Retrogressive Thaw Slumps Research Articles

Classification of cryogenic-landslide landforms for mapping and prediction

A classification of cryogenic-landslide landforms is developed for mapping their distribution and dynamics. It is based on the previously suggested classification subdividing cryogenic landsliding into two main types: cryogenic translational landslides (or active-layer detachment slides), and cryogenic earth flows (or retrogressive thaw slumps). The increased proportion of retrogressive thaw slumps compared to active layer detachments in the North of West Siberia in the last decade creates the need for an expanded classification of cryogenic earth flows. One of the important issues is separating the process of landsliding and resulting landforms, which in English are covered by one term ‘retrogressive thaw slump’. In dealing with the landforms, we distinguish (1) open and (2) closed ones. Open cryogenic-landslide landforms are those formed by the retreating of the coast bluff due to the thaw of ice or ice-rich deposits with an additional impact from wave or stream action. Closed cryogenic-landslide landforms are those initiated on a slope landward, and thawed material is delivered to the coast or stream through an erosional channel. Morphologically we distinguish thermocirques and thermoterraces depending on the shape of the retreating headwall, crescent or linear, respectively. An important issue is the type of ground ice subjected to thaw: tabular, ice-wedge or constitutional ground ice are distinguished. Landforms can be active, stabilized or ancient. One can find both single landforms and their combination. The classification is based on a significant amount of field studies and interpretation of remote sensing data. Mapping of the cryogenic-landslide landforms is suggested using the proposed classification and indication features. The classification is based on the experience obtained mainly in the north of West Siberia. Applying it to other regions may require additional studies.

High-resolution assessment of retrogressive thaw slump susceptibility in the Qinghai-Tibet Engineering Corridor

Under the rapidly warming climate in the Arctic and high mountain areas, permafrost is thawing, leading to various hazards at a global scale. One common permafrost hazard termed retrogressive thaw slump (RTS) occurs extensively in ice-rich permafrost areas. Understanding the spatial and temporal distributive features of RTSs in a changing climate is crucial to assessing the damage to infrastructure and decision-making. To this end, we used a machine learning-based model to investigate the environmental factors that could lead to RTS occurrence and create a susceptibility map for RTS along the Qinghai-Tibet Engineering Corridor (QTEC) at a local scale. The results indicate that extreme summer climate events (e.g., maximum air temperature and rainfall) contributes the most to the RTS occurrence over the flat areas with fine-grained soils. The model predicts that 13% (ca. 22,948 km2) of the QTEC falls into high to very high susceptibility categories under the current climate over the permafrost areas with mean annual ground temperature at 10 m depth ranging from −3 to −1 °C. This study provides insights into the impacts of permafrost thaw on the stability of landscape, carbon stock, and infrastructure, and the results are of value for engineering planning and maintenance.

Identifying active retrogressive thaw slumps from ArcticDEM

Permafrost degradation in the Arctic is accelerating and affects northern communities, ecosystems, and global soil carbon storage. However, the extent, distribution, and rates of permafrost degradation in the pan-Arctic remain unknown, contributing to the challenges of mapping and monitoring it in a harsh and remote environment. We applied feature extraction, deep learning, and crowdsourcing to an open-access, high-resolution (2 m), and multi-temporal digital elevation models (i.e. ArcticDEM), to identify retrogressive thaw slumps (RTSs), a dynamic form of permafrost degradation widespread across the Arctic. Specifically, we (1) developed an automated pipeline to process approximately 200 TB of ArcticDEM data; (2) designed feature extractors (pixel-wise elevation differences, polygons of elevation reductions, lines of narrow-steep slopes, and RTS headwall lines) to identify slumps; (3) trained a super-efficient object detection algorithm based on deep learning (YOLOv4) and used it to locate RTSs from composite imagery derived from the ArcticDEM; (4) combined the extracted features and the bounding boxes output by YOLOv4 to obtain mapping results at a manageable level; and (5) developed a crowdsourcing system and invited volunteers to validate and refine the results. The final map included 2494 RTSs (actively expanding during ArcticDEM observation) across the Arctic. The results also show that (1) it is necessary to combine the extracted features and deep learning to remove many false positives in the scenario with limited training data, but large regions to map; (2) some hotspots of RTSs need further and detailed investigation, including an RTS cluster in Greenland; (3) the crowdsourcing system is useful for the validation of a large dataset, but responses to this work were limited, possibly because RTSs are a quite specific topic that not many people are familiar with. The results likely miss many RTSs due to the limitation in the method for identifying RTSs and uncertainties as well as short observation periods of ArcticDEM at many locations. This study provides data and serves as a starting point to develop a global inventory and better understand permafrost thaw in the pan-Arctic using very high resolution remote sensing.

