Slope failures resulting from thaw slumps in permafrost regions, have developed widely under the influence of climate change and engineering activities. The shear strength at the interface between the active layer and permafrost (IBALP) at maximum thawing depth is a critical factor to evaluate stability of permafrost slopes. Traditional direct shear, triaxial shear, and large-scale in-situ shear experiments are unsuitable for measuring the shear strength parameter of the IBALP. Based on the characteristics of thaw slumps in permafrost regions, this study proposes a novel test method of self-weight direct shear instrument (SWDSI), and its principle, structure, measurement system and test steps are described in detail. The shear strength of the IBALP under maximum thaw depth conditions is measured using this method. The results show that under the condition that the permafrost layer is thick underground ice and the active layer consists of silty clay with 20% water content, the test results are in good agreement with the results of field large-scale direct shear tests and are in accordance with previous understandings and natural laws. The above analysis indicates that the method of the SWDSI has a reliable theoretical basis and reasonable experimental procedures, and meets the needs of stability assessment of thaw slumps in permafrost regions. The experimental data obtained provide important parameter support for the evaluation of related geological hazards.

The thermal coupling between the atmosphere and the subsurface on the Qinghai-Tibetan Plateau (QTP) governs permafrost stability, surface energy balance, and ecosystem processes, yet its spatiotemporal dynamics under accelerated warming are poorly understood. This study quantifies soil-atmosphere thermal coupling ((3) at the critical 0.1 m root-zone depth using in-situ data from 99 sites (1980-2020) and a machine learning framework. Results show significantly weaker coupling in permafrost (PF) zones (mean (3 = 0.42) than in seasonal frost (SF) zones (mean (3 = 0.50), confirming the powerful thermal buffering of permafrost. Critically, we find a widespread trend of weakening coupling (decreasing (3) at 66.7 % of sites, a phenomenon most pronounced in SF zones. Our driver analysis reveals that the spatial patterns of (3 are primarily controlled by surface insulation from summer rainfall and soil moisture. The temporal trends, however, are driven by a complex and counter-intuitive interplay. Paradoxically, rapid atmospheric warming is the strongest driver of a strengthening of coupling, likely due to the loss of insulative snow cover, while trends toward wetter conditions drive a weakening of coupling by enhancing surface insulation. Spatially explicit maps derived from our models pinpoint hotspots of accelerated decoupling in the eastern and southern QTP, while also identifying high-elevation PF regions where coupling is strengthening, signaling a loss of protective insulation and increased vulnerability to degradation. These findings highlight a dynamic and non-uniform response of land-atmosphere interactions to climate change, with profound implications for the QTP's cryosphere, hydrology, and ecosystems.

This study assesses the stability of the Bei'an-Hei'he Highway (BHH), located near the southern limit of latitudinal permafrost in the Xiao Xing'anling Mountains, Northeast China, where permafrost degradation is intensifying under combined climatic and anthropogenic influences. Freeze-thaw-induced ground deformation and related periglacial hazards remain poorly quantified, limiting regional infrastructure resilience. We developed an integrated framework that fuses multi-source InSAR (ALOS, Sentinel-1, ALOS-2), unmanned aerial vehicle (UAV) photogrammetry, electrical resistivity tomography (ERT), and theoretical modeling to characterize cumulative deformation, evaluate present stability, and project future dynamics. Results reveal long-term deformation rates from -35 to +40 mm/yr within a 1-km buffer on each side of the BHH, with seasonal amplitudes up to 11 mm. Sentinel-1, with its 12-day revisit cycle, demonstrated superior capability for monitoring the Xing'an permafrost. Deformation patterns were primarily controlled by air temperature, while precipitation and the topographic wetness index enhanced spatial heterogeneity through thermo-hydrological coupling. Wavelet analysis identified a 334-day deformation cycle, lagging climate forcing by similar to 107 days due to the insulating effects of peat. Early-warning analysis classified 4.99 % of the highway length as high-risk (subsidence 10.91 mm/yr). The InSAR-based landslide prediction model achieved high accuracy (Area Under the Receiver Operating Characteristic (ROC) Curve, or AUC = 0.9486), validated through field surveys of subsidence, cracking, and slow-moving failures. The proposed 'past-present-future' framework demonstrates the potential of multi-sensor integration for permafrost monitoring and provides a transferable approach for assessing infrastructure stability in cold regions.

