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.
As an important water conservation area of the upper Yellow River， the Zoige Wetland is crucial in maintaining the stability of the plateau ecosystem and regional climate. Under the background of climate warming， the water resources in Zoige Wetland has changed significantly， which has caused a series of ecological and environmental problems， and further threatened the ecological security and economic development of the middle and lower reaches of the Yellow River. At present， the water resource status of Zoige Wetland has been widely concerned. However， many scientific problems about the water resources in this region have not been solved yet. For example， what are the law of climate change and water resources change in Zoige Wetland in recent decades？ What are the impact process and impact mechanism of the climate change on the water resources in this region？ To solve these problems systematically， based on the observation data at meteorological stations and the hydrological stations in Zoige Wetland， and the ERA-Interim monthly mean reanalysis dataset from 1981 to 2015， this paper research on the regional climate response characteristics and their impact on runoff in Zoige Wetland， by adopting the following research methods， including observation analysis and theoretical research. For the first step， this paper analyze the basic climate change characteristics of runoff in Zoige Wetland. On this basis， we explore the impact process of climate change on runoff in Zoige Wetland. The research work can provide theoretical support for water resources protection and coping strategies of climate change in Zoige Wetland. The results show that， the annual and seasonal average runoff in Zoige Wetland showed a downward trend before 2008 and showed an increased trend after 2008， and the monthly average runoff showed a “bimodal” distribution， with the runoff mainly concentrated in the flood season during 1981 to 2015. Precipitation is the main factor to impact the runoff in Zoige Wetland， and summer precipitation having the most significant impact. The precipitation decreased before 2008 and then increased after 2008， while the continuous rise of temperature， the snowmelt water and evaporation also increased significantly in Zoige Wetland. The evaporation in summer and autumn had the most significant impact on runoff. The annual and seasonal average water vapor flows in via the western and northern boundaries and flows out via the eastern boundary in Zoige Wetland. However， the annual / spring / summer / autumn average water vapor flows in via the southern boundary， and the winter average water vapor flows out via the southern boundary. The inflow from the western boundary is more than from the southern boundary. The net water vapor budget is positive for the whole year and spring/summer/autumn， which is the largest in summer. It is indicated that summer is the season with the most active water vapor transport. Therefore， the Zoige Wetland is an obvious water vapor sink for the whole year and in spring， summer， and autumn， while water vapor is exported from Zoige Wetland in winter. The annual （summer） precipitation is significantly positively correlated with the water vapor inflow at the southern， western boundary and net water vapor budget of the Zoige Wetland in the same period. Southwest wind water vapor transport is an important water vapor transport that causes precipitation anomalies in Zoige Wetland. When the anticyclonic circulation on the southern side of the plateau are strengthened （weakened）， while the anticyclonic circulation in South China and the Western Pacific region are strengthened （weakened）， the southwesterly water vapor from the South China Sea， Western Pacific， and Bay of Bengal transport along the south edge of the Qinghai-Xizang Plateau are strengthened （weakened）， the anomalous southwesterly （easterly） airflow and water vapor convergence （divergence） persist in Zoige Wetland， resulting in more （less） precipitation in summer， and eventually leading to more （less） runoff in Zoige Wetland. We hope this paper can provide a theoretical basis and scientific support for water security and water resources management in the Yellow River basin. Furthermore， the future climate change of Zoige Wetland will be predicted based on the CMIP6 global climate model， and the variation characteristics of water resources in this region under the background of future climate change will be studied. Finally， we try to explore the coping strategies and technical measures of water resources in Zoige Wetland to adapt to climate change.
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.
Snow cover on Antarctic sea ice significantly influences the processes of sea ice growth and melting， regulates energy exchange between the atmosphere and ocean， and plays a critical role in global climate change. This study explores the application of China’s HY-2B satellite microwave radiometer （SMR） data for retrieving high-precision snow depth information over Antarctic sea ice. Using HY-2B passive microwave brightness temperature （BT） data at 6.925 GHz， 10.7 GHz， 18.7 GHz， 23.8 GHz， and 37.0 GHz， we analyzed the brightness temperature gradient ratios （GRs） derived from pairwise combinations of the BTs. Among these， only the 23.8 GHz channel provides data in vertical polarization （V）， while the other channels offer both vertical （V） and horizontal （H） polarization modes. A total of 10 GRs were calculated and correlated with ship-based snow depth observations to identify the GR with the strongest relationship to snow depth. The optimal GR， derived from vertically polarized BTs at 18.7 GHz and 37.0 GHz， was used alongside snow depth data collected during China’s 35th Antarctic scientific expedition aboard the research vessel Xuelong and Antarctic Sea Ice Processes and Climate （ASPeCt） ship-based observations to develop a snow depth retrieval model for HY-2B SMR data. The model construction excluded the influence of open water and liquid water on the BTs of sea ice surfaces. To evaluate the performance of the newly developed model， four statistical metrics—Bias， mean absolute error （MAE）， root mean square error （RMSE）， and correlation coefficient （r）—were used to compare the retrieved snow depths with those from traditional models （Markus98 and Comiso03） and the GCOM-W1 AMSR-2 Antarctic sea ice snow depth product released by the National Snow and Ice Data Center （NSIDC）.The results show that the snow depths retrieved using the new model exhibit spatial distributions similar to those derived from the Markus98 and Comiso03 models， all displaying clear seasonal variations. Compared to the traditional Markus98 and Comiso03 models， the new model demonstrated higher accuracy， with a bias of only -1.70 cm， and outperformed the existing GCOM-W1 AMSR-2 Antarctic sea ice snow depth product. The snow depths retrieved from HY-2B SMR data for 2019 and those from the GCOM-W1 AMSR-2 product both ranged between 10 and 30 cm， with similar daily and monthly mean snow depth trends. The overall bias between the two products ranged from -5 to 8.4 cm， with larger differences observed in December， January， and February. During the snow accumulation and stabilization periods （April to October）， the average bias between the two datasets was only 1.71 cm， with a high correlation coefficient of 0.8， indicating strong consistency between the two snow depth datasets during this period. This consistency further validates the reliability of the newly developed model. The main differences during this period were concentrated in the southeastern Antarctic Ocean and the Ross Sea. During the melting period （November to March）， the average bias increased to 3.12 cm， with the largest differences observed in the southwestern Antarctic Ocean and the southern Weddell Sea. Although the biases between HY-2B SMR-derived snow depths and the GCOM-W1 AMSR-2 product were larger during the melting period， the overall annual mean bias between the two datasets was 1.42 cm. Furthermore， the snow depths retrieved from HY-2B SMR data were generally higher than those from the GCOM-W1 AMSR-2 product and closer to actual field observations. This study demonstrates the potential of using HY-2B SMR data for retrieving snow depths over Antarctic sea ice and provides high-accuracy， large-scale datasets to support operational monitoring of Antarctic ice and snow dynamics. It also offers valuable technical references for advancing research on Antarctic sea ice processes and their interactions with global climate change.