Carbon dioxide release from retrogressive thaw slumps in Siberia

Thawing of ice-rich permafrost soils in sloped terrain can lead to activation of retrogressive thaw slumps (RTSs) which make organic matter available for decomposition that has been frozen for centuries to millennia. Recent studies show that the area affected by RTSs increased in the last two decades across the pan-Arctic. Combining a model of soil carbon dynamics with remotely sensed spatial details of thaw slump area and a soil carbon database, we show that RTSs in Siberia turned a previous quasi-neutral ecosystem into a strong source of carbon dioxide of 367 ± 213 gC m-1 a-1. On a global scale, recent CO2 emissions from Siberian thaw slumps of 0.42 ± 0.22 Tg carbon per year are negligible so far. However, depending on the future evolution of permafrost thaw and hence thaw slump-affected area, such hillslope processes can transition permafrost landscapes to become a major source of additional CO2 release into the atmosphere.

Potential of Multi-temporal InSAR for Detecting Retrogressive Thaw Slumps: A Case of the Beiluhe Region of the Tibetan Plateau

Permafrost degradation due to climate warming is severely reducing slope stability by increasing soil pore water pressure and decreasing shear strength. Retrogressive thaw slumps (RTSs) are among the most dynamic landforms in permafrost areas, which can result in the instability of landscape and ecosystem. However, the spatiotemporal characteristics of surface deformation of RTSs are still unclear, and the potentials of deformation properties in mapping large-scale RTSs need to be further assessed. In this study, we applied a multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) method to map the spatiotemporal variations in surface deformation of RTSs in the Beiluhe region of the Tibetan Plateau by using 112 scenes of Sentinel-1 SAR data acquired from 2017 to 2021. The deformation rates of RTSs ranged from − 35 to 20 mm/year, and three typical motion stages were inferred by analyzing the deformation variation trend of the headwall of RTSs: stable, abrupt thaw, and linear subsidence. A total of 375 RTSs were identified in the Mati Hill region by combining InSAR-based deformation results with visual interpretation of optical remote sensing images. Among them, 76 RTSs were newly developed, and 26% more than the inventory derived from the optical images alone. This study demonstrated that the combination of InSAR-derived deformation with optical images has significant potential for detecting RTSs with high accuracy and efficiency at the regional scale.

Three-Dimensional Numerical Modeling of Ground Ice Ablation in a Retrogressive Thaw Slump and Its Hydrological Ecosystem Response on the Qinghai-Tibet Plateau, China

Retrogressive thaw slumps (RTSs), which frequently occur in permafrost regions of the Qinghai-Tibet Plateau (QTP), China, can cause significant damage to the local surface, resulting in material losses and posing a threat to infrastructure and ecosystems in the region. However, quantitative assessment of ground ice ablation and hydrological ecosystem response was limited due to a lack of understanding of the complex hydro-thermal process during RTS development. In this study, we developed a three-dimensional hydro-thermal coupled numerical model of a RTS in the permafrost terrain at the Beilu River Basin of the QTP, including ice–water phase transitions, heat exchange, mass transport, and the parameterized exchange of heat between the active layer and air. Based on the calibrated hydro-thermal model and combined with the electrical resistivity tomography survey and sample analysis results, a method for estimating the melting of ground ice was proposed. Simulation results indicate that the model effectively reflects the factual hydro-thermal regime of the RTS and can evaluate the ground ice ablation and total suspended sediment variation, represented by turbidity. Between 2011 and 2021, the maximum simulated ground ice ablation was in 2016 within the slump region, amounting to a total of 492 m3, and it induced the reciprocal evolution, especially in the headwall of the RTS. High ponding depression water turbidity values of 28 and 49 occurred in the thawing season in 2021. The simulated ground ice ablation and turbidity events were highly correlated with climatic warming and wetting. The results offer a valuable approach to assessing the effects of RTS on infrastructure and the environment, especially in the context of a changing climate.

