Permafrost degradation on the Tibetan Plateau (TP) has triggered widespread retrogressive thaw slumps (RTSs), affecting hydrology, carbon sequestration and infrastructure stability. To date, there is still a lack of long-term monitoring of RTSs across the TP, the thaw dynamics and comprehensive driving factors remain unclear. Here, using time-series Landsat imagery and change detection algorithm, we identified RTSs on permafrost regions of the TP from 1986 to 2020. Existing RTSs inventories and high-resolution historical imagery were employed to verify the identified results, the temporal validation of RTSs disturbance pixels demonstrated a high accuracy. In the study area, a total of 3537 RTSs were identified, covering a total area of 5997 ha, representing a 26-fold increase since 1986, and 69.2 % of RTSs formed since 2010. Most RTSs are located on gentle slope (4-12 degrees) at elevations between 4500 m and 5300 m, with a tendency to form in alpine grassland and alpine meadow. Annual variations in RTSs area exhibited a significant positive correlation with minimum air temperature, mean land surface temperature, and annual thawing index, while it showing a significant negative correlation with the decrease in downward shortwave radiation. Spatially, RTSs were more common in areas with higher soil water content and shallower active layer. Landsat imagery captured the vast majority of RTSs on the TP and revealed interannual disturbance details, but the 30 m resolution remains inadequate for delineating the refined boundaries of some micro-scale (< 0.18 ha) RTSs. Detected RTSs disturbances on the TP will aid in hazard management and carbon feedback assessments, and our findings provide novel insights into the impacts of climate change and permafrost environments on RTSs formation.

Soil freeze-thaw state influences multiple terrestrial ecosystem processes, such as soil hydrology and carbon cycling. However, knowledge of historical long-term changes in the timing, duration, and temperature of freeze-thaw processes remains insufficient, and studies exploring the combined or individual contributions of climatic factors-such as air temperature, precipitation, snow depth, and wind speed-are rare, particularly in current thermokarst landscapes induced by abrupt permafrost thawing. Based on ERA5-Land reanalysis, MODIS observations, and integrated thermokarst landform maps, we found that: 1) Hourly soil temperature from the reanalysis effectively captured the temporal variations of in-situ observations, with Pearson' r of 0.66-0.91. 2) Despite an insignificant decrease in daily freeze-thaw cycles in 1981-2022, other indicators in the Qinghai-Tibet Plateau (QTP) changed significantly, including delayed freezing onset (0.113 d yr- 1), advanced thawing onset (-0.22 d yr- 1), reduced frozen days (-0.365 d yr- 1), increased frozen temperature (0.014 degrees C yr- 1), and decreased daily freeze-thaw temperature range (-0.015 degrees C yr- 1). 3) Total contributions indicated air temperature was the dominant climatic driver of these changes, while indicators characterizing daily freeze-thaw cycles were influenced mainly by the combined effects of increased precipitation and air temperature, with remarkable spatial heterogeneity. 4) When regionally averaged, completely thawed days increased faster in the thermokarstaffected areas than in their primarily distributed grasslands-alpine steppe (47.69%) and alpine meadow (22.64%)-likely because of their stronger warming effect of precipitation. Locally, paired comparison within 3 x 3 pixel windows from MODIS data revealed consistent results, which were pronounced when the thermokarst-affected area exceeded about 38% per 1 km2. Conclusively, the warming and wetting climate has significantly altered soil freeze-thaw processes on the QTP, with the frozen soil environment in thermokarstaffected areas, dominated by thermokarst lakes, undergoing more rapid degradation. These insights are crucial for predicting freeze-thaw dynamics and assessing their ecological impacts on alpine grasslands.

