In order to explore the characteristics of winter temperature changes and extreme cold weather in Heilongjiang Province， cope with extreme climate events， daily temperature data and ERA5 reanalysis data from 84 national meteorological stations in Heilongjiang Province during 1961/1962—2022/2023 were selected， and the spatio-temporal distribution characteristics of multi-year temperature in Heilongjiang Province were analyzed by means of synoptic and statistical methods. the atmospheric circulation during extreme temperature was discussed， and the atmospheric circulation index which had better indication of winter temperature was selected. The results show that the winter mean temperature and extreme minimum temperature in Heilongjiang Province are higher in the south and lower in the north， and both show a warming trend， and the extreme cold weather shows a decreasing trend， but the longest duration and maximum influence range of the extreme cold weather have no obvious decreasing trend. The extreme cold weather mainly occurred in the north of Heilongjiang Province， where the Greater Khingan Mountains accounted for 68.7% of the total extreme cold days， followed by the Lesser Khingan Mountains 27.6%， and other regions only 3.7%. Most of the extreme cold weather occurred in January， accounting for 56.9% of the total number of extreme cold weather in winter， followed by December （24.5%）， and February （18.6%）. The abrupt changes in winter mean temperature and extreme cold station occurred in the 1980s， while the abrupt changes in extreme minimum temperature occurred in the 1990s， which are more consistent with the abrupt changes in the Arctic Oscillation， the East Asian trough intensity， and the Arctic sea ice area in autumn. The winter temperature in Heilongjiang Province is mainly affected by the polar circulation， and the southerly polar vortex may lead to a long duration of extremely cold weather in Heilongjiang Province. The Kuroshio Current SST Index， East Asian Trough Intensity Index and Scandinavian Pattern have good correlation with winter temperature in Heilongjiang Province， which have good indication for predicting future extreme temperature changes.

The rapid formation， expansion， and outburst of glacial lakes are the prominent consequences of global warming and glacier recession. Large-scale glacial lake mapping， glacial lake outburst flood （GLOF） compilations， and risk assessments are essential for understanding regional environmental changes and implementing effective disaster prevention and management strategies. Centered on the Tibetan Plateau， the Asian Water Tower is one of the major global glacial lake regions and has experienced rapid glacial lake expansion and significant disaster impacts over the past few decades. This study focuses on glacial lake changes， outbursts， and associated risks in the Asian Water Tower and summarizes recent advances in glacial lake research. The results show that： （1） The overall rate of glacial lake area expansion in the Asian Water Tower from 1990 to 2018 ［（31.6±4.0） km²·（5a）^-1］ exceeded the average rate observed during 2018—2022 ［（22.7±8.5） km²·（5a）^-1］. Proglacial lakes， accounting for 83% of the total area increase during 2018—2022， emerge as the primary contributor to the current expansion of glacial lakes in the Asian Water Tower， with changes mainly concentrated in the eastern Himalayas and southeastern Xizang. Under the future ice-free scenarios， a total of 15 826 potential glacial lakes with areas exceeding 0.02 km² are projected to emerge， covering an area of （2 253.95±1 291.29） km² with a water volume of （60.49±28.94） km³. The western glacier-rich region is projected to experience a greater emergence of new and rapidly expanding lakes， although with a time lag compared to eastern regions such as the eastern Himalayas and southeastern Xizang， due to different climate patterns. （2） From 1900 to 2020， 145 credible moraine-dammed GLOFs were recorded in the region， with a marked increase in frequency after the 1980s. Specifically， the average annual number increased from 1.5 GLOFs during 1981—1990 to 2.7 GLOFs during 2011—2020. Spatially， GLOF activity has intensified in southeastern Xizang and China-Nepal border area. Additionally， 183 GLOFs stemming from ice-dammed lakes are documented， mainly concentrated in the Karakoram and western Tianshan Mountains， demonstrating watershed-scale clustering and periodic outbursts. （3） The ongoing expansion and frequent outbursts of glacial lakes have significantly increased disaster risk across the region. A systematic hazard assessment has identified 1 499 glacial lakes classified as high or very high hazard level， potentially affecting a flood inundation area of 6 353 km². In terms of disaster-bearing bodies， approximately 55 808 buildings， 105 existing or planned hydropower projects， 194 km² of farmland， 5 005 km of roads， and 4 038 bridges are vulnerable to potential GLOFs. Approximately 190 000 peoples are directly exposed along GLOF-prone pathways. Subsequently， 85 lakes are classified as very high risk level and 113 as high risk level. The eastern Himalaya and southeastern Xizang exhibit the highest levels of GLOF hazard， exposure， and risk. This situation is exacerbated by the rapid expansion of glacial lakes， the increased frequency of GLOFs， the density of dangerous glacial lakes， and the significant potential disaster volume in the Asian Water Tower. These findings highlight the urgent need for more proactive GLOF prevention and mitigation efforts and the necessity of constructing a regionally coordinated and dynamic disaster management system. Overall， while glacial lake research in the Asian Water Tower has progressed rapidly over the past decade， critical challenges remain. These include the need for standardized and high-precision lake mapping， improved understanding of GLOF-climate relationships， and more refined and watershed-scale risk assessments.

The Qinghai-Xizang Plateau （Tibetan Plateau， TP）， renowned as one of the most distinctive geographical landmarks globally， stands as a critical tipping point within the climate system. As the “Asian Water Tower”， this region is highly sensitive to the effects of climate change and could significantly impact the regional climate patterns. Its influence extends profoundly， affecting the climate of East Asia and even the broader Northern Hemisphere. Therefore， it is imperative to comprehend the historical climate patterns and current dynamics of TP. As a proxy for reconstructing past climates， tree rings have been widely utilized in TP climate reconstruction studies. Dendrochronological works in TP have demonstrated a significant potential for developing long tree-ring chronologies over 1 000 years from living trees， archaeological samples， and in situ timber remnants. These records contain temperature and precipitation variability information at multidecadal scales with perfect annual resolution. While numerous literature reviews have focused on different tree-ring parameters， a comprehensive review of dendroclimatological research on TP is still lacking. Therefore， it is necessary to systematically review the research progress of tree ring climate studies in this region. This study retrieved tree-ring chronology data from 1 436 sampling sites across the TP， outlining the progress of dendroclimatology research since 1990. The analysis revealed that most studies utilized tree-ring width （TRW）， followed by maximum latewood density （MXD）， stable oxygen isotopes （δ¹⁸O）， and stable carbon isotopes （δ¹³C）. TRW studies were widely distributed， encompassing nearly 70 tree species， with the length of the chronologies ranging from 300 to 600 years. MXD studies were concentrated in the Hengduan Mountains， focusing mainly on Picea balfouriana， with shorter chronologies and no millennial records. MXD is predominantly used to reconstruct regional temperatures during the growing or late growing season. Studies of δ¹⁸O are primarily located in the surrounding areas of the Hengduan Mountains， Qilian Mountains， and Himalayas. The studied species is substantial， with the longest δ¹⁸O chronology dating back to 4680 B.C. δ¹⁸O mainly records hydroclimatic signals. Studies of δ¹³C are relatively weak， mainly concentrated near the cities of Linzhi and Chamdo and the surrounding areas of the Qilian Mountains. The species most commonly used for δ¹³C studies are Picea crassifolia and Sabina przewalskii. Most of the δ¹³C chronologies are shorter than 200 years， with the longest chronology reaching 1 171 years. Tree-ring δ¹³C is used to reconstruct changes in temperature and hydroclimatic signals. In the future， the accuracy of climate reconstructions can be improved by extending the length of MXD and isotope chronologies， conducting multi-parameter comprehensive analyses， and refining the detrending methods. Expanding sampling sites， particularly in the western and southern TP， is essential to address these geographic gaps. Future research should also explore the climate responses of shrubs and non-coniferous species to enhance the regional dendroclimatological database. Extending the length of tree-ring density and isotope chronologies—especially in northern and western regions—is critical. Given the current reliance of most studies on a single parameter from a single species for climate reconstruction， multi-species and multi-parameter approaches remain uncommon. Therefore， future research should prioritize integrated analyses， combining diverse dendrochronological indicators with climate models to improve the accuracy and temporal depth of multifactor climate reconstructions. Researchers can gain deeper insights into this region's changing frequency and intensity of extreme climate events by integrating tree-ring records and modern meteorological data and combining tree-ring ecology and physiology studies. This study offers a comprehensive overview of advancements in dendroclimatological research in TP， thus providing readers with a clear understanding of the latest developments and the persistent challenges in these studies. Furthermore， this review also summarizes the shortcomings of existing research and lays a theoretical foundation for future in-depth investigations in this field.

Soil carbon pools are among the largest carbon reservoirs in the Earth’s surface ecosystems and play a key role in the global carbon cycle due to their large storage capacity. This study took the northern Xizang （Tibet Autonomous Region） as the study area and obtained measured data of soil organic carbon stock and physicochemical properties in the 0~30 cm soil layer through field experiments. Combined with meteorological station data in the study area and meteorological and soil data from sampling points from 2014 to 2023， the DNDC model was driven， calibrated， and validated. Subsequently， the model was employed to simulate soil organic carbon stocks across the study area of northern Xizang. Statistical methods including the coefficient of variation， spatial interpolation， correlation analysis， and partial least squares regression analysis were used to analyze the spatiotemporal characteristics of soil organic carbon stocks in different soil layers of northern Xizang， and quantitatively assess the influence of climatic and environmental factors on soil organic carbon stocks. The results showed that： （1） Based on the results of previous studies and the actual data of the grasslands on the Qinghai-Xizang Plateau， the model parameters were iteratively adjusted through repeated experiments. The R² value between the simulated and measured values from the DNDC model was greater than 0.8， with a root-mean-square error （RMSE） of 5.16 and a Nash-Sutcliffe efficiency coefficient （NSE） of 0.61. The localized DNDC model demonstrated strong applicability for simulating soil organic carbon stocks in the grasslands on the Qinghai-Xizang Plateau， though its simulation results required simple correction. （2） On the time scale， from 2014 to 2023， the soil organic carbon stock in the 0~10 cm soil layer in northern Xizang showed a fluctuating decline trend. The soil organic carbon stock in the 10~20 cm soil layer remained relatively stable， with a slight increase in 2023. In the 20~30 cm soil layer， the soil organic carbon stock was stable from 2014 to 2022 but declined significantly in 2023. On the spatial scale， the soil organic carbon stock exhibited a distribution pattern characterized by low values in the central area and high values around the periphery. The soil organic carbon stock was concentrated in the eastern part of the study area， such as Baqen County， Sêni District， and Lhari County， while it was lower in the central and western parts of the study area， such as Nyima County， Xainza County and Gar County. （3） Average precipitation and soil water content were the main factors positively correlated with carbon storage. In contrast， soil bulk density， pH value， sand-to-gravel ratio， and elevation were negatively correlated factors， and the annual average temperature had a weak influence on the soil organic carbon stocks in northern Xizang. The results of partial least squares （PLS） regression showed that elevation， soil bulk density， sand-to-gravel ratio， and soil water content had relatively strong explanatory importance for the dependent variable （soil organic carbon stocks）. This study holds significant implications for grassland management and ecosystem conservation in northern Xizang. In future carbon pool management， attention should be paid to environmental factors， land use， and ecological restoration measures， especially the importance of precipitation and elevation in the dynamic changes of soil organic carbon. This will promote the stabilization or growth of soil organic carbon stocks. This study provides data support for the carbon cycling processes of grassland ecosystems and offers a practical basis for ecological environmental protection and construction in northern Xizang.