Assessing multi-hazard susceptibility to cryospheric hazards: Lesson learnt from an Alaskan example

Classifying a given landscape on the basis of its susceptibility to surface processes is a standard procedure in low to mid-latitudes. Conversely, these procedures have hardly been explored in periglacial regions. However, global warming is radically changing this situation and will change it even more in the future. For this reason, understanding the spatial and temporal dynamics of geomorphological processes in peri-arctic environments can be crucial to make informed decisions in such unstable environments and shed light on what changes may follow at lower latitudes. For this reason, here we explored the use of data-driven models capable of recognizing locations prone to develop retrogressive thaw slumps (RTSs) and/or active layer detachments (ALDs). These are cryospheric hazards induced by permafrost degradation, and their development can negatively affect human settlements or infrastructure, change the sediment budget and release greenhouse gases. Specifically, we test a binomial Generalized Additive Modeling structure to estimate the probability of RST and ALD occurrences in the North sector of the Alaskan territory. The results we obtain show that our binary classifiers can accurately recognize locations prone to RTS and ALD, in a number of goodness-of-fit (AUCRTS = 0.83; AUCALD = 0.86), random cross-validation (mean AUCRTS = 0.82; mean AUCALD = 0.86), and spatial cross-validation (mean AUCRTS = 0.74; mean AUCALD = 0.80) routines. Overall, our analytical protocol has been implemented to build an open-source tool scripted in Python where all the operational steps are automatized for anyone to replicate the same experiment. Our protocol allows one to access cloud-stored information, pre-process it, and download it locally to be integrated for spatial predictive purposes.

Distribution and Morphometry of Thermocirques in the North of West Siberia, Russia

The Arctic zone of West Siberia (Yamal and Gydan peninsulas) is an area with continuous permafrost and tabular ground ice close to the surface, active thermodenudation, and related landforms: retrogressive thaw slumps (RTS); in Russian referred to as thermocirques (TC). The dimensions of most TCs have not been determined so far. We use Sentinel 2 imagery to measure each TC area ranging from 0.55 to 38 ha with a median of 2.5 ha. Around 95% of TCs have an area of less than 10 ha. The largest areas are gained due to the merging of several neighboring TCs. The ArcticDEM is used to determine TC edge elevation and slope angle. In general, the Median TC of the Yamal peninsula has an area of 1.8 ha, an elevation of the edge of 17.7 m, and a slope angle of 2.5°. The Median TC of the Gydan peninsula has an area of 2.6 ha, elevation of the edge of 29.4 m, and slope angle of 3°. TCs of the Gydan peninsula occupy higher positions and slightly steeper slopes compared to TCs of the Yamal peninsula. The ranges of the median and the largest TC areas are consistent with the reported RTS dimensions in North America.

Batagay megaslump: A review of the permafrost deposits, Quaternary environmental history, and recent development

