slope failures due to the thawing of ice-rich permafrost. They typically retreat and expand at high rates, damaging infrastructure, and releasing carbon preserved in frozen ground. Along the critical and essential corridor, RTSs are commonly distributed but remain poorly investigated. To compile the first comprehensive inventory of RTSs, this study uses an iteratively semi-automatic method built on deep learning to delineate thaw slumps in the 2019 PlanetScope CubeSat images over a ∼ 54 000 km2 corridor area. The method effectively assesses every image pixel using DeepLabv3+ with limited training samples and manually inspects the deep-learning-identified thaw slumps based on their geomorphic features and temporal changes. The inventory includes 875 RTSs, of which 474 are clustered in the Beiluhe region, and 38 are near roads or railway lines. The dataset is available at https://doi.org/10.5281/zenodo.6397029​​​​​​​ (Xia et al., 2021a), with the Chinese version at DOI: https://doi.org/10.11888/Cryos.tpdc.272672 (Xia et al. 2021b). These RTSs tend to be located on north-facing slopes with gradients of 1.2–18.1∘ and distributed at medium elevations ranging from 4511 to 5212 m a.s.l. They prefer to develop on land receiving relatively low annual solar radiation (from 2900 to 3200 kWh m−2), alpine meadow covered, and loam underlay. Our results provide a significant and fundamental benchmark dataset for quantifying thaw slump changes in this vulnerable region undergoing strong climatic warming and extensive human activities.

Automated Detection of Retrogressive Thaw Slumps in the High Arctic Using High-Resolution Satellite Imagery

Retrogressive thaw slumps (RTS) are considered one of the most dynamic permafrost disturbance features in the Arctic. Sub-meter resolution multispectral imagery acquired by very high spatial resolution (VHSR) commercial satellite sensors offer unique capacities in capturing the morphological dynamics of RTSs. The central goal of this study is to develop a deep learning convolutional neural net (CNN) model (a UNet-based workflow) to automatically detect and characterize RTSs from VHSR imagery. We aimed to understand: (1) the optimal combination of input image tile size (array size) and the CNN network input size (resizing factor/spatial resolution) and (2) the interoperability of the trained UNet models across heterogeneous study sites based on a limited set of training samples. Hand annotation of RTS samples, CNN model training and testing, and interoperability analyses were based on two study areas from high-Arctic Canada: (1) Banks Island and (2) Axel Heiberg Island and Ellesmere Island. Our experimental results revealed the potential impact of image tile size and the resizing factor on the detection accuracies of the UNet model. The results from the model transferability analysis elucidate the effects on the UNet model due the variability (e.g., shape, color, and texture) associated with the RTS training samples. Overall, study findings highlight several key factors that we should consider when operationalizing CNN-based RTS mapping over large geographical extents.

AUTOMATED RECOGNITION OF PERMAFROST DISTURBANCES USING HIGH-SPATIAL RESOLUTION SATELLITE IMAGERY AND DEEP LEARNING MODELS

Abstract. The accelerated warming conditions of the high Arctic have intensified the extensive thawing of permafrost. Retrogressive thaw slumps (RTSs) are considered as the most active landforms in the Arctic permafrost. An increase in RTSs has been observed in the Arctic in recent decades. Continuous monitoring of RTSs is important to understand climate change-driven disturbances in the region. Manual detection of these landforms is extremely difficult as they occur over exceptionally large areas. Only very few studies have explored the utility of very high spatial resolution (VHSR) commercial satellite imagery in the automated mapping of RTSs. We have developed deep learning (DL) convolution neural net (CNN) based workflow to automatically detect RTSs from VHRS satellite imagery. This study systematically compared the performance of different DLCNN model architectures and varying backbones. Our candidate CNN models include: DeepLabV3+, UNet, UNet++, Multi-scale Attention Net (MA-Net), and Pyramid Attention Network (PAN) with ResNet50, ResNet101 and ResNet152 backbones. The RTS modeling experiment was conducted on Banks Island and Ellesmere Island in Canada. The UNet++ model demonstrated the highest accuracy (F1 score of 87%) with the ResNet50 backbone at the expense of training and inferencing time. PAN, DeepLabV3, MaNet, and UNet, models reported mediocre F1 scores of 72%, 75%, 80%, and 81% respectively. Our findings unravel the performances of different DLCNNs in imagery-enabled RTS mapping and provide useful insights on operationalizing the mapping application across the Arctic.