In recent decades， global warming has caused a notable “Arctic amplification effect” in the Arctic region， which poses a significant threat to permafrost. This study focuses on the Tiksi region in the Arctic and investigates the dynamic changes in the surface thermal environment and their impact on permafrost in the context of global warming. First， based on measured data from meteorological stations， long-term trends in the annual average temperature， freeze-thaw frequency， freezing index， and melting index from 1976 to 2022 were statistically analyzed. Additionally， using Landsat remote sensing data， changes in surface temperature and land cover in the Tiksi region from 2000 to 2021 were retrieved. The main driving factors influencing surface temperature were analyzed using the geographic detector method. The results show that from 1976 to 2022， the annual average temperature increased at a rate of 0.07 ℃·a-1， the annual freezing index decreased at a rate of -20.98 ℃·d·a-¹， and the annual melting index increased at a rate of 7.11 ℃·d·a-¹. From 2000 to 2021， the average surface temperature in July and August rose by 3.14 ℃. Geographic detector analysis revealed that soil moisture and the built-up index had the highest explanatory power for surface temperature， with elevation and vegetation index also playing significant roles. Long-term ground temperature monitoring indicates that ground temperatures at different depths have shown warming trends， though the rate of warming decreases with depth. The area of thermal melt ponds near the airport has continuously expanded， further indicating widespread permafrost degradation. This study provides scientific insights into the impact of climate change on permafrost in the Arctic and offers theoretical and technical support for infrastructure planning and environmental protection along Arctic shipping routes.
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.
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 black soil region of Northeast China is one of only four major black soil areas in the world， and also one of the most serious areas of potential soil erosion in China， while snow accumulation is important for regulating soil biological and chemical cycles and changing soil properties. It is of great significance to clarify the characteristics of snow depth， snow phenology and snow area changes in the black soil region of Northeast China in order to promote the sustainable development of the black soil and ensure national food security. In this paper， we will use the day-by-day remote sensing inversion data of snow depth from July 1979 to June 2021 to elucidate the spatial and temporal characteristics of the parameters of snow depth， the first day of snow accumulation， the last day of snow accumulation， the period of stable snow accumulation as well as the area of snow accumulation in the black soil region of Northeast China by means of regression analysis and other methods. The results show that： （1） In hydrological years from 1979 to 2020， the average annual snow depth in the northeast black earth region was 2.70 cm， the average annual stable snow area was 1.07×106 km2， The first day of snow accumulation is the 1st of December and the last day of snow accumulation is the 22nd of March of the following year， and the length of the stable snow accumulation period was 112.0 days. The average snow depth and stable snow area in all seasons and months of the year showed the characteristics of a single-peak distribution， with February being the highest peak month. （2） The spatial distribution of each now cover parameter shows almost uniform spatial variability， and under the influence of factors such as topography and latitude， they all show a horseshoe-shaped spatial distribution centred on the hinterland of the black earth region. （3） Significant inter-annual variations in snow depth as well as snow phenology were found， except for the now cover area， which did not change significantly. Annual average， seasonal， and month-by-month snow depths show a consistent downward trend. Compared to 1979—1988， the average annual snow depth has decreased by 29.48 % in the last 10 years. The first day of stable now cover was significantly pushed back at a rate of 1.4 d⋅（10a）-1. Stable now cover end date significantly advanced at a rate of -1.6 d⋅（10a）-1. This resulted in a significant shortening of the stable now cover period at a rate of 3.0 d⋅（10a）-1. （4） The characteristics of the spatial variation of each now cover parameter are slightly different. The annual average snow depth and the spatial variation of snow depth in all seasons and months are more consistent， and all of them show the differential spatial variation characteristics of significant decrease of snow depth in high latitude and high altitude areas， and significant increase of snow depth in the core area of the black soil region. The spatial variability of the snow cover was significant in the northern and eastern parts of the black soil region， while the spatial variability of the final days of the stable now cover was sporadic and strip-like. Compared with the existing studies， this paper will further focus on the black soil region of Northeast China to conduct a more comprehensive and systematic study on the main snow parameters and to further clarify the characteristics of typical regional snow changes. The results of this paper will provide preliminary results for the subsequent series of studies on the black soil region of Northeast China based on snow accumulation parameters， provide a quantitative basis for revealing the changes of soil properties in the black soil region of Northeast China， and provide scientific support for guaranteeing national food security.
