Retrogressive Thaw Slumps Research Articles

Review article: Retrogressive thaw slump characteristics and terminology

Abstract. Retrogressive thaw slumps (RTSs) are spectacular landforms that occur due to the thawing of ice-rich permafrost or melting of massive ground ice, often in hillslope terrain. RTSs occur in the Arctic, the subarctic, and high mountain (Qinghai–Tibet Plateau) permafrost regions and are observed to expand in size and number due to climate warming. As the observation of RTSs is receiving more and more attention due to their important role in permafrost thaw; impacts on topography; mobilization of sediment, carbon, nutrients, and contaminants; and their effects on downstream hydrology and water quality, the thematic breadth of studies increases and scientists from different scientific backgrounds and perspectives contribute to new RTS research. At this point, a wide range of terminologies originating from different scientific schools is used, and we identified the need to provide an overview of variable characteristics of RTSs to clarify terminologies and ease the understanding of the literature related to RTS processes, dynamics, and feedbacks. We review the theoretical geomorphological background of RTS formation and landform characteristics to provide an up-to-date understanding of the current views on terminology and underlying processes. The presented overview can be used not only by the international permafrost community but also by scientists working on ecological, hydrological, and biogeochemical consequences of RTS occurrence and by remote-sensing specialists developing automated methods for mapping RTS dynamics. The review will foster a better understanding of the nature and diversity of RTS phenomena and provide a useful base for experts in the field but also ease the introduction to the topic of RTSs for scientists who are new to it.

A Labeling Intercomparison of Retrogressive Thaw Slumps by a Diverse Group of Domain Experts

ABSTRACTDeep‐learning (DL) models have become increasingly beneficial for the detection of retrogressive thaw slumps (RTS) in the permafrost domain. However, comparing accuracy metrics is challenging due to unstandardized labeling guidelines. To address this, we conducted an experiment with 12 international domain experts from a broad range of scientific backgrounds. Using 3 m PlanetScope multispectral imagery, they digitized RTS footprints in two sites. We evaluated label uncertainty by comparing manually outlined RTS labels using Intersection‐over‐Union (IoU) and F1 metrics. At the Canadian Peel Plateau site, we see good agreement, particularly in the active parts of RTS. Differences were observed in the interpretation of the debris tongue and the stable vegetated sections of RTS. At the Russian Bykovsky site, we observed a larger mismatch. Here, the same differences were documented, but several participants mistakenly identified non‐RTS features. This emphasizes the importance of site‐specific knowledge for reliable label creation. The experiment highlights the need for standardized labeling procedures and definition of their scientific purpose. The most similar expert labels outperformed the accuracy metrics reported in the literature, highlighting human labeling capabilities with proper training, site knowledge, and clear guidelines. These findings lay the groundwork for DL‐based RTS monitoring in the pan‐Arctic.

Formation and evolution of thermokarst landslides in the Qinghai-Tibet Plateau, China

Thermokarst landslide (TL) activity in the Qinghai-Tibet Plateau (QTP) is intensifying due to climate warming-induced permafrost degradation. However, the mechanisms driving landslide formation and evolution remain poorly understood. This study investigates the spatial distribution, annual frequency, and monthly dynamics of TLs along the Qinghai-Tibet engineering corridor (QTEC), in conjunction with in-situ temperature and rainfall observations, to elucidate the interplay between warming, permafrost degradation, and landslide activity. Through the analysis of high-resolution satellite imagery and field surveys, we identified 1298 landslides along the QTEC between 2016 and 2022, with an additional 386 landslides recorded in a typical landslide-prone sub-area. In 2016, 621 new active-layer detachments (ALDs) were identified, 1.3 times the total historical record. This surge aligned with unprecedented mean annual and August temperatures. The ALDs emerged primarily between late August and early September, coinciding with maximum thaw depth. From 2016 to 2022, 97.8 % of these ALDs evolved into retrogressive thaw slumps (RTSs), identified as active landslides. Landslides typically occur in alpine meadows at moderate altitudes and on gentle northward slopes. The thick ice layer near the permafrost table serves as the material basis for ALD occurrence. Abnormally high temperature significantly increased the active layer thickness (ALT), resulting in melting of the ice layer and formation of a thawed interlayer, which was the direct causing factor for ALD. By altering the local material, micro-topography, and thermal conditions, ALD activity significantly increases RTS susceptibility. Understanding the mechanisms of ALD formation and evolution into RTS provides a theoretical foundation for infrastructure development and disaster mitigation in extreme environments.