Understanding the impact of snow phenology on vegetation carbon sequestration is crucial for evaluating ecosystem responses to climate change. This is particularly important in arid and semi-arid regions of Xinjiang， where snowmelt serves as one of the region’s major water resources. Based on global daily carbon flux simulation data from 2001 to 2018 and the AVHRR China snow phenology dataset， this study analyzed the spatiotemporal variation characteristics of vegetation carbon sequestration indicators such as gross primary production （GPP） and net primary production （NPP） in Xinjiang， and snow phenology parameters such as snow cover days （SCD）， snow cover start dates （SCS）， and snow cover melt dates （SCM） in Xinjiang using Theil-Sen Median trend analysis， Mann-Kendall test， geodetector， and partial correlation analysis. Additionally， the impact of snow phenology in Xinjiang on the spatial differentiation and temporal variation of vegetation carbon sequestration was explored. The results showed that： （1） From 2001 to 2018， the carbon sequestration of vegetation in Xinjiang showed an overall increasing trend. Among them， from 2001 to 2007， the GPP and NPP of vegetation in Xinjiang decreased at a temporal rate of 9.83 gC⋅m^-2⋅a^-1 and 5.1 gC⋅m^-2⋅a^-1， respectively， and the areas with significant spatial decreasing trends accounted for 38.78% and 36.50%， respectively. From 2007 to 2018， the GPP and NPP of vegetation in Xinjiang increased at a temporal rate of 6.62 gC⋅m^-2⋅a^-1 and 3.26 gC⋅m^-2⋅a^-1， respectively， with areas showing significant spatial increasing trends accounting for 48.05% and 49.84%， respectively. From 2001 to 2018， the SCS in Xinjiang showed a trend of first delaying and then advancing， the SCM showed a trend of first advancing and then delaying， and the SCD showed a trend of first decreasing and then increasing. （2） The results of the geodetector showed that the interaction between any two driving factors had a greater impact on GPP and NPP than a single factor， showing nonlinear enhancement or two-factor enhancement. Among them， the interactions between snow phenology and elevation， vegetation type， and temperature were mainly nonlinear enhancement， while the interaction with precipitation and solar radiation exhibited mainly two-factor enhancement. The interaction between topography and snow phenology had the highest explanatory power for the spatial differentiation of vegetation carbon sequestration. （3） Partial correlation results showed that the response of vegetation carbon sequestration in Xinjiang to snow phenology exhibited a “positive and negative coexistence” characteristic. Overall， vegetation GPP and NPP were mainly negatively correlated with the SCS， with the proportions of pixels showing significant negative correlation being 10.01% and 11.27%， respectively. Vegetation GPP and NPP were mainly positively correlated with the SCM， with the proportions of pixels demonstrating significant positive correlation being 13.73% and 10.86%， respectively. Additionally， vegetation GPP and NPP were primarily positively correlated with the SCD， of which the proportion of pixels with significant positive correlation was 11.14% and 13.35%， respectively. This indicated that an earlier SCS， a delayed SCM， and an increased SCD were more conducive to vegetation growth and carbon absorption. These findings help deepen our understanding of the impact of snow phenology on vegetation carbon sequestration under climate warming. They provide a reference for the evaluation of terrestrial carbon sinks and ecological support capacity， as well as for the formulation of ecologically sustainable development policies， offering a theoretical basis for ecological protection and sustainable development in Xinjiang.

Dust microparticles， as key components of atmospheric aerosols， have significant impacts on climate change and atmospheric environments. Based on continuous observations from November 2022 to January 2024， this study systematically analyzed the deposition characteristics of dust microparticles in snow and ice， glacial meltwater， as well as atmospheric precipitation and meltwater-fed Mingyong river water in the Mingyong Glacier， Meili Snow Mountain， southeastern Qinghai-Xizang Plateau. The results showed that： （1） The number concentration of microparticles in glacier meltwater runoff had pronounced seasonal variations， and the monsoon season was higher than in the non-monsoon season. （2） During the period of intense glacier melting （May—October）， the number concentration of microparticle in the Mingyong river water showed notable diurnal variations， nighttime concentrations were higher than daytime values. This phenomenon was mainly attributed to a decrease in the rate of glacier ablation at night， which slowed down meltwater runoff and thus prolonged the retention time of suspended particles in the river water. Through the continuous observation of meltwater runoff during day-night， it was found that the peak concentration of microparticles appeared around 20：00 CST， which further proved that there was a responsive relationship between the diurnal variation of microparticles in meltwater runoff and the strong ablation process of the glacier. （3） Fine particles （0.57~2 μm in diameter） dominated the number concentration of microparticles in water bodies. The volume-diameter distribution of microparticles in different water bodies generally exhibited a unimodal pattern， with small median particle sizes， indicating that dust microparticles in this glacier region predominantly originated from long-range atmospheric transport and deposition. This study revealed the deposition characteristics of dust microparticles in snow， ice and water bodies in the Meili Snow Mountain glacier area， providing significant insights for analyzing the mechanisms of the rapid ablation of the cryosphere and its response to regional climate change in the context of global warming.

River is a channel for carbon transport between land and ocean and an important reactor for the metabolism of aquatic ecosystems. It plays an important role in the migration and transformation of carbon in terrestrial and marine aquatic continuums and the carbon budget of watershed ecosystems. The Qinghai-Xizang Plateau is the largest high-altitude cold region in the mid-latitude region. As an Asian water tower， it has developed many major rivers and plays an important role in the regional carbon cycle. In the context of climate change， the process of riverine carbon cycle in the Qinghai-Xizang Plateau has received extensive attention in recent years. This study focuses on the process of river carbon cycle in the Qinghai-Xizang Plateau， and systematically summarizes the progress of riverine carbon cycle in many source areas such as the Yellow River， the Yangtze River， the Lancang River， the Nujiang River and the Yarlung Zangbo River in the past decade. It is found that： （1） The dissolved carbon in the rivers of the Qinghai-Xizang Plateau is dominated by inorganic carbon， and the concentration of dissolved organic carbon is relatively low. The factors such as thawing of frozen soil， enhanced weathering， changes in hydrological processes， and increased erosion and sediment production caused by future climate change will further increase the lateral carbon transport flux in the rivers of the Qinghai-Xizang Plateau. （2） Current observations show that rivers on the Qinghai-Xizang Plateau are important sources of carbon dioxide and methane emissions， and their processes are also affected by climate change and have an increasing trend； （3）The river carbon flux in the Qinghai-Xizang Plateau has an important impact on the carbon budget of the basin ecosystem. The large river carbon horizontal and vertical carbon fluxes offset some terrestrial carbon sinks and are an important part of the ecosystem carbon process. With the warm and humid climate of the Qinghai-Xizang Plateau， its river carbon cycle process will inevitably change significantly， which in turn affects regional carbon sinks， but the extent of its impact is still unclear. In the future， in-situ observation， remote sensing inversion， machine learning and other means should be further used to accurately describe the mechanism of river carbon process， enhance the in-depth understanding of the microscopic mechanism of river carbon cycle， and develop a carbon cycle model suitable for alpine rivers on the Qinghai-Xizang Plateau based on relevant mechanisms， so as to strengthen the simulation and prediction ability of carbon cycle in plateau rivers. This study can provide a scientific basis for further understanding the role of rivers in the carbon neutrality of the Qinghai-Xizang Plateau in the context of changing environment.

Reconstructing glacier retreat from the Last Glacial Maximum （LGM） to the early Holocene using cosmogenic ¹⁰Be exposure dating of moraines is essential for understanding the climatic transition between glacial and interglacial periods. This period， spanning approximately 26.5~10 ka， is marked by major reorganizations in the global climate system， including rapid temperature shifts， fluctuations in atmospheric CO₂， changes in ocean circulation， and variations in orbital forcing. These processes collectively shape ice sheet dynamics and influence cryospheric feedbacks in the Northern Hemisphere. However， the fragmented preservation of moraine sequences at individual sites has hindered efforts to resolve the spatiotemporal patterns of glacier retreat on a continental scale. Single-site records often fail to capture the timing and magnitude of broader deglacial trends. To overcome this limitation， this study compiled a comprehensive dataset of 4 003 published ¹⁰Be exposure ages from 802 well-dated moraines spanning the period between 26.5 and 10 ka. The dataset encompassed a wide range of glacial settings and climates， offering a rare opportunity to conduct integrated regional and hemispheric-scale analyses. The moraines were grouped into four major regions—Asia， Europe， North America， and Greenland—to enable comparative analysis. All ¹⁰Be exposure ages were recalculated using a consistent， globally averaged ¹⁰Be production rate and the LSDn （time-dependent） scaling scheme. This recalibration ensured inter-study comparability and minimized biases caused by inconsistent production rates or scaling methods across different studies. To resolve the temporal distribution of glacier retreat events， the Probabilistic Cosmogenic Age Analysis Tool （P-CAAT） was employed based on probability density estimation （PDE） of exposure ages. This method identified statistically significant clusters of moraine ages by fitting the composite age distribution with multiple Gaussian components， enabling the identification of peak periods of moraine formation or abandonment. Gaussian component analysis was conducted independently for each region and for a combined Northern Hemisphere dataset excluding Greenland. The Greenland moraines were analyzed separately due to preservation patterns heavily constrained by the spatial extent and temporal evolution of the ice sheet margin， differing from other regions. This analytical framework enabled the identification of eight statistically significant hemispheric-scale glacier retreat episodes， centered at （23.9±1.0） ka， （20.5±1.3） ka， （18.3±0.7） ka， （16.6±0.8） ka， （14.7±0.8） ka， （13.0±0.7） ka， （11.4±0.4） ka， and （10.3±0.6） ka. The first two episodes occurring before 19 ka were primarily attributed to increases in Northern Hemisphere summer insolation due to orbital forcing. In contrast， the third through seventh episodes occurred after 19 ka and broadly coincided with abrupt increases in atmospheric CO₂， indicating that greenhouse gas forcing became the dominant driver of glacier retreat. The final retreat episode around 10.3 ka aligned with the peak in summer insolation during the early Holocene. Regional comparison showed a high degree of synchronicity in retreat timing， although the earliest phase （~24 ka） was absent from the Greenland and North American records， likely due to differing responses between ice sheet and mountain glacier systems. Some variability in the number or prominence of retreat episodes across regions could also be attributed to differences in sample density and uncertainties in exposure ages. This study represents one of the most extensive syntheses of ¹⁰Be moraine chronologies in the Northern Hemisphere to date. By integrating a large， globally distributed dataset and applying a standardized analytical workflow， this study improves the temporal resolution of deglacial patterns and provides critical geological constraints for climate-cryosphere interaction models. The findings enhance the understanding of the timing and drivers of glacier retreat and have implications for refining ice sheet reconstructions， glacial isostatic adjustment models， and past sea-level estimation. Moreover， the observed regional differences highlight the importance of glacier type， geographic context， and climate forcing in shaping the heterogeneous responses of ice masses to global climate change.

