The measurement of ice thickness is the key to the ice volume estimation. In 2019， a ground penetrating radar system （with a 100 MHz antenna） was used to sound ice thickness along one longitudinal transect and eight transverse transects on the trunk glacier of Tuolainanshan Glacier No. 6 in the Qilian Mountains. The map of ice thickness distribution of trunk glacier was acquired by using ordinary Kriging method， the spatial ice thickness gradually thickening from margin to inner， and the average ice thickness was found to be about （39.61±5.32） m. The total ice volume ［（0.0504±0.0082） km3］ of Tuolainanshan Glacier No. 6 was estimated by combine with shallow ice approximation， and the maximum thickness ［（100.78±1.78） m］ was sounded in the depressed basin located near the 4 770 m a.s.l. of longitudinal transect. The transverse transects of ice bed was typical U-shape， and the width of glacier valley decreases with rising elevation. The existing volume-area scaling of glaciers was not suitable for estimation of the ice volume for single glacier， but the scaling fitting by glacier shape had the potential to reduce the error of the estimated results.
Solifluction terraces is a typical slope cryogenic glacial landform. A large number of ancient paleo-solifluction terraces are found in the eastern Tibetan Plateau， and their spatial distribution is important for reconstructing the distribution of ancient permafrost and paleoclimatic environment in the region. The complex texture， different geometric shapes and diverse surface coverage of solifluction terraces make remote sensing interpretation and automatic extraction of solifluction terraces very difficult. However， deep learning methods can acquire contextual multi-scale semantic information and improve feature representation， providing an important means for large-scale extraction of solifluction terraces. Therefore， this paper proposed an automatic extraction method of solifluction terraces based on the DeepLab V3+ deep learning model and high-resolution optical remote sensing images， and conducted experimental research in the surrounding area of Xinduqiao， Ganzi Prefecture， Sichuan. The results show that： （1） Compared with the visual interpretation results， the comprehensive accuracy of the extraction results of this method is above 0.68， and its effectiveness were verified by field investigation； （2） A total of 9 203 solifluction terraces were identified in this area， mainly distributed on both sides of the valley near Xinduqiao Town； （3） The solifluction terraces are mainly in the northwest direction， with a concentrated distribution of slopes ranging from 20° to 25°， most of the elevation is from 3 650 to 3 750 m， and the main surface cover type is grass.
The crystallization pressure on the surface of the pore wall lead to the frost heave of porous materials during the growth of ice crystals in the cooling process. In this paper， the calculation models of crystallization pressure for crystals of different shapes are given respectively， and the application conditions of the classical calculation formula of crystallization pressure are analyzed. Then， based on crystallization kinetics theory and considering the interaction between crystal and pore wall， a growth model of pore ice crystallization during the cooling process was established to calculate the pore deformation during the growth of ice crystals， and the influence mechanism of crystal nucleus density， pore size， load and freezing amount on the pore crystallization deformation was analyzed. The results show that the increase of initial pore diameter and aspect ratio inhibits crystallization deformation by decreasing the proportion of expanded crystals in ice crystal volume. The inhibition mechanism of load on pore deformation is that the increase of load forces salt crystals to grow more laterally （the length-to-width ratio increases）， leading to the decrease of the proportion of expansive crystals.There are two modes of crystal growth in the pore： full filling mode and partial filling mode. In the partial filling mode， the increase of crystal nucleus density， load and pore size will lead to the increase of crystal filling rate in the pore， thus affecting the pore crystallization deformation. This model reveals the deformation mechanism of ice crystals in a freezing single pore， which provides a new idea for solving the problem of frost heave deformation and failure of porous media.
Accurate extraction of temporal-spatial information of the river ice is important to the ice prevention and sustainable development of Inner Mongolia section in Yellow River basin. Based on the remote sensing image data （93 scenes） of the winter river ice in the Inner Mongolia section of the Yellow River from 1989 to 2019， this study proposes Normalized Difference Unfrozen Water Index（NDUWI） to replace the single-band reflectance value of stripping water information in SNOMAP algorithm， and determines the threshold value of dividing river ice and water pixel. The information of river ice in the Inner Mongolia section of the Yellow River is extracted by the modified SNOMAP algorithm， and the spatial and temporal distribution and variation characteristics of river ice is analyzed. The results show that the middle reach of the study area （Bayangaole-Sanhu Estuary） is abounded with more river ice than the other reach. Since 1989， river ice of the study area sequentially underwent “stabilized phase” （1989—1997）， “expansion phase” （1998—2000）， and “shrinkage phase” （2001—2019）. Among all sub-sections， the distribution of river ice of R1 section prior to 2015 was stable； since 2015， the frozen section of the river has witnessed reduced in length； the area variations of river ice of the R2 section and R3 section are similar to that of the full section. The shrinkage rate of the left bank of R2 section is faster than that of its right bank， and the distribution of river ice of R3 section is primarily concentrated on its both banks. The significant fluctuation of river ice of R4 section is steadily decelerating with the passage of time. The distribution of river ice is mainly driven by the change of river regime. The research results can be used as reference for reservoir traffic control in the upper reaches of Inner Mongolia section of the Yellow River and the determination of potential risk dike sections.