AbstractThe Batagay megaslump, in the Yana Uplands of northern Yakutia, Russia, is the largest known retrogressive thaw slump in the world. The slump exposes a remarkable sequence of Ice Age permafrost deposits that record the interaction of colluvial, eolian and periglacial processes on a hillslope episodically forested during the last 650 ka or more in response to climate variability on glacial–interglacial timescales. Numerous bones, teeth, and occasional carcasses of Pleistocene and Holocene mammals have been recovered from the permafrost. The megaslump developed over the course of several decades in three stages: (1) gullying, (2) thaw slumping, and (3) megaslumping. After disturbance to the taiga vegetation cover in the 1940s–1960s, a hillslope gully formed by the early 1960s. The gully initiated thaw slumping along its central part during the 1980s, with the slump enlarging to megaslump (>20 ha) proportions during the 1990s. By 2019, the area of the slump had reached about 80 ha and its headwall above the slump floor was up to about 55 m high. The main geomorphic processes of slump growth are headwall ablation and thermal erosion, producing a distinctive terrain of icy badlands on the slump floor. Though much of the megaslump is rapidly growing at present, it will probably stabilize eventually as an irregular terrain characterized by sandy ridges and sand‐filled elongate depressions formed by degradation of the badlands. Comparison of the Batagay megaslump with megaslumps from northwest Canada reveals several similarities and differences in terms of their geomorphology, permafrost deposits, and Quaternary history.

Missing nitrogen source during ecosystem succession within retrogressive thaw slumps in Alaska

Retrogressive thaw slumps (RTS)—thermal erosion of soil and vegetation after ground ice thaw—are increasing. Recovery of plant biomass after RTS is important for maintaining Arctic carbon (C) stocks and is regulated by nutrient availability for new plant growth. Many RTS are characterized by verdant shrub growth mid-succession, atypical of the surrounding nutrient-limited tundra. Here, we investigated the potential for internal and external sources of nitrogen (N) and phosphorus (P) to support mid-successional shrub growth at three Alaskan RTS chronosequences. We assessed patterns of soil and microbial CNP, soil NP cycling rates and stocks, N inputs via biological N2-fixation, and thaw leachate over time after disturbance. We found a clear transfer of P stocks from mineral to organic soils with increasing site age, yet insufficient N from any one source to support observed shrub growth. Instead, multiple mechanisms may have contributed to mid-successional shrub growth, including sustained N-cycling with reduced plant biomass, N leaching from undisturbed tundra, uninvestigated sources of N2-fixation, and most promising given the large resource, deep mineral soil N stocks. These potential mechanisms of N supply are critical for the regulation of the Arctic C cycle in response to an increasingly common climate-driven disturbance.

Dramatic Carbon Loss in a Permafrost Thaw Slump in the Tibetan Plateau is Dominated by the Loss of Microbial Necromass Carbon.

Thaw slumps can lead to considerable carbon loss in permafrost regions, while the loss of components from two major origins, i.e., microbial and plant-derived carbon, during this process remains poorly understood. Here, we provide direct evidence that microbial necromass carbon is a major component of lost carbon in a retrogressive permafrost thaw slump by analyzing soil organic carbon (SOC), biomarkers (amino sugars and lignin phenols), and soil environmental variables in a typical permafrost thaw slump in the Tibetan Plateau. The retrogressive thaw slump led to a ∼61% decrease in SOC and a ∼25% SOC stock loss. As evident in the levels of amino sugars (average of 55.92 ± 18.79 mg g-1 of organic carbon, OC) and lignin phenols (average of 15.00 ± 8.05 mg g-1 OC), microbial-derived carbon (microbial necromass carbon) was the major component of the SOC loss, accounting for ∼54% of the SOC loss in the permafrost thaw slump. The variation of amino sugars was mainly related to the changes in soil moisture, pH, and plant input, while changes in lignin phenols were mainly related to the changes in soil moisture and soil bulk density.

Evidence for preservation of organic carbon interacting with iron in material displaced from retrogressive thaw slumps: Case study in Peel Plateau, western Canadian Arctic