Potentilla parvifolia is a typical alpine shrub widely distributed in the Qilian Mountains. In the context of climate change， it has accelerated its migration trend toward higher altitudes in recent years. Soil microorganisms， as crucial biological communities with transformation potential， are strongly influenced by the root activities of P. parvifolia. Therefore， this study aims to use Illumina Miseq high-throughput sequencing technology to analyze rhizosphere microbial communities and their functional transformation characteristics across different altitudinal habitats and identify the key driving factors， thereby providing an important basis for in-depth investigation of the mechanisms of soil ecological function evolution in alpine regions due to plant migration. The study found that at the low-altitude site （3 204 m）， the coverage of P. parvifolia significantly increased soil total carbon （TC）， available phosphorus （AP）， and nitrate nitrogen （NO₃^--N） contents， while enhancing the activities of sucrase （SUC）， urease （URE）， and cellobiohydrolase （CBH） （P<0.05）. The coverage of P. parvifolia increased the diversity and richness of soil microbial communities， with a more pronounced response observed in fungal communities compared to bacterial communities. Furthermore， P. parvifolia increased the relative abundances of Proteobacteria and Ascomycota. Analysis based on microbial community assembly revealed that stochastic processes dominated bacterial community assembly at low （3 204 m） and middle （3 550 m） altitudes， whereas deterministic processes prevailed at the high-altitude site （3 650 m）. In contrast， fungal community assembly was governed by deterministic processes across all three altitudes. A total of 23 conserved COG functional categories were identified through PICRUSt functional prediction. The coverage of P. parvifolia significantly increased the relative abundance of nitrogen cycle-related functional genes （gudB/rocG， nirK， narH/narY/nxrB） in the soil （P<0.001）， while the abundance of these genes generally decreased with increasing altitude. These findings indicate that P. parvifolia coverage positively affects soil ecological functions at high altitudes in the Qilian Mountains by improving soil physicochemical properties， enhancing key enzyme activities， altering microbial community structure， and regulating functional gene expression. The microbially mediated nitrogen cycling reinforcement may serve as a key driver for the successful migration and niche occupation of P. parvifolia.

High-altitude glacial ecosystems serve as sensitive indicators of global climate change. Within these systems， aquatic microbial communities， acting as core carriers of biodiversity and ecological function， are crucial for maintaining ecosystem stability. Selin Co， the largest glacier-fed endorheic lake on the Qinghai-Xizang Plateau， provides a unique environment that serves as an ideal platform for investigating the ecological patterns of microbial communities under extreme conditions. This study focused on analyzing the diversity patterns and assembly mechanisms of bacterial communities in the waters of Selin Co Lake and assessing their potential response dynamics to environmental changes. Systematic sampling was conducted in June 2022 at the south shore， north shore， and main inflow of Selin Co. Key physicochemical parameters， including pH， total dissolved solids （TDS）， turbidity （TUR）， and nutrients， were measured concurrently. Using 16S rRNA gene high-throughput sequencing， the bacterial community composition and structure were deeply analyzed， yielding a total of 1 327 612 high-quality sequences clustered into 8 587 operational taxonomic units （OTUs）， spanning 28 phyla and 98 genera， thereby revealing exceptionally high taxonomic diversity. Community structure analysis showed that Proteobacteria， Bacteroidota， and Actinomycetota were the absolutely dominant phyla， with Loktanella， Belliella， and Aquiluna as the predominant genera at the genus level. Although α-diversity indices exhibited no statistically significant differences among sampling regions， richness and diversity showed an increasing spatial trend from the south shore to the north shore and inflow， indicating potential influence of local environmental heterogeneity or hydrological inputs. β diversity decomposition （based on Bray-Curtis dissimilarity） demonstrated that differences in bacterial community composition across the regions in Selin Co were primarily driven by species turnover rather than nestedness， reflecting strong environmental filtering or dispersal limitation. Microbial co-occurrence network analysis showed that bacterial taxa were mainly significantly positively correlated， indicating widespread cooperative interactions or niche overlap within the community. This pattern suggested that implementing “a simultaneous multi-region conservation strategy without prioritization” might be more effective for protecting aquatic microbial diversity than prioritizing specific areas. To further analyze community assembly mechanisms， this study applied both the neutral community model （NCM） and checkerboard score （C-score） test. The results showed that deterministic processes predominated in structuring bacterial communities in Selin Co， while stochastic processes played a relatively minor role. Further linear regression modeling of the relationship between bacterial OTU niche breadth and environmental factors identified pH， TDS， and TUR as key drivers significantly influencing bacterial niche breadth. Using multiple community ecological analytical approaches， this study systematically revealed the high diversity， spatial pattern characteristics， and assembly mechanisms of bacterial communities in Selin Co， Qinghai-Xizang Plateau. The findings provide an important case for understanding microbial biogeography in high-altitude glacial lakes， highlighting the central role of environmental selection in shaping microbial communities within extreme aquatic environments. More importantly， the identified key environmental driving factors （pH， TDS， TUR） are highly susceptible to regional climate changes （e.g.， accelerated input of debris from glacial retreat， altered precipitation patterns， and rising water temperatures）. Therefore， the community-environment relationship model established in this study provides a solid scientific foundation for predicting the dynamic responses of microbial community structure and function in Selin Co and similar high-altitude glacial lake ecosystems under future climate change scenarios. This study holds significant scientific value for assessing the impact of global change on vulnerable alpine aquatic ecosystems and for formulating conservation strategies.

Against the backdrop of the ongoing implementation of the Belt and Road Initiative， infrastructure construction in cold regions of China has entered a phase of rapid development. Influenced by the unique climatic environment in cold regions， rock mass engineering faces severe challenges. Diurnal and seasonal freeze-thaw cycles cause repeated ice-water phase transitions in water contained within the pores and fractures of rock masses， generating frost heave stress accompanied by complex moisture migration. This leads to the initiation， propagation， and interconnection of micro-cracks inside the rocks， resulting in significant degradation of their macroscopic mechanical properties. Investigating the evolution mechanisms of mesoscopic structural damage in sandstone under the coupled action of freeze-thaw cycles and mechanical loading holds significant theoretical value and practical engineering implications. Taking freeze-thaw sandstone as the research object， in-situ CT monitoring tests on sandstone under freeze-thaw cycles were systematically conducted. Based on deep learning algorithms， a fully convolutional network （FCN） architecture was integrated with the representative elementary volume （REV） theory to develop a multiscale characterization method linking mesoscopic and macroscopic damage throughout the entire uniaxial compression process of freeze-thaw sandstone. This method accurately extracted the actual mesostructures of internal fractures and their geometric parameters in sandstone. It revealed the controlling mechanisms of freeze-thaw cycles on the anisotropic deterioration of the rock mass， and clarified the cross-scale correlation between micropore reorganization and macroscopic mechanical response. The main contributions and conclusions were as follows. （1） The freeze-thaw rock damage identification algorithm based on the FCN achieved high-precision automatic segmentation of the internal pore （fracture） network in freeze-thaw rocks， enabling quantitative identification of mesoscopic damage. （2） A voxel size of 350×350×350 was selected as the minimum REV. The variations in connected porosity across scanning layers of freeze-thaw sandstone under uniaxial compression were obtained. The sharp increase in internal connected pores of freeze-thaw rocks led to sudden failure under compressive loading. （3） Under identical loading conditions， sandstone subjected to a greater number of freeze-thaw cycles exhibited faster growth in bulk porosity and more rapid internal damage development. The sudden increase in bulk porosity was directly related to the loss of rock strength， serving as a sensitive indicator for predicting failure. （4） A pore-throat network model was established using the maximal ball method. Quantitative analysis of the three-dimensional reconstructed REV of freeze-thaw sandstone indicated that the number of pore-throats inside the rock samples increased significantly under freeze-thaw cycles. Pore-throats were the main pathways for the transmission of frost heave force and damage propagation. The pore-throat system evolved dynamically， transitioning from an increase in frost-induced small pores to an increase in load-assisted medium pores， and finally to large pores before peak stress. （5） The failure process of freeze-thaw rocks under uniaxial compressive loading was essentially the result of the synergistic evolution of pore structure expansion and throat network reorganization. This process induced progressive damage accumulation. Ultimately， dominated by the pore coalescence effect， the percolation channel network formed rapidly， leading to the instability and failure of the rock samples. The coupling action of freeze-thaw cycles and loading profoundly influenced the evolution path of pore structures and failure modes. These findings provide a scientific basis for the stability assessment of rock engineering in cold regions.

The accelerated glacier melting under the intensification of global warming has significantly altered glacial erosion and weathering processes within the cryosphere， with unprecedented enhancement observed in recent decades. In warm glacier catchments， these enhanced processes may exert substantial impacts on the global carbon balance and climate change. Specifically， chemical weathering regulates the carbon cycle by consuming atmospheric CO₂， playing a crucial regulatory role in global climate change over geological timescales. However， with the accelerated melting of glaciers， the chemical weathering rate may change， and its specific impact on the carbon cycle requires further investigation. Mingyong Glacier catchment， located on the southeastern margin of the Qinghai-Xizang Plateau， was selected as the research area. A two-year monitoring campaign （from October 2018 to October 2020） of river water hydrological indicators and daily sampling was conducted， ultimately acquiring 731 river water samples. Multiple analytical approaches were employed， including mass balance calculations， Gibbs diagram analysis， mathematical statistics， and hydrochemical characterization. The hydrochemical characteristics of the river water in Mingyong Glacier catchment were investigated， and the chemical weathering rate of rocks and the carbon sink/source rate in the catchment were quantified. The results showed that river water exhibited significant seasonal variations in Mingyong Glacier catchment， with higher anion and cation concentrations in river water during the non-monsoon periods and lower values during the monsoon periods； the water hydrochemical type was classified as HCO₃-Ca. The anions and cations were predominantly derived from carbonate rock weathering， with participation of sulfuric acid in carbonate weathering contributing the largest proportion （62.1%） to chemical composition in river water. Other sources include carbonic acid in carbonate rock weathering （32.4%）， silicate weathering （4.5%）， and atmospheric deposition （1.0%）. Quantitatively， the silicate weathering rate and atmospheric CO₂ consumption flux averaged 0.16 t·km^-2·a^-1 and 0.31×10³ mol·km^-2·a^-1， respectively. In contrast， sulfuric acid-driven carbonate weathering exhibited higher rates （3.34 t·km^-2·a^-1） and corresponding CO₂ release （4.00×10³ mol·km^-2·a^-1）， yielding a net carbon source flux of 3.70×10³ mol·km^-2·a^-1. This indicated that CO₂ release rates through chemical weathering in the study area exceeded its consumption rates by up to 13 times， exhibiting pronounced seasonal variability with monsoon periods showing significantly higher values than non-monsoon periods. Further analysis of the influencing factors for the above characteristic changes revealed that runoff was identified as the primary controlling factor affecting chemical weathering rates in the Mingyong Glacier catchment， with air temperature exerting greater impact than precipitation. The carbon fluxes in the Mingyong Glacier catchment were directly controlled by chemical weathering rates， while runoff and rainfall indirectly controlled these fluxes by modulating the weathering processes. In conclusion， chemical weathering in warm glacier catchments plays a pivotal role in regulating both local hydrochemical characteristics and ecosystems， as well as global carbon balance and climate change. Consequently， research in this field holds substantial scientific and social value， and deserves widespread attention. Future research should focus on the interaction mechanism between chemical weathering process and climate change to provide a scientific basis and decision-making support for addressing global climate change.