The Atacama Plateau in the Central Andes (28-22 degrees S) is characterised by a dry and cold periglacial tundra due to the high altitude, low precipitation, and high evaporation. Endogenous freshwater sources - e.g.: seasonal streams and lakes, subsurface reservoirs, surface snow/ice patches - are available, though they are highly sensitive to climatic changes. The near surface hydrological network is highly modified by the distribution and seasonal evolution of perennial frozen ground, i.e. permafrost, which is also expected to change in the future. The interplay between permafrost and hydrology, especially in relation to future climate change, is poorly explored. To address this issue, we carry out long-term ground temperature measurement and modelling, snow coverage survey, tritium- and stable isotope analysis of surface waters on the Ojos del Salado Massif, which is representative of high altitude mountains on the Atacama Plateau. According to our results, a highly transient surface hydrological network - lakes, springs and streams - forms during each summer where permafrost is widespread and ground thawing (i.e. active layer) is present (similar to 4900-6500 m a.s.l.). In this system, the water is of meteoric origin and relatively young (<10 years). The development of the network is strongly influenced by the active layer, which plays a crucial role in storing, seeping, and discharging groundwater. However, future permafrost degradation is expected to reduce the seasonal presence of shallow water, and hence, modify groundwater recharge patterns.

Alpine wet meadow (AWM), an important wetland type on the Qinghai-Tibet Plateau (QTP), is sensitive to climate change, which alters the soil hydrothermal regime and impacts ecological and hydrological functions in permafrost regions. The mechanisms underlying extreme AWM degradation in the QTP and hydrothermal factors controlling permafrost degradation remain unclear. In this study, soil hydrothermal processes, soil heat migration, and the permafrost state were measured in AWM and extremely degraded AWM (EDAWM). The results showed that the EDAWM exhibited delayed onset of both soil thawing and freezing, shortened thawing period, and extended freezing period at the lower boundary of the active layer. The lower ground temperatures resulted in a 0.2 m shallower active layer thickness in the EDAWM compared with the AWM. Moreover, the EDAWM altered soil thermal dynamics by redistributing energy, modifying soil moisture, preserving soil organic matter, and adjusting soil thermal properties. As for energy budget, a substantial amount of heat in the EDAWM was consumed by turbulent heat fluxes, particularly latent heat flux, which reduced the amount of heat transferred to the ground. Additionally, the higher soil organic matter content in EDAWM decreased the annual mean soil thermal conductivity from 1.42 W m- 1 K-1 in AWM to 1.26 W m- 1 K-1 in EDAWM, slowing down heat transfer within the active layer and consequently mitigating permafrost degradation. However, with continued climate warming, the soil organic matter content in EDAWM will inevitably decline due to microbial decomposition in the absence of new organic inputs. As the soil organic matter content diminishes, soil heat transfer processes will likely accelerate, and the permafrost warming rate may surpass that in undistributed AWM. These findings enhance our understanding of how alpine ecosystem succession influences regional hydrological cycles and greenhouse gas emissions.

Ongoing climate warming and increased human activities have led to significant permafrost degradation on the Qinghai-Tibet Plateau (QTP). Mapping the distribution of active layer thickness (ALT) can provide essential information for understanding this degradation. Over the past decade, InSAR (Interferometric synthetic aperture radar) technology has been utilized to estimate ALT based on remotely-sensed surface deformation information. However, these methods are generally limited by their ability to accurate extract seasonal deformation and model subsurface water content of active layer. In this paper, an ALT inversion method considering both seasonal deformation from InSAR and smoothly multilayer soil moisture from ERA5 is proposed. Firstly, we introduce a ground seasonal deformation extraction model combining RobustSTL and InSAR, and the deformation extraction accuracy by considering the deformation characteristics of permafrost are evaluated, proving the effectiveness of RobustSTL in extracting seasonal deformation of permafrost. Then, using ERA5 soil moisture products, a smoothed multilayer soil moisture model for ALT inversion is established. Finally, integrating the seasonal deformation and multilayer soil moisture, the ALT can be estimated. The proposed model is applied to the Yellow River source region (YRSR) with Sentinel-1A images acquired from 2017 to 2021, and the ALT retrieval accuracy is validated with measured data. Experimental results show that the vertical deformation rate of the study area generally ranges from -30 mm/year to 20 mm/year, with seasonal deformation amplitude ranging from 2 mm to 30 mm. The RobustSTL method has the highest accuracy in extracting seasonal deformation of permafrost, with an RMSE (root mean square error) of 0.69 mm, and is capable of capturing the freeze-thaw characteristics of the active layer. The estimated ALT of the YRSR ranges from 49 cm to 450 cm, with an average value of 145 cm. Compared to the measured data, the proposed method has an average error of 37.5 cm, which represents a 21 % improvement in accuracy over existing methods.