Using remote sensing technology to identify river ice phenology information can effectively assess the growth trend of river ice and improve the information management level of ice conditions. based on Sentinel-1 time series remote sensing images， this study took the Inner Mongolia section of the Yellow River as an example， combined curve slope method and dynamic threshold method to extract a five-year （2015—2020） phenological information （FUS， FUE， BUS and BUE） of river ice from the tail of Haibowan reservoir， the lower reach of Sanshenggong Sluise， Sanhuhekou reach， Toudaoguai reach and the tail of Wanjiazhai reservoir， which are five sub sections of Inner Mongolia section of the Yellow River， and analyzed its dynamic characteristics. The result showed that the optimal extraction thresholds for the FUS and the BUE of these five sub-sections are 0.1， 0.2， 0.1， 0.05， and 0.05 times the upper or lower limit of the Logistic curve， and the identification deviations are within 3 days. The FUE and the BUS appear at the maximum （minimum） value of the slope of the Logistic curve， and the identification deviation are within 5 days. The BUS of the five sub sections had a late trend in the past five years， and the rate of change was 1.4 d·a-1， 1.0 d·a-1， 0.8 d·a-1， 0.2 d·a-1 and 0.4 d·a-1， respectively. The freezing rate of the the tai of Haibowan reservoir l and Toudaoguai sections from the BUS to the BUE is increasing at 0.2 d·a-1 and 1.4 d·a-1 year by year， while the Bayangola section and the tail of Wanjiazhai reservoir decrease at 1.4 d·a-1 and 1.0 d·a-1， and the lower reach of Sanshenggong Sluise stays basically the same. Research results of this paper can provide scientific basis for ice flood prediction and construction of bank embankment.
Ice flood is a natural disaster unique to high-latitude rivers， which seriously threatens the safety of river hydraulic structures and the stability of river bank ecosystems. Ice thickness is an important basic information for ice formation analysis， ice condition simulation and forecasting， and it can provide an important basis for ice prevention and disaster mitigation. Whether it is ice prevention or ice utilization， ice thickness is a key parameter and a physical indicator that is difficult to monitor. How to estimate it accurately and effectively has always been the focus and difficulty in river ice research. The Inner Mongolia section of the Yellow River has a cold flood season of up to 4 months each year. The river is meandering and is a key section for prevention and control of floods. The acquisition of its ice thickness information is of great significance to the prevention and mitigation of floods in the Yellow River. This paper aims to use Sentinel-1 radar image combined with Sentinel-2 optical image to estimate the thickness of river ice in the Inner Mongolia section of the Yellow River. Taking the Baotou to Toudaoguai hydrological station in Inner Mongolia as an example， the Sentinel-2 optical image is first processed to extract the boundary of the Yellow River main channel before the ice flood season. Then the Sentinel-1 radar image is processed， 2 intensity information and 4 polarization decomposition parameters are extracted， and the correlation between the 6 radar characteristic parameters and the thickness of the river ice is analyzed. The parameters with the highest correlation were selected， and the linear regression model of ice thickness inversion was established by statistical regression method. The adjusted R2 of the model was 0.657， and the RMSE was verified to be 9.82 cm， MRE was 13.46%， and MAE was 8.26 cm. Inversion of ice thickness during the ice flood season， analysis of the characteristics of temporal and spatial changes of ice thickness， and estimation of ice storage， while discussing the scattering mechanism of river ice. It proves the feasibility of active microwave remote sensing data in the inversion of river ice thickness， and provides a reference for blizzard prevention and disaster reduction in the Inner Mongolia section of the Yellow River.
Liaodong Bay is the most serious ice condition sea area in China， which is significantly affected by sea ice in winter every year. The prediction and evaluation of ice condition can provide technical basis for anti-ice and anti-ice activities in the Liaodong Bay. By establishing the correlation between air temperature， water temperature and ice condition， and combining with the high-precision continuous meteorological data and high-precision ice condition prediction model， the rapid prediction and evaluation of local and small-scale ice condition can be realized. Based on the observation data of meteorological， water temperature and ice conditions in the waters near Hongyanhe in the eastern coast of Liaodong Bay in the winter of 2017—2018， this paper deduced the correlation between local air temperature， water temperature and ice conditions evaluation parameters （ice extent and ice thickness） at different scales， and combined with the large-scale overall ice conditions （floe ice area） in the Liaodong Bay. A method for selecting heat flux under ice was proposed based on the variation of temperature in different temperature ranges， and then a temperature-water-ice condition assessment method was established for Liaodong Bay. The correlation between air temperature， water temperature and ice condition was established， and the high precision continuous meteorological data and the high precision ice condition prediction model were combined The results show that： there was a clear correlation between the temperature and water temperature of the beginning of freezing period and end of the winter， and the correlation coefficients were 0.940 and 0.864 （passed the 0.05 significant test）； the seawater temperature can be divided into ice area （air temperature is less than -10 ℃）， transition area （air temperature is between -10 ℃ and -5 ℃）， and melting area （air temperature is greater than -5 ℃） according to the air temperature； The coverage of ice area in Liaodong Bay was negatively related to the air temperature， and the negative correlation coefficient was -0.557 （passed the 0.05 significant test）；Analysis of the relationship between the daily change of ice area in Liaodong Bay and the daily average temperature， it is found that when the daily change of ice area is 0， the temperature threshold is -5 degrees， and the cumulative negative temperature based on this temperature can be well fitted to this year’s ice area. The heat flux at the bottom of the ice obtained by the observation data can reproduce the change of ice near the observation station well， and it is proved that it is feasible to take 2.2×10-5 heat transfer coefficient between ice water. A method for calculating the heat flux under the ice and a method for evaluating the ice condition in all areas of the Liaodong Bay are proposed.