Snow Water Equivalent （SWE） is a critical hydrological variable for assessing the water content stored in snowpacks， particularly in alpine and high-altitude regions like the Qinghai-Xizang Plateau. Given the region’s complex topography， harsh climatic conditions， and the scarcity of in-situ snow measurements， SWE estimation remains a major scientific challenge. This study presents a novel hybrid framework that combines physical modeling and deep learning to simulate daily SWE across the Qinghai-Xizang Plateau， offering a new technical pathway for SWE estimation under data-scarce conditions. The proposed methodology integrates two core models. First， the Factorial Snow Model （FSM）， a physically based process model， is employed to simulate daily snow depth. FSM uses meteorological inputs including air temperature， precipitation， radiation， humidity， wind speed， and pressure to simulate key snowpack processes such as accumulation， compaction， energy exchange， and melt. Second， snow density is estimated using a CNN-BiLSTM-Attention model， which leverages Convolutional Neural Networks （CNN） to extract local spatiotemporal features， Bidirectional Long Short-Term Memory networks （BiLSTM） to capture forward and backward temporal dependencies， and an attention mechanism to dynamically emphasize the most influential features across time steps. Meteorological and snow density data were obtained from ERA5 reanalysis datasets spanning 1979 to 2014. Six key input variables were selected via Pearson correlation analysis： longwave radiation， snowfall， rainfall， temperature， wind speed， and relative humidity. The CNN-BiLSTM-Attention model was trained on data from 1979 to 2003 and tested on data from 2004 to 2014. The model achieved strong predictive performance， with MSE=71.66 kg⋅m^-3， RMSE=8.465 kg⋅m^-3， MAE=6.378 kg⋅m^-3， MAPE=4.556， and R²=0.732， indicating its high accuracy in modeling snow density over long timescales. SWE was calculated by multiplying simulated snow depth from FSM with snow density predicted by the deep learning model. The daily SWE time series from 2006 to 2014 revealed clear seasonal patterns. SWE begins accumulating in October， peaks between December and February， and melts rapidly from March to May. The average daily SWE across the historical period was 0.278 cm， with a maximum of 0.838 cm observed in late December， reflecting the seasonal snow accumulation and melt dynamics typical of the region. The modeled SWE was further validated against two reference datasets： a high-resolution 0.01° SWE dataset and a 0.25° national fused SWE product. Comparisons showed that the proposed model closely tracked observed seasonal and interannual SWE trends， particularly during the critical accumulation and melt periods. It exhibited better agreement with high-resolution data than with coarser products， especially in representing peak values and transitional dynamics. This study introduces an effective and scalable method for SWE estimation in regions lacking dense observational networks. By decoupling the estimation of snow depth and snow density and applying specialized models to each， the framework combines the physical interpretability of FSM with the pattern recognition strength of deep learning. This hybrid modeling approach captures both the mechanistic and statistical characteristics of snowpack evolution， providing a reliable basis for snow resource evaluation. The CNN-BiLSTM-Attention model， which has rarely been applied to snow density modeling before， demonstrated a strong ability to model complex spatiotemporal interactions. When integrated with FSM， it forms a robust and adaptable modeling system that can be generalized to other alpine or cryospheric environments. The results provide valuable support for snow hydrology， water resource planning， and climate change impact assessment on the Qinghai-Xizang Plateau and similar high-mountain regions.

The Laptev Sea， as a typical marginal sea of the Arctic Ocean， occupies a pivotal position within the Arctic climate system and marine environment. This region functions as a critical zone for Arctic sea ice formation， freshwater input， and land-ocean heat exchange. Its unique geographical location not only regulates the surface freshwater flux and heat budget of the North Atlantic but also modulates key biogeochemical processes in the Arctic， such as nutrient cycling， primary productivity， and carbon transport. Furthermore， as a strategic hub of the Arctic Northeast Passage， the dynamic characteristics of its sea ice directly determine the navigational potential of the route. Consequently， a comprehensive analysis and systematic review of the multi-dimensional characteristics of sea ice are imperative for fully understanding the mechanisms of sea ice change in the Laptev Sea. This study integrated multi-source observational data to systematically investigate the spatiotemporal evolution of total sea ice area， fast ice area， floating ice area， sea ice thickness， and sea ice age in the Laptev Sea from 1979 to 2024. Additionally， by incorporating model data， this study elucidated and summarized the driving mechanisms of sea ice change in this region and their ecological and environmental effects. The results showed that from 1979 to 2024， the total sea ice area in the Laptev Sea exhibited a significant decreasing trend during the melting season （June-October）， with the largest decline rate observed in October， reaching 0.95×10⁴ km² a^-1. Additionally， the fast ice area exhibited a continuous shrinking trend， with the most pronounced decrease in July， reaching 0.14×10 ⁴ km² a^-1. The fast-ice-free period extended from 78.4 days in the 1980s to 111.2 days in the past decade， and the date of complete ablation advanced by an average of 22.8 days. The floating ice area exhibited a significant decreasing trend during the melting season， whereas it showed an increasing trend during the freezing season （December-January）， reflecting an enhanced conversion from fast ice to floating ice. The floating ice thickness showed thinning trends of 0.13 m per decade in April and 0.23 m per decade in August， and the ice age structure exhibited a trend toward younger ice. Sea ice change in the Laptev Sea was jointly driven by coupled atmosphere-ocean forcing. Among atmospheric factors， offshore wind fields， the Arctic Oscillation （AO）， and the Arctic Dipole （AD） mode influenced sea ice dynamics through momentum and heat transport. At the oceanic level， the enhanced heat flux induced by Atlantification （with a 400% increase in winter over the past two decades） significantly altered the thermodynamic balance during the freezing season. The synergistic effects of meridional winds and warm， humid air masses during the melting season significantly accelerated sea ice ablation， whereas the freezing season was dominated by thermal processes. Due to its unique functions in sea ice export， entrainment of terrestrial sediments， and regulation of permafrost， the Laptev Sea has become a key node connecting the Arctic and the global climate system. The persistent retreat of sea ice in this region laid the foundation for the commercial operation of the Arctic Northeast Passage. Finally， this study summarized the key future research directions for sea ice in the Laptev Sea， including critical scientific issues such as the analysis of sea ice dynamic mechanisms， the feedback effects of sea ice anomalies on the Arctic climate system， and the prediction of route navigability. This study provides theoretical references and directional guidance for interdisciplinary research in the context of rapid Arctic change.

The Qinghai-Xizang Railway （QXR）， a crucial strategic corridor and vital support for Xizang’s economic development in China， extensively employed bridges instead of embankments to protect permafrost during its construction. A total of 156 kilometers of the railway consists of bridges rather than conventional embankments， with 447 bridges constructed， including 125 kilometers of dry bridges， leading to a higher proportion of embankment-bridge transition sections （EBTSs） compared to ordinary railways. However， with global warming and increasing human engineering activities， numerous engineering distresses have emerged in the EBTS in permafrost regions， such as differential embankment settlement， subsidence of wing walls， and bridge structure deformation. Addressing these issues is crucial for ensuring the safe and sustainable operation of this vital infrastructure. This paper reviews and summarizes the current research on engineering distress of the EBTS along the QXR from four aspects： distress types， causes， existing treatment measures， and analysis of faced problems. Additionally， it integrates the latest observations from the QXR’s experimental project on integrated treatment technologies， proposing future directions and recommendations for engineering distress treatment in EBTSs. Research findings indicate that the non-uniform degradation of permafrost induced by the thermal effects of bridge structures is the primary cause of differential settlements in EBTS， contributing up to approximately 50% of the observed settlement. For the numerous dry bridges along the QXR， foundation settlement constitutes the main source of embankment settlement in EBTSs， accounting for over 80% of the total settlement. Bridge structural deformation primarily results from decreased bearing capacity of permafrost foundations and seasonal frost heaving of backfill. Given the lower elevation of EBTSs， protection-cone slopes are subjected to substantial groundwater thermal erosion and intense frost heave， leading to subsidence and cracking deformations. With ongoing warm and rainfall increases on the Qinghai-Xizang Plateau， EBTSs along the QXR face increased risks of distress occurrence in the future. Traditional reinforcement measures are inadequate to address future challenges. The integrated treatment technology has shown promising results in addressing engineering distresses in EBTSs. This technology employs a multi-faceted approach， utilizing innovations such as horizontal thermosyphons， ventilated slope and targeted thermosyphon arrays to achieve comprehensive cooling and regulation of embankment slopes， cone slopes， and foundations. Field monitoring data indicate that this technology has successfully transformed warm permafrost into cryogenic permafrost within a year， concurrently achieving rapid enhancement in structural stability. This technology holds practical significance for enhancing the engineering quality and promoting sustainable development of the railway.

In the context of global climate change， the persistent reduction of Arctic sea ice has created more favorable conditions for the opening of Arctic shipping routes. The Northeast Passage， in particular， has garnered the interest of shipping companies due to its potential distance and cost benefits. However， the passage presents significant navigational safety challenges stemming from its complex and variable climatic and sea ice conditions. Therefore， there is an urgent demand for an intelligent path planning method to optimize the utilization of Arctic routes. This study introduces an intelligent route planning approach that integrates the Polar Operational Limit Assessment Risk Indexing System （POLARIS） with deep reinforcement learning. POLARIS assesses navigational risks by evaluating sea ice conditions along potential routes. The researchers integrate POLARIS with traditional A* algorithms and Deep Reinforcement Learning （DRL） to enhance the path planning process. This integration is crucial as it improves the capacity to manage dynamic environments—typical of Arctic conditions—more effectively than static algorithms such as A*. Experimental results indicate that DRL significantly surpasses the traditional A* algorithm in computational efficiency， achieving approximately 50 times faster processing. This efficiency is vital for real-time route planning， necessary to adapt to the rapidly changing Arctic environment. The article explores the mechanics of DRL， elucidating its superiority in managing complex and dynamic conditions. Unlike traditional methods that struggle with large state spaces and require predefined heuristic functions， DRL employs neural networks to learn optimal strategies through trial and error， making it adept at navigating the unpredictable Arctic environment. The study highlights DRL’s capacity for learning and adaptation， providing a practical solution for real-time decision-making in shipping route planning. Moreover， the study offers a detailed explanation of the employed methodologies. It categorizes DRL approaches into value-based， policy-based， and actor-critic methods， with the research selecting a value-based approach. The Deep Q-Network （DQN） algorithm is employed for its ability to handle large state spaces and efficiently learn optimal policies. The research describes the environmental setup， where Arctic sea ice and weather data are modeled on a grid to simulate navigational scenarios. The DQN model is trained on this data to predict optimal routes， taking into account navigational risk and efficiency. The researchers conducted case studies using historical sea ice data to validate the model， comparing the predicted routes against actual shipping paths taken by vessels such as the ship “Yong Sheng” in 2013. The results show a strong alignment， with the model’s predictions reflecting practical and safe routes that avoid areas of high ice concentration， akin to decisions made manually by ship captains. In conclusion， the article establishes that DRL serves as a suitable core algorithm for Arctic route planning systems. It provides a robust and efficient solution for managing the challenging and variable conditions of the Arctic， facilitating the development of intelligent， automated route planning systems. The study underscores the importance of integrating advanced AI techniques like DRL into maritime navigation to enhance safety， efficiency， and adaptability， thereby contributing to the development of the Arctic as a viable international shipping corridor. The findings suggest a promising future for automated decision-making in maritime logistics， particularly in regions where environmental conditions are critical and volatile.