Permafrost is undergoing rapid changes due to climate warming, potentially exposing a vast reservoir of carbon to be released to the atmosphere, causing a positive feedback cycle. Despite the importance of this feedback, its specifics remain poorly constrained, because representing permafrost dynamics still poses a significant challenge for Earth System Models (ESMs). This review assesses the current state of permafrost representation in land surface models (LSMs) used in ESMs and offline permafrost models, highlighting both the progress made and the remaining gaps.We identify several key physical processes crucial for permafrost dynamics, including soil thermal regimes, freeze-thaw cycles, and soil hydrology, which are underrepresented in many models. While some LSMs have advanced significantly in incorporating these processes, others lack fundamental elements such as latent heat of freeze-thaw, deep soil columns, and Arctic vegetation dynamics. Offline permafrost models provide valuable insights, offering detailed process testing and aiding the prioritization of improvements in coupled LSMs.Our analysis reveals that while significant progress has been made in incorporating permafrost-related processes into coupled LSMs, many small-scale processes crucial for permafrost dynamics remain underrepresented. This is particularly important for capturing the complex interactions between physical and biogeochemical processes required to model permafrost carbon dynamics. We recommend leveraging advancements from offline permafrost models and progressively integrating them into LSMs, while recognizing the computational and technical challenges that may arise in coupled simulations. We highlight the importance of enhancing the representation of physical processes, including through improvements in model resolution and complexity, as this is a fundamental precursor to accurately incorporate biogeochemical processes and capture the permafrost carbon feedback.

Soil organic carbon (SOC) in the active layer (0-2 m) of the Tibetan Plateau (TP) permafrost region is sensitive to climate change, with significant implications for the global carbon cycle. Environmental factors-including parent material, climate, vegetation, topography, soil, and human activities-inevitably drive SOC variations. However, vegetation and climate are likely the two most influential factors impacting SOC variations. To test this hypothesis, we conducted experiments using 31 environmental variables combined with the recursive feature elimination (RFE) algorithm. These experiments showed that RFE retained all vegetation variables [Land cover types (LCT), normalized difference vegetation index (NDVI), leaf area index (LAI), and gross primary productivity (GPP)] as well as two climate variables [Moisture index (MI) and drought index (DI)], supporting our hypothesis. We then analyzed the relationship between SOC and the retained vegetation and climate variables using random forest (RF), Shapley additive explanations (SHAP), and GeoDetector models to quantify the independent and interactive drivers of SOC distribution and to identify the optimal conditions for SOC accumulation. The RF model explained 68% and 42% of the spatial variability in SOC at depths of 0-1 m and 1-2 m, respectively, with SOC stocks higher in the southeast and lower in the northwest. Additionally, SOC stock at 0-1 m was significantly higher (p 0.05). Spearman correlation coefficients results indicated that NDVI, LAI, GPP, and MI had highly significant positive correlations with SOC (p < 0.01), whereas DI had a highly significant negative correlation with SOC (p < 0.01). SHAP analysis revealed environmental thresholds for SOC variations, with notable shifts at NDVI (0.40), LAI (7), GPP (250 g C m(-)(2) year(-)(1)), MI (0.40), and DI (0.50). The spatial distribution of these thresholds aligns with the 400 mm equivalent precipitation line. Additionally, GeoDetector results emphasized that interactions between climate and vegetation factors enhance the explanatory power of individual variables on SOC variations. The swamp meadow type, with an NDVI range of 0.73-0.84, LAI range of 11.06-15.94, and MI range of 0.46-0.56, was identified as the most favorable environment for SOC accumulation. These findings are essential for balancing vegetation and climate conditions to sustain SOC levels and mitigate climate change-driven carbon release.