Obtaining air-ice-sea environmental characteristic data has become an important task for field observation in winter， it is used to accurately and quickly carry out regional ice assessment and prediction to meet the needs of sea ice disaster prevention and mitigation. Based on the winter observation data of the Bayuquan Radar Sea Ice Observation Station （15 years） and the sea area near Wentuozi （9 years） on the east coast of Liaodong Bay， the basic characteristics of the sea ice environment such as wind， water temperature and distribution of sea ice are discussed. Analysis of the data of the Bayuquan Station and the winds greater than Level 4 shows that the northerly winds have changed from N， NNE and NE to NE since 2016， and the southerly winds have changed from SSW to WSW since 2016. The water temperature observation data of Wentuozi were analyzed， and it was found that the water temperature showed the cooling period， the stabilization period and the heating period. The cooling period occurred from December to mid-January of the following year， the stabilization period occurred from late January to mid-February， and the heating period occurred in late February. When the air temperature was lower than -10 ℃， the water temperature was basically maintained near -1.4 ℃； when the air temperature was at -10~-5 ℃， the water temperature was basically distributed in the range of -1.4~-0.5 ℃； when the air temperature was at -5~5 ℃， and the water temperature at the observation point was basically distributed at -1.4~3 ℃. The proportion of ice floes in the visible range of Bayuquan and Wentuozi Stations was analyzed， it was found that the sea ice area showed an increase zone， a stable zone and a decrease zone. Bayuquan Station was an increase zone from December 12 to January 9 of the following year， the sea ice range was a stable zone from around January 9 to February 8， and the decrease zone from around February 8 to around March 15； the Wentuozi Station was similar to the Bayuquan Station. Statistics of ice period in the waters near Bayuquan and Wentuozi are given， and the average first-ice date， final-ice date and ice period of the two locations are given， and compared with historical data. Combined with the analysis of the environmental elements of first-ice date and final-ice date， the regular characteristics of the first ice and the final ice are summarized， and it is of great significance for the assessment of ice conditions in Liaodong Bay.
In passive microwave snow water equivalent retrieval algorithms， the change characteristics of snow cover physical parameters over time affect the inversion accuracy. This paper uses Nordic Snow Radar Experiment （NoSREx） datasets from 2009 to 2013 to study the snow cover evolution characteristics over time and its influence on the microwave brightness temperature. Based on the change of snow depth and temperature， the snow period in the northern arctic inland regions is divided into the snow accumulation period （October to February）， the snow stable period （February to April） and the snow melting period （April to May）. Firstly， characteristics of the snow evolution process in different periods are analyzed. The shape of snow particles is mainly melting forms （MF） in the early accumulation period， and rounded grains （RG）， faceted crystals （FC）， and depth hoar （DH） in the late accumulation period and stable period， the snow melting period is dominated by MF； from the accumulation period to the snow melting period， the snow particles in the bottom layer will grow from small to large and then small. The maximum particle size appears in the annual stable period （February to March）， the value is about 2.5~4.0 mm， all appear in the layer near the ground surface， the surface particle size is always small and relatively stable. Secondly， through the analysis of the relationship between snow depth and microwave brightness difference （18 and 37 GHz）， the brightness temperature difference has different dependence on snow depth in different snow accumulation periods. During the accumulation period and the stable period， the changes of snow depth and the brightness temperature difference are positively similar； during the melting period， the correlation is not obvious due to the influence of snow melting. Thirdly， combined with the simultaneous observation of ground-based radiometers and the Microwave Emission Model of Layered Snowpacks （MEMLS）， a forward one-dimensional microwave simulation environment was constructed， the results showed that three periods of 10.65 GHz and 18.7 GHz and the simulation results under vertical polarization are better at 37 GHz and 90 GHz； under 10.65 GHz， at the stable period，vertical polarization and an incident angle of 50°， the microwave brightness temperature simulation results are the best， RMSE is 2.49 K； compared with vertical polarization， the simulation results under three periods of 90 GHz are better under horizontal polarization； due to changes in the surface snow， the 90 GHz simulation results are unstable， especially during the snow melting period， the minimum RMSE reached 42.7 K. This research is helpful to understand the characteristics of snow cover evolution over time and its influence on microwave radiation simulation. It shows that in the passive microwave snow water equivalent retrieval algorithm， the dynamic process of snow cover evolution needs to be considered in different snow periods.
