The Qinghai-Tibetan Plateau (QTP) and the Arctic are prime examples of permafrost distribution in high-altitude and high-latitude regions. A nuanced understanding of soil thermal conductivity (STC) and the various influencing factors is essential for improving the accuracy of permafrost simulation models in these areas. Nevertheless, no comparative analysis of STC between these two regions has been conducted. Therefore, this study aims to investigate the characteristics and influencing factors of STC at varying depths within the active layer (5 to 60 cm) during freezing and thawing periods in the QTP and the Arctic, using the regional-scale STC data products simulated through the XGBoost method. The findings indicate the following: (1) the mean STC of permafrost in the QTP is higher than that in the Arctic permafrost region. The STC in the QTP demonstrates a declining trend over time, while the Arctic permafrost maintains relative stability. The mean STC values in the QTP permafrost region during the thawing period are significantly higher than those during the freezing period. (2) STC of the QTP exhibits a fluctuating pattern at different depths, in contrast, the average STC value in the Arctic increases steadily with depth, with an increase rate of approximately 0.005 Wm-1 K-1/cm. (3) The analysis of influencing factors revealed that although moisture content, bulk density, and porosity are the primary drivers of regional variations in STC between the QTP and the Arctic permafrost, moisture elements in the QTP region have a greater influence on STC and the effect is stronger with increasing depth and during the freeze-thaw cycles. Conversely, soil saturation, bulk density, and porosity in the Arctic have significant impacts. This study constitutes the first systematic comparative analysis of STC characteristics.

With the global climate change, glaciers on the Qinghai-Tibet Plateau (QTP) and its adjacent mountainous regions are retreating rapidly, leading to an increase in active rock glaciers (ARGs) in front of glaciers. As crucial components of water resources in alpine regions and indicators of permafrost boundaries, ARGs reflect climatic and environmental changes on the QTP and its adjacent mountainous regions. However, the extensive scale of rock glacier development poses a challenge to field investigations and sampling, and manual visual interpretation requires substantial effort. Consequently, research on rock glacier cataloging and distribution characteristics across the entire area is scarce. This study statistically analyzed the geometric characteristics of ARGs using high- resolution GF-2 satellite images. It examined their spatial distribution and relationship with local factors. The findings reveal that 34,717 ARGs, covering an area of approximately 6873.54 km2, with an average area of 0.19 +/- 0.24 km2, a maximum of 0.0012 km2, and a minimum of 4.6086 km2, were identified primarily in north-facing areas at elevations of 4300-5300 m and slopes of 9 degrees-25 degrees, predominantly in the Karakoram Mountains and the Himalayas. Notably, the largest concentration of ARGs was found on north-facing shady slopes, constituting about 42 % of the total amount, due to less solar radiation and lower near-surface temperatures favorable for interstitial ice preservation. This research enriches the foundational data on ARG distribution across the QTP and its adjacent mountainous regions, offering significant insights into the response mechanisms of rock glacier evolution to environmental changes and their environmental and engineering impacts.

Permafrost degradation varies spatially; however, the underlying mechanism remains partially unclear. In this study, we predicted permafrost variation under the influence of climate change to investigate the sensitivity of permafrost degradation to geological and climatic conditions. The results revealed that geological strata can strongly impact the permafrost degradation process. Mainly due to the greater thermal conductivity of sandy gravel in the Arctic, the complete thaw of permafrost will be greatly delayed by more than 160 years compared with that on the Qinghai-Tibet Plateau (QTP). Climatic conditions, such as snow depth, can also greatly affect the degradation process of permafrost: The thaw of permafrost will be delayed by more than 140 years when the snow depth decreases from 0.7 to 0.1 m. Peat soil thickness at ground surface can also affect permafrost degradation. The permafrost temperature increases as peat soil thickens when the thickness is less than 1.0 m, whereas there is a critical peat soil thickness (approximately 0.2 and 0.5 m on the QTP and in the Arctic, respectively) under which permafrost will thaw at the fastest rate. The findings highlight the influence of geology and climate over permafrost degradation.

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock-ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate-cryosphere-hydrology-hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze-thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole.

Global climate warming is accelerating permafrost degradation. The large amounts of soil organic matter in permafrost-affected soils are prone to increased microbial decomposition in a warming climate. Along with permafrost degradation, changes to the soil microbiome play a crucial role in enhancing our understanding and in predicting the feedback of permafrost carbon. In this article, we review the current state of knowledge of carbon-cycling microbial ecology in permafrost regions. Microbiomes in degrading permafrost exhibit variations across spatial and temporal scales. Among the short-term, rapid degradation scenarios, thermokarst lakes have distinct biogeochemical conditions promoting emission of greenhouse gases. Additionally, extreme climatic events can trigger drastic changes in microbial consortia and activity. Notably, environmental conditions appear to exert a dominant influence on microbial assembly in permafrost ecosystems. Furthermore, as the global climate is closely connected to various permafrost regions, it will be crucial to extend our understanding beyond local scales, for example by conducting comparative and integrative studies between Arctic permafrost and alpine permafrost on the Qinghai-Tibet Plateau at global and continental scales. These comparative studies will enhance our understanding of microbial functioning in degrading permafrost ecosystems and help inform effective strategies for managing and mitigating the impacts of climate change on permafrost regions.