Spatial distribution and dynamics of thermocirques in a key area of Central Yamal based on remote sensing data

Т. In recent decades, the distribution and activation of thermodenudation, which leads to the formation of specific landforms — thermocirques (also referred to as retrogressive thaw slumps, RTS), have been intensively studied. In different regions of the cryolithozone and at different time intervals, both activation and stabilization of thermocirques are observed. As a rule, studies focus on the climatic controls of the phenomena observed, the environmental controls are discussed less often. This study presents an analysis of the dynamics of thermocirques in relation to the relief features in a specific key area of Central Yamal. To achieve this aim, the spatial distribution of thermocirques at different geomorphological levels is considered based on multi-temporal remote sensing data. Satellite images obtained in 2009, 2018, and 2023, as well as a global digital elevation model (ArcticDEM), were used. We outlined five geomorphic levels and determined their parameters: area, altitude, steepness, and the aspect of the slopes. Thermocirques were identified in the images and their parameters were measured. The dynamics of the thermocirques were analyzed by their number, area, length, width, slope aspect and angle for the periods 2009–2018 and 2018–2023, and for 14 years in total, separately for each geomorphic level. It was found that thermocirques predominate on the slopes of the III alluvial-marine plain, 5–12° steep. In 14 years, the total area of thermocirques increased by 296 %, and their number — by 61 %. A larger increase in the total area and number of thermocirques occurred during the period 2009–2018 in response to climate extremes in 2012 and 2016. Thermocirques that face west cover a higher total area, partly due to the predominance of such slopes over the area of the key site. In all the years of observation, the average areas and lengths of thermocirques are maximum on south-facing slopes. Some of the results are close to those obtained in other regions of Russia and in North America. In many of the areas studied, the increase in the total area of thermocirques exceeded the increase in their number, which means that the expansion of the existing forms prevails over the inception of the new ones. The discrepancies observed in different studies in the results of assessing the effect of relief on thermocirques are due to both the regional features and differences in satellite imagery and methods of its processing.

Life Cycles and Polycyclicity of Mega Retrogressive Thaw Slumps in Arctic Permafrost Revealed by 2D/3D Geophysics and Long‐Term Retreat Monitoring

AbstractMega retrogressive thaw slumps (MRTS, >106 m3) are a major threat to Arctic infrastructure, alter regional biogeochemistry, and impact Arctic carbon budgets. However, processes initiating and reactivating MRTS are insufficiently understood. We hypothesize that MRTS preferentially develop a polycyclic behavior because the material is thermally and mechanically prepared for subsequent generation failure. In contrast to remote sensing, geophysical reconnaissance reveals the inner structure and relative thermal state of MRTS decameters beneath slump surfaces, potentially controlling polycyclicity. Based on their life cycle development, five (M)RTS were studied on Herschel Island, an MRTS hotspot on the Canadian Beaufort coast. We combine >2 km of electrical resistivity tomography (ERT), 500 m of ground‐penetrating radar (GPR) and annual monitoring of headwall retreat from 2004 to 2013 to reveal the thermal state, internal structure, and volume loss of slumps. ERT data were calibrated with unfrozen‐frozen transitions from frost probing of active layer thickness and shallow boreholes. In initial stage MRTS, ERT displays surficial thermal perturbations a few meters deep, coincident with recent mud pool and mud flow development. In early stage polycyclic MRTS, ERT shows decameter deep‐reaching thermal perturbations persisting even 300 years after the last activation. In peak‐stage polycyclic MRTS, 3D‐ERT highlights actively extending deep‐reaching thermal perturbations caused by gully incisions, mud slides and mud flows. GPR and headwall monitoring reveal structural disturbance by historical mud flows, ice‐rich permafrost, and a decadal quantification of headwall retreat and slump floor erosion. We show that geophysical signatures identify long‐lasting thermal and mechanical disturbances in MRTS predefining their susceptibility to polycyclic reactivation.