Infrastructure in northern regions is increasingly threatened by climate change, mainly due to permafrost thaw. Prediction of permafrost stability is essential for assessing the long-term stability of such infrastructure. A key aspect of geotechnical problems subject to climate change is addressing the surface energy balance (SEB). In this study, we evaluated three methodologies for applying surface boundary conditions in longterm thermal geotechnical analyses, including SEB heat flux, n-factors, and machine learning (ML) models by using ERA5-Land climate reanalysis data until 2100. We aimed to determine the most effective approach for accurately predicting ground surface temperatures for climate-resilient design of northern infrastructure. The evaluation results indicated that the ML-based approach outperformed both the SEB heat flux and n-factors methods, demonstrating significantly lower prediction errors. The feasibility of long-term thermal analysis of geotechnical problems using ML-predicted ground surface temperatures was then demonstrated through a permafrost case study in the community of Salluit in northern Canada, for which the thickness of the active layer and talik were calculated under moderate and extreme climate scenarios by the end of the 21st century. Finally, we discussed the application and limitations of surface boundary condition methodologies, such as the limited applicability of the n-factors in long-term analysis and the sensitivity of the SEB heat flux to inputs and thermal imbalance. The findings highlight the importance of selecting suitable boundary condition methodologies in enhancing the reliability of thermal geotechnical analyses in cold regions.

Here, we present the result of different models for active layer thickness (ALT) in an area of the Italian Central Alps where a few information about the ALT is present. Looking at a particular warm year (2018), we improved PERMACLIM, a model used to calculate the Ground Surface Temperature (GST) and applied two different versions of Stefan's equation to model the ALT. PERMACLIM was updated refining the temporal basis (daily respect the monthly means) of the air temperature and the snow cover. PERMACLIM was updated also to minimize the bias of the snow cover in summer months using the PlanetScope images. Moreover, the contribution of the solar radiation was added to the air temperature to improve the summer GST. The modelled GST showed a good calibration and, among the two versions of Stefan's equation, the first (ALT1) indicates a maximum active layer thickness of 7.5 m and showed a better accuracy with R2 of 0.93 and RMSE of 0.32 m. The model underlined also the importance of better definition of the thermal conductivity of the ground that can strongly influence the ALT.

The Three-Rivers Headwater Region (TRHR) is located on the Tibetan Plateau, within a transitional zone between seasonally frozen ground and continuous permafrost. Over 70 % of the region is predominantly covered by alpine grasslands, a vulnerable ecosystem increasingly threatened by ongoing permafrost degradation. This study utilized satellite data to analyze permafrost degradation by measuring active layer thickness (ALT) and the soil non-frozen period (NFP), and to investigate their impacts on alpine grassland growth. Results showed significant permafrost degradation from 2000 to 2020, with ALT thickening at a rate of 7.79 cm/decade (p < 0.05) and NFP lengthening by 1.1 days/yr (p < 0.05). Simultaneously, grassland vegetation exhibited a significant greening trend (0.0014 yr(-1), p < 0.01). Using the partial least squares (PLS) regression method, the study evaluated the relationships between grassland dynamics and permafrost degradation, while jointly accounting for climate variables (temperature, precipitation, and sunshine duration). ALT thickening was the dominant explanatory variable for grassland growth in 11.09 % of the region, and it was positively correlated in relatively cold western and alpine areas, but negatively correlated in the relatively warm eastern and central regions. NFP extension was the dominant explanatory variable for grassland growth in 10.38 % of the region, although its positive correlation weakened as climate conditions transitioned from relatively cold-dry to relatively warm-wet. Although permafrost degradation was positively correlated with grassland greening in relatively cold regions, the diminishing benefit of NFP extension and the adverse effects of ALT thickening may increasingly undermine grassland stability in relatively warm regions under further climate warming.

Understanding soil organic carbon (SOC) distribution and its environmental controls in permafrost regions is essential for achieving carbon neutrality and mitigating climate change. This study examines the spatial pattern of SOC and its drivers in the Headwater Area of the Yellow River (HAYR), northeastern Qinghai-Xizang Plateau (QXP), a region highly susceptible to permafrost degradation. Field investigations at topsoils of 86 sites over three summers (2021-2023) provided data on SOC, vegetation structure, and soil properties. Moreover, the spatial distribution of key permafrost parameters was simulated: temperature at the top of permafrost (TTOP), active layer thickness (ALT), and maximum seasonal freezing depth (MSFD) using the TTOP model and Stefan Equation. Results reveal a distinct latitudinal SOC gradient (high south, low north), primarily mediated by vegetation structure, soil properties, and permafrost parameters. Vegetation coverage and above-ground biomass showed positive correlation with SOC, while soil bulk density (SBD) exhibited a negative correlation. Climate warming trends resulted in increased ALT and TTOP. Random Forest analysis identified SBD as the most important predictor of SOC variability, which explains 38.20% of the variance, followed by ALT and vegetation coverage. These findings likely enhance the understanding of carbon storage controls in vulnerable alpine permafrost ecosystems and provide insights to mitigate carbon release under climate change.