The Qinghai-Tibet Plateau is an important region due to its dynamic and thermal effect. In view of the fact that air surface temperature increase rate of Qinghai-Tibet Plateau in winter is much higher than that in summer， the extreme climate sensitivity and important research value of Qinghai-Tibet Plateau， and the lack of research status on the thermal condition of Qinghai-Tibet Plateau in winter， it is of great significance to study the thermal condition of Qinghai-Tibet Plateau in winter. Based on the air surface temperature data of China Meteorological Administration， JRA-55 reanalysis data and NOAA extended reconstructed sea surface temperature data from 1961 to 2017， the interdecadal characteristics and the linkage with sea surface temperature for the air surface temperature of Qinghai-Tibet Plateau in winter are studied. The wintertime air surface temperature of Qinghai-Tibet Plateau presents a 16-years interdecadal variation signal. Further correlation analysis shows an apparent interdecadal relationship between the air surface temperature of Qinghai-Tibet Plateau and the sea surface temperature anomaly in Barents-Kara Sea. The abnormal warming or cooling of sea surface temperature of Barents-Kara Sea in winter causes the convergence and divergence of local upper atmosphere anomalies， resulting in the anomaly of 300 hPa geopotential height and the oscillation of geopotential height field causes the propagation of Rossby wave. The Rossby wave generated by Barents-Kara Sea propagates eastward， affecting Siberia and causing the anomaly of geopotential height. At the same time， the abnormal convergence or divergence is generated， accompanied by the enhancement or weakening of the subtropical westerly jet in the north and south of Siberia， respectively. The corresponding shear caused by the strengthening or weakening of the subtropical westerly jet produces abnormal convergence or divergence in the upper layer of Qinghai-Tibet Plateau， the subsequent abnormal subsidence or ascent will modulate the plateau air surface temperature through the diabatic heating process.
Land surface temperature （LST） is one of the important indicators reflecting the ecological environment and has a strong correlation with air temperature. Against the backdrop of rising global air temperature， Tibet， a sensitive area for climate change， is worthy of being monitored the spatiotemporal change for LST by remote sensing technology on a large scale， which is helpful for gaining insight into the evolutionary process of the Tibetan thermal environment and for long-term monitoring of basic ecological change in plateau areas. However， remote sensing images in plateau areas generally face the problem of cloud shading， with the annual average cloud amount exceeding 50%， which greatly reduces the availability of remote sensing data and makes it difficult to effectively monitor the LST in plateau areas over a large range and long-time span. In addition， the research on LST and surface parameters is mainly based on classical linear regression and geographically weighted regression， and the scale effects of different surface parameters on the spatial differentiation pattern of LST are not fully considered， which results in an unstable regression.In this paper， with the help of Google Earth Engine （GEE）， the MODIS LST data of Tibet without cloud cover from 2000 to 2020 were obtained by the methods of cloud identification， could masking， image overlay and mean value composite to address the problem of cloud cover in the plateau region. LST was classified into 5 classes by normalized classification method， and then the spatiotemporal distribution characteristics of Tibet’s LST is explored using trend analysis， landscape pattern indexes and movement analysis of the center of gravity. Moreover， the surface parameters which effect LST such as normalized difference vegetation index （NDVI）， bare soil index （BI）， perpendicular impervious surface index （PISI）， humidity （WET） are retrieved and digital elevation model （DEM） is attained. In view of the fact that traditional regression methods cannot take into account the spatial scale variations， the paper use multi-scale geographically weighted regression which can address this problem and the effect on LST and the spatial scale variations of LST influencing factors are analyzed. The following conclusions are drawn from the research analysis： The mean value of LST increased from 18.72 ℃ to 20.28 ℃ over the past two decades （2000—2020） with an annual growth of 0.09 ℃， showing a weak increased trend. The interannual variation law of LST in Tibet is as follows： fluctuating increase—continuous decrease—rapid increase—large decrease—slight increase. From the perspective of ecological regions， the interannual changes of LST in each ecological region are significantly different. LST and its increased trend are spatially uneven in Tibet， with a higher value in northwest than in southeast. The spatial structure of the LST is dominated by the medium LST zone and above， which accounts for about 70% of the study area. Low LST zone and high LST zone is aggregated with simple and regular shapes while sub-low LST， medium LST， and sub-high LST zones are fragmented with complex shapes. The distribution of the center of gravity for each LST zones during 2000—2020 has obvious directionality and the movements of that have significant differences. In particular， the movements of the center of gravity in the low LST zone and high LST zone show a shift from toward each other to moving in opposite directions， reflecting that the regional LST gap between the east and west of study area has experienced a process from narrowing to widening. DEM and WET have negative effects on LST， BI， PISI and NDVI have positive effects， and the intercept have different influence in different ecological zones. Among of them， DEM has fairly small scale and the strongest effect； the intercept has the smallest scale and fairly strong effect.
The extreme cold events that occur occasionally ought to be valued under the trend of global warming. Meanwhile， exploring the influences of large-scale driving factors on extreme cold events in Xinjiang is of great significance to predict and cope with disasters caused by climate change. Based on extreme cold indices which are calculated by daily air surface temperature data from 53 meteorological stations covering the period from 1961 to 2016. Temporal and spatial characteristics of extreme cold events in Xinjiang have been analyzed using linear regression analysis and Inverse Distance Weighted， it is found that the average extreme cold indices have a significant change in spatial point， this means the temperature of Xinjiang has a increasing trend. In the spatial-scale， the variation range of cold indices in northern Xinjiang， eastern Xinjiang and Ili River Valley have dramatic change rather than other region. Cross wavelet transform are used in order to analyze the relationship between extreme cold indices， including frost days（FD）， ice days（ID）， daily minimum air tempreture （TNn）， maximum of daily maximum tempreture （TXn）， cold nights（TN10p）， cold days（TX10p）， and large-scale driving factors ［Arctic Oscillation（AO）， North Atlantic Oscillation（NAO）， EI Ni?o and the Southern Oscillation（ENSO）］， the result shows that AO and NAO have strong relevance with extreme cold indices， the overall influence of large-scale driving factors on extreme cold indices is AO>NAO>ENSO. Using parametric hypothesis tests verify statistically significant changes in the cold indices characteristics from one phase to another of each oscillation and also in coupled phases. During the individual factor driving modes， the extreme cold events have occurred easier on AO negative phase， NAO negative phase， and La Ni?a event. During the coupled factors driving modes， the number of cold days are more on EI Ni?o-AO positive phase and EI Ni?o-NAO positive phase， and the lower temperature values of extreme cold events would be smaller on EI Ni?o-NAO negative phase. Extreme cold events are more likely to occur during the La Ni?a-AO negative phase and La Ni?a-NAO positive phase. The EI Ni?o （La Ni?a） event has a modulating effect on the AO （NAO）. To explore the mechanism for the coupled modes， the atmospheric circulation systems are discussed via anomalies composite analysis by using National Centers for Environmental Prediction（NCEP） reanalysis data. The extreme cold events in Xinjiang are more likely to occur during the La Ni?a-AO negative phase and La Ni?a-NAO positive phase， and the causes are related to the westward path of cold air due to the anomalous mid and high latitude potential in Eurasia， the strengthening of Ural blocking， and the northerly airflow affecting Xinjiang.