Thermokarst landslides susceptibility evaluation across the permafrost region of the central Qinghai-Tibet Plateau: Integrating a machine learning model with InSAR technology

Thermokarst landslides (TLs), which are made up of retrogressive thaw slumps (RTSs) and active-layer detachments slides (ALDSs), are quickly increasing in the central Qinghai-Tibet Plateau (QTP) permafrost area. TLs induce many environmental problems and threaten the safety of infrastructure. A landslide susceptibility map is crucial to prevent the negative impacts of these landslides. However, traditional thermokarst landslides susceptibility (TLS) evaluations do not consider real-time surface deformation information. Therefore, we propose a novel method that integrates a conventional machine learning model with ground surface deformation. Nine influencing factors, including slope, normalized difference vegetation index, elevation, precipitation, thawing degree days, soil content, active layer thickness, water content, and vegetation type were selected based on the q-index detector, and support vector machine was employed to obtain an initial model (IM). We obtained surface deformation in the study area using the enhanced Small Baseline Subset (SBAS) method. The accuracy of the InSAR results was validated through comparison with data from two field monitoring cross-sections. Subsequently, we established an integrated model (ITM) by combining the initial model with surface deformation using the contribution matrix, and confirmed the fusion model’s higher rationality and accuracy through comparison with the IM. Furthermore, we examined the impact of the quadtree segmentation method on atmospheric correction and validated the TLS results obtained using the ITM with high-resolution optical remote sensing imagery from GF6. Finally, the influencing factors and distribution characteristics of TLS was analyzed, and the results indicated that climatic conditions are the primary factors affecting the distribution of TLS.

Observed Retrogressive Thaw Slump Evolution in the Qilian Mountains

Climate warming can lead to permafrost degradation, potentially resulting in slope failures such as retrogressive thaw slumps (RTSs). The formation of and changes in RTSs could exacerbate the degradation of permafrost and the environment in general. The mechanisms of RTS progression and the potential consequences on the analogous freeze–thaw cycle are not well understood, owing partly to necessitating field work under harsh conditions and with high costs. Here, we used multi-source remote sensing and field surveys to quantify the changes in an RTS on Eboling Mountain in the Qilian Mountain Range in west-central China. Based on optical remote sensing and SBAS-InSAR measurements, we analyzed the RTS evolution and the underlying drivers, combined with meteorological observations. The RTS expanded from 56 m2 in 2015 to 4294 m2 in 2022, growing at a rate of 1300 m2/a to its maximum in 2018 and then decreasing. Changes in temperature and precipitation play a dominant role in the evolution of the RTS, and the extreme weather in 2016 may also be a primary contributor to the accelerated growth, with an average deformation of −8.3 mm during the thawing period, which decreased slope stability. The RTS evolved more actively during the thawing and early freezing process, with earthquakes having potentially contributed further to RTS evolution. We anticipate that the rate of RTS evolution is likely to increase in the coming years.

A Comparison of Satellite Imagery Sources for Automated Detection of Retrogressive Thaw Slumps

A Comparison of Satellite Imagery Sources for Automated Detection of Retrogressive Thaw Slumps

Thermo‐hydro‐mechanical coupled material point method for modeling freezing and thawing of porous media