Snow cover is one of the important underlying surfaces of numerical models， especially in seasonal snow covered areas， and its accuracy is crucial for the calculation of surface flux and the subsequent prediction of atmospheric variables. Liaoning Province is located in the southern part of Northeast China and is a region with seasonal snow cover. The temperature simulations in the Liaoning Province have exhibited a cold bias. To assess the sensitivity of temperature simulations to initial snow cover fields in numerical models， the WRFv4.1 model was utilized， with January 2020 selected as a representative winter month for Liaoning Province. The first 24 hours served as the model’s spin-up period， and the simulation results from 00：00 on January 2nd to 23：00 on January 31st were analyzed. Four experiments were designed， Control experiment （CTL_Exp）： The initial fields of snow depth and snow water equivalent （SWE） in the WRF model were directly provided by the initial and lateral boundary conditions FNL data. Snow initial fields sensitivity experiments： ERA5L_Exp： The initial fields of snow depth and SWE in the WRF model were obtained from the ERA5-Land reanalysis data at 00：00 on January 1， 2020. NCDCS_Exp： The initial fields of snow depth and SWE in the WRF model were derived from the China 25 km daily snow water equivalent product for the period 1980—2020， using data from January 1， 2020.OBS_Exp： The initial fields of snow depth and SWE were interpolated from observations at 62 national meteorological stations in Liaoning Province on January 1， 2020. The four experiments are identical except for the initial conditions of snow depth and snow water equivalent （SWE）. The results show that： （1） Enhancing the accuracy of the initial fields of snow depth and SWE reduced the cold bias in daily maximum， minimum， and mean temperatures. The cold bias of daily mean temperatures simulated by ERA5L_Exp， NCDCS_Exp， and OBS_Exp were improved by 0.74， 0.85 ℃， and 0.8 ℃ respectively compared to CTL_Exp， with an improvement of more than 0.6 ℃ in the cold bias of the daily maximum temperature， and the cold bias of the daily minimum temperature turned into warm bias. The RMSE decreased by 0.38 to 0.62 ℃ compared to CTL_Exp， and the correlation coefficients also showed improved. Overall， temperature simulations were sensitive to the initial snow cover conditions throughout the simulation period. （2） Influenced by the snow parameterization scheme in the land surface model， using snow depth observation data to improve the initial conditions of snow depth has a limited effect on improving air temperature. The cold bias in mean and extreme temperatures is reduced by only 0.04 to 0.21 ℃， with RMSE improvements of 0.04 to 0.25 ℃. However， updating the initial conditions of SWE using SWE observation data produces results similar to those in the OBS_Exp experiment. The cold bias in mean and maximum temperatures improves by 0.58 to 0.74 ℃， the cold bias in minimum temperatures shifts to a warm bias， and RMSE improves by 0.33 to 0.57 ℃. This indicates that improving the initial conditions of SWE is more important than improving snow depth alone. （3） Changes in the initial field of snow cover mainly affect the net radiation by altering the surface albedo， which in turn affects the sensible heat flux and ground heat flux， with a relatively smaller impact on the latent heat flux， ultimately affecting the near-surface air temperature. The hydrological effect of snow on soil moisture is relatively minor. （4） The improvement of the initial field of snow cover reduces the cold bias of the maximum temperature primarily by enhancing the simulation of net radiation during the day， and then reallocating it to sensible， latent， and ground heat fluxes. It reduces the cold bias of the minimum temperature by improving the simulation of nighttime long-wave radiation. This study will provide a certain basis for numerical forecasting and climate dynamical downscaling.
Permafrost degradation has great impacts on engineered infrastructures and socioeconomics through modifying or altering hydrothermal regimes， hydrology and water resources， ecology and ecological safety， and carbon and nitrogen regimes and cycles， and further accelerating climate change by releasing cryotically long-preserved organic carbon and nitrogen. Permafrost in Northeast China has experienced ground temperature rise and permafrost degradation， but field studies relevant to the distributive and other characteristics of permafrost and active layer are still grossly inadequate. This article aims to present a review on studies on the features of distributive patterns and ground thermal regimes of the northern Da Xing’anling （Hinggan） permafrost based on the long-term observations of ground temperatures at key sites， such as those in Mangui， Genhe， Yitulihe， and Nanwenghe， and focuses on summarizing and explaining the changing trends in ground temperature in the northern Da Xing’anling Mountains during the last two decades. Study results show that in the northern Da Xing’anling Mountains， permafrost temperature and thickness are primarily controlled by latitude and elevation. As annual mean air temperature lowers northwards and upwards， permafrost temperature also declines from about 0 °С to -2.83 °С， and permafrost thickens from 20 m to >100 m. On the local scale， permafrost temperature and thickness are heavily dependent on hydroclimate and environmental factors， but the hydrothermal effect of such local factors on permafrost features tends to weaken with rising latitudes. During the observation period （2009—2022）， thinning active layer， increasing annual maximum frost depth in talik area and lowering ground temperature above the depth of dividing point between permafrost cooling and warming occurred in many places in the Da Xing’anling Mountains， possibly due to the global warming hiatus. However， permafrost under the depth of dividing point between permafrost cooling and warming did not show a clear trend to the global warming hiatus， and evidently rising temperature was observed. This study is of great importance to understanding the changing temperature of latitudinal permafrost and its driving factors， and to providing data support and references for the management of the ecological and hydrological environment of the northern Da Xing’anling Mountains and the Heilongjiang-Amur River basin.