为研究黄河源区径流演变规律，以WEP-QTP（The Water and Energy transfer Processes in the Qinghai-Tibet Plateau）模型为基础构建基于水热耦合的黄河源区冻土水文模型。采用玛曲站2019—2021年冻融期逐日土壤温度及土壤液态含水率对模型进行验证，率定期及验证期决定系数（R2）均值为0.8左右，均方根误差（RMSE）均值分别为1.0℃及0.04左右；采用8个冻土监测点1971—2000年冻融期逐日冻土深度进行验证，决定系数（R2）均值为0.89,均方根误差（RMSE）均值为214.81 mm。模型模拟黄河源区1956—2020年逐月流量过程，效率系数（NSE）为0.8左右，相对误差（RE）为5%左右，表明模型能较好地模拟黄河源区径流过程。利用M-K趋势检验分析得到1956—2020年黄河源区径流呈不显著增加趋势，其变化趋势是降水与气温共同影响的结果。冻融期、非冻融期径流与全年趋势一致。降水增加、气候变暖及冻土退化使径流组分发生变化，地表径流及地下径流均呈增加趋势，但地下径流在全...

为研究黄河源区径流演变规律，以WEP-QTP（The Water and Energy transfer Processes in the Qinghai-Tibet Plateau）模型为基础构建基于水热耦合的黄河源区冻土水文模型。采用玛曲站2019—2021年冻融期逐日土壤温度及土壤液态含水率对模型进行验证，率定期及验证期决定系数（R2）均值为0.8左右，均方根误差（RMSE）均值分别为1.0℃及0.04左右；采用8个冻土监测点1971—2000年冻融期逐日冻土深度进行验证，决定系数（R2）均值为0.89,均方根误差（RMSE）均值为214.81 mm。模型模拟黄河源区1956—2020年逐月流量过程，效率系数（NSE）为0.8左右，相对误差（RE）为5%左右，表明模型能较好地模拟黄河源区径流过程。利用M-K趋势检验分析得到1956—2020年黄河源区径流呈不显著增加趋势，其变化趋势是降水与气温共同影响的结果。冻融期、非冻融期径流与全年趋势一致。降水增加、气候变暖及冻土退化使径流组分发生变化，地表径流及地下径流均呈增加趋势，但地下径流在全...

为研究黄河源区径流演变规律，以WEP-QTP（The Water and Energy transfer Processes in the Qinghai-Tibet Plateau）模型为基础构建基于水热耦合的黄河源区冻土水文模型。采用玛曲站2019—2021年冻融期逐日土壤温度及土壤液态含水率对模型进行验证，率定期及验证期决定系数（R2）均值为0.8左右，均方根误差（RMSE）均值分别为1.0℃及0.04左右；采用8个冻土监测点1971—2000年冻融期逐日冻土深度进行验证，决定系数（R2）均值为0.89,均方根误差（RMSE）均值为214.81 mm。模型模拟黄河源区1956—2020年逐月流量过程，效率系数（NSE）为0.8左右，相对误差（RE）为5%左右，表明模型能较好地模拟黄河源区径流过程。利用M-K趋势检验分析得到1956—2020年黄河源区径流呈不显著增加趋势，其变化趋势是降水与气温共同影响的结果。冻融期、非冻融期径流与全年趋势一致。降水增加、气候变暖及冻土退化使径流组分发生变化，地表径流及地下径流均呈增加趋势，但地下径流在全...

为研究气候变化条件下高原寒区径流演变规律，利用WEP-QTP模型模拟了1956～2020年长江源区水循环过程，分析了长江源区径流及其组分演变规律，并基于多因素归因分析方法定量分析了径流变化的驱动机制。结果表明：1956～2020年长江源区径流组分中降雨径流、融雪径流及融冰径流占比分别为79.4%,17.2%和3.4%。对比基准期（1956～1998年）与变化期（1999～2020年）,气候影响下径流变化量为21.4亿m3,气温和降水对径流增加的贡献率分别为-8.4%和108.4%。对径流组分进行分析，气候影响下降雨径流变化量为24.8亿m3,气温和降水对降雨径流增加的贡献率分别为36.2%和63.8%;气候影响下融雪径流变化量为-3.1亿m3,气温和降水对融雪径流减少的贡献率分别为348.1%和-248.1%;气候影响下融冰径流变化量为-0.3亿m3,气温和降水对融冰径流减少的贡献率分别为-21.5%和121.5%。对径流及其组分逐月过程进行分析，气候变化对径流及其组分的影响主要集中在6～10月。

为研究气候变化条件下高原寒区径流演变规律，利用WEP-QTP模型模拟了1956～2020年长江源区水循环过程，分析了长江源区径流及其组分演变规律，并基于多因素归因分析方法定量分析了径流变化的驱动机制。结果表明：1956～2020年长江源区径流组分中降雨径流、融雪径流及融冰径流占比分别为79.4%,17.2%和3.4%。对比基准期（1956～1998年）与变化期（1999～2020年）,气候影响下径流变化量为21.4亿m3,气温和降水对径流增加的贡献率分别为-8.4%和108.4%。对径流组分进行分析，气候影响下降雨径流变化量为24.8亿m3,气温和降水对降雨径流增加的贡献率分别为36.2%和63.8%;气候影响下融雪径流变化量为-3.1亿m3,气温和降水对融雪径流减少的贡献率分别为348.1%和-248.1%;气候影响下融冰径流变化量为-0.3亿m3,气温和降水对融冰径流减少的贡献率分别为-21.5%和121.5%。对径流及其组分逐月过程进行分析，气候变化对径流及其组分的影响主要集中在6～10月。