Accelerated mobilization of organic carbon from retrogressive thaw slumps on the northern Taymyr Peninsula

Abstract. With climate change, Arctic hillslopes above ice-rich permafrost are vulnerable to enhanced mass wasting and organic carbon mobilization. In this study we use TanDEM-X-derived (TerraSAR-X add-on for Digital Elevation Measurement; synthetic-aperture radar) digital elevation models to document an approximately 43-fold increase in thaw slumping and concomitant 28-fold increase in carbon mobilization on the northern Taymyr Peninsula from 2010 to 2021. The available observations allowed us to compare two time periods, from 2010/11 to 2016/17 and from 2017/18 to 2020/21, and contrast retrogressive thaw slump (RTS) activity between these periods. We find that all quantities describing RTS activity increased in the observed period. The total volumetric change per year increased from about 0.17×106 to 7.4×106m3yr-1, a 43-fold increase. The observed surge in RTS activity is mainly driven by the initiation of new RTS, indicated by the 17-fold increase in active RTS numbers from 82 to 1404 and the relatively low average volumetric change rate per RTS increase of 2.3. In annual Sentinel-2 imagery, the number of detected RTSs in a subregion increased 10-fold in 2020. This coincides with a severe heatwave that occurred in northern Siberia in 2020. The area-to-volume scaling of the RTSs varied only slightly over time, despite the 2020 heatwave, indicating a robustness of the relationship to such an event. To estimate the slump-mobilized organic carbon, we intersected the elevation changes with a soil organic carbon (SOC) map, with contrasting assumptions about the deep carbon pool and massive-ice content. We estimated that the SOC mobilization rate increases 28-fold. The normalization of the SOC mobilization rate to our study region yields values of 11gCyr-1m-2 with a confidence interval of 5 to 38gCyr-1m-2. A comparison to an independent estimate of the net ecosystem exchange of 4.1±13.0gCyr-1m-2 illustrates the importance of RTS activity to the carbon cycle. These results underscore that mass wasting is an important but commonly neglected component of the Arctic carbon cycle and particularly sensitive to extreme events.

Accuracy, Efficiency, and Transferability of a Deep Learning Model for Mapping Retrogressive Thaw Slumps across the Canadian Arctic

Deep learning has been used for mapping retrogressive thaw slumps and other periglacial landforms but its application is still limited to local study areas. To understand the accuracy, efficiency, and transferability of a deep learning model (i.e., DeepLabv3+) when applied to large areas or multiple regions, we conducted several experiments using training data from three different regions across the Canadian Arctic. To overcome the main challenge of transferability, we used a generative adversarial network (GAN) called CycleGAN to produce new training data in an attempt to improve transferability. The results show that (1) data augmentation can improve the accuracy of the deep learning model but does not guarantee transferability, (2) it is necessary to choose a good combination of hyper-parameters (e.g., backbones and learning rate) to achieve an optimal trade-off between accuracy and efficiency, and (3) a GAN can significantly improve the transferability if the variation between source and target is dominated by color or general texture. Our results suggest that future mapping of retrogressive thaw slumps should prioritize the collection of training data from regions where a GAN cannot improve the transferability.

