Research in geocryology is currently principally concerned with the effects of climate change on permafrost terrain. The motivations for most of the research are (1) quantification of the anticipated net emissions of CO2 and CH4 from warming and thaw of near-surface permafrost and (2) mitigation of effects on infrastructure of such warming and thaw. Some of the effects, such as increases in ground temperature or active-layer thickness, have been observed for several decades. Landforms that are sensitive to creep deformation are moving more quickly as a result, and Rock Glacier Velocity is now part of the Essential Climate Variable Permafrost of the Global Climate Observing System. Other effects, for example, the occurrence of physical disturbances associated with thawing permafrost, particularly the development of thaw slumps, have noticeably increased since 2010. Still, others, such as erosion of sedimentary permafrost coasts, have accelerated. Geochemical effects in groundwater from trace elements, including contaminants, and those that issue from the release of sediment particles during mass wasting have become evident since 2020. Net release of CO2 and CH4 from thawing permafrost is anticipated within two decades and, worldwide, may reach emissions that are equivalent to a large industrial economy. The most immediate local concerns are for waste disposal pits that were constructed on the premise that permafrost would be an effective and permanent containment medium. This assumption is no longer valid at many contaminated sites. The role of ground ice in conditioning responses to changes in the thermal or hydrological regimes of permafrost has re-emphasized the importance of regional conditions, particularly landscape history, when applying research results to practical problems.

Soil parameters form the foundation of hydrogeological research and are crucial for studying engineering construction and maintenance, climate change, and ecological environment effects in cold regions. However, the soil properties in the permafrost region of the Qinghai-Tibet Plateau (QTP) remain unclear. Hence, in this study, soil temperature (Ts), volumetric specific heat capacity (C), thermal conductivity (K), thermal diffusivity (D), soil water content (SWC), electric conductivity (EC), vertical (Kv) and horizontal (Kh) saturated hydraulic conductivity, bulk density (rho b), and soil texture near the Qinghai-Tibet Railway were measured, and their effects on the freeze-thaw process were evaluated. The results revealed a predominantly sandy loam soil texture, with Kh and Kv showing strong spatial variability, while the other parameters presented moderate spatial variability. Thermokarst lake had a limited influence on D, C, K, and rho b but significantly reduced Kh and Kv. Groundwater affected SWC, Ts, and EC. The model results showed that all parameters indicated small sensitivities to the maximum thawing depth (MTD), with MTD positively responding to all parameters except for Kv and porosity (rho p). Except for Kh and Kv, all parameters showed high sensitivities to the time from starting to complete freezing (TSCF). TSCF responded positively to C, rho p, and density (rho d) and negatively to K and Kh. This study expanded the quantification of soil properties in the QTP, which can help improve the accuracy of cryohydrogeologic models, thus guiding the construction and maintenance of infrastructure engineering.

Rapid surface and subsurface changes in the Arctic polygonal tundra landscapes due to the melting of ice wedges, known as thermokarst processes, have significant implications for Arctic ecosystems. However, the integration of thermokarst processes into widely used global climate models for projections poses an important question. Here we use an integrated permafrost thermal hydrology model to explore the decoupled nature of two thermokarst processes - microtopography evolution and ground subsidence - in six Arctic locations. Our study specifically investigates this decoupled nature during the transformation of poorly drained low-centered polygons to welldrained high-centered polygons. Spanning diverse climates in polygonal tundra landscapes under the RCP8.5 climate scenario, our findings reveal small variations in permafrost thaw and ground subsidence rates - 2-10 % and 2-4 %, respectively - with and without the representation of microtopography evolution. This suggests that neglecting surface microtopography and its evolution is unlikely to have significant impacts on permafrost projections, regardless of the climate and location. As a result, we suggest the representation of microtopography in Earth System Models may not be imperative. Disclaimer: Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Commerce, National Oceanic and Atmospheric Administration.

Our knowledge on permafrost carbon (C) cycle is crucial for understanding its feedback to climate warming and developing nature-based solutions for mitigating climate change. To understand the characteristics of permafrost C cycle on the Tibetan Plateau, the largest alpine permafrost region around the world, we summarized recent advances including the stocks and fluxes of permafrost C and their responses to thawing, and depicted permafrost C dynamics within this century. We find that this alpine permafrost region stores approximately 14.1 Pg (1 Pg=1015 g) of soil organic C (SOC) in the top 3 m. Both substantial gaseous emissions and lateral C transport occur across this permafrost region. Moreover, the mobilization of frozen C is expedited by permafrost thaw, especially by the formation of thermokarst landscapes, which could release significant amounts of C into the atmosphere and surrounding water bodies. This alpine permafrost region nevertheless remains an important C sink, and its capacity to sequester C will continue to increase by 2100. For future perspectives, we would suggest developing long-term in situ observation networks of C stocks and fluxes with improved temporal and spatial coverage, and exploring the mechanisms underlying the response of ecosystem C cycle to permafrost thaw. In addition, it is essential to improve the projection of permafrost C dynamics through in-depth model-data fusion on the Tibetan Plateau.