AbstractClimate warming accelerates permafrost thawing, causing warming‐driven disasters like ground collapse and retrogressive thaw slump (RTS). These phenomena, involving intricate multiphysics interactions, phase transitions, nonlinear mechanical responses, and fluid‐like deformations, and pose increasing risks to geo‐infrastructures in cold regions. This study develops a thermo‐hydro‐mechanical (THM) coupled single‐point three‐phase material point method (MPM) to simulate the time‐dependent phase transition and large deformation behavior arising from the thawing or freezing of ice/water in porous media. The mathematical framework is established based on the multiphase mixture theory in which the ice phase is treated as a solid constituent playing the role of skeleton together with soil grains. The additional strength due to ice cementation is characterized via an ice saturation‐dependent Mohr–Coulomb model. The coupled formulations are solved using a fractional‐step‐based semi‐implicit integration algorithm, which can offer both satisfactory numerical stability and computational efficiency when dealing with nearly incompressible fluids and extremely low permeability conditions in frozen porous media. Two hydro‐thermal coupling cases, that is, frozen inclusion thaw and Talik closure/opening, are first benchmarked to show the method can correctly simulate both conduction‐ and convection‐dominated thermal regimes in frozen porous systems. The fully THM responses are further validated by simulating a 1D thaw consolidation and a 2D rock freezing example. Good agreements with experimental results are achieved, and the impact of hydro‐thermal variations on the mechanical responses, including thaw settlement and frost heave, are successfully captured. Finally, the predictive capability of the multiphysics MPM framework in simulating thawing‐triggered large deformation and failure is demonstrated by modeling an RTS and the settlement of a strip footing on thawing ground.

Recent rapid initiation and growth of retrogressive thaw slumps in the Hoh Xil region of the Qinghai-Tibetan Plateau

With persistent climate warming, a rapid increase in retrogressive thaw slump (RTS) activity has been observed in the permafrost region of the Qinghai-Tibetan Plateau (QTP). However, the initiation and growth dynamics of these RTSs have not yet been fully investigated. In this study, annual RTS observations from 2013 to 2021 within the Hoh Xil region (HXR), based on field investigations and satellite imagery interpretations, are presented. The total number and affected areas of RTSs in the HXR significantly increased during the last 8 years, and such an increase mainly occurred in 2016 when an anomalously high air temperature appeared during the thawing season, which induced the massive occurrence of active layer detachment slides. The mean headwall retreat rate of RTSs in the HXR ranged from 2.4–3.2 m/y from 2013, or their initialization, to 2021, and the highest headwall retreat occurred in the first 2–3 years after RTS initiation. In addition, the headwall retreat rate of RTSs in the study region was significantly affected by the climatic factors, including average daily air temperature, thawing degree days, and cumulative precipitation and also controlled by the ratio of slump floor slope to undisturbed terrain slope, the headwall height, and permafrost conditions. The study’s findings can serve as a reference for hazard mitigation and regional carbon cycle assessment of the QTP.

Spatiotemporal Mechanism-Based Spacetimeformer Network for InSAR Deformation Prediction and Identification of Retrogressive Thaw Slumps in the Chumar River Basin

The increasing incidence of retrogressive thaw slumps (RTSs) in permafrost regions underscores the need for detailed spatial and temporal analysis using InSAR technology to monitor and predict dynamic changes in the process of RTSs. Nevertheless, current InSAR deformation forecasting methods employing deep learning strategies such as the traditional long short-term memory (LSTM) and recent transformer models encounter difficulties in effectively capturing temporal features. Moreover, they are limited in their ability to directly integrate spatial information. In this paper, an innovative deep learning approach named Spacetimeformer is proposed for predicting medium- and short-term InSAR deformation of RTSs in the Chumar River area. This method employs a transformer architecture with a spatiotemporal attention mechanism, which enhances the long-term prediction capabilities of time series models and dynamic spatial modeling. It is applicable to multivariate InSAR spatiotemporal deformation prediction problems. The findings include a list of 72 RTSs compiled based on derived InSAR deformation maps and Sentinel-2 optical images, of which 64 have an average deformation rate exceeding 10 mm/year, indicating signs of permafrost degradation. The density distribution of the displacement maps predicted by the Spacetimeformer model aligned well with the InSAR deformation maps obtained from the small baseline subset (SBAS) method, with the overall prediction deviation controlled within 20 mm. In addition, the point-scale prediction results were compared with LSTM and transformer models. This study indicates that the Spacetimeformer network achieved good results in predicting the deformation of RTSs, with a root mean square error of 1.249 mm. The Spacetimeformer method for deformation prediction with the spacetime mechanism presented in this study can serve as a general framework for multivariate deformation prediction based on InSAR results. It can also quantitatively assess the spatial deformation characteristics and deformation trends of RTSs.