Snow cover is the main controlling factor of the freeze-thaw cycle of seasonally frozen ground in northern Xinjiang， and seasonally frozen ground affects the infiltration of snowmelt water by changing the freeze-thaw phase of shallow soil. However， the freeze-thaw state of shallow soil in the thawing snowmelt season in this region is not clear， making it difficult to accurately assess the synergistic regulation of snow cover and frozen soil on soil moisture from the mechanism level. Therefore， based on the monitoring data of snow cover and frozen ground at six meteorological stations in the Altai Mountains from 1961 to 2011， this study applied the Gaussian model and the Boltzmann model to analyze the basic characteristics of snow cover and seasonally frozen ground in northern Xinjiang on the basis of dividing high snowfall years， low snowfall years and normal years， and discussed the freeze-thaw state of shallow soil during the melting period in detail. The results show that the average duration of snow cover is 123.2 days and the average maximum depth of snow is 29.7 cm. The average annual freezing period of seasonal frozen soil is 150.9 days， and the average maximum freezing depth is 120.3 cm. In general， the snow cover showed an increasing trend， mainly manifested as an increase in snow depth. On the other hand， the frozen soil shows a degradation trend， which is mainly reflected in the shortening of the freezing period and the decrease in maximum freezing depth. The comparative analysis of the end time of the frozen soil melting and the end time of snow melt in different types of snow cover years shows that the end time of frozen soil melting in 70% snowy years and 60.5% normal years is 8.2 and 5.5 days earlier than the end time of snow melt， respectively. The end time of frozen soil melting in the year with low snowfall is 13.2 days later than the end time of snow melting. Overall， the results indicate that as snowfall increases， the probability of seasonally frozen ground being in a thawed state during the snowmelt period also increases significantly. The snowmelt water can recharge soil moisture， which leads to a longer retention time of snowmelt water in the soil. This snow-frozen soil synergistic mechanism significantly affects the infiltration of snowmelt water and alters the snow hydrology runoff process. This process can facilitate more snowmelt water replenishing the soil and strengthens the exchange between meltwater and groundwater， contributing to the effective utilization of snowmelt water in arid regions.

Ice and snow tourism is increasingly emerging as a vital and rapidly expanding sector within the global tourism industry. The presence and effective utilization of ice and snow resources form the essential bedrock upon which this niche tourism thrives. As a prominent example of this development， the Northeast region of China stands out as a national leader and pioneer in ice and snow tourism. This region has been proactive in enlarging its resource base and strategically optimizing the spatial configuration of its offerings to maximize appeal and economic benefits. Consequently， a comprehensive understanding of the spatial and temporal dynamics of ice and snow tourism resources is crucial to advancing the sustainable and high-quality development of this sector. This study adopts a geographical spatio-temporal framework to scrutinize the evolution and distribution of ice and snow tourism resources across the three northeastern provinces of China， Heilongjiang， Jilin， and Liaoning， from 1990 to 2022. Through the application of advanced tools such as ArcGIS spatial analysis and geographic detectors， the research investigates the patterns of resource distribution and the underlying drivers of change across four critical dimensions： temporal evolution， spatial configuration， clustering characteristics， and influencing factors. The study’s findings present several key insights： （1） Growth Phases and Regional Specialization： the development trajectory of ice and snow tourism in the three northeastern provinces over the period from 1990 to 2022 can be delineated into four distinct phases： “initial emergence”， “steady expansion”， “adjustment phase”， and “rapid development”. Each phase reflects a unique period of growth and transformation in the region's tourism landscape. Heilongjiang Province， with its rich ice resources， has established itself as a dominant hub for “ice” tourism. Jilin Province， on the other hand， has gained recognition for its specialization in “snow” tourism， leveraging its unique geographical and climatic conditions. Meanwhile， Liaoning Province has differentiated itself by offering a combination of “ice and hot springs” tourism， creating a diverse and complementary tourism product that appeals to a broader market. （2） Spatial Distribution and Correlation Patterns： the spatial distribution of ice and snow tourism resources within these provinces demonstrates a significant positive correlation， indicating a high degree of clustering. Over time， there has been a discernible shift in the spatial concentration of these resources， moving from hot spots in the southeastern areas to cold spots in the northwestern regions. This migration reflects both natural and socio-economic factors that influence where and how tourism resources are developed and utilized. （3） Evolution of Distribution Patterns： from 1990 to 2022， the spatial distribution pattern of ice and snow tourism resources in the three northeastern provinces has evolved through several stages， reflecting broader trends in regional development and planning. Initially， the distribution was more “dispersed”， followed by a phase of “agglomeration”， which then transitioned into a stage characterized by “agglomeration and diffusion”. The final stage saw a process of “re-agglomeration”， leading to the emergence of a diagonal “T”-shaped distribution pattern. This structure is anchored around two core axes： Daqing-Harbin-Mudanjiang and Harbin-Changchun-Shenyang. The general trend of this spatial distribution is along a “northeast-southwest” axis， with the gravitational center initially shifting northeast before moving southwest. As a result， disparities in the distribution of high-quality ice and snow tourism resources among the three provinces have decreased， signaling a trend towards more balanced and equitable regional development. （4） Influencing Factors and Underlying Drivers： the spatial and temporal distribution of ice and snow tourism resources in Northeast China is shaped by a complex interplay of various factors， including the natural environment， socio-economic conditions， the level of tourism development， and the quality and extent of transportation infrastructure. Among these， the most significant influences are exerted by domestic and international tourism revenue and road mileage. These findings highlight the “tourism-dependent” and “transportation-driven” characteristics that are pivotal to understanding the spatial dynamics of ice and snow tourism in this region. The correlation between transportation infrastructure and tourism growth suggests that improved accessibility and connectivity are crucial for future development strategies. By employing a spatio-temporal perspective， this study aims to broaden the scope of research on ice and snow tourism and enrich the understanding of its spatial and temporal structure. It advocates for a more rational and strategic distribution of ice and snow tourism resources across the three northeastern provinces， aiming to foster regional integration and leverage the complementary advantages of each province. Ultimately， the insights derived from this research are intended to support the sustainable and high-quality development of ice and snow tourism in the post-Winter Olympics era， ensuring.

The chain reaction of glacial lake outburst floods in the alpine and high altitude mountains area poses a significant threat due to its extensive impact range， large flood scale， and high flow velocity. Traditional prevention and control measures downstream of the disaster chain are often difficult to implement and economically unfeasible. Moreover， the primary triggering factors， such as ice collapses， are usually located at altitudes above 5，000 m， making access and engineering interventions extremely challenging. To address these issues， this study proposes a novel prevention and control approach focusing on the overflow outlet of the moraine dam， based on field investigations and statistical analysis. A new integrated protective system， combining rigid and flexible reinforcement layers， is developed to enhance the stability of the moraine dam’s armor layer at the overflow section. The system consists of scaffold steel pipes， high-strength metal flexible nets， and anchor rods， forming a “rigid-flexible integrated plate-like” protective structure. Laboratory physical model experiments were conducted to verify its effectiveness under simulated flood conditions. The results indicate that as protection measures are strengthened， the initiation flow velocity of the armor layer increases significantly. Compared to the unprotected state， a single-row rigid connection provides a minimal increase in stability， whereas a double-row rigid connection increases the initiation flow velocity by approximately 2%， a triple-row rigid connection by about 5%， and the combined rigid-flexible system by approximately 8%. The effectiveness of the protection system improves with an increase in the protected area， demonstrating its potential for mitigating moraine dam breaches. This research provides a feasible and technically viable solution for preventing moraine dam failures in high-altitude， cold environments. The proposed system offers advantages in terms of adaptability， cost-effectiveness， and ease of implementation in remote mountainous regions. It has the potential to serve as an effective first line of defense against glacial lake outburst floods and contribute to disaster risk reduction in alpine and high-altitude areas.

With global warming and increasing human activities， slope geohazards such as landslides， thaw collapse， and gelifluctions have become increasingly frequent on the Qinghai-Xizang Plateau. A large number of slope geohazards have developed along the Gongyu High-grade Highway on the eastern edge of the Qinghai-Xizang Plateau， posing serious threats to infrastructure construction and safe operation in the region. However， the distribution patterns and factors influencing unstable slopes along the entire highway remain unclear， which severely constrains the construction and development of highways. Using IPTA-InSAR technology， this study conducted surface deformation monitoring， early-stage identification of unstable slopes， and field validation along the Gongyu High-grade Highway. Then， the spatial distribution and types of unstable slopes were summarized， and their influencing factors of deformation were analyzed. The results showed that the surface deformation rate along the radar line-of-sight direction in the study area ranged from -249 to 335 mm·a^-¹. The overall area remained relatively stable， with significant surface uplift observed near the Duogerong Basin and Bayan Har Mountains. Based on the InSAR deformation monitoring results， a total of 974 unstable slopes were identified. Unstable slopes were widely distributed in the study area， mainly concentrated in the Heka Mountain to Wenquanxiang segment and Qingshuihe County to Yushu segment， with minor distributions near Maduo County and Yeniugou. Types of unstable slopes included creep， colluvial landslides， and bedrock landslides. Among them， one large-scale bedrock landslide was located along the Qinggenhe in Wenquanxiang. The development of this landslide was structurally controlled. Under the influence of rainfall and river erosion， the slope experienced sliding. In the future， it may block the Qinggen River， potentially triggering disaster chain effects such as barrier lake and dam break. One colluvial landslide was located in the mountainous area of Heka Mountain， mainly influenced by rainfall and temperature. The persistent and cyclic freeze-thaw actions caused extensive cracks on the slope surface. Precipitation infiltrated through cracks， altering the thermal state at the base of the colluvium and promoting ground-ice melting on the bedrock interface. This process formed a sliding surface. Meltwater and rainwater further eroded and transported the colluvium， triggering sliding. Additionally， the sliding of the lower gully channel at the slope’s toe exerted a retrogressive pull， causing the middle section of the slope to slide. There were 972 creep slopes distributed along the entire highway. One typical creep slope was located near the Heka Mountain. This slope was located in a small watershed catchment area with consistently high water or ice content. Under the influence of road construction and excavation disturbances， the underground ice layer was exposed and gradually melted. As the underlying materials became saturated with water， the soil structure was damaged， leading to an increase in pore water pressure and a reduction in shear strength. This resulted in the formation of a weak sliding surface， triggering slope failure. In the future， this landslide may gradually evolve into other types of geohazards. The deformation of unstable slopes in the study area is primarily controlled by the loose soil characteristics of grassland and desert areas. Increased rainfall and temperature variations serve as the direct driving factors for slope deformation. This study reveals the development characteristics and influencing factors of slope geohazards along the Gongyu High-grade Highway， providing scientific support for risk prevention， hazard control， and the safe operation of the Gongyu High-grade Highway.

In the fields of national defense and civil engineering， such as the explosion-proof structure design of tunnels， railways， highways， and pipeline networks in cold regions， and the vertical shaft excavation using the freezing method in coal mines， frozen soil is often subjected to impact loading during activities like drilling and blasting construction， weapon damage， and seismic events. Investigating the strength characteristics， deformation and failure mechanisms， and stress wave propagation patterns of frozen soil within a high strain rate range is of great theoretical and practical significance for improving the efficiency of frozen soil excavation and fragmentation and for analyzing the safety and stability of frozen soil masses. This study summarizes the research status of the dynamic characteristics of frozen soil under impact loading from three aspects： frozen soil SHPB test system and data processing， laboratory SHPB tests on frozen soil， and dynamic constitutive relationship of frozen soil. First， the advantages and disadvantages of the developed temperature-controlled SHPB test system for frozen soil are analyzed. It is found that the current temperature control system has disadvantages such as large temperature fluctuations， cumbersome test process， and low refrigerant utilization efficiency. Second， the effects of parameters such as temperature， strain rate， stress state， moisture content， and fracture distribution on the dynamic strength， deformation modulus， and failure characteristics of frozen soil are systematically summarized. It is found that frozen soil exhibits typical characteristics of “freezing brittleness” and “dynamic brittleness”. Confining pressure and axial pressure help enhance the dynamic compressive strength of frozen soil. The presence of prefabricated fissures significantly reduces the bearing capacity of frozen soil specimens. Finally， the methods for establishing constitutive models of frozen soil and their advantages and disadvantages are summarized and evaluated. It is concluded that the Z-W-T model can better characterize the relationship between the strength and deformation of frozen soil under impact loading. Based on the summary of existing research， prospects are provided for the urgent problems to be solved and future research directions in frozen soil dynamics.