Retrogressive thaw slumps in the Alaskan Low Arctic may influence tundra shrub growth more strongly than climate

AbstractThermokarst disturbance in permafrost landscapes is likely to increase across the tundra biome with climate warming, resulting in changes to topography, vegetation, and biogeochemical cycling. Tundra shrubs grow on permafrost, but shrub–thermokarst relationships are rarely studied in detail. Since the 1980s, Alaska's North Slope has experienced increased thermokarst activity, including retrogressive thaw slumps (RTSs) on hillslopes. Within decades, RTSs near Toolik Lake, Alaska, were colonized by tall (≥0.5 m) deciduous shrubs. We used dendrochronology methods on 66 shrubs (182 stem cross sections) representing dominant deciduous species: willows (Salix pulchra and S. glauca) and dwarf birch (Betula nana) at two RTS chronosequences on Alaska's North Slope comprising seven sites, to quantify thermokarst and climate effects (25 years of temperature and precipitation records) on shrub secondary growth (i.e., annual rings) in RTS‐disturbed and undisturbed moist acidic tussock (MAT) tundra. Across species, average growth ring widths were two times wider for shrubs in RTSs than in MAT, and ring widths decreased with RTS age. A 1°C June temperature increase was associated with 2% wider rings across species and sites, but shrubs showed marginal growth in warmer summers, supporting tundra‐wide shrub climate sensitivity studies. A 4.5% average ring width increase per 1 mm of previous year's September precipitation was seen in shrubs in mid‐successional RTSs, suggesting protective effects of early snowfall in RTSs versus open tundra. Retrogressive thaw slump age category explained 47% and 30% of average ring width variance of willows and dwarf birch, respectively, in linear mixed‐effects models. Climate variables explained 2% average ring width variance across species. Our results suggest that RTS exerts strong successional effects on tundra shrub growth. Climate effects appear to show weaker synoptic patterns across the study area. Retrogressive thaw slumps will likely contribute to tundra greening where RTS activity is increasing.

Sources of CO2 Produced in Freshly Thawed Pleistocene-Age Yedoma Permafrost

The release of greenhouse gases from the large organic carbon stock in permafrost deposits in the circumarctic regions may accelerate global warming upon thaw. The extent of this positive climate feedback is thought to be largely controlled by the microbial degradability of the organic matter preserved in these sediments. In addition, weathering and oxidation processes may release inorganic carbon preserved in permafrost sediments as CO2, which is generally not accounted for. We used 13C and 14C analysis and isotopic mass balances to differentiate and quantify organic and inorganic carbon released as CO2 in the field from an active retrogressive thaw slump of Pleistocene-age Yedoma and during a 1.5-years incubation experiment. The results reveal that the dominant source of the CO2 released from freshly thawed Yedoma exposed as thaw mound is Pleistocene-age organic matter (48–80%) and to a lesser extent modern organic substrate (3–34%). A significant portion of the CO2 originated from inorganic carbon in the Yedoma (17–26%). The mixing of young, active layer material with Yedoma at a site on the slump floor led to the preferential mineralization of this young organic carbon source. Admixtures of younger organic substrates in the Yedoma thaw mound were small and thus rapidly consumed as shown by lower contributions to the CO2 produced during few weeks of aerobic incubation at 4°C corresponding to approximately one thaw season. Future CO2 fluxes from the freshly thawed Yedoma will contain higher proportions of ancient inorganic (22%) and organic carbon (61–78%) as suggested by the results at the end, after 1.5 years of incubation. The increasing contribution of inorganic carbon during the incubation is favored by the accumulation of organic acids from microbial organic matter degradation resulting in lower pH values and, in consequence, in inorganic carbon dissolution. Because part of the inorganic carbon pool is assumed to be of pedogenic origin, these emissions would ultimately not alter carbon budgets. The results of this study highlight the preferential degradation of younger organic substrates in freshly thawed Yedoma, if available, and a substantial release of CO2 from inorganic sources.