The fine-scale controls of active layer dynamics remain poorly understood, particularly at the southern boundary of continuous permafrost. We examined how environmental conditions associated with upland tundra heath, open graminoid fen, and palsa/peat plateau landforms affected active layer thermal regime (timing, magnitude, and rate of thaw) in a subarctic peatland in the Hudson Bay Lowlands, Canada. A significant increase in active layer thaw depth was evident between 2012 and 2024. Within-season thaw patterns differed among landforms, with tundra heath exhibiting the highest thaw rates and soil temperatures, succeeded by fen and palsa. Air temperature mediated by soil properties, topography, and vegetation affected thaw patterns. The increased thermal conductivity of gravel/sandy tundra heath soils exerted a more pronounced influence on thaw patterns relative to fens and palsas, both of which had a thicker organic layer. Near-surface soil moisture was the lowest in tundra, followed by palsas, and fens. Increased soil moisture impeded active layer thaw, likely due to a combination of soil surface evaporation and meltwater percolation. These findings elucidate the relationship between the biophysical properties of landform features and climate, revealing their role in influencing active layer thaw patterns in a subarctic ecosystem.

This paper investigates the spatiotemporal dynamics and their changes of the southern limit of latitudinal permafrost (SLLP) and the lower limit of mountain permafrost (LLMP) in Northeast China, emphasizing the roles of climate change and human activities. Permafrost in this region is primarily distributed in the northern parts of the Da and Xiao Xing'anling mountain ranges and in the upper parts of the Changbai Mountains and at the summits of the Huanggangliang Mountains in the southern part of the Da Xing'anling Mountain Range. Permafrost degradation, ongoing since at least the local Holocene Megathermal Period (8.5-6.0 ka BP), has intermittently reversed during cooler climatic intervals but continues to exert significant impacts on regional environments, infrastructure stability, and carbon storage. Notably, the northward retreats of the SLLP since the mid-19th century underscore the sustained nature of this degradation, especially in southern patchy permafrost zones increasingly sensitive to warming and anthropogenic influences. LLMP variability is similarly shaped by a combination of climatic, hydrometeorological, ecological, and topographic factors. The distributions of SLLP and LLMP are further complicated by the presence of relict and sporadic permafrost, as well as the hydrothermal effects of vegetation and snow cover. Addressing the challenges of mapping and modeling boreal permafrost in Northeast China requires comprehensive field investigations, long-term in situ monitoring via station networks, and advanced numerical modeling. Emerging technologies, including satellite and airborne remote sensing (RS), geographic information systems (GIS), unmanned aerial vehicles (UAVs), surface geophysical methods, and big data analytics, offer new possibilities for enhancing permafrost monitoring and mapping. Integrating these tools with conventional field studies can significantly improve our understanding of permafrost dynamics. Continued efforts in monitoring, technological innovation, multidisciplinary collaboration, and international cooperation are essential to meet the challenges posed by permafrost degradation in a changing climate.

Global warming due to climate change has substantial impact on high-altitude permafrost affected soils. This raises a serious concern that the microbial degradation of sequestered carbon can result in alteration of the biogeochemical cycles. Therefore, the characterization of permafrost affected soil microbiomes, especially of unexplored high-altitude, low oxygen arid region, is important for predicting their response to climate change. This study presents the first report of the bacterial diversity of permafrost-affected soils in the Changthang region of Ladakh. The relationship between soil pH, organic carbon, electrical conductivity, and available micronutrients with the microbial diversity was investigated. Amplicon sequencing of permafrost affected soil samples from Jukti and Tsokar showed that Proteobacteria and Actinobacteria were the dominant phyla in all samples. The genera Brevitalea, Chthoniobacter, Sphingomonas, Hydrogenispora, Clostridium, Gaiella, Gemmatimonas were relatively abundant in the Jukti samples whereas the genera Thiocapsa, Actinotalea, Syntrophotalea, Antracticibcterium, Luteolibacter, Nitrospirillum dominated the Tsokar sample. Correlation analyses highlighted the influence of soil geochemical parameters on the bacterial community structure. PCoA analyses showed that the bacterial beta diversity varied significantly between the sampling locations (PERMANOVA test (F-value: 2.3316; R2 = 0.466, p = 0.001) and similar results were also obtained while comparing genus abundance data using the ANOSIM test (R = 0.345, p = 0.007).