Retrogressive thaw slump susceptibility in the northern hemisphere permafrost region

AbstractMean annual temperatures in the Arctic and subarctic have increased in recent decades, increasing the number of permafrost hazards. Retrogressive thaw slumps (RTSs), triggered by the thawing of ground ice in permafrost soil, have become more common in the Arctic. Many studies report an increase in RTS activity on a local or regional scale. In this study, the primary goals are to: (i) examine the spatial patterns of the RTS occurrences across the circumpolar permafrost region, (ii) assess the environmental factors associated with their occurrence and (iii) create the first susceptibility map for RTS occurrence across the Northern Hemisphere. Based on our results, we predicted high RTS susceptibility in the continuous permafrost regions above the 60th latitude, especially in northern Alaska, north‐western Canada, the Yamal Peninsula, eastern Russia and the Qinghai‐Tibetan Plateau. The model indicated that air temperature and soil properties are the most critical environmental factors for the occurrence of RTSs on a circumpolar scale. Especially, the climatic conditions of thaw season were highlighted. This study provided new insights into the circumpolar susceptibility of ice‐rich permafrost soils to rapid permafrost‐related hazards like RTSs and the associated impacts on landscape evolution, infrastructure, hydrology and carbon fluxes that contribute to global warming.

The first hillslope thermokarst inventory for the permafrost region of the Qilian Mountains

Abstract. Climate warming and anthropogenic disturbances result in permafrost degradation in cold regions, including in the Qilian Mountains. These changes lead to extensive hillslope thermokarst (HT) formation, such as retrogressive thaw slumps, active-layer detachment slides, and thermal erosion gullies. These in turn cause, e.g., degradation of local vegetation, economic losses, infrastructure damages, and threats to human safety. However, despite its importance, there is currently no thermokarst inventory for the Qilian Mountains. Through manual visual interpretation and field validation, we therefore produce the first quantification of HT features. We count a total of 1064 HT features, with 67 % located in the upper reaches of the Heihe River basin, which encompasses ∼ 13 % of the Qilian Mountains region. We further identified that 187 HT features (18 %) existed before 2010, while the remaining 874 (82 %) were initiated in the recent period. More specifically, 392 sites (37 %) were initiated during 2010–2015 and 482 (45 %) after 2015. Thermokarst terrain is observed primarily in areas with shallow active-layer depths (average thickness 2.98 m) on northern shaded slopes of 3–25°, with low solar radiation and moderate elevations ranging from 3200 to 4000 m. This first inventory of HT features is an important and missing piece in documenting changes on the Qinghai–Tibetan Plateau, and this new dataset also provides an important basis for further studies, such as automated extraction of HT features, susceptibility analysis of HT, and estimation of losses caused by HT. The datasets are available from the National Tibetan Plateau/Third Pole Environment Data Center and can be downloaded from https://doi.org/10.11888/Cryos.tpdc.300805 (Peng and Yang, 2023).