The Gurbantunggut Desert is the only desert in China with long-term snow cover in winter. In such a special geographical environment， the temporal and spatial variation of winter snow depth and boundary layer height over the desert and its surrounding areas are not clear. To explore its temporal and spatial variations and relationships， this paper compiled 2014—2019 SMMR （Scanning Multichannel Microwave Radiometer）， SSM/I （Special Sensor Microwave/Imager）， SSMI/S （Special Sensor Microwave Imager/Sounder） passive microwave snow depth data， snow depth observations in the hinterland of the Gurbantunggut Desert， and boundary layer height data from the Fifth Generation ECMWF Reanalysis， for the analysis of the temporal and spatial variation of winter snow depth and boundary layer height in desert and its surrounding areas， using trend analysis method and Pearson correlation analyses are analyzed. It provides a reference for understanding the impact of Gurbantunggut Desert snow on regional atmospheric boundary layer. The results show that the annual average winter snow depth in the Gurbantunggut Desert and its surrounding areas is 8.45 cm， showing a deep snow cover in the northeast and south as a whole， and a shallow snow cover in other areas. The snow cover gradually decreases from the center of the desert to the surrounding areas. The snow depth in the Gurbantunggut Desert and its northeast and south adjacent areas shows a rising trend， while the remaining areas show a downward trend. The average annual height of the boundary layer in the Gurbantunggut Desert and its surrounding areas in winter is 105.54 m， which is high in the southeast and northwest， and shallow in the central， northeast and southwest desert area. The height of the boundary layer in the desert and its surrounding areas increases while that in other areas decreases. The winter snow depth in Gurbantunggut Desert is negatively correlated with the spatial and temporal changes of the atmospheric boundary layer height， of which 93.17% of the desert areas are negatively correlated， with an average correlation coefficient of -0.32， the highest of -0.58， and a spatial correlation coefficient of -0.42 （P<0.05）.
The spatial distribution of permafrost ground temperature is significant to identify the changes in the active layer thickness， which can provide insight into the prevention of the geohazard. This paper takes the permafrost coverage area from Kunlun Mountain to Chiqu Valley around the Qinghai-Tibet Railway as the study area， and uses the geographically weighted ridge regression Kriging （GWRRK） method to simulate the spatial distribution of its ground temperature from July to September in 2001， in order to reveal the variation of its associated thawing depth. The results show that the permafrost ground temperature in mountainous areas is generally lower than that in the plains and basins， and it decreases with the increase of ground depth. The temperature varies greatly in the ground depth interval of 0~5 m， and the average temperature difference is 10.3 ℃. However， in the ground depth interval of 5~15 m， the temperature remains nearly unchanged， and the average difference is only 0.2 ℃. By comparing the performance of the GWRRK method with Kriging with an external drift （KED） method and geographically weighted ridge regression （GWRR） method， it is identified that the simulation accuracy of the former is better than the latter two methods.
Aiming at the phenomenon that shallow landslides often occur in the slope of roadbed in cold and arid regions of northern China due to insufficient tensile and shear strength， high and low temperature alternating box is used to simulate the effects of different freeze and thaw actions in cold regions. Simulation and arid regions by the method of steam balance to control the soil is in a state of high suction， and the high suction soil water characteristic curve method of data with axis-translation get low suction test data combined with soil and water characteristic curve plotted by different broad suction under the action of freeze and thaw soil water characteristic curve， through conventional triaxial test and uniaxial tensile test to explore the strength characteristics of unsaturated soil freeze and thaw cycles and high suction. The results show that the water-holding capacity of soil decreases with the increase of freeze-thaw cycles. In terms of mechanics， the tensile strength and shear strength of unsaturated soil with high suction decrease gradually with the increase of the number of freeze-thaw cycles. With the increase of matric suction， the shear strength increases and the tensile strength decreases. The uniaxial tensile strength obtained from the shear strength data obtained from the conventional triaxial test is much larger than that obtained from the actual uniaxial tensile strength test for unsaturated soil with high suction. Because of the traditional formula of tensile shear joint not well reflect the high suction of unsaturated soil tensile area after unfreezing the actual situation of the failure envelope， by introducing the concept of “boundary value”， this paper proposes a new paint tensile area failure envelope method to modify the traditional formula， the revised formula has good applicability. It can provide a reliable theoretical basis for the calculation of unsaturated soil slope stability in cold and arid areas.