The changes in Qilian Mountain glaciers are not only a direct reflection of climate change， but also has an important impact on fresh water resources. In recent years， the glaciers in Qilian Mountain have been in a state of retreat and instability， to deeply understand the response of the glaciers in the Qilian Mountain to climate change in the 21st century and the magnitude of the change， clarify the characteristics of the Qilian Mountain glaciers change， the process and the reasons， and further predict the trend and magnitude of glacier change， and analyze the Qilian Mountain glaciers destabilization and imbalance in the climate warming background of the mechanism， we selected the Qiyi Glacier， which is strongly influenced by the westerllies， and the Lenglongling No.2 Glacier， which is strongly influenced by the monsoon as examples by analyzing remote sensing images and numerical simulation， it is found that the two glaciers show obvious retreat at the end of the glacier in 1990 to 2022， with the area reduced by 10.28% and 19.04% respectively， and the retreat rate of Lenglongling No.2 Glacier is obviously larger than that of Qiyi Glacier， which is attributed to the fact that the eastern part of the Qilian Mountain is warmer than the western part， and the eastern part of the glacier has decreased in precipitation. To explore the future changes of the two glaciers， the OGGM model considering glacier flow was driven by CMIP6 model data.The relative errors were within 2.5% by comparing the glacier area of the two glaciers simulated by the model and the visually interpreted area from 2008 to 2022. The simulated values of the mass balance of the Qiyi Glacier were more in line with the observed values， with an average difference of 88 mm w.e. Between the two mass balances， the difference between the two is 88 mm w.e.， which indicates that the OGGM model can simulate the glacier changes better.The simulation results show that under the high-emission scenario （SSP5-8.5）， the area and volume of the Qiyi Glacier will decrease by 77% and 94%， and the area and volume of the Lenglongling No.2 Glacier will decrease by 93% and 99% by 2060， and by the 21st century 80s， the air temperature in the area of the Qiyi Glacier will increase by 4.1 ℃ compared with that of the period of 1990 to 2015， while the increase in precipitation will not be obvious， and the glacier will be completely extinguished by that time. In contrast， temperatures in the eastern section of the Qilian Mountain will rise even faster， and it is predicted that the Lenglongling No.2 Glacier will be completely extinct around 2075. Even under the most optimal carbon emission scenario （SSP1-2.6）， a temperature increase of 1.0 ℃ in the Qilian Mountain by 2050 compared with the 1990 to 2015 prognosis will lead to the retreat of both glaciers into ice buckets by 2080 when the area and volume of Qiyi Glacier will be only 22% and 8% of that of 2020， and the Lenglongling No.2 Glacier will be only 17% and 6%. The rapid ablation of glaciers will cause changes in glacier runoff， and the runoff of the Qiyi Glacier and the Lenglongling No.2 Glacier will peak in 2035 and 2024， respectively， and the runoff of the two glaciers will be reduced by 28.98% and 41.82% by the end of the century under the SSP5-8.5 scenario， respectively. Therefore， regardless of climate scenarios， Qilian Mountain glaciers will retreat significantly in the 21st century， or even disappear， due the temperature， precipitation atmospheric circulation， and other influences， which will also make the eastern Qilian Mountain glaciers than the western glaciers retreat faster. Clarifying the trend and magnitude of glacier changes， and understanding the process mechanism of Qilian Mountain glaciers imbalance and destabilization in the context of climate change will deepen our understanding of the current and future glacier changes in the Qilian Mountain region， so as to cope with the environmental and water resource problems caused by the glacier changes， which require us to plan in advance.

Slope instability and collapse due to freeze-thaw cycles are significant challenges in infrastructure development in permafrost regions. To explore and analyze the causes and stability variation patterns of slope instability in seasonally frozen soil areas in Southwest China， this study focuses on the slopes of the Batang region. A research approach combining theoretical analysis， numerical simulation， and laboratory experiments is employed. A permeability coefficient model for the negative temperature zone， considering the unfrozen water and pore ice states， is applied within a soil hydrothermal coupling model. Through direct shear tests， the variation patterns of shear strength parameters for remolded slope soils were further obtained. Using the COMSOL finite element software， a customized slope stability model was developed. The slope height was set to 10 m with a gradient of 1∶1.5， and both the slope top and bottom extended 50 m and 60 m， respectively， with the slope base extending 10 m downward. This model was used to analyze the changes in temperature and seepage fields， as well as the slope instability mechanisms induced by freeze-thaw cycles affecting the soil’s shear strength parameters. Results show that the modified hydrothermal model， which incorporates the adjusted permeability coefficient， has higher accuracy， as verified by comparing the calculated values with those from one-dimensional soil column experiments. Temperature fluctuations within 1 m of the slope surface are significant. From January to March， air temperatures remain low， with deeper soil temperatures being relatively high， and soil temperature increases monotonically with depth. Between April and September， surface soil temperatures rise faster than the deeper layers， which maintain relatively high temperatures. From October to December， surface temperatures decrease more rapidly than in deeper layers， where temperatures remain relatively stable. The seepage field shows minimal variation beyond 2.5 m depth. From November to March， the slope surface freezes first， leading to a decrease in water content， although internal moisture migration is limited. Water content initially decreases with depth， then increases. Between April and June， during the thawing period， shallow soil melts， with water content decreasing with depth. From July to October， the water content initially increases with depth before decreasing again. In March， water content variation with depth is most pronounced， and due to the surface thawing while the subsurface remains frozen， excess water above the frozen layer cannot drain effectively， resulting in the lowest slope stability with a safety factor of only 1.59. The mechanism of slope failure in seasonally frozen regions is attributed to saturation above the thaw-freeze interface during the thawing period， where water cannot drain， reducing soil friction and leading to slope collapse. By dynamically adjusting the soil strength parameters to calculate slope stability， it is determined that the most significant strength degradation occurs at the thaw-freeze interface， primarily resulting in shallow slides within 1.2 m of the surface. The sliding surface is relatively flat and arc-shaped， with the primary failure mode being slumping collapse. This study provides valuable insights for slope design and disaster prevention in Southwest China， particularly for engineering projects in seasonally frozen regions， offering significant practical implications.

This study investigates the significant role of Antarctic sea ice in the global climate system， with a specific focus on the contribution of coastal polynyas， particularly in the Ross Ice Shelf region. Polynyas， areas of open water within the sea ice cover， are known to be critical sites for sea ice production， influenced predominantly by wind-driven processes. The Ross Ice Shelf polynya， a prominent feature in the Southern Ocean， is one of the most important regions for understanding the mechanisms of sea ice production and its implications for global climate dynamics. The primary aim of this research is to accurately determine the extent of the polynya and the volume of sea ice produced within it， under the influence of wind. Traditional methods for estimating sea ice volume often do not account for the contributions of polynyas， leading to an incomplete understanding of their role in sea ice dynamics. This study addresses this gap by using advanced remote sensing techniques to identify the spatial extent of the polynya， measure the frequency of sea ice production events， and calculate the corresponding volume of sea ice produced. By achieving this， the research provides more accurate estimates of sea ice production in the Antarctic and contributes to a better understanding of its role in the global climate system. To achieve these objectives， the study employs a combination of Sentinel-1 Synthetic Aperture Radar （SAR） data and Advanced Microwave Scanning Radiometer 2 （AMSR2） data. The Sentinel-1/SAR data is used to determine the spatial extent of the polynya during wind-driven events. This high-resolution data allows for precise detection of open water areas within the sea ice cover， which are crucial for understanding the dynamics of polynyas. In parallel， the AMSR2 data is used to measure the thickness of the sea ice that forms within the polynya and is subsequently blown away by wind. By integrating these two datasets， the study is able to accurately identify the boundaries of the polynya during wind events and quantify the volume of sea ice produced. The research focuses on the period from 2019 to 2021 in the Ross Sea region， identifying each wind-driven polynya event and the resulting sea ice production. Additionally， the study analyzes the intra-annual and inter-annual variations in the area， thickness， and volume of ice produced by these events from 2017 to 2021. This comprehensive analysis provides insights into the temporal variability of sea ice production in the Ross Sea polynya and its potential impact on the broader climate system. The findings of the study reveal that wind-driven polynya events in the Ross Sea predominantly occur between mid-March and mid-November each year， with the peak of sea ice production happening in July， August， and September. The average ice thickness during these events ranges from 1 to 30 cm， with the annual frequency of events varying between 72 and 114 occurrences. The volume of sea ice produced in these events ranges from 196 to 284 cubic kilometers， showing a trend of initial increase followed by a decline over the five-year study period. This research makes a significant contribution to the field of polar studies by presenting a novel approach to precisely identify and quantify the extent and impact of polynyas on sea ice production in the Antarctic region. The use of advanced satellite data， combined with a detailed analysis of wind-driven processes， provides new scientific evidence that enhances the understanding of the role of Antarctic sea ice in global climate change. The findings underscore the importance of considering polynyas in sea ice dynamics and highlight their potential impact on climate models and predictions of future changes in the Antarctic environment. This study not only advances the understanding of polar processes but also offers valuable insights into the broader context of global climate variability and change.

Ice thickness and glacier topography data are the basis of glacial dynamics simulation. The analysis of ice thickness distribution and terrain features is important to understand the characteristics of glacier flow velocity， stress and its change. In September 2021， a ground penetrating radar system was used to probe the ice thickness of the Ningchan River Glacier No.3 in the Qilian Mountains， then using Ordinary Kriging interpolation method to process ice thickness and glacier surface elevation data， and based on the processed data we analyzed the glacier’s profile and whole characteristics of ice thickness distribution and glacier topography， and also studied glacier’s changes in recent years. The results showed that： the thicker parts of the Ningchan River Glacier No.3 were mainly located in the areas where the bed terrain was relatively flat， and broadly the ice thickness value decreased from the center to the edge， and increased first and then decreased with the rise in altitude； compared with glacier surface， the bed terrain of the Ningchan River Glacier No.3 fluctuated more strongly， and the shape of glacier bed in each transverse section appeared in various forms， such as slope， double trough valley and V-shaped valley； the glacier area， maximum ice thickness， mean ice thickness and ice volume of the Ningchan River Glacier No.3 in 2021 were respectively about 1.08 km²， 60 m， 24.1 m and 0.026 km³， and the glacier area， mean ice thickness and ice volume had respectively reduced about 0.123 km²， 3.3 m and 0.007 km³ from 2009 to 2021， and their mean annual change rate were respectively about -1.03×10^-2 km²·a^-1 （-0.86%）， -0.28 m·a^-1 （-1.00%） and -5.83×10^-4 km³·a^-1 （-1.77%）， compared with the period 1972—2009， the glacier shrunk faster， and its primary cause was the rise of mean summer （June-August） air temperature； the change of ice volume was in the form of thickness thinning all the time from 1972 to 2021.