In northern high latitudes, rapid warming is set to amplify carbon-climate feedbacks by enhancing permafrost thaw and biogeochemical transformation of large amounts of soil organic carbon. However, between 30 % and 80 % of permafrost soil organic carbon is considered to be stabilized by geochemical interactions with the soil mineral pool and thus less susceptible to be emitted as greenhouse gases. Quantification of the nature of and controls on mineral-organic carbon interactions is needed to better constrain permafrost-carbon-climate feedbacks, particularly in ice-rich environments resulting in rapid thaw and development of thermokarst landforms. On sloping terrain, mass wasting features called retrogressive thaw slumps are amongst the most dynamic forms of thermokarst. These multi-decadal disturbances grow due to ablation of an ice-rich headwall, and their enlargement due to warming of the Arctic is mobilizing vast stores of previously frozen materials. Here, we investigate headwall profiles of seven retrogressive thaw slumps and sediments displaced from these mass wasting features from the Peel Plateau, western Canadian Arctic. The disturbances varied in their headwall height (2 to 25 m) and affected land surface area (<1 to > 30 ha). We present total and water extractable mineral element concentrations, mineralogy, and mineral-organic carbon interactions in the headwall layers (active layer, permafrost materials above an early Holocene thaw unconformity, and Pleistocene-aged permafrost tills) and in displaced material (suspended sediments in runoff and material accumulated on the debris tongue). Our data show that the main mechanism of organic carbon stabilization through mineral-organic carbon interactions within the headwall is the complexation with metals (mainly iron), which stabilizes 30 ± 15 % of the total organic carbon pool with higher concentrations in near-surface layers compared to deep permafrost. In the displaced material, this proportion drops to 18 ± 5 %. In addition, we estimate that up to 12 ± 5% of the total organic carbon is stabilized by associations to poorly crystalline iron oxides, with no significant difference between near-surface layers, deep permafrost and displaced material. Our findings suggest that the organic carbon interacting with the sediment mineral pool in slump headwalls is preserved in the material mobilized by slumping and displaced as debris. Overall, up to 32 ± 6 % of the total organic carbon displaced by retrogressive thaw slumps is stabilized by organo-mineral interactions in this region. This indicates that organo-mineral interactions play a significant role in the preservation of organic carbon in the material displaced from retrogressive thaw slumps over years to decades after their development resulting in decadal to centennial scale sequestration of this retrogressive thaw slump-mobilized organic carbon interacting with the soil mineral pool.

A transfer learning approach for automatic mapping of retrogressive thaw slumps (RTSs) in the western Canadian Arctic

ABSTRACT Retrogressive thaw slumps (RTSs) are thermokarst landforms that develop on slopes in permafrost regions when thawing permafrost causes the land surface to collapse. RTSs are an indicator of climate change and pose a threat to human infrastructure and ecosystems in the affected areas. As the availability of ready-to-use high-resolution satellite imagery increases, automatic RTS mapping is being explored with deep learning methods. We employed a pre-trained Mask-RCNN model to automatically map RTSs on Banks Island and Victoria Island in the western Canadian Arctic, where there is extensive RTS activity. We tested the model with different settings, including image band combinations, backbones, and backbone trainable layers, and performed hyper-parameter tuning and determined the optimal learning rate, momentum, and decay rate for each of the model settings. Our final model successfully mapped most of the RTSs in our test sites, with F1 scores ranging from 0.61 to 0.79. Our study demonstrates that transfer learning from a pre-trained Mask-RCNN model is an effective deep learning model that has the potential to be applied for RTS mapping across the Canadian Arctic.

Quantifying the effect of a retrogressive thaw slump on soil freeze–thaw erosion in permafrost regions on the Qinghai–Tibet Plateau, China

AbstractThermokarst terrain is developing at an accelerating pace in the ice‐rich permafrost on the Qinghai–Tibet Plateau (QTP), China, and the most dramatic of these terrain‐altering thermokarsts is retrogressive thaw slump (RTS). The freeze–thaw erosion (FTE) impacts are sharply increasing on the Plateau due to RTS, especially as a result of the migration of fine sediments in cold climates, these impacts are still not quantified due to the limitation of hydro‐thermal‐mass transport laws in RTS development. Moreover, it is difficult to assess the impact of RTS on the ecology and environment, especially on soil erosion. This study developed a heat–water‐mass transport coupled model of a RTS in the Beiluhe River Region on the QTP, considering the actual topography, water‐ice phase change, latent heat, and surface heat exchange layer. Based on the observed data of ground temperature, unfrozen water content, and heat flux, the coupled model herein is practicable for presenting the geotemperature regime and groundwater flow in the RTS area, thereby quantifying the ice‐rich permafrost thaw and mass wasting. The results presented indicate that: (1) the seepage velocity of the superficial zone (0–1.5 m depth) is two orders of magnitude higher than that of the permafrost table; (2) the mean ice‐rich permafrost thaw volume was 13.4 m2 from 2016 to 2021; and (3) the cumulative mass transport volume was 22 m2 from July 2020 to September 2021. In addition, the relation between the FTE (shown as the migration of sediments) and the amount of ground ice ablation can be fitted by an exponential equation. This work proposes a reliable method for quantifying the effect of FTE and is helpful to assess the eco‐environmental impacts of RTS.