Permafrost in Northeast China is situated at the southern edge of the circum-Arctic permafrost zones， exhibiting low areal continuity of permafrost distribution. The spatial heterogeneity of permafrost distribution and other characteristics in this region is notably higher than in other regions due to its high sensitivity to changes in topography and land cover types. Despite numerous research had been conducted on permafrost distribution in Northeast China， the understanding of the laws of permafrost distribution in this region still remain inadequate， significantly limiting our prudent and correct adaption to the responses of permafrost to climate change， as well as the proper and scientific assessment and estimation of the impacts of changing climate， on ecology， hydrology， and engineering construction in Northeast China. Clarifying the distribution of permafrost holds great significance for research in climate environment， forest ecosystem， hydrological processes， economic development and infrastructure in Northeast China. This study took the Genhe River basin， the core distribution area of permafrost in Northeast China， as the research area， and analyzed the distribution laws of permafrost in Northeast China based on field investigation and observation data. The survey data used in this study mainly came from the catalog of 20 boreholes produced in the survey work in 2023 and the ground temperature monitoring data at different depths in boreholes. All drilling work was carried out on six survey profiles laid out from west to east along the lower to the upper reaches of Genhe River basin. There were significant differences in the combination of environmental factors， such as elevation， land cover types， and topography among each profile. The results revealed that in the grassland and farmland areas in the Genhe River basin， permafrost mainly distributed in strips in low-lying and wet areas. Some roads in this area had rolling surfaces resulted from ground thaw settlement and pavement breakups， as well as cracks. They were caused by ground freezing and thawing of foundation soils and thawing and creeping permafrost roadbed. In the forest area in the Genhe River basin， the distribution range of permafrost is relatively large， but the areal continuity of its distribution is relatively low. In forest areas in the Genhe River basin， the distribution of permafrost is greatly affected by topographic factors. Permafrost was found to be well developed in flat valleys between mountains and low-lying areas. No permafrost was found on steep and sunny slopes， or in areas near the riverbed of the main stream of Genhe River valley， or in naturally dried marshes and wetlands. The distribution of permafrost in Northeast China is closely related to the land cover types， and the actual extent of permafrost in Northeast China may be considerably smaller than that indicated by previous simulations. In the future， the mapping of permafrost in Northeast China needs to comprehensively consider the overall impact of local factors， such as land cover type， topography， and river course， on the distribution of permafrost.
Saline soils are widely distributed across permafrost regions， posing significant challenges to engineering projects， especially when it comes to constructing foundations. These regions are particularly vulnerable to salt erosion due to the seasonal climate variations， which often result in one of the most prominent engineering disasters—salt-induced damage to infrastructure. As temperatures fluctuate， the soil and its composition undergo complex changes that can affect the integrity and stability of structures built upon them. In recent research， it has been found that frozen soil， when subjected to an electric field， experiences a movement of polar water molecules and cations， which migrate from the anode to the cathode. This phenomenon is influenced by the concentration of ions in the soil， particularly in unfrozen water. As the solution’s ion concentration increases， the migration of water becomes more pronounced， providing a potentially effective solution to address issues of water accumulation in frozen soil， which is a primary cause of freezing damage. Electroosmosis， a technique that involves manipulating the flow of water through an electric field， has shown promise in controlling water migration and preventing the formation of ice that could damage engineering structures in these regions. The experimental results from studies conducted at a temperature of -4 ℃ revealed that the migration of water is significantly affected by the number of particles and the ion migration channels present in the soil samples. Higher concentrations of ions in the unfrozen water lead to stronger soil conductivity， which in turn results in a higher peak current in the electroosmotic process. Additionally， an increase in ion migration channels shortens the time it takes to reach this peak current， further indicating the enhanced migration capabilities in the presence of higher ion concentrations. Moreover， the research highlighted that when salt is added to the soil， water migration is noticeably increased compared to conditions without salt. However， it was also noted that there was no significant difference in the water migration between saline soils with sodium chloride concentrations of 0.20%， 0.25%， and 0.30%. This suggests that for low salinity frozen soils， the impact on water migration might be negligible， and variations in salt concentration within this range can be overlooked in terms of their influence on the electroosmotic process. Overall， these findings offer valuable insights into the potential application of electroosmotic methods in permafrost regions. They can help engineers and researchers determine the suitability of this approach for mitigating freezing damage caused by water accumulation in saline and frozen soils. By optimizing the electroosmotic process， it may be possible to significantly reduce the risk of infrastructure failure due to soil freezing， especially in regions prone to seasonal salt erosion and extreme temperature fluctuations.
