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 Sanjiangyuan region, known as the Chinese Water Tower, serves as a crucial ecological zone that is highly sensitive to climate change. In recent years, rising temperatures and increased precipitation have led to permafrost melt and frequent occurrences of thermokarst landslides, exacerbating soil erosion issues. Although studies have explored the impact of freeze-thaw action (FTA) on soil properties, research on this phenomenon within the unique geomorphological unit of thermokarst landslides, formed from degrading permafrost, remains sparse. This study, set against the backdrop of temperature-induced soil landslides, combines field investigations and controlled laboratory experiments on typical thermokarst landslide bodies within the permafrost region of Sanjiangyuan to systematically investigate the effects of FTA on the properties of soils within thermokarst landslides. Furthermore, this study employs the EPIC model to establish an empirical formula for the soil erodibility (SE) factor before and after freeze-thaw cycles (FTCs). The results indicate that: (1) FTCs significantly alter soil particle composition, reducing the content of clay particles in the surface soil while increasing the content of sand particles and the median particle size, thus compromising soil structure and enhancing erodibility. (2) FTA initially significantly increases soil organic matter content (OMC); however, as the number of FTCs increases, the magnitude of these changes diminishes. The initial moisture content of the soil significantly influences the effects of FTA, with more pronounced changes in particle composition and OMC in soils with higher moisture content. (3) With an increasing number of FTCs, the SE K-value first significantly increases and then tends to stabilize, showing significant differences across the cycles (1 to 15) (p < 0.05). This study reveals that FTCs, by altering the physicochemical properties of the soil, significantly increase SE, providing a scientific basis for soil erosion control and ecological environmental protection in the Sanjiangyuan area.

In the context of global warming, landscapes with ice-rich permafrost, such as the Qinghai-Tibet Plateau (QTP), are highly vulnerable. The expansion of thermokarst lakes erodes the surrounding land, leading to collapses of various scales and posing a threat to nearby infrastructure and the environment. Assessing the susceptibility of thermokarst lakes in remote, data-scarce areas remains a challenging task. In this study, Landsat imagery and human-computer interaction were employed to improve the accuracy of thermokarst lake classification. The study also identified the key factors influencing the occurrence of thermokarst lakes, including the lake density, soil moisture (SM), slope, vegetation, snow cover, ground temperature, precipitation, and permafrost stability (PS). The results indicate that the most susceptible areas cover 19.02% of the QTP's permafrost region, primarily located in southwestern Qinghai, northeastern Tibet, and the Hoh Xil region. This study provides a framework for mapping the spatial distribution of thermokarst lakes and contributes to understanding the impact of climate change on the QTP.

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.