The stability of permafrost subgrades is closely related to the surrounding hydrothermal environment. Under the influence of climate warming and humidification on the Qinghai-Xizang Plateau， the extensive scale and segmented design of permafrost subgrades in expressways lead to significant ponding issues. Based on long-term field investigations， unmanned aerial vehicle image modelling， and ground-penetrating radar surveys of Gonghe-Yushu Expressway （GYE）， combined with ground temperature and moisture monitoring in typical ponding sections， this study analyzed the spatial distribution of permafrost subgrade distress and their relationship with roadside ponding in the 340-km section from Elashan to Qingshuihe of the GYE. Additionally， it revealed how roadside ponding affected the development of large-scale permafrost subgrade distress. The results showed that subgrade distress in the permafrost sections of the GYE were primarily classified into three types： uneven settlement （77.3%）， transition settlement （13.5%）， and cracks （8.6%）. In the 340-km study section， more than 60% of the sections showed signs of subgrade distress， which were mainly distributed in the high plains and intermountain basins from Changshitoushan to Duoqinankelang and from Yeniugou to Qingshuihe. The distress rate reached up to 60% in permafrost regions with high temperatures and high ice content. The distress rate of the entire study section was 8.4%， while the distress rate in the permafrost section was significantly higher， reaching 18.05%. A strong correlation was observed between roadside ponding and the development of subgrade distress. Approximately 31% of the permafrost sections exhibited roadside ponding， which was mainly distributed in flat terrain. About 66.1% of subgrade distress was associated with roadside ponding. The severity of these distress increased with larger ponding area， greater ponding depth， and shorter distance to the subgrade slope foot. In the ponding sections， moisture content and temperature at the subgrade foot were significantly higher than those in the non-ponding sections， and the permafrost table beneath the subgrade was notably lower than that in the non-ponding sections. Roadside ponding served as a long-term heat source， continuously transferring heat to the permafrost layer beneath the subgrade. During the warm season， ponding absorbed external heat and stored it internally， while simultaneously transferring heat to the permafrost layer. In the early-to-mid cold season， the ponding surface froze， forming an ice layer that acted as a barrier to heat exchange with the external environment. However， due to the high salt content of ponding water， the freezing point was lowered， enabling continued heat transfer from the bottom of the ponding to the permafrost layer. This resulted in a shorter cold season for the soil beneath the ponding area， reducing the amount of cold absorbed by the soil. As a result， the ground temperature in the ponding area increased significantly， leading to asymmetric temperature distribution in the permafrost subgrade area. This process， coupled with increased moisture content， ultimately led to partial thawing of the permafrost layer beneath the subgrade， disrupting the subgrade and inducing various types of subgrade distress. The findings of this study provide valuable insights into the relationship between roadside ponding and subgrade distress in permafrost regions. The results provide a scientific basis for formulating preventive measures and maintenance strategies to mitigate ponding-induced distress in permafrost subgrades. Additionally， the study contributes to a deeper understanding of hydrothermal dynamics affecting subgrade stability and offers practical guidance for future construction and maintenance of subgrades in water-rich permafrost areas. Furthermore， the study highlights the critical role of roadside ponding in the management of permafrost subgrades.

Snow depth is a fundamental parameter in hydrology， cryosphere science， weather forecasting， and climate modeling. Accurate monitoring of snow depth is essential for water resource management， natural hazard assessment， and climate change prediction. Passive microwave remote sensing， owing to its strong penetration capability， enables all-weather and all-time observation of the land surface， providing significant advantages for snow depth estimation. The first passive microwave snow depth retrieval algorithm was proposed by Chang et al. in 1987. Since then， numerous snow depth and snow water equivalent products based on passive microwave data have been developed. However， due to differences in retrieval algorithms， results from these snow products often show significant discrepancies. The Fengyun-3 （FY-3） satellite series， China’s first system to provide multi-frequency passive microwave remote sensing data， has played a vital role in improving the autonomy and reliability of climate monitoring. The operational satellites in this series currently include FY-3B， FY-3C， and FY-3D， each of which operates in both descending and ascending orbits. Snow depth retrieval algorithms have been developed using FY-3B and FY-3D microwave brightness temperature data， but their results are inconsistent. Furthermore， the compatibility of other snow depth retrieval algorithms with FY-3 series satellite data requires further investigation and validation. To explore the compatibility of different snow depth algorithms in China， this study applied three typical remote sensing algorithms—KELLY， CHE， and JIANG—to retrieve snow depth from FY-3 MWRI data. The accuracy of these algorithms was evaluated against in situ snow depth measurements from meteorological stations between 2010 and 2019， and the causes of discrepancies were analyzed. The performance and applicability of the KELLY， CHE， and JIANG algorithms were evaluated in three regions—Inner Mongolia-Northeast China， Qinghai-Xizang Plateau， and northern Xinjiang—using root mean square error （RMSE）， bias （Bias）， mean relative error （MRE）， and correlation coefficient （r）. Overall， the KELLY algorithm showed the lowest accuracy， significantly overestimating snow depths between 0 and 60 cm compared to the other two algorithms， with RMSE values ranging from 3.99 cm to 8.23 cm. The CHE and JIANG algorithms demonstrated comparable performance， with RMSEs of 2.78~5.48 cm and 2.88~4.99 cm， respectively. When in situ snow depth exceeded 60 cm， all algorithms tended to underestimate the depth， highlighting a limitation of brightness temperature gradient methods for snow depth retrieval. Regionally， in northern Xinjiang where snow cover was primarily distributed over mountainous terrain， all three algorithms exhibited underestimation in the ascending orbits of FY-3B and FY-3D because of daytime overpass. However， the KELLY algorithm showed relatively smaller underestimation in this region. For other orbits， the KELLY algorithm consistently demonstrated the lowest accuracy across all subregions. In contrast， the CHE and JIANG algorithms demonstrated comparable performance and achieved the highest accuracy in the Inner Mongolia-Northeast China region and the Qinghai-Xizang Plateau. Temporally， the CHE algorithm outperformed the JIANG algorithm during the shallow snow period （November to January）， while the JIANG algorithm outperformed the CHE algorithm during the deep snow period （January to March）. Both the CHE and JIANG algorithms achieved better performance in the Qinghai-Xizang Plateau and Northeast China than in northern Xinjiang. This was attributed to larger interannual snow variability and deep snow causing signal saturation in northern Xinjiang. Overall， the local algorithms （CHE and JIANG） were more suitable for snow depth estimation in China. However， due to variations in snow characteristics， these methods could not fully capture the seasonal patterns of snow depth. Additionally， although cross-platform calibration was conducted to reduce the systematic bias in brightness temperature， snow depth derived from different platforms still showed obvious disparities. In summary， these findings offer valuable insights and technical support for snow depth algorithm improvement and for the reasonable application of passive microwave remote sensing data.

In the context of global warming， the ice and snow in the source regions of the Yangtze River and the Yellow River are melting at an accelerated rate. Quantitatively evaluating the runoff effect of glacier melting is crucial for the management of water resources in high-altitude cold regions of the Qinghai-Xizang Plateau. The meteorological station data （daily precipitation and daily temperature） provided by the National Climate Data Center were used in combination with the 90 m×90 m digital elevation model （SRTM DEM） and the vector data of the second glacier inventory. Subsequently， a degree-day factor model was employed to reconstruct the multi-year glacial mass balance and the historical evolution characteristics of glacial meltwater runoff and its components in the source regions of the Yangtze River and the Yellow River from 1958 to 2022 （including rainfall runoff， snowmelt runoff， and ice melt runoff from glacial areas）. The extent of the impact of climate change on the melting of glaciers in the source regions was explored. The main findings were summarized as follows： （1） over the past 60 years， the glacier mass balance in the source regions of the Yangtze River and the Yellow River showed a significant negative equilibrium. The annual average glacier mass balance was -117.2 mm and -84.3 mm， respectively， and the cumulative mass balance was -7.03 m and -5.48 m， respectively. （2） The glacier equilibrium line altitudes （ELAs） in these two source regions showed a significant upward trend. The upward rates were 5.57 m·a^-1 and 3.93 m·a^-1， respectively， and the ELAs of the source regions of the Yangtze River and the Yellow River increased by 334.2 m and 313.6 m， respectively. （3） The total runoff of glacial meltwater in these source regions generally showed an increasing trend. The multi-year average total runoff of meltwater in the two source regions was 18.43×10⁸ m³ and 1.87×10⁸ m³， respectively. The variation trend of the ice melt runoff in the source regions was consistent with that of the total meltwater runoff， and its proportion showed an increasing trend year by year. The proportions of ice melt runoff in summer reached 91.88% and 90.95%， respectively. Snowmelt runoff showed a slight increase in the source region of the Yangtze River and a slight downward trend in the source region of the Yellow River. The seasonal distribution characteristics indicated that summer （June to August） was the primary period for glacial meltwater runoff， with its runoff volume accounting for 90.05% and 88.23% of the total annual runoff volume of the Yangtze River source and the Yellow River source， respectively. The proportions of runoff in spring and autumn decreased significantly. The spring runoff volumes of the Yangtze River source and the Yellow River source were 3.79% and 1.95%， respectively， and those in autumn were 6.17% and 9.82%， respectively. In winter （December to February）， there was basically no runoff generation. （4） The sensitivity of glacier mass balance to temperature was much higher than that to precipitation. The sensitivity of glaciers in the source region of the Yellow River to climate change was higher than that in the source region of the Yangtze River， which was related to the scale of glaciers in the source regions. In conclusion， this study systematically analyzes the variation patterns of glacial mass balance and meltwater runoff components under climate change， and quantifies the contribution of glacial ablation to streamflow. The findings provide critical insights into the implications of cryospheric changes for water security.