Three-dimensional subsurface architecture and its influence on the spatiotemporal development of a retrogressive thaw slump in the Richardson Mountains, Northwest Territories, Canada

ABSTRACT The development of retrogressive thaw slumps (RTS) is known to be strongly influenced by relief-related parameters, permafrost characteristics, and climatic triggers. To deepen the understanding of RTS, this study examines the subsurface characteristics in the vicinity of an active thaw slump, located in the Richardson Mountains (Western Canadian Arctic). The investigations aim to identify relationships between the spatiotemporal slump development and the influence of subsurface structures. Information on these were gained by means of electrical resistivity tomography (ERT) and ground-penetrating radar (GPR). The spatiotemporal development of the slump was revealed by high-resolution satellite imagery and unmanned aerial vehicle–based digital elevation models (DEMs). The analysis indicated an acceleration of slump expansion, especially since 2018. The comparison of the DEMs enabled the detailed balancing of erosion and accumulation within the slump area between August 2018 and August 2019. In addition, manual frost probing and GPR revealed a strong relationship between the active layer thickness, surface morphology, and hydrology. Detected furrows in permafrost table topography seem to affect the active layer hydrology and cause a canalization of runoff toward the slump. The three-dimensional ERT data revealed a partly unfrozen layer underlying a heterogeneous permafrost body. This may influence the local hydrology and affect the development of the RTS. The results highlight the complex relationships between slump development, subsurface structure, and hydrology and indicate a distinct research need for other RTSs.

Mapping retrogressive thaw slumps using deep neural networks

Retrogressive thaw slumps (RTS) are thermokarst features in ice-rich hillslope permafrost terrain, and their occurrence in the warming Arctic is increasingly frequent and has caused dynamic changes to the landscape. RTS can significantly impact permafrost stability and generate substantial carbon emissions. Understanding the spatial and temporal distribution of RTS is a critical step to understanding and modelling greenhouse gas emissions from permafrost thaw. Mapping RTS using conventional Earth observation approaches is challenging due to the highly dynamic nature and often small scale of RTS in the Arctic. In this study, we trained deep neural network models to map RTS across several landscapes in Siberia and Canada. Convolutional neural networks were trained with 965 RTS features, where 509 were from the Yamal and Gydan peninsulas in Siberia, and 456 from six other pan-Arctic regions including Canada and Northeastern Siberia. We further tested the impact of negative data on the model performance. We used 4-m Maxar commercial imagery as the base map, 10-m NDVI derived from Sentinel-2 and 2-m elevation data from the ArcticDEM as model inputs and applied image augmentation techniques to enhance training. The best-performing model reached a validation Intersection over Union (IoU) score of 0.74 and a test IoU score of 0.71. Compared to past efforts to map RTS features, this represents one of the best-performing models and generalises well for mapping RTS in different permafrost regions, representing a critical step towards pan-Arctic deployment. The predicted RTS matched very well with the ground truth labels visually. We also tested how model performance varied across different regional contexts. The result shows an overall positive impact on the model performance when data from different regions were incorporated into the training. We propose this method as an effective, accurate and computationally undemanding approach for RTS mapping.