The frost heaving damage of canal lining is common in cold regions. The anti-freezing engineering design of the canal mostly depends on engineering practical experience and qualitative understanding， which has a certain randomness and blindness. Therefore， there is a lack of concise and reasonable method to calculate the frost heaving force on the lining structure. In this paper， considering the interaction between frozen soil and lining and the continuity of frozen soil foundation， the difference equation of frost heave deflection curve of canal lining plate is derived based on the generalized Winkler foundation beam theory and the finite difference method. Then， the mechanical model of frost heaving force of trapezoidal canal is established. The calculation methods of normal and tangential frost heaving force of lined canal are given. At the same time， this paper considers the ultimate bearing capacity of the frost heaving failure of the canal lining and the influence of the uplift displacement of the slope toe on the actual frost heaving force in the process of frost heaving， so as to avoid the excessive calculated value of the frost heaving force and the internal force of the lining structure. In order to verify the rationality of this model， taking the trapezoidal canal of Jinghui Main Canal in Gansu Province as the research object， the frost heave damage is calculated. The results show that because the interaction between lining structure and frozen soil is considered in this model， the normal frost heaving force of canal lining plate presents a nonlinear distribution， and the linear distribution assumption of engineering mechanics model is modified. The frost heaving force increases at the foot of the slope， decreases in the middle of the span and increases on the bottom plate than that of the engineering mechanical model， so that the calculation results are more in line with the engineering practice. Therefore， the frost heaving force mechanical model proposed in this paper is scientific， reasonable， simple and fast， and has better universality， which can provide a reference for the frost heaving resistance design of canals in cold regions.
Accurate and reliable runoff prediction is of great significance for the scientific management and planning of water resources， especially in the arid and semi-arid areas where water resources are scarce. Runoff prediction has important practical significance for the efficient utilization of water resources and the economic operation of water conservation projects.In view of the problem that it is difficult to make use of the advantages of each prediction model because a single method is usually used for modeling and prediction of runoff prediction. In this paper， the Extreme Learning Machine （ELM） model， Support Vector Machine （SVM） model and Multivariate Adaptive Regression Spline （MARS） model were used to develop the runoff prediction model in the upper reaches of Shule River in 1 to 7 day. On this basis， the Bayesian Model Average （BMA） method was also used to combine the prediction results of ELM， SVM and MARS models， and a combined runoff prediction model was constructed to obtain more reliable predictions. The 95% confidence interval of BMA was obtained by Monte Carlo sampling method， and the uncertainty of the predictions was analyzed. The results show that ELM， SVM， MARS model and BMA combination model are suitable for medium and long term daily runoff prediction in arid and semi-arid areas； BMA has higher prediction accuracy than the single models and can provide more reliable and accurate predictions； The 95% confidence interval of BMA has high coverage of measured values， and can provide better deterministic and probabilistic predictions. The results suggest that BMA has better prediction performance than the single models under the condition of limited data， and can be an effective method for medium and long-term daily runoff prediction in arid and semi-arid areas.
This study used 349 precipitation event samples collected at 4 stations in Lanzhou from April 2018 to April 2021 to study the characteristics and influencing factors of precipitation isotope changes in Lanzhou. The results show that the precipitation isotopes in Lanzhou show seasonal variation characteristics of high summer and low winter， and the spatial variability of precipitation isotopes in the summer half year is more significant than that in the winter half year. The local meteoric water line （LMWL） is δ2H=7.34δ18O+7.28 （R2=0.96， P<0.01）， and it reflects the arid climate characteristics of Lanzhou. In terms of influencing factors， the precipitation isotope in Lanzhou exhibits a temperature effect. The backward trajectory indicates that the precipitation in the summer half of the year in Lanzhou is affected by the monsoon water vapor and the water vapor in the westerly zone， and the precipitation in the winter half of the year mainly comes from the transportation of water vapor in the westerly zone. Using potential source contribution function （PSCF） analysis and concentration weighted trajectory （CWT） analysis， it was found that potential evaporation sources were mainly distributed in the eastern part of Lanzhou in the summer half year， while there were almost no potential evaporation sources in the winter half year. In addition， in the summer half year， the convective activities in the days before the precipitation events have an integrated effect on the precipitation isotopes. The accumulated time is related to the intensity and frequency of the convective activities， and the accumulated time is longer at the beginning and end of the monsoon. During the winter half year， the temperature in the days before the precipitation event will have an integrated effect on the precipitation isotopes， and the accumulated time is influenced by the southward frequency of the cold air. The above understanding will provide a new perspective for the study of the water circulation process in the monsoon marginal area.