Assessing volumetric change distributions and scaling relations of retrogressive thaw slumps across the Arctic

Abstract. Arctic ice-rich permafrost is becoming increasingly vulnerable to terrain-altering thermokarst, and among the most rapid and dramatic of these changes are retrogressive thaw slumps (RTSs). They initiate when ice-rich soils are exposed and thaw, leading to the formation of a steep headwall which retreats during the summer months. The impacts and the distribution and scaling laws governing RTS changes within and between regions are unknown. Using TanDEM-X-derived digital elevation models, we estimated RTS volume and area changes over a 5-year time period from winter 2011/12 to winter 2016/17 and used for the first time probability density functions to describe their distributions. We found that over this time period all 1853 RTSs mobilized a combined volume of 17×106 m3 yr−1, corresponding to a volumetric change density of 77 m3 yr−1 km−2. Our remote sensing data reveal inter-regional differences in mobilized volumes, scaling laws, and terrain controls. The distributions of RTS area and volumetric change rates follow an inverse gamma function with a distinct peak and an exponential decrease for the largest RTSs. We found that the distributions in the high Arctic are shifted towards larger values than at other study sites We observed that the area-to-volume scaling was well described by a power law with an exponent of 1.15 across all study sites; however the individual sites had scaling exponents ranging from 1.05 to 1.37, indicating that regional characteristics need to be taken into account when estimating RTS volumetric changes from area changes. Among the terrain controls on RTS distributions that we examined, which included slope, adjacency to waterbodies, and aspect, the latter showed the greatest but regionally variable association with RTS occurrence. Accounting for the observed regional differences in volumetric change distributions, scaling relations, and terrain controls may enhance the modelling and monitoring of Arctic carbon, nutrient, and sediment cycles.

Assessment of the sediment and associated nutrient/contaminant continuum, from permafrost thaw slump scars to tundra lakes in the western Canadian Arctic

AbstractWithin the Canadian Arctic, vast areas of previously frozen sediments and carbon are being released into aquatic ecosystems via the occurrence of permafrost thaw and retrogressive thaw slumps (RTSs). While knowledge of mass wasting RTS processes are more advanced, the significance of exposed retrogressive thaw slump scars (RTSSs) at various phases of stabilization to yield additional large quantities of ecologically relevant sediment to lakes and rivers is not well constrained. Using laboratory simulation (linked rainfall and lake flow dynamics), RTS sediments were investigated to assess the sediment continuum from the terrestrial RTSSs to depositional zones within two Arctic tundra lakes. Using an estimate of 30% of the RTSS areas contributing sediment under hypothetical 20‐ and 100‐year rainfall events, up to 598 and 997 kg hr−1 of RTSS sediment washoff was projected respectively. Eroded particle size, regardless of lake or initial bulk RTSS size distribution, was dominated by individual clay particles (<5 μm) that were winnowed from the RTSS surface sediment. Given this is the most biogeochemical relevant fraction, it has the potential for significant ecological impact on the lakes. This deposited fine sediment was found to be very unstable with a critical shear stress for erosion close to that of the critical shear for deposition (0.05 Pa). As such, wave energy is expected to have an impact on remobilization of fine sediments and associated compounds with concomitant implications for lake‐ecosystem health.

Remote sensing annual dynamics of rapid permafrost thaw disturbances with LandTrendr

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 (8.1×106km2). 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 high-resolution RapidEye and PlanetScope imagery.Our study presents the first automated detection and assessment of RTS and their temporal dynamics at large-scale 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: 20,158ha, 2001–2019: 66,699ha). Contrary to this, 5 focus sites show spatio-temporal 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.

Developing and Testing a Deep Learning Approach for Mapping Retrogressive Thaw Slumps