In order to construct the mechanical model of heterogeneous artificial frozen wall suitable for multi-circle freezing in water-rich strata and reasonably analyze the safety and stability of this kind of frozen wall， it is necessary to study the stress and deformation characteristics of multi-refrigerant heterogeneous artificial frozen wall in viscoelastic state. The characteristic cross section at 1/4 tube distance from the main surface of freezing temperature field is selected， and the temperature field of the heterogeneous freezen wall of “brine-carbon dioxide” combined freezing is equivalent to the distribution of “three-stage first function”. Considering the unloading effect of the soil inside the frozen wall during the excavation process and the interaction between the frozen wall and the surrounding unfrozen soil， based on the viscoelastic theory， the Mises incremental rule and the Newton Cotes numerical integration technique， the calculation formula of stress， strain and displacement of the heterogeneous frozen wall is derived， and the calculation results of the formula are compared with the calculation results of the homogeneous frozen wall. The analysis results show that in this example， in the 36 hours from the initial unloading of the frozen wall to the completion of the construction of the shaft lining， the radial displacement and strain of the heterogeneous and homogeneous frozen wall show a nonlinear attenuation trend from the inside to the outside， and the maximum displacement and strain occur inside the frozen wall. With the passage of time， the shaft displacement and outer wall displacement of the frozen wall increase with the increase of creep time， but the growth rate gradually decreases and enters the steady-state creep stage. After considering the creep characteristics and unloading effect， the external load acting on the frozen wall is less than the initial horizontal stress of the soil， and decreases with the increase of empty wall time， and finally tends to be stable. Corresponding to the same time and depth， the external load of heterogeneous frozen wall is greater than that of homogeneous frozen wall， while the strain and displacement of heterogeneous frozen wall are less than that of homogeneous frozen wall. The research results can provide important theoretical basis and reference value for the design of multi-refrigerant combined freezing curtain in water-rich strata.
In order to tackle the four seasons cross country ski tunnel of the negative temperature impact of the lower side of the snow road， the lower side of the snow road structure is transformed into a new type of thermal insulation structure in the form of a superelevation arch， with the water-cement ratio， blowing agent dosage， fiber dosage as the main factors， through the orthogonal test to prepare a high-strength thermal insulation foam concrete insulation materials with good thermal insulation properties， according to the compressive strength test， thermal conductivity measurement to get its optimal ratio， through the latent heat of the earth’s phase change the freezing depth of the surrounding rock is obtained and when calculating the heat flow， because of the negative temperature in the tunnel cave all year round， the depth of the surrounding rock is taken as the freezing depth， and the heat flow of the tunnel under the freezing depth of the uninsulated heat preservation and thermal insulation layer is obtained， and the temperature of the contact surface between the uninsulated perimeter lining is calculated according to the theory of heat transfer from the cylinder， and when the tunnel is the superelevation arch heat preservation form， it is not possible to use the traditional model to calculate， and the calculation model will be optimized and used to calculate the heat flow of the tunnel. MATLAB on the pitch arch insulation form of the tunnel under the contact surface temperature of each material finite difference calculation， and with no insulation layer under the tunnel theoretical calculations for comparative analysis， the following conclusions： the same foam content， the greater the water-cement ratio， the higher the compressive strength， the higher the fiber content， the lower the thermal conductivity； in the case of the water-cement ratio of 0.6， the fiber content of 0.2%， the compressive strength and thermal insulation of concrete has the best compressive strength and thermal insulation performance. Numerical simulation results show that the lining insulation structure has a better linear growth than when there is no insulation layer， while the temperature at the interface of the first and second lining increases by 13.59 ℃， and the numerical simulation result is -14.2 ℃， and the simulation result is -0.61 ℃. The temperature at the interface between the first lining and the surrounding rock was compared， and the result obtained was -14.01 ℃， and the numerical simulation result was -0.53 ℃， which was 13.48 ℃ higher than the theoretical calculation.
On January 7， 2025， Dingri County in Shigatze， Xizang （Tibet） experienced a significant earthquake that measured 6.8 on the Surface-wave Magnitude Scale. This seismic event resulted in considerable casualties and extensive economic damage. The region is known for its vulnerability to glacial lake outburst floods （GLOFs）， underscoring the importance of understanding and mitigating the risks associated with such devastating occurrences. In light of the potential for glacial lake outbursts following the earthquake， our study utilizes advanced Landsat imagery and Geographic Information System （GIS） methodologies to examine the distribution and evolving characteristics of glacial lakes in the affected area. By analyzing various indicators related to glacial lake types and sizes， we have identified glacial lakes that pose significant risks， drawing on historical records of glacial lake disasters in the region. The key findings of our analysis are as follows： （1） In the 6-degree intensity zone of the Tingri earthquake， we identified 463 glacial lakes in 2022， covering an area of approximately （43.70±5.01） km²， with an estimated total water volume of 10.05×10⁸ m³； （2） Our data indicates a consistent increase in the number of glacial lakes over four distinct time periods—1992， 2000， 2009， and 2022， which raises concerns about their stability； （3） Notably， we identified 17 glacial lakes deemed potentially hazardous. Although these lakes have not previously experienced outburst events， some have continued to expand from 2022 to 2025. While no major changes were observed in these glacial lakes from late 2024 through the first two months following the earthquake in 2025， we recommend ongoing monitoring. If any warnings or signs of potential outbursts appear， prompt and appropriate interventions should be initiated to reduce secondary disaster risks caused by the earthquake， thereby minimizing their negative impacts on both the local community and infrastructure.
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.