The largest area of permafrost in China is located on the Qinghai-Tibet Plateau， where the ecosystem is very sensitive to climate change. Changes in air temperature and precipitation have been reported to affect soil thermodynamics and hydrodynamics， which are thought to be critical for thawing and freezing processes， and this process further affects the seasonality of soil nitrogen cycling. However， the seasonal dynamics of soil nitrogen on the Qinghai-Tibet Plateau and its relationship with temperature and moisture remain unclear. The study sites are located in permafrost regions along the Qinghai-Tibet Highway， which has a typical cold and dry continental alpine climate. We selected two typically different alpine ecosystems， alpine meadows and alpine grasslands， located in the Fenghuo Mountain and Tedaqiao areas， respectively. Soil samples were collected at 10 cm depth during each month from April 2016 to March 2017 （the entire thaw-freeze cycle of the active layer in the permafrost regions， including a growing season and a non-growing season）， except for December 2016 and February 2017. Soil temperature was also measured at 10 cm depth using a thermocouple temperature sensor. Soil samples were transported to the laboratory and analyzed for total nitrogen （TN）， ammonium nitrogen （NH4+-N）， nitrate nitrogen （NO3--N）， microbial biomass nitrogen （MBN）， dissolved organic nitrogen （DON）， soil volume water content （VWC）， and pH. The results showed that soil available N and MBN in the two alpine grassland ecosystems varied significantly between months and had similar trends， while the fluctuation range of alpine meadows in the Fenghuo Mountain area was larger than that of alpine grasslands in the Tedaqiao area. This pattern suggests that the trends of soil available N and MBN are influenced by similar climatic conditions and plant growth periods in both grassland ecosystems， rather than by different vegetation types. The large fluctuation range of alpine meadows in the Fenghuo Mountain area was attributed to the high content of TN in the soil. Soil NH4+-N and DON content were higher in the non-growing season than in the growing season， while soil NO3--N content was higher in the growing season than in the non-growing season. Because soil moisture content was higher in the growing season than in the non-growing season， higher soil moisture resulted in higher nitrification， which led to higher soil NO3--N content. The lower DON content in soils during the growing season was attributed to plant uptake and leaching during the growing season. Alpine meadows in the Fenghuo Mountains region had high soil NH4+-N content during the thawing phase. This was because the strong nitrification during the growing season led to low NH4+-N content in the soil. In alpine meadows of the Fenghuo Mountains region， freeze-thaw cycles could kill microorganisms in the soil and release inorganic N ions and organic N of small molecular mass， while NH4+-N could be absorbed and accumulated by the soil. Soil MBN decreased at the beginning of the growing season and increased at the end of the growing season， and this pattern may be attributed to the relatively low NH4+-N content in the soil during the growing season， while soil microorganisms prefer to assimilate NH4+-N. Further， soil temperature and moisture showed a weak correlation with soil available N and MBN. This pattern could be explained by the fact that soil available N had been assimilated by plants during the growing season， while soil temperature and moisture were higher during the growing season. Soil MBN content in alpine meadows in the Fenghuo Mountain area and available N in alpine grasslands in the Tedaqiao area were positively correlated with TN. This pattern suggests that soil TN can effectively influence soil available N and MBN in alpine meadows in the Fenghuo Mountain area and alpine grasslands in the Tedaqiao area. Our results indicate that soil TN content， vegetation uptake， and freeze-thaw effects may cause seasonal changes in soil available N and MBN under different grassland types in permafrost regions on the Qinghai-Tibet Plateau.
Polycyclic aromatic hydrocarbons （PAHs） widely distributed in the environment medium， which is a strong carcinogenic persistent pollutant. About 90% PAHs in terrestrial environment accumulate in soils. With the development of resources in the cold region and the intensification of human activity， the soil pollution of PAHs in permafrost regions caused by oil leakage， landfill leach and sewage discharge has become increasingly prominent. Under the background of global warming， there is a risk of secondary pollution due to the re-release of PAHs from permafrost. The study on the distribution characteristics and migration process of soil PAHs pollution have great significance to the assessment of ecological environment risks， the prevention and control of persistent organic matter pollution in soil， and the protection of life and health safety of residents in permafrost region. In this review， we summarized the latest research on the distribution characteristic， source identification， vertical migration and risk assessment of PAHs in soil of permafrost regions. As the remote area on the earth， the pollution level of PAHs in the soil of permafrost region is much lower than that in the densely populated areas of middle and low latitudes， which can represent the background value of PAHs in the soil of the earth. Due to the high latitude or altitude and cold climate， a common and most important source of PAHs in soil of permafrost region is long-distance atmospheric transport. The freezing-thawing effect of the active layer affects the vertical distribution of PAHs in soil profile by changing the physical and chemical properties of soil and controlling the direction of unfrozen water migration. Meanwhile， the low permeability of permafrost can hinder the vertical migration of PAHs. Based on the overview of research results， it is concluded that the current research on soil PAHs pollution in permafrost region is mainly focused on the investigation of the distribution and source analysis of the pollution in the surface soil， while the research on the migration of PAHs in the deeper active layer and permafrost is only limited to the explanatory analysis of its distribution in the soil profile. The effects of freezing-thawing on the migration， transformation and destination of PAHs in soil are still unclear. In view of the problems and shortcomings of the current researches， it is believed that the future effort on PAHs in soil of permafrost region will focus on the migration and transformation mechanism and pollution control technology. The simulation model for migration behavior of PAHs in soil of permafrost region needs to be developed urgently to achieve the quantitative prediction of PAHs pollution reserves and migration flux. In addition， the in-depth study of soil pollution in permafrost regions needs to be carried out by closely associating with multi-layer， multi-interface， multi-medium， multi-factor and multi-target pollutants.