Thermokarst lakes are typical representatives of severe degradation of permafrost， where dissolved organic matter （DOM） from permafrost enters thermokarst lakes. Due to intense solar radiation on the Qinghai-Xizang Plateau， DOM in thermokarst lakes undergoes significant photodegradation processes. However， few studies have reported the photodegradation characteristics of DOM in thermokarst lakes on the Qinghai-Xizang Plateau， which may lead to bias in understanding carbon cycle and carbon-climate feedback under permafrost degradation. This study sampled water from thermokarst lakes under four distinct vegetation types on the Qinghai-Xizang Plateau， including alpine wet meadows， alpine meadows， alpine steppes and alpine deserts. For each vegetation type， dark control and light-exposed groups were established to conduct in-situ photodegradation experiments. By measuring dissolved organic carbon （DOC） concentrations， ultraviolet-visible absorption spectra， and three-dimensional fluorescence spectra—coupled with parallel factor analysis （3D-EEM-PARAFAC）， to investigates the effects of solar radiation on DOM content， optical properties， and composition in thermokarst lakes on the Qinghai-Xizang Plateau. Results show that after 10 days of in-situ experiments， DOM in thermokarst lakes under dark conditions exhibited limited changes across all four vegetation types. Although no significant variation in DOM content was observed under light exposure， significant alterations occurred in DOM optical properties and composition under light treatment. Chromophoric dissolved organic matter （CDOM） decreased by 23.6% to 36.7%， indicating that sunlight radiation significantly degrades the CDOM content in DOM. Under the same ultraviolet radiation intensity， the degree of CDOM photodegradation is greater in systems with low DOC concentrations. The decrease in surface water CDOM content may lead to enhanced photodegradation in deep water， resulting in more organic carbon being released into the atmosphere. The aromatic index （SUVA₂₅₄） decreases by 18.9% to 37.1%， indicating that sunlight radiation degrades aromatic compounds in DOM； while the spectral slope ratio （S_R） increases by 45.5% to 124.2%， indicating that sunlight radiation converts high molecular weight DOM to low molecular weight DOM. The significant decrease in humification index （HIX） indicates that solar radiation substantially reduced the humification degree of DOM， while the significant increase in freshness index （BIX） suggests that solar radiation promoted the production of more fresh DOM. These findings are consistent with the decreasing trend of humic-like components and the increasing trend of protein-like components under light exposure. Humic-like substances （C1， C3） exhibit greater photosensitivity than protein-like substances （C2， C4）， with C3 being lost faster than C1， and C2 accumulating faster than C4. Moreover， allochthonous DOM component C3 shows greater photoreactivity than the autochthonous DOM component C2. The study supports the idea that a portion of the protein-like component C4 is a photodegradation product of terrestrial humic component C3. The above findings demonstrate that during the 10-day in-situ observation period， the photochemical mineralization quantum yield of DOC was evidently low. This may be attributed to the relatively static conditions of thermokarst lakes in permafrost regions during summer， combined with prolonged water residence times， which collectively constrain the photomineralization of DOM in these water bodies-otherwise， DOM mineralization would be significantly more pronounced. The photobleaching effect led to a reduction in the absorption coefficient of CDOM， thereby increasing the maximum depth of light penetration in the water column. This phenomenon consequently exerts significant influence on the structure and function of aquatic ecosystems. Solar radiation preferentially degrades terrestrially-derived humic substances in Tibetan Plateau thermokarst lakes， thereby promoting the photodegradation of DOM in these water bodies. These observations collectively indicate that solar radiation plays a crucial role in the migration and transformation of DOM in thermokarst lakes. Under future climate warming and permafrost degradation scenarios， substantial quantities of DOM from thermokarst lakes will be introduced into aquatic ecosystems and exposed to solar radiation， creating favorable conditions for DOM photodegradation and significantly altering its migration and transformation behaviors.

Supraglacial debris comprising rock fragments， soil， and gravel， accumulates on glacier surfaces primarily due to processes such as mountain collapses， glacial erosion， and the exposure of englacial debris. It influences sub-debris ablation patterns through energy exchange and albedo variations， resulting in distinctive mass balance responses， hydrological effects， and hazard processes in debris-covered glaciers compared to debris-free glaciers. The impact of debris on underlying glacier melt is predominantly determined by the unique thermal processes driven by variations in debris thickness. When debris thickness is below a threshold （approximately 2~3 cm）， the absorbed heat is efficiently transferred to the sub-debris ice， accelerating glacial melt and generating basal meltwater that facilitates glacier movement. In contrast， thicker debris layers impede heat transfer， reducing melt rates， and potentially leading to over- or underestimation of glacier runoff. The spatially heterogeneous distribution of debris thickness results in differential melting across the glacier surface， giving rise to ice cliffs and supraglacial lakes. Ice cliffs absorb more solar radiation， intensifying localized melt and posing risks of collapse， while expanding supraglacial lakes can lead to glacial lake outburst floods （GLOFs）， threatening downstream populations and ecosystems. The thickness of supraglacial debris is thus a key factor in simulating glacier melt and accurately quantifying glacier runoff. It provides essential data support for research on dynamics， mass balance， and hydrological modeling of debris-covered glaciers， contributing to enhanced disaster risk management and water resource planning in downstream areas. The Langtang basin， located in the central Himalayas， is characterized by extensive supraglacial debris cover. Its unique geographical setting and glacial features make it a critical area for studying Himalayan glaciers and their responses to climate change. However， current research on supraglacial debris in this region remains predominantly focused on field measurements， with limited investigations into debris thickness and its spatial distribution at the watershed scale. It significantly impedes a comprehensive understanding of glaciers with debris coverage within this area. In response to the above questions， using Landsat 8 remote sensing imagery and high temporal resolution meteorological station data， this study calculated surface temperature， sensible heat flux， and net radiation flux in the Langtang region to solve the energy balance equation. Based on this approach， the spatial distribution of supraglacial debris thickness across glaciers in the Langtang region was retrieved. The study further analyzed the spatial distribution characteristics of debris thickness in representative glaciers and investigated the factors contributing to the heterogeneity in supraglacial debris thickness distribution. The study yielded the following findings： （1） The average thickness of supraglacial debris in the Langtang Basin glaciers was （0.25±0.02） m. Specifically， Lirung glacier had a thickness of （0.55±0.02） m， Shalbachum glacier （0.48±0.02） m， Langshisha glacier （0.31±0.02） m， and Langtang glacier （0.25±0.02） m. （2） Along the longitudinal profiles， the supraglacial debris thickness increased from the upper to the lower parts of the ablation zones. For Lirung， Shalbachum， and Langtang glaciers， the debris accumulation rates first decreased and then increased along the longitudinal profiles， whereas Langshisha glacier showed a continuous decrease. In terms of the transverse profiles， the thickness of the supraglacial debris was greater on the right side of the glacier movement direction for Lirung， Shalbachum， and Langtang glaciers， while Langshisha glacier exhibited thicker debris on both sides and thinner debris in the middle. （3） The variation in debris accumulation rates along the longitudinal profiles was primarily attributed to differences in surface flow velocities in the lower ablation zones. （4） The uneven distribution of debris thickness along the transverse profiles near the glacier termini was mainly due to differences in surface flow velocities， melt rates， and topographic features.

With the continuous expansion of global natural resource exploitation and infrastructure development into cold and high-altitude regions， the stability of geotechnical engineering under extremely low-temperature conditions has become a growing concern within the engineering community. Among the various challenges， frost heave damage is recognized as a critical factor affecting the service performance and operational safety of underground engineering. Its occurrence mechanism is closely related to the pore structure of rock mass and the state of pore water. Although previous studies have confirmed that the mechanical properties of frozen rocks are significantly influenced by temperature， pore size distribution， and water saturation， systematic investigations into the unfrozen water content during the freezing process remain limited. In particular， the mechanisms by which water-ice phase transitions at different pore scales contribute to frost heave damage are still unclear. In this study， low-field nuclear magnetic resonance （NMR） technology was employed as the primary analytical technique. Six representative sandstone samples from diverse geological backgrounds were selected and subjected to temperature-controlled freezing experiments from room temperature to -50 ℃. The dynamic evolution of pore water states was monitored in real time， with a focus on analyzing the variation of transverse relaxation time （T₂ spectra） with temperature， thereby revealing the transformation trends and patterns of unfrozen water and ice content across different pore size ranges. The results showed that all samples exhibited typical bimodal T₂ spectral distribution characteristics， with both peak positions and areas decreasing significantly as temperature dropped， indicating the progressive freezing of pore water. Water in larger pores froze almost completely at around -5 ℃， while in smaller pores， particularly those with diameters less than 0.1 μm， the freezing point was significantly depressed due to pore size effects. As a result， a considerable amount of water remained unfrozen in the form of bound water， even at temperatures below -20 ℃. Moreover， the rate of decrease in unfrozen water content and the freezing behavior varied significantly among the sandstone samples， which was closely related to their pore structure characteristics such as pore size distribution and connectivity. These results underscored the critical role of pore-scale features in governing the controlling phase transition process and the associated frost heave risk. This study not only deepens the understanding of the evolution of pore water states in frozen rocks but also elucidates the microscopic physical mechanisms underlying frost heave damage under low-temperature conditions. The findings provide a theoretical basis for evaluating the stability of rock masses in cold regions. Furthermore， the results offer valuable references for risk identification and structural optimization in the design phase of major cold-region infrastructure projects， including polar railways， highways， tunnels， and hydropower stations， and provide a theoretical basis for the development of materials and technologies for frost damage prevention and control.

Wildfire activities， an important part of natural ecosystems， are strongly influenced by climate change and vegetation types. As one of the best proxies for reconstructing wildfire history and wildfire occurrence mechanisms， the quantitative statistics and morphological analysis of charcoal can not only recover the frequency， intensity and changes of wildfire activities over geologic historical periods， but also understand the types of vegetation （woody and herbaceous plants） in which wildfires occurred. In addition， different sizes of charcoal can reflect the distance between the wildfire site and the depositional area. The Qinghai-Xizang Plateau with its high-altitude is very sensitive to the environment changes. However， there are few reliable charcoal records in the Qinghai-Xizang Plateau， and the research results mainly focused on macroscopic charcoal anatomy and the charcoal records in the late Holocene archaeological sites. In this study， a Holocene sedimentary profile with the complete sedimentary sequences was founded in the Zoîgé Basin on the eastern Qinghai-Xizang Plateau through extensive field investigations. The history of Holocene wildfire activities and climate changes in the Zoîgé Basin were reconstructed using a variety of paleoclimate proxies （charcoal， magnetic susceptibility and total organic carbon）， and the relationship between Holocene environment change and wildfire activities was revealed. The results showed that the wildfire activities in the Zoîgé Basin were dominated by regional wildfire activities during the Holocene， and local wildfire activities were dominated by burning woody plants， regional wildfire activities were dominated by burning herbaceous plants. However， the low magnetic susceptibility （mean 7.9×10^-8 m³/kg） and TOC （mean 0.23%） of the aeolian sand layer in the early Holocene （before 8.5 ka） were presented indicated that the temperature has increased， but the climate was still dry and cold， with frequent dust storms and weak biopedogenesis. The lowest value of total charcoal concentration （mean 33 390 grains/g） was presented in the aeolian sand layer， indicating that there was less vegetation and low coverage， limited plant biomass limited the occurrence of wildfire activities， and the occurrence frequency of regional and local wildfires was low. In the middle Holocene （from 8.5 to 3.1 ka）， under the moist and warm climate， the paleosol layer developed. The magnetic susceptibility of paleosol layer reached the highest value （mean 7.9×10^-8 m³/kg）， and the TOC value was also high （0.23%）， indicating that the increase of temperature and precipitation in the middle Holocene led to lush vegetation in the Zoîgé Basin. The value of total charcoal concentration （mean 45 315 grains/g） was relatively high in the paleosol layer， reaching the highest level in the middle Holocene， reflecting the frequent occurrence of wildfire activities in the middle Holocene. However， due to the increase of temperature and precipitation， the plant biomass in the Zoîgé Basin increased， leading to the frequent occurrence of regional and local wildfire activities related to the increase of plant biomass. In the late Holocene （after 3.1 ka）， The magnetic susceptibility （mean 16.6×10^-8 m³/kg） and TOC （mean 0.55%） of the modern meadow soil layer in the late Holocene decreased compared with that in the mid-Holocene. It was inferred that under the condition of lower temperature and precipitation in the late Holocene， weathering and soil formation in the Zoîgé Basin weakened， and vegetation decreased， Vegetation coverage decreased. The total carbon concentration was relatively stable and low in the late Holocene， with an average of 34 615 grains /g， indicating that the intensity of wildfire activities in the Zoîgé Basin was relatively stable and the frequency of wildfire activities was low， which may be due to the decrease of plant biomass during this period， and the lack of combustible biomass led to the decline of wildfire activities. However， the intensification of local wildfires activities during this period may also be caused by human activities. The results of this study will contribute to the in-depth understanding of wildfire history and climate evolution in the Zoîgé Basin.