Avalanches are critical natural hazards in alpine regions， characterized by their unpredictability， rapid movement， and immense destructive power. These events often disrupt transportation corridors， endanger lives， and significantly hinder sustainable economic development in mountainous areas. For infrastructure safety and disaster risk mitigation， understanding avalanche dynamics， particularly throw distances， is essential. This study investigates avalanche events and computational methods for assessing throw distances in the Wenquan-Khorgas traffic corridor along the G219 highway， located in the Chet Aksu Gully of the West Tien Shan Mountains， China. Five major avalanches from late winter 2023—2024 were reconstructed using the RAMMS-AVALANCHE numerical model. Additionally， the applicability of four methods for throw distance calculations—RAMMS numerical simulation， tanα mapping， bi-inclination mapping， and equivalent friction methods—was evaluated to identify the most suitable approach for the region.The study area features steep U-shaped valleys， where snow instability is heightened by topographic variability， high winter precipitation， and specific snowpack conditions. Field surveys revealed 89 avalanche occurrences in the region， with snow throw distances ranging from 70 meters to 1 900 meters. Five representative avalanches were selected for detailed analysis. High-resolution digital elevation models （DEMs） were obtained through drone surveys， and meteorological data were collected using automated monitoring stations. These datasets served as inputs for RAMMS numerical simulations. Validation of these models was performed by comparing simulation results with field observations of avalanche shapes， movement directions， and deposition volumes. The RAMMS model achieved high similarity scores ranging from 84.6% to 93.3%， confirming its reliability in accurately reconstructing avalanche dynamics in complex terrains.Comparative analysis of throw distance calculation methods revealed distinct advantages and limitations. The RAMMS numerical simulation method demonstrated the highest accuracy， with a mean absolute percentage error （MAPE） of 4.93%. This method reliably captured complex avalanche behaviors， such as bifurcations and irregular deposition patterns， making it the most versatile tool for precise hazard assessments. The tanα mapping method， though less accurate （MAPE of 6.64%）， was found to be effective for rapid assessments of single-path avalanches without significant diversion. In contrast， the bi-inclination mapping （MAPE of 8.99%） and equivalent friction methods （MAPE of 6.90%） were less accurate and struggled to account for the specific geomorphic and snowpack characteristics of the West Tien Shan region.The findings of this study provide actionable insights for avalanche risk management. First， the RAMMS model is recommended for high-precision applications， such as designing mitigation structures and evaluating risks for transportation infrastructure. Its ability to simulate the full dynamics of avalanches makes it particularly valuable in complex scenarios. Second， the tanα mapping method is suitable for large-scale preliminary assessments during infrastructure planning stages， where rapid evaluation of potential risks is prioritized. Meanwhile， the bi-inclination and equivalent friction methods require significant refinement. Improvements such as the introduction of region-specific parameters and additional variables， including snow density， moisture content， and terrain roughness， could enhance their accuracy and applicability.This research contributes to a deeper understanding of avalanche dynamics and the evaluation of computational methods. By integrating high-resolution field data， numerical modeling， and comparative analyses， the study establishes a robust framework for regional hazard assessments. The integration of empirical observations and computational simulations offers practical guidance for disaster risk mitigation in alpine regions. Specifically， the insights gained can inform the selection of highway routes， design of mitigation structures， and placement of protective measures along the G219 corridor and similar high-altitude transportation networks.Future research should focus on expanding the dataset to include avalanches across multiple seasons and under diverse meteorological conditions， enabling more comprehensive model calibration. Additionally， the introduction of more complex variables， such as vegetation effects and hydrological dynamics， could further enhance predictive accuracy. The study also identifies avenues for improving bi-inclination and equivalent friction methods. Redefining dependent variables and incorporating localized data would help address current limitations and increase their suitability for regions with unique environmental conditions like the West Tien Shan.Overall， this study emphasizes the critical role of selecting appropriate computational methods tailored to specific regional needs. While RAMMS numerical simulations provide unparalleled precision for detailed assessments， simplified approaches like tanα mapping remain valuable for initial evaluations. The combined application of these methods can optimize both the efficiency and accuracy of avalanche hazard management in alpine transportation corridors.