As a special type of mountain glacier， debris-covered glaciers show different response to climate change due to the presence of debris. By analyzing the spectral characteristics， topographic and surface temperature of debris， we proposed a new method for automatically delineating debris-covered glacier， i.e.， TDSI （temperature NDDI slope ice）. Based on the Landsat TM/ETM+/OLI remote sensing images and ASTER DEM data from 2011 to 2020， the TDSI method was used to extract six continental glaciers in Karakoram Range and Tien Mountains and three maritime glaciers in Nyainqentanglha Range. The results show： the overall area accuracy of the TDSI method for extracting the debris-covered glaciers is 91.23%， with 91.20% and 90.97% for continental and maritime debris-covered glaciers， respectively. The area of six continental glaciers and three maritime glaciers decreased by 0.06% and 0.11% on average from 2011 to 2020， while the area of debris increased by 11.92% and 18.35%. The debris of continental glaciers are mainly located below median altitude， while the distributions of maritime glaciers’ debris are more extensive. During the past decade， the debris of both continental glaciers and maritime glaciers showed an obvious tendency of expanding to the upper part of glaciers. Rising temperature is the main reason for the decrease in glacier and the increase in debris. Meanwhile the change of ice velocities and the evolution of proglacial lakes near glacier terminus have a certain influence on the debris changes.
Glaciers as important indicators of climate change， the melting of glaciers will not only cause sea level rise， but also cause disasters such as ice avalanches and glacial lake break. Glacier extent monitoring is important to the regional ecological environment and human social life. Glacier extent monitoring based on remote sensing technology is widely used. However， in the conventional remote sensing monitoring method， there is a phenomenon that glaciers and glacial lakes are indistinguishable in the extraction results of the ice and snow index threshold method. The object-oriented classification method is limited by the spectral texture information of ground objects， and the occurrence of different types of land cover with the same spectrum’ phenomena will appear. In order to make up for the above shortcomings， in this paper， improved ice and snow index that can distinguish glacial lakes and glaciers is proposed， and it is integrated into the object-oriented classification method， and an object-oriented-improved ice and snow index method is constructed. Taking the Geladandong Glacier as the test area （the surface of the glacier in this area is clean）， the extraction results of this method are compared with the results of the Qinghai-Tibet Plateau glacier data products and the results of conventional remote sensing monitoring methods. Validation of the object-oriented-improved ice and snow index method for effectiveness and robustness. The results demonstrate that： Ice and snow have strong reflection characteristics in the blue band and strong absorption in the near-infrared long-wave band. These two bands are sensitive bands for glacier identification. They not only have a high degree of recognition for glaciers， but also there are also obvious effects in distinguishing glaciers and glacial lakes. The band characteristics of the Landsat-8 OLI sensor were analyzed， and the glacier sensitive bands were Coastal band and SWIR1 band. Three improved ice and snow indices RSI， NDSI* and DSI are proposed for the glacier-sensitive band of Landsat-8 data. Taking the Geladandong Glacier as the test area， the glacier extent was extracted based on three improved ice and snow indices， and the extraction results were compared with the 2017 glacier data on the Qinghai-Tibet Plateau. The results show that the three ice and snow indices can extract the glacier boundary well and the extraction accuracy can reach more than 95%. In the recognition of confusing ground objects， the three indices can distinguish clouds and glaciers， but in the difference between glaciers and glacial lakes， the DSI ice and snow index is significantly better than others. The object-oriented-improved ice and snow index method was used to extract the extent of Geladandong Glacier， and the overall accuracy of the extraction results was as high as 97.26%. This method combines the advantages of the ice and snow index threshold extraction method and the object-oriented classification method to make up their respective shortcomings， and identify the glacier boundary more accurately. The object-oriented-improved ice and snow index method solves the problem of the same spectrum foreign matter in the process of glacier extraction to a certain extent， especially for the distinction between glacial lakes and glaciers. However， there are still some deficiencies. The original data of this study are images at the end of glacier ablation and with a high solar elevation angle. This study did not consider in detail the indistinguishability of snow covered in glacial and non-glacial areas and the difficulty of identifying glaciers in shaded areas. In the follow-up， in view of the above shortcomings， it is proposed to use multi-source remote sensing data （high resolution images， DEM， etc.）， integrate multiple glacier characteristic indicators， and further improve the object-oriented-improved ice and snow index method， and strive to fundamentally solve the difficult problem of clean glacier classification.
The frozen soil hollow cylinder apparatus is the main experiment instrument to study the mechanical characteristics of frozen soil under the complex stress paths including stress rotation. The sample used in frozen soil hollow cylinder test is mostly hollow cylinder sample to achieve the continuous rotation of the main stress axis direction. However， it is difficult to prepare hollow cylinder specimen because of the thin wall， which can have a great influence on the further study in frozen soil hollow cylindrical tests. Therefore， based on the preparation method of clay sample in triaxial test， a novel preparation device for remolded hollow cylinder specimen of frozen soil is designed， which can improve the efficiency of sample making and reduce the influence of human factors in the sample making process. Based on the novel preparation device， a practical preparation method for frozen clay hollow cylindrical specimens is proposed. It is found the moisture content and dry density of the clay samples prepared by this method is uniform and the mechanical properties are stable， which can meet the requirements of indoor mechanical tests of frozen soil. Two parallel remodeled hollow cylinder soil samples of frozen clay prepared under the same conditions are tested in the different principal stress directions tests， and the consistency of the results indicates that the preparation method for samples has sound repeatability. Thus， the proposed preparation method can be used for further systematical researches on the static and dynamic mechanical behaviors of frozen soil under complex condition.