Characterizing Batagay megaslump topography dynamics and matter fluxes at high spatial resolution using a multidisciplinary approach of permafrost field observations, remote sensing and 3D geological modeling

Retrogressive thaw slumps (RTS) are an important landform of rapid permafrost degradation in regions with very high ground ice contents. RTS mobilize significant amounts of sediment, meltwater and organic carbon and impact downstream hydrological systems by directly affecting topography and water quality. The term megaslump has previously been coined for RTS exceeding 20 ha in size. The Batagay megaslump in the Yana highlands of NE Siberia with an area of 87.6 ha (in 2023, including the bowl-shaped part and the erosional outlet) has been identified as the largest megaslump on Earth. We use very high resolution remote sensing from satellite data and drones, geological structure modeling, and field data to assess how much and what material is thawed and mobilized in the Batagay megaslump. The total volume of permafrost thaw and material loss from the Batagay RTS amounts to about 1 million m3 per year. The material is by one third composed of thawed sediments and by two thirds of melted ground ice. About 4000 to 5000 tons of previously permafrost-locked organic carbon is released every year. Organic carbon content has been measured as Total Organic Carbon (TOC) of sediments and as Dissolved Organic Carbon (DOC) of ground ice. From its formation in the 1970s until 2023, the Batagay RTS – due to thermal denudation and headwalls retreat – mobilized a total volume of about 34.7 million m3 of which 23.4 million m3 were melted ground ice and 11.3 million m3 were thawed deposits including a total of about 169,500 t organic carbon. With these rates of sediment and carbon mobilization, the Batagay megaslump is not only a prominent local feature of rapid permafrost thaw, but offers excellent conditions to study rates and mechanisms of rapid permafrost degradations and to calculate the stock and release of, e.g., organic matter.

Study on Shear Characteristics of Herbs Plant Root–Soil Composite System in Beiluhe Permafrost Regions under Freeze–Thaw Cycles, Qinghai–Tibet Highway, China

In order to study the root–soil composite system shear characteristics under the action of freeze–thaw cycles in the permafrost regions along the Qinghai–Tibet Highway (QTH) from the Beiluhe–Tuotuohe (B-T) section, the slopes in the permafrost regions along the QTH from the B-T section were selected as the object of the study. The direct shear test of root–soil composite systems under different amounts of freeze–thaw (F-T) cycles and gray correlations were used to analyze the correlation between the number of F-T cycles, water content, root content, and the soil shear strength index. The results show that the cohesion of the soil in the area after F-T cycles exhibits a significant stepwise decrease with an increase in F-T cycles, which can be divided into three stages: the instantaneous stage (a decrease of 46.73–56.42%), the gradual stage (a decrease of 14.80–25.55%), and the stabilization stage (a decrease of 0.61–2.99%). The internal friction angle did not exhibit a regular change. The root–soil composite system showed significant enhancement of soil cohesion compared with soil without roots, with a root content of 0.03 g/cm3 having the most significant effect on soil cohesion (increasing amplitude 65.20–16.82%). With an increase in the number of the F-T cycles, while the water content is greater than 15.0%, the greater the water content of the soil, the smaller its cohesion becomes. Through gray correlation analysis, it was found that the correlation between the number of F-T cycles, water content, root content, and soil cohesion after F-T cycles were 0.63, 0.72, and 0.66, respectively, indicating that water content had the most significant impact on soil cohesion after F-T cycles. The results of this study provide theoretical support for further understanding the variation law of the shear strength of root–soil composite systems in permafrost regions under F-T cycles and the influencing factors of plant roots to enhance soil shear strength under F-T cycles, as well as for the scientific and effective prevention and control of retrogressive thaw slump in the study area, the QTH stretches across the region.

Applications of ArcticDEM for measuring volcanic dynamics, landslides, retrogressive thaw slumps, snowdrifts, and vegetation heights