Under the influence of climate warming， the flood risk of rivers in arid regions has increased significantly. In order to study the future flood risk of Fuyun County on the banks of the Irtysh River， this paper uses SRM to predict runoff in the upper reaches of Fuyun County， and couples the MIKE 11 and MIKE 21 FM models in Fuyun County to explore the flood risk of Fuyun County under future climate scenarios. In this study， the SRM model parameters of the runoff area were calibrated by using the measured data of three hydrological stations in the upper reaches of the Irtysh River， the measured snow area data and the temperature and precipitation data to calibrate the SRM model parameters of the runoff area， the DEM data and the measured river section data to construct the MIKE 11 hydrodynamic model of the evolution area， the MIKE 11 coupled MIKE 21 FM model in the oasis area by using the dem data， the measured river section data and the underlying surface roughness， and the three climate models of CMIP6 and two future climate change scenarios （SSP2-4.5， SSP5-8.5） data-driven model， which completed the simulation of future flood elements in Fuyun County， and finally made a flood risk zoning map. The results show that： （1） The flood risk prevention and control scope of Fuyun County in the Irtysh River Basin is mainly on the north bank of the river. The south side of the river is high and the north side is low， and the width of the river channel in three areas in the central and western regions of the county is significantly smaller than before， and the river runoff is prone to inundation when the river runoff increases. The simulation results of the coupled model show that from 2025 to 2065， the once-in-a-century flood under two different scenarios will be inundated on the north bank of the river. Among the inundation areas， more than 90% of the areas have a maximum water depth of more than 1 m， more than 60% of the areas have been inundated for more than half of the time， and more than 75% of the areas have reached the high-risk level. （2） The flood risk of the Irtysh River gradually decreases from southeast to northwest in the inundation area. The central part of the county is low-lying， the ground is mostly cement surface， the roughness is low， the inundation depth and flow rate are high， and the flood risk is high. There is a certain slope in Northwest China， the ground is mostly low trees， the roughness rate is high， and the depth and flow velocity of flood inundation gradually decrease from the river channel to the northwest， and the flood risk decreases accordingly. （3） The flood risk under the SSP5-8.5 scenario is more severe than that under the SSP2-4.5 scenario. Under the SSP5-8.5 scenario， there were 7.2% more high-risk areas than SSP2-4.5， and 7.7% fewer medium-risk areas than SSP2-4.5. The main difference between the two is reflected in the central part of the county， where the area is high-risk under the SSP5-8.5 scenario and medium-risk under the SSP2-4.5 scenario. The reason is that the maximum water depth in this area is 1-2 m in the SSP5-8.5 scenario and 0.5~1 m in the SSP2-4.5 scenario. The analysis of flood inundation elements shows that the average water depth of the inundation area under the SSP5-8.5 scenario is 0.19 m deeper than that of the SSP2-4.5 scenario， the average flow velocity is 0.49 m·s-1， and the average duration is 0.20 days longer than that of the SSP2-4.5 scenario. The reason is that the runoff output of the SRM model under the SSP5-8.5 scenario is 15.77 m³·s-1 more than that in the SSP2-4.5 scenario. This study provides a basis for flood control measures and has practical significance for the development planning and disaster prevention work of the county.
The Tianshan Mountains are the most extensive in the global arid zone， with glaciers developing at high altitudes. The glacial meltwater of the Tianshan Mountains provides valuable freshwater resources for the ecological and socio-economic development of downstream oases. Significant warming has intensified the ablation of Tianshan glaciers， altering their morphology and profoundly changing the regional water allocation. Therefore， quantitative assessment of glacier changes in the Tianshan region was critical. In this paper， focusing on all glaciers in the Tianshan region， we simulated the glacier mass balance and glacier runoff by driving a monthly-scale mass balance model using temperature and precipitation data from ERA5. At the same time， the glacier area data of the Tianshan region from two glacier inventories and the mass balance results from ASTER were used to calibrate and validate the model parameters of individual glaciers. The results showed that the glacier mass balance in the Tianshan region was in a state of deficit from 1961 to 2020， with a multi-year average value of -0.36 m w.e.·a-1. With 1990 as the boundary， the average annual glacier mass balance after 1990 was -0.43 m w.e.·a-1， which was 0.15 m w.e.·a-1 less than before 1990. The Tarim Basin had the most minor glacier mass loss due to the lowest mean annual temperature at the basin scale. In contrast， the Turpan-Hami Basin had the most severe loss of glacier mass balance due to the highest mean annual temperature and lowest annual precipitation. Regarding spatial changes in the glacier mass balance， most areas of the Tianshan region were below 0 m w.e.·a-1， i.e.， most of the Tianshan region was in a state of mass deficit in the past. However， glacier mass accumulation and smaller deficits were concentrated in the western and central parts of the Tianshan region， while the eastern part faced more severe deficits. Glacier runoff in the Tianshan region increased throughout the study period， with a multi-year average value of 58.86×108 m3. However， driven by a higher mass deficit， resulting in an increase in the average annual glacier runoff after 1990 by 5.91×108 m3 （10.58%）. Among the four basins， the Tarim Basin had the largest glacier runoff， while the Turpan-Hami Basin had the smallest glacier runoff due to the relative minimum of glaciers. Sensitivity analysis of glacier changes in the Tianshan region found that for every 0.5 ℃ increase in temperature， the glacier mass balance decreased by an average of 0.16 m w.e.·a-1. In contrast， for every 10% increase in precipitation， the glacier mass balance increased by an average of 0.03 m w.e.·a-1. Although the climate of the Tianshan region showed a warming and humidification trend， a comprehensive analysis of the value of changes in temperature and precipitation revealed that the effect of increasing temperature on glacier change was significantly more significant than that of increasing precipitation. Finally， the effect of atmospheric circulation transition on the glacier mass balance in the past 60 years was discussed. It was found that the Tianshan glacier mass balance was mainly controlled by high-altitude cyclones before 1990， with the adiabatic rise of the air and the decrease of the surface temperature， which led to a relatively small deficit. After 1990， the control of high-altitude anticyclonic circulation exacerbated the adiabatic warming of the sinking airflow， resulting in a sustained and larger deficit of glacier mass in the Tianshan region. By calibrating and validating the parameters of a single glacier and accurately modelling the glacier mass balance and runoff across the entire Tianshan region， we not only deepened our understanding of the overall characteristics of glacier changes in the Tianshan region but also provided methodological references for regional water resource management and future prediction studies.
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.