In a warming Arctic, permafrost-related disturbances, such as retrogressive thaw slumps (RTS), are becoming more abundant and dynamic, with serious implications for permafrost stability and bio-geochemical cycles on local to regional scales. Despite recent advances in the field of earth observation, many of these have remained undetected as RTS are highly dynamic, small, and scattered across the remote permafrost region. Here, we assessed the potential strengths and limitations of using deep learning for the automatic segmentation of RTS using PlanetScope satellite imagery, ArcticDEM and auxiliary datasets. We analyzed the transferability and potential for pan-Arctic upscaling and regional cross-validation, with independent training and validation regions, in six different thaw slump-affected regions in Canada and Russia. We further tested state-of-the-art model architectures (UNet, UNet++, DeepLabv3) and encoder networks to find optimal model configurations for potential upscaling to continental scales. The best deep learning models achieved mixed results from good to very good agreement in four of the six regions (maxIoU: 0.39 to 0.58; Lena River, Horton Delta, Herschel Island, Kolguev Island), while they failed in two regions (Banks Island, Tuktoyaktuk). Of the tested architectures, UNet++ performed the best. The large variance in regional performance highlights the requirement for a sufficient quantity, quality and spatial variability in the training data used for segmenting RTS across diverse permafrost landscapes, in varying environmental conditions. With our highly automated and configurable workflow, we see great potential for the transfer to active RTS clusters (e.g., Peel Plateau) and upscaling to much larger regions.

Coastal Retreat Due to Thermodenudation on the Yugorsky Peninsula, Russia during the Last Decade, Update since 2001–2010

Thermodenudation on the Kara seacoast, the Yugorsky Peninsula, Russia, is studied by analyzing remote-sensing data. Landforms resulting from the thaw of tabular ground ice, referred to as thermocirques, are formed due to polycyclic retrogressive thaw slumps, during the last decade 2010–2020. We calculate the retreat rate of the thermocirque edge using various statistical approaches. We compared thermocirque outlines by the end of each time interval defined by the dates of available very-high-resolution imagery. Six thermocirques within two key sites on the Yugorsky peninsula are monitored. We correlate each of the thermocirque edge’s retreat rates to various climatic parameters obtained at the Amderma weather station to understand the interrelation patterns better. As a result, we find a very low correlation between the retreat rate of each thermocirque and summer warmth, rainfall, and wave action. In general, the activity of thermodenudation decreases in time from the previous decade (2001–2010) to 2010–2020, and from 2010 towards 2020, although the summer warmth trend increases dramatically. A single thermocirque or series of thermocirques expand in response to environmental and geological factors in coastal retreat caused by thermodenudation.

Daily Field Observations of Retrogressive Thaw Slump Dynamics in the Canadian High Arctic

With observed increases in retrogressive thaw slump (RTS) number, rates, and size in recent decades, there is a need to understand these highly dynamic landforms as they impact surrounding ecosystems and infrastructure. There is a general lack of detailed (e.g., daily) field observations of change in RTSs; we help fill this gap by monitoring three RTSs for much of the 2017 thaw period by setting up and tracking survey transects on a near daily basis. We correlated mean daily and cumulative retreat to mean daily air temperature (MDAT), total daily precipitation (TDP), and cumulative thawing degree days (TDD) using various polynomial regressions and Pearson correlation techniques. Our results show that July retreat was highly variable, and periods of increased RTS retreat did not always align with periods of increased air temperature. Also, multiple periods of increased retreat largely driven by sediment distribution in the RTS floor could occur within a single period of increased air temperature. Retreat rates decreased suddenly in early August, indicating a threshold of either air temperature, solar radiation or a combination of both must be reached for increased retreat rates. A statistically significant correlation was found between daily mean and mean cumulative retreat with MDAT (p > 0.001) and TDD (p > 0.001 and > 0.0001) but not with total daily precipitation. Correlating mean cumulative retreat and TDD using polynomial regression generated R2 values greater than 0.99 for all three sites. Both cumulative retreat and TDD account for past and current conditions, as well as lag responses, within the monitoring period. The high R2 values for the correlation of mean cumulative retreat and TDD suggest the potential for accurately modelling RTS retreat with minimal field data (air temperature and headwall position), however modelling is currently restricted to individual RTSs and only within short time scales. Monitoring RTSs on a daily scale allows RTS behaviour and trends to be identified that may be obscured at annual time scales and highlights the importance of all system inputs when considering RTS retreat dynamics.