Understanding changes in water balance and land-atmosphere interaction under climate change is crucial for managing water resources in alpine regions, especially in the Qinghai-Tibet Plateau (QTP). Evapotranspiration (ET), a key process in the land-atmosphere interaction, is influenced by permafrost degradation. As the active layer in permafrost regions deepens due to climate warming, the resulting shifts in surface hydrologic connectivity and water storage capacity affect vegetation's ability to access water, thereby influencing its growth and regulating ET dynamics, though the full complexity of this process remains unclear. This study employs the Budyko-Fu model to assess the spatiotemporal dynamics of ET and the ET ratio (the ratio of ET to precipitation) on the QTP from 1980 to 2100. While ET shows a continuous upward trend, the ET ratio exhibits a non-monotonic pattern, increasing initially and then decreasing. More than two-thirds of permafrost areas on the QTP surpassed the critical ET ratio threshold by 2023, under three emission scenarios. By 2100, nearly all areas are projected to reach the tipping point, with 97 % affected under the SSP5-8.5 scenario. Meadow and steppe regions are expected to encounter this threshold earlier, whereas forested areas will be less affected, with over 80 % unlikely to reach the tipping point by 2100. Basin-level differences are notable: nearly 90 % of the Qaidam basin exceeded the threshold before 2023, compared to less than 50 % in the Yangtze basin. By 2100, more than 80 % of regions in all basins are expected to cross the tipping point due to ongoing permafrost degradation. This study advances understanding of land-atmosphere interactions in alpine regions, providing critical insights for water resource management and improving extreme weather predictions.

Thaw hazards in high-latitude and glaciated regions are becoming increasingly frequent because of global climate warming and human activities, posing significant threats to infrastructure stability and environmental sustainability. However, despite these risks, comprehensive investigations of thaw-hazard susceptibility in permafrost regions remain limited. Here, this gap is addressed by a systematic and long-term investigation of thaw hazards in China's Qinghai Province as a representative permafrost area. A detailed inventory of 534 thawhazard sites was developed based on remote sensing, field verification, and surveys by a UAV, providing critical data for susceptibility analysis. Eleven environmental factors influencing thaw hazards were identified and analyzed using information gain and Shapley additive explanation. By using the random forest model, a susceptibility map was generated, categorizing the study area into five susceptibility classes: very low, low, moderate, high, and very high. The key influencing factors include precipitation, permafrost type, temperature change rate, and human activity. The results reveal that 17.5 % of the permafrost region within the study area is classified as high to very high susceptibility, concentrated primarily near critical infrastructure such as the Qinghai-Tibet Railway, potentially posing significant risks to its structural stability. The random forest model shows robust predictive capability, achieving an accuracy of 0.906 and an area under the receiver operating characteristic curve of 0.965. These findings underscore the critical role of advanced modeling in understanding the spatial distribution and drivers of thaw hazards, offering actionable insights for hazard mitigation and infrastructure protection in permafrost regions under a changing climate.

Aeolian sand, as a primary medium of desertification, changes surface energy budgets and consequently affects both ecological systems and infrastructure stability on the Tibetan Plateau. Accurate interpretation of upper boundary conditions is critical for assessing aeolian sand's effects on subsurface hydrothermal processes. Nevertheless, current numerical simulations typically rely on empirical thermal boundaries and neglect surface radiation components and latent heat exchange, causing, significant deviations between simulations and field observations. This study establishes a thermal boundary model to calculate net surface energy (Q) based on energy balance theory and 13 sets of reflectance experiments. Using meteorological data from 2003 to 2019, soil temperature evolution was simulated under three aeolian sand coverage conditions: dry, 5 % moisture, and 10 % moisture. Results indicated that the simulated outputs exhibit strong correlations with observed data in terms of trend direction, phase timing of peaks and troughs, and temperature amplitude (R > 0.93, p < 0.0001). At the sand-atmosphere interface (-0.05 m), the annual mean temperature under dry aeolian sand cover reached 5.300 degrees C, which is 4.823 degrees C higher than that of the exposed surface (0.477 degrees C) during 2005-2006. When including moisture, the latent heat-driven cooling effect became evident, and the annual mean temperature at the sand surface dropped significantly to 0.930 degrees C (5 % moisture) and 1.461 degrees C (10 % moisture). More importantly, moisture cooling effects in shallow layers (-0.05 to-0.4 m) exhibit non-monotonic behavior: 10 % moisture yields higher annual mean temperatures than 5 % moisture (e.g., 1.370 degrees C vs. 0.858 degrees C at-0.2 m), suggesting aeolian sand's thermal impact on underlying permafrost involves critical moisture thresholds.