Impact of a retrogressive thaw slump on surrounding vegetation communities in the Fenghuoshan mountains, Qinghai-Tibet Plateau

Under global warming, permafrost around the world is experiencing degradation which is especially so on the Third Pole, the Qinghai-Tibet Plateau (QTP), China. Retrogressive thaw slump (RTS) is one of the thermokarst features caused by rapid degradation of ice rich permafrost, which transforms landforms and threatens infrastructures, and even affects the terrestrial carbon cycle. In this work, vegetation communities surrounding a RTS in the Fenghuoshan Mountains of the interior portion of the Qinghai-Tibet Plateau have been investigated to examine the impact from RTS. This investigation indicates that the occurrence of RTS influences the vegetation community by altering their habitats, especially the soil water content, which forces the vegetation community to evolve in order to adapt to the alterations. In the interior part of RTS where it has been disturbed tremendously, alterations have produced a wider niche and richer plant species. This favors species of a wet environment in a habitat where it was a relatively dry environment of alpine steppe prior to the occurrence of RTS. This study adds to limited observations regarding the impact of RTS to vegetation community on the QTP and helps us to reach a broader understanding of the effects of permafrost degradation as well as global warming.

Transient Freeze‐Thaw Deformation Responses to the 2018 and 2019 Fires Near Batagaika Megaslump, Northeast Siberia

AbstractWildfires in Arctic regions impact landforms via permafrost degradation and subsequent deformation that can last for many years. However, it remains uncertain on if and how much deformations occur, and what controls their magnitude, particularly during the first couple of years. Here, we examine the transient post‐fire deformation responses near the Batagaika megaslump, which is the world's largest retrogressive thaw slump at Batagay, Sakha Republic. There were wildfires in the summers of 2018 and 2019 on the same slope, which could trigger the formation of another megaslump; many fires occurred nearby in 2019. We use interferometric synthetic aperture radar (InSAR) to measure surface displacements, including both post‐fire and span‐fire images. We also perform onsite measurements of temperature and thaw depth around the two scars near Batagaika megaslump in 2019, 2020, and 2021 and around the 2014 scar in 2019. At the three fire scars formed in 2018 and 2019, we demonstrate year‐to‐year and location‐specific changes in the amplitude of subsidence, heave, and duration. The 2018 scar shows cumulative subsidences of up to 10 cm by March 2021, more clearly than the nearby 2019 scar. On the other hand, another 2019 scar adjacent to the 2014 scar shows up to 13 cm net subsidence during the first span‐fire year, although the subsiding area is limited. These diverse transient post‐fire responses demonstrate that under the yedoma area the spatial heterogeneities of the active layer depth and the timing of fires will control subsequent thermokarst processes.

MT-InSAR Unveils Dynamic Permafrost Disturbances in Hoh Xil (Kekexili) on the Tibetan Plateau Hinterland

Hoh Xil is an uninhabited extremity secluded on the Tibetan Plateau hinterland. A complete mapping of ground motion variation in Hoh Xil is essential for in-depth understanding of terrain’s responses to climate change on the Tibetan Plateau. However, the inaccessibility and extremely harsh environment impeded extensive field investigations on landform alteration and its formative process. Such difficulty can be resolved by Interferometric Synthetic Aperture Radar (InSAR), which enables a broad detection of subtle permafrost motions at millimeter precision. This study, for the first time, accomplished a Multi-temporal InSAR (MT-InSAR) deformation mapping from 2015 to 2020 in Hoh Xil, with a wide coverage of about 200,000 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . 1,592 Sentinel-1 images were processed based on the small baseline subset (SBAS) technique. The results show that Hoh Xil was experiencing dynamic permafrost disturbances. Thawing permafrost with both the linear subsidence rate higher than 2 mm/yr and the periodic amplitude over 2 mm was primarily detected in areas of flat or gentle slopes. The InSAR cumulative deformation is highly correlated with permafrost thawing depth. Significant lag times were identified between seasonal oscillation of InSAR deformation and land surface temperature (LST). Thermokarst landforms of retrogressive thaw slumps and thermokarst lakes broadly formed and dynamically evolved as a consequence of permafrost degradation. Particularly, widespread thawing permafrost characterized by the spatial clustering of thermokarst lakes appeared to occur in areas adjacent to large lakes. The discovered dynamic permafrost disturbances in Hoh Xil manifested even the secluded Tibetan Plateau hinterland was facing the threat of climate change.

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.