Wildfire strongly influences permafrost environment and soil organic carbon (SOC) pool. In this study, we reviewed the effects of fire severity, time after a fire, and frequency on SOC in boreal permafrost regions. This review highlighted several key points: the effect of wildfires on SOC increased with an increase of fire severity, and the amount of vegetation returned and surface organic matter replenished was less in a short term, which resulted in a significantly lower SOC content compared to that of before the fire. Within a short period after fire, the SOC in near-surface permafrost and the active layer decreased significantly due to the loss of above ground biomass, permafrost thaw, and increased microbial decomposition; as the years pass after a fire, the SOC gradually accumulates due to the contributions of litter layer accumulation and rooting systems from different stages of succession. The increase in fire frequency accelerated permafrost thawing and the formation of thermokarst, resulting in the rapid release of a large amount of soil carbon and reduced SOC storage. Therefore, the study on the effects of wildfires on SOC in the boreal permafrost region is of great significance to understanding and quantifying the carbon balance of the ecosystem.

Climate change is causing permafrost in the Qinghai-Tibet Plateau to degrade, triggering thermokarst hazards and impacting the environment. Despite their ecological importance, the distribution and risks of thermokarst lakes are not well understood due to complex influencing factors. In this study, we introduced a new interpretable ensemble learning method designed to improve the global and local interpretation of susceptibility assessments for thermokarst lakes. Our primary aim was to offer scientific support for precisely evaluating areas prone to thermokarst lake formation. In the thermokarst lake susceptibility assessment, we identified ten conditioning factors related to the formation and distribution of thermokarst lakes. In this highly accurate stacking model, the primary learning units were the random forest (RF), extremely randomized trees (EXTs), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost) algorithms. Meanwhile, gradient boosted decision trees (GBDTs) were employed as the secondary learning unit. Based on the stacking model, we assessed thermokarst lake susceptibility and validated accuracy through six evaluation indices. We examined the interpretability of the stacking model using three interpretation methods: accumulated local effects (ALE), local interpretable model-agnostic explanations (LIME), and Shapley additive explanations (SHAP). The results showed that the ensemble learning stacking model demonstrated superior performance and the highest prediction accuracy. Approximately 91.20% of the total thermokarst hazard points fell within the high and very high susceptible areas, encompassing 20.08% of the permafrost expanse in the QTP. The conclusive findings revealed that slope, elevation, the topographic wetness index (TWI), and precipitation were the primary factors influencing the assessment of thermokarst lake susceptibility. This comprehensive analysis extends to the broader impacts of thermokarst hazards, with the identified high and very high susceptibility zones affecting significant stretches of railway and highway infrastructure, substantial soil organic carbon reserves, and vast alpine grasslands. This interpretable ensemble learning model, which exhibits high accuracy, offers substantial practical significance for project route selection, construction, and operation in the QTP.

Recently, as global climate change and local disturbances such as wildfires continue, long- and short-term changes in the high-latitude vegetation systems have been observed in various studies. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, there has been limited understanding of various landscape changes at different spatiotemporal scales, their mutual relationships, and overall long-term landscape changes. The objective of this study is to devise a change monitoring strategy that can effectively observe landscape changes at different spatiotemporal scales in the boreal ecosystems from temporally sparse time series remote sensing data. We presented a new post-classification-based change analysis scheme and applied it to time series Landsat data for the central Yakutian study area. Spectral variability between time series data has been a major problem in the analysis of changes that make it difficult to distinguish long- and short-term land cover changes from seasonal growth activities. To address this issue effectively, two ideas in the time series classification, such as the stepwise classification and the lateral stacking strategies were implemented in the classification process. The proposed classification results showed consistently higher overall accuracies of more than 90% obtained in all classes throughout the study period. The temporal classification results revealed the distinct spatial and temporal patterns of the land cover changes in central Yakutia. The spatiotemporal distribution of the short-term class illustrated that the ecosystem disturbance caused by fire could be affected by local thermal and hydrological conditions of the active layer as well as climatic conditions. On the other hand, the long-term class changes revealed land cover trajectories that could not be explained by monotonic increase or decrease. To characterize the long-term land cover change patterns, we applied a piecewise linear model with two line segments to areal class changes. During the former half of the study period, which corresponds to the 2000s, the areal expansion of lakes on the eastern Lena River terrace was the dominant feature of the land cover change. On the other hand, the land cover changes in the latter half of the study period, which corresponds to the 2010s, exhibited that lake area decreased, particularly in the thermokarst lowlands close to the Lena and Aldan rivers. In this area, significant forest decline can also be identified during the 2010s.