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 Bidirectional Reflectance Distribution Function （BRDF） characterizes the anisotropic distribution of surface reflectance and is a key parameter in quantitative remote sensing studies. In this study， BRDF measurements of different types of snow and lake ice during the ice-covered period of Chagan Lake were collected using an ASD Field Spec 4 portable spectroradiometer and a multi-angle observation mount. Spectral data were processed using the Savitzky-Golay filtering method. Based on ground observations， the BRDF reflectance characteristics of black ice， white ice， gray ice， blue ice， and snow were analyzed， and the effects of bubbles and impurities on the BRDF reflectance spectra of ice and snow were explored. The results show that the reflectance of lake ice and snow increases with the zenith angle， exhibiting distinct anisotropy when the azimuth angles are 0° and 45°. The spectral reflectance of different types of lake ice follows a common trend： between 350~600 nm， reflectance increases with wavelength， peaking around 600 nm； between 600~1 300 nm， reflectance decreases with increasing wavelength， eventually being fully absorbed. A comparison of black ice spectral properties with varying bubble sizes revealed that bubbles primarily affect reflectance in the 350~1 000 nm range， with their presence leading to an increase in black ice reflectance. This study provides empirical data and insight into the mechanisms for remote sensing inversion algorithms of lake ice.
Snow accumulation is a crucial component of the cryosphere， characterized by high albedo， low thermal conductivity， and sensitivity to climate change， exerting significant influence on water resource distribution， energy cycles， and surface radiation balance. The snowline， which delineates the boundary between snow-covered and non-snow-covered areas， serves as a sensitive indicator of climate change. Against the backdrop of global warming， remote sensing and simulation of regional snowline altitudes are crucial for deeper exploration into the changing trends and mechanisms of the cryosphere in high mountain regions. Simulating and analyzing changes in the snowline altitude is crucial for understanding regional water resource variations. This has significant implications for ecological sustainability and flood disaster prevention. This study focuses on the Yarkant River Basin， utilizing snowline altitude data extracted from MODIS snow products and ERA5 meteorological reanalysis. Various algorithmic models for simulating snowline altitudes were constructed， including gradient boosting decision trees （GBDT）， adaptive boosting （AdaBoost）， light gradient boosting （LightGBM）， random forest （RF）， and extreme gradient boosting （XGBoost）. On this foundation， simulating snowline altitude data across various temporal scales and analyzing their trends provides theoretical support for understanding regional water resource changes and sustainable development. This study employs nine meteorological data types from the Yarkant River Basin spanning the years 2001 to 2021 as predictor variables， and daily snowline altitude datasets extracted from MODIS as the target variable. The dataset is randomly divided into training and testing sets in a 7∶3 ratio to construct a base learner simulation model. After validating the accuracy， the learning capabilities are compared. The inter-error correlation among models is calculated using the Kendall τ correlation coefficient， and three algorithms with significant diversity are selected as base learners. A meta-learner with strong generalization ability is chosen. The main evaluation and validation metrics include the Root Mean Square Error （RMSE）， Mean Absolute Error （MAE）， Mean Absolute Percentage Error （MAPE）， and the coefficient of determination （R2）. Results indicate that temperature is the most influential factor affecting snowline altitude change， accounting for 27% of the variance. Other factors in descending order of importance include snow cover， solar radiation， runoff， snowmelt， snowfall， and precipitation. Model accuracy verification demonstrates that all five learning algorithms achieve a goodness of fit （R2） exceeding 0.8， ranked from highest to lowest accuracy as GBDT， AdaBoost， LightGBM， XGBoost， RF. Based on simulation accuracy and dissimilarity metrics， AdaBoost， XGBoost， and RF algorithms were selected as base learners， while GBDT was employed as the meta-learner within a Stacking ensemble learning framework. This ensemble model outperforms individual learners （RMSE=88.73 m， MAE=57.99 m， R2=0.93）. Compared with other models， the algorithm effectively mitigating overfitting and singular value issues， demonstrating enhanced robustness， improved generalization ability， and more stable prediction results. Furthermore， a snowline altitude model across multiple time scales was developed to simulate trends from 1991 to 2000， revealing an overall annual growth rate of 24.02 m·a⁻¹ in snowline altitude. The study underscores that temperature is the predominant factor influencing the variation in the snowline altitude， accounting for 27% of the significance. The results are consistent with existing research conclusions on the glaciers of the Yarkant River Basin， thereby validating the high reliability of the snowline altitude data simulated by the Stacking ensemble learning model. The ranking of other factors in terms of importance is as follows： snow accumulation， solar radiation， runoff， snowmelt， snowfall， and precipitation.The Stacking ensemble learning algorithm， integrating multiple models， enhances the accuracy and generalization of snowline altitude simulations， resulting in enhanced reliability of predictions and expanded scope of applicability， providing an accurate and efficient approach for monitoring snowline altitude at the watershed scale.
Cryospheric science plays a crucial role in the global climate system. However， under the influence of global warming， the rapid degradation of various elements within the cryospheric is profoundly impacting both the environment and human society. Although China has achieved significant progress in cryosphere research， public outreach efforts in cryospheric science remain relatively underdeveloped. The dissemination of knowledge is hindered by limited educational resources， outdated communication methods， and a lack of authoritative sources. This paper provides a comprehensive analysis of the current state of cryospheric science popularization in China， highlighting its achievements and shortcomings. Issues such as the shortage of science museums， a lack of specialized talent， the absence of youth-targeted educational books， fragmented content， and limited diversity in outreach formats are identified as challenges. Based on these observations， the paper proposes future development trends and recommendations to enhance public understanding of cryospheric science， strengthen the capacity of the whole people to cope with climate change， and promote a more professional， systematic， and innovative approach to cryospheric science popularization.