Topographical changes are of fundamental interest to a wide range of Arctic science disciplines faced with the need to anticipate, monitor, and respond to the effects of climate change, including geohazard management, glaciology, hydrology, permafrost, and ecology. This study demonstrates several geomorphological, cryospheric, and biophysical applications of ArcticDEM – a large collection of publicly available, time-dependent digital elevation models (DEMs) of the Arctic. Our study illustrates ArcticDEM's applicability across different disciplines and five orders of magnitude of elevation derivatives, including measuring volcanic lava flows, ice cauldrons, post-failure landslides, retrogressive thaw slumps, snowdrifts, and tundra vegetation heights. We quantified surface elevation changes in different geological settings and conditions using the time series of ArcticDEM. Following the 2014–2015 Bárðarbunga eruption in Iceland, ArcticDEM analysis mapped the lava flow field, and revealed the post-eruptive ice flows and ice cauldron dynamics. The total dense-rock equivalent (DRE) volume of lava flows is estimated to be (1431 ± 2) million m3. Then, we present the aftermath of a landslide in Kinnikinnick, Alaska, yielding a total landslide volume of (400 ± 8) × 103 m3 and a total area of 0.025 km2. ArcticDEM is further proven useful for studying retrogressive thaw slumps (RTS). The ArcticDEM-mapped RTS profile is validated by ICESat-2 and drone photogrammetry resulting in a standard deviation of 0.5 m. Volume estimates for lake-side and hillslope RTSs range between 40,000 ± 9000 m3 and 1,160,000 ± 85,000 m3, highlighting applicability across a range of RTS magnitudes. A case study for mapping tundra snow demonstrates ArcticDEM's potential for identifying high-accumulation, late-lying snow areas. The approach proves effective in quantifying relative snow accumulation rather than absolute values (standard deviation of 0.25 m, bias of −0.41 m, and a correlation coefficient of 0.69 with snow depth estimated by unmanned aerial systems photogrammetry). Furthermore, ArcticDEM data show its feasibility for estimating tundra vegetation heights with a standard deviation of 0.3 m (no bias) and a correlation up to 0.8 compared to the light detection and ranging (LiDAR). The demonstrated capabilities of ArcticDEM will pave the way for the broad and pan-Arctic use of this new data source for many disciplines, especially when combined with other imagery products. The wide range of signals embedded in ArcticDEM underscores the potential challenges in deciphering signals in regions affected by various geological processes and environmental influences.

Machine learning-based predictions of current and future susceptibility to retrogressive thaw slumps across the Northern Hemisphere

Retrogressive thaw slumps (RTSs) caused by the thawing of ground ice on permafrost slopes have dramatically increased and become a common permafrost hazard across the Northern Hemisphere during previous decades. However, a gap remains in our comprehensive understanding of the spatial controlling factors, including the climate and terrain, that are conducive to these RTSs at a global scale. Using machine learning methodologies, we mapped the current and future RTSs susceptibility distributions by incorporating a range of environmental factors and RTSs inventories. We identified freezing-degree days and maximum summer rainfall as the primary environmental factors affecting RTSs susceptibility. The final ensemble susceptibility map suggests that regions with high to very high susceptibility could constitute (11.6 ± 0.78)% of the Northern Hemisphere's permafrost region. When juxtaposed with the current (2000–2020) RTSs susceptibility map, the total area with high to very high susceptibility could witness an increase ranging from (31.7 ± 0.65)% (SSP585) to (51.9 ± 0.73)% (SSP126) by the 2041–2060. The insights gleaned from this study not only offer valuable implications for engineering applications across the Northern Hemisphere, but also provide a long-term insight into the potential change of RTSs in permafrost regions in response to climate change.

Advances in retrogressive thaw slump research in permafrost regions

AbstractA retrogressive thaw slump (RTS) is a slope failure formed by slope thaw settlement and retrogressive slump following the thawing of ice‐rich permafrost or the melting of massive ice. Here, we review recent literature on RTSs, one of the main geomorphological landscapes developed in the process of permafrost degradation. The main topics are as follows: development and temporal evolution, mechanisms and processes, influencing factors, evaluation susceptibility and calculation, and assessment of engineering and environmental impacts. There has been a rapid increase in the number and distribution area of RTSs over permafrost in recent years. Climate warming events, extreme rainfall, forest fires, bank and coast erosion, and anthropogenic activity are the primary factors leading to RTSs in permafrost regions, disrupting the initial hydrothermal equilibrium of permafrost slopes. This causes a rise in ground temperature and the thaw of ice‐rich permafrost. Meltwater seeps down and collects on the ice surface, weakening freeze–thaw interface shear resistance and resulting in soil collapse. The development of RTSs may last several decades or longer. RTSs destabilize infrastructure, destroy vegetation, boost soil erosion and land desertification, alter the environment of nearby waters, and increase emissions of some major greenhouse gases. Numerous methods have been developed and adopted to explore RTSs, including geographic information systems (GIS) and equilibrium, numerical, and reliability analysis methods. However, research on formation mechanisms and processes, quantitative prediction, engineering and environmental influences, and mitigative measures of RTSs under a warming climate are still inadequate. Existing research methods, such as numerical simulations, remote sensing, airborne ground‐based geophysical surveys, investigations and mapping, and hydrothermal and deformation field monitoring, should be systematically integrated. Additionally, equipment for laboratory testing and numerical models for simulating RTSs may need to be timely introduced and better developed.

Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model’s Generalizability in Permafrost Mapping

This paper assesses trending AI foundation models, especially emerging computer vision foundation models and their performance in natural landscape feature segmentation. While the term foundation model has quickly garnered interest from the geospatial domain, its definition remains vague. Hence, this paper will first introduce AI foundation models and their defining characteristics. Built upon the tremendous success achieved by Large Language Models (LLMs) as the foundation models for language tasks, this paper discusses the challenges of building foundation models for geospatial artificial intelligence (GeoAI) vision tasks. To evaluate the performance of large AI vision models, especially Meta’s Segment Anything Model (SAM), we implemented different instance segmentation pipelines that minimize the changes to SAM to leverage its power as a foundation model. A series of prompt strategies were developed to test SAM’s performance regarding its theoretical upper bound of predictive accuracy, zero-shot performance, and domain adaptability through fine-tuning. The analysis used two permafrost feature datasets, ice-wedge polygons and retrogressive thaw slumps because (1) these landform features are more challenging to segment than man-made features due to their complicated formation mechanisms, diverse forms, and vague boundaries; (2) their presence and changes are important indicators for Arctic warming and climate change. The results show that although promising, SAM still has room for improvement to support AI-augmented terrain mapping. The spatial and domain generalizability of this finding is further validated using a more general dataset EuroCrops for agricultural field mapping. Finally, we discuss future research directions that strengthen SAM’s applicability in challenging geospatial domains.

Investigating Soil Water Retention and Water Content in Retrogressive Thaw Slumps in the Qinghai-Tibet Plateau, China

Retrogressive thaw slumps (RTSs) are becoming more common on the Qinghai-Tibet Plateau as permafrost thaws, but the hydraulic properties of thaw slumps have not been extensively studied. To fill this knowledge gap, we used the “space-for-time substitution method” to differentiate three stages of RTSs: original grassland, collapsing, and collapsed. Our study included on-site investigations, measurements in the laboratory, and measured and simulated analyses of soil water retention curves and estimated hydrological properties. Our findings show that the measurements and simulated analyses of soil water retention were highly consistent across RTSs, indicating the accuracy of the Van Genuchten model in reproducing soil hydraulic parameters for different stages of RTSs. The original grassland stage had the highest soil water retention and content due to its high soil organic carbon (SOC) content and fine-textured micropores. In contrast, the collapsed stage had higher soil water retention and content compared to the collapsing stage, primarily due to increased proportions of soil micropores, SOC content, and lower bulk density (BD). From original grassland stage to collapsed stage, there were significant changes on the structure of each RTS site, which resulted in a decrease in SOC content and an increase in BD in general. However, the absence of soil structure and compaction led to the subsequent accumulation of organic matter, increasing SOC content. Changes in field capacity, permanent wilting point, and soil micropore distribution aligned with variations in SOC content and water content. These findings highlight the importance of managing SOC content and water content to mitigate the adverse effects of freeze-thaw cycles on soil structure and stability at different thaw collapse stages of RTSs. Effective management strategies may include incorporating organic matter, reducing soil compaction, and maintaining optimal water content. Further research is needed to determine the most suitable management practices for different soil types and environmental conditions.