Climate warming has caused the active layer of permafrost to thicken, leading to permafrost melting and surface collapse, forming thermokarst landforms. These changes significantly affect regional vegetation, soil properties, and water processes, thereby impacting regional carbon cycling. This study examined the relationships between soil respiration rate (Rs), soil temperature (T), and volumetric water content (VWC) in the thermokarst depression zones of Qinghai Lake's headwater wetlands. The results showed a significant positive correlation between soil temperature and Rs, and a significant negative correlation between VWC and Rs. The inhibitory effect of VWC on Rs was stronger in thermokarst areas compared to natural conditions. Temperature had a greater influence on Rs, especially during the day, while VWC inhibited Rs more at night. The study highlights the combined impact of temperature and humidity on soil respiration, revealing that Rs in thermokarst areas is more sensitive to temperature changes at night. These findings improve our understanding of carbon cycling in wetland ecosystems and help predict wetland carbon emissions under climate change. As the climate warms, the thickening of the active layer of permafrost has led to permafrost melting and surface collapse, forming thermokarst landforms. These changes significantly impact regional vegetation, soil physicochemical properties, and hydrological processes, thereby exacerbating regional carbon cycling. This study analyzed the relationship between soil respiration rate (Rs), soil temperature (T), and volumetric water content (VWC) in the thermokarst depression zone of the headwater wetlands of Qinghai Lake, revealing their influence on these soil parameters. Results showed a significant positive correlation between soil temperature and Rs (p < 0.001), and a significant negative correlation between VWC and Rs (p < 0.001). The inhibitory effect of VWC on Rs in the thermokarst depression zone was stronger than under natural conditions (p < 0.05). Single-factor models indicated that the temperature-driven model had higher explanatory power for Rs variation in both the thermokarst depression zone (R-2 = 0.509) and under natural conditions (R-2 = 0.414), while the humidity-driven model had lower explanatory power. Dual-factor models further improved explanatory power, slightly more so in the thermokarst depression zone. This indicates that temperature and humidity jointly drive Rs. Additionally, during the daytime, temperature had a more significant impact on Rs under natural conditions, while increased VWC inhibited Rs. At night, the positive correlation between Rs and temperature in the thermokarst depression zone increased significantly. The temperature sensitivity (Q(10)) values of Rs were 3.32 and 1.80 for the thermokarst depression zone and natural conditions, respectively, indicating higher sensitivity to temperature changes at night in the thermokarst depression zone. This study highlights the complexity of soil respiration responses to temperature and humidity in the thermokarst depression zone of Qinghai Lake's headwater wetlands, contributing to understanding carbon cycling in wetland ecosystems and predicting wetland carbon emissions under climate change.

Our understanding of tundra fire effects in Northern Alaska is limited because fires have been relatively rare. We sampled a 70+ year -old burn visible in a 1948 aerial photograph for vegetation composition and structure, soil attributes, terrain rugosity, and thermokarst pit density. Between 1948 and 2017 the burn initially became wetter as ice wedges melted but then drained and dried as the troughs became hydrologically connected. The reference tundra has become wetter over the last few decades and appears to be lagging through a similar sequence. The burn averaged 2.5 degrees C warmer than the reference tundra at 30 cm depth. Thinning of organic soil following fire appears to dramatically accelerate the background degradation of ground-ice features in response to climate change and promotes a plant community that is distinct in terms of taxa and structure, dominated by tall willows and other competitive, rather than cold-tolerant, species. The cover of sedges and mosses is low while that of willows and grass is high relative to the reference tundra. The changes in plant community composition and structure, increasing ground temperature, and thermokarst lead us to expect the observed biophysical changes to the tundra will persist centuries into the future.

Due to the effects of global climate change, the permafrost temperature in the Qinghai-Tibet Plateau (QTP) has rapidly increased over the past decades. The development of thermokarst landforms is one distinctive indicator of permafrost degradation, while the change of the rate of permafrost degradation in recent 10 years has not been systematically investigated in QTP. In this paper, the annual average growth rate (AAGR) of ground deformation, the change of thaw slump areas, and the change of active layer thickness (ALT) of thermokarst landforms are monitored integrating SAR (synthetic aperture radar) and optical images for years 2007 to 2020 in Qilian Mountain, northern QTP. The ground deformation rate and seasonal amplitude were estimated by InSAR method, and the descending and ascending InSAR data are compared the validate the results. Based on the deformation results, AAGR was introduced to evaluate the permafrost degradation degree. Moreover, the ALT were estimated based on the seasonal deformation amplitude and Stefan model. The spatio-temporal characteristics of ground deformation and its relationship with thaw slump and temperature are explored. Experimental results show that the deformation rate increased about 150 % from 2007 to 10 to 2017-20. The maximum AAGR of deformation rate in the study area can reach 20.6 %. The thaw slump area has an obvious trend of expansion from 2009 to 2015, and its distribution agreed well with the deformation map. The ALT results ranged from 0.5 m to 2.8 m, indicating an obvious increase trend from 2007 to 2020. Based on the estimated increased ground deformation, thaw slump area, and ALT, it is inferred that frozen ground was undergoing serious degradation in the last 10 years. This study demonstrates the capability of multi-temporal InSAR in observing the accelerated permafrost thaw-freezing process and monitoring the permafrost parameters.