Thaw-driven mass wasting couples slopes with downstream systems, and effects propagate through Arctic drainage networks

Abstract. The intensification of thaw-driven mass wasting is transforming glacially conditioned permafrost terrain, coupling slopes with aquatic systems, and triggering a cascade of downstream effects. Within the context of recent, rapidly evolving climate controls on the geomorphology of permafrost terrain, we (A) quantify three-dimensional retrogressive thaw slump enlargement and describe the processes and thresholds coupling slopes to downstream systems, (B) investigate catchment-scale patterns of slope thermokarst impacts and the geomorphic implications, and (C) map the propagation of effects through hydrological networks draining permafrost terrain of northwestern Canada. Power-law relationships between retrogressive thaw slump area and volume (R2=0.90), as well as the thickness of permafrost thawed (R2=0.63), combined with the multi-decadal (1986–2018) increase in the areal extent of thaw slump disturbance, show a 2 order of magnitude increase in catchment-scale geomorphic activity and the coupling of slope and hydrological systems. Predominant effects are to first- and second-order streams where sediment delivery, often indicated by formation of recent debris tongue deposits, commonly exceeds the transport capacity of headwater streams by orders of magnitude, signaling centennial- to millennial-scale perturbation of downstream systems. Assessment of hydrological networks indicates that thaw-driven mass wasting directly affects over 5538 km of stream segments, 889 km of coastline, and 1379 lakes in the 994 860 km2 study area. Downstream propagation of slope thermokarst indicates a potential increase in the number of affected lakes by at least a factor of 4 (n>5692) and impacted stream length by a factor of 8 (>44 343 km), and it defines several major impact zones on lakes, deltas, and coastal areas. Prince of Wales Strait is the receiving marine environment for greatly increased sediment and geochemical fluxes from numerous slump-impacted hydrological networks draining Banks Island and Victoria Island. The Peel and Mackenzie rivers are globally significant conveyors of the slope thermokarst cascade, delivering effects to North America's largest Arctic delta and the Beaufort Sea. Climate-driven erosion of ice-rich slopes in permafrost-preserved glaciated terrain has triggered a time-transient cascade of downstream effects that signal the rejuvenation of post-glacial landscape evolution. Glacial legacy, ground-ice conditions, and continental drainage patterns dictate that terrestrial, freshwater, coastal, and marine environments of western Arctic Canada will be an interconnected hotspot of thaw-driven change through the coming millennia.

Automatically quantifying evolution of retrogressive thaw slumps in Beiluhe (Tibetan Plateau) from multi-temporal CubeSat images

Retrogressive thaw slumps (RTSs) are among the most dynamic landforms resulting from the thawing of ice-rich permafrost. However, RTS distribution and evolution are poorly quantified because most of them occur in remote and inaccessible areas. In this study, we propose a method that integrates deep learning, change detection, and medial axis transform, aiming to automatically quantify the RTS development on multi-temporal images in the Beiluhe region on the Tibetan Plateau from 2017 to 2019. The images are taken by the Planet CubeSat constellation with high spatial and temporal resolution. The experiments show that automatic delineation based on deep learning can produce similar results to manual delineation, providing the potential of using these results to quantify the changes of RTS boundaries in different years. Our method reveals that among manually-delineated 342 RTSs in the Beiluhe region, 83% and 76% of them expanded from 2017 to 2018 and 2018 to 2019, respectively. For the expansion from 2017 to 2018, the average and maximum expanding areas are 0.20 ha and 1.47 ha, while the average and maximum retreat distances are 21.3 m and 91 m, respectively. For 2018 to 2019 the average and maximum expansion areas and retreat distances are 0.22 ha, 2.53 ha, 25.0 m, and 212 m, respectively. The results show that the method can quantify RTS development automatically on multi-temporal images but may miss some small and subtle RTSs. Moreover, this study provides the very first quantitative report on RTS development on the Tibetan Plateau, which helps to advance the understanding of permafrost degradation.