The glaciers in northwest China are mainly distributed in the northern part of the Himalayas, the Kunlun Mountains, and the Tianshan Mountains. Glaciers are an important freshwater resource in the northern part of the Kunlun Mountains, and the melting of glaciers and snow provides an assured source of water for rivers on the southern edge of the Tarim Basin. Based on the first glacier inventory dataset on China (1968), the second glacier inventory dataset on China (2009), and the glacier inventory dataset on Western China in 2018, this study used DEM data, Landsat remote sensing images, and ERA5 atmospheric reanalysis data to investigate glacier change and its influencing factors with respect to the northern part of the Kunlun Mountains. The results showed that there were 9273 glaciers in the northern part of the Kunlun Mountains in 2018, with an area of about 11,762.72 km², an ice inventory of about 1168.53 km³, and an average length per glacier of about 1.68 km. The glaciers were mainly distributed at altitudes of 5300–6100 m (7574.66 km²). From 1968 to 2018, the number of glaciers in the northern stretch of the Kunlun Mountains increased by 343, while the glacier area decreased by 2452.80 km² (−0.14%/a). From 2009 to 2018, the glacier area at the altitude of 4900 m to 6100 m decreased in the northern section of the Kunlun Mountains, and the glacier area at the remaining altitude increased slightly (10.67 km²). From 1968 to 2018, the glacier area and glacier length in all river basins decreased. The relative rate of glacier area change in the Qarqan River basin from 2009 to 2018 was five times that of 1968–2009, and this needs significant attention. From 1968 to 2018, both temperature and precipitation increased to varying degrees, and the increase in precipitation was beneficial to the accumulation of glaciers. Therefore, the increase in temperature was the main cause of glacier change in the northern section of the Kunlun Mountains.

Influenced by global warming, glaciers in High Mountains Asia (HMA) generally show a trend of retreat and thinning, but in Karakoram, Pamir, and West Kunlun there is a trend of glacier stabilization or even a weak advance. In this study, using a bibliometric analysis, we systematically sorted the area, mass balance, and elevation changes of the glaciers in Karakoram and summarized the glacier surges in HMA. The study shows that, since the 1970s, the glaciers in the Karakoram region have experienced a weak positive mass balance, with weakly reducing area and the increasing surface elevation. The north slope of Chogori Peak and the Keltsing River Basin presented a glacier retreat rate with a fast to slow trend. The anomaly is mainly due to low summer temperatures and heavy precipitation in winter and spring in the Karakoram region. There are a large number of surging glaciers in the Karakoram Mountains, the Pamir Plateau, and the West Kunlun region in the western part of HMA, especially in the Karakoram Mountains and the Pamir Plateau, which account for more than 70% of the number of surging glaciers in the entire HMA. The glaciers in the Karakoram and Kunlun Mountains are mainly affected by the synergistic influence of various factors, such as hydrothermal conditions, atmospheric circulation, and topography. However, the glaciers in the Pamir region are mainly influenced by the thermal mechanism of the glacier surge. The glaciers in and around Karakoram are critical to the hydrological response to climate change, and glacial meltwater is an important freshwater resource in arid and semi-arid regions of South and Central Asia, as well as in western China. Therefore, changes in the Karakoram anomaly will remain a hot research topic in the future.

Permafrost is one of the crucial components of the cryosphere, covering about 25% of the global continental area. The active layer thickness (ALT), as the main site for heat and water exchange between permafrost and the external atmosphere, its changes significantly impact the carbon cycle, hydrological processes, ecosystems, and the safety of engineering structures in cold regions. This study constructs a Stefan CatBoost-ET (SCE) model through machine learning and Blending integration, leveraging multi-source remote sensing data, the Stefan equation, and measured ALT data to focus on the ALT in the Qinghai-Tibet Plateau (QTP). Additionally, the SCE model was verified via ten-fold cross-validation (MAE: 20.713 cm, RMSE: 32.680 cm, R²: 0.873, and MAPE: 0.104), and its inversion of QTP’s ALT data from 1958 to 2022 revealed 1998 as a key turning point with a slow growth rate of 0.25 cm/a before 1998 and a significantly increased rate of 1.26 cm/a afterward. Finally, based on multiple model input factor analysis methods (SHAP, Pearson correlation, and Random Forest Importance), the study analyzed the ranking of key factors influencing ALT changes. Meanwhile, the importance of Stefan equation results in SCE model is verified. The research results of this paper have positive implications for eco-hydrology in the QTP region, and also provide valuable references for simulating the ALT of permafrost.

Worldwide, there are great challenges for meteorological monitoring and glacier ablation monitoring in high-altitude mountain areas. It is often difficult to capture fine-scale climate and glacial changes in high-altitude mountainous areas due to the harsh natural environment and the extreme lack of observational sites. Based on high-altitude meteorological stations erected on the eastern shore of Aqikkule Lake (AQK) and at the terminus of Shenshechuan Glacier (SSG), as well as on mass balance data from SSG, the characteristics and correlation of temperature, solar radiation, relative humidity, precipitation, wind speed and direction of the two regions, and the mass balance in the ablation area of SSG from 30 May 2022 to 18 May 2023 were analyzed, and the average melting depth of SSG was simulated. The results indicate the following: (1) The average annual temperature of AQK and the terminus of SSG is −3.7 °C and −7.7 °C, respectively, and the vertical lapse rate of temperature in the summer half of the year is greater than that in the winter half of the year. Precipitation timing has a great influence on daily temperature differences. (2) Precipitation in both places is concentrated in summer; the glaciers in this area are of the summer recharge type, and precipitation has a significant reducing effect on the solar incident radiation and increases the relative humidity in this region. (3) AQK and SSG both have local circulation development, in the area of AQK all year round due to the lake effect, while the terminus of SSG only has the development of valley winds in the summer, being controlled in the winter by the westerly wind belt. (4) The average mass balance value of the ablation area of SSG was −1786 mm as measured by the range poles method. The average annual ablation depth of SSG simulated by using the empirical formula was 587–597 mm, which is not large compared with other glacier areas in the Tibetan Plateau, and it has the characteristics of typical continental-type glaciers.

As a crucial component of the cryosphere, snow exhibits high sensitivity to climate change and exerts a significant influence on the hydrological cycle and ecological framework. Snow depth is one of the important information about snow, so studying high-resolution snow depth data is of great significance for regional climate, hydrology, and disasters. Utilizing passive microwave data from AMSR-2 and optical remote sensing data from SSE mod FSC, along with DEM and Land Cover Type datasets, the Extreme Gradient Boosting (XGBoost) model is employed to leverage the advantages of multi-source remote sensing information. Subsequently, The spatial-environmental-XGBoost (SE-XGB) model is constructed for the downscaling retrieval of snow depth. In the Arid Region of Northwest China, the inversion results of SE-XGB model were validated against measured snow depth at the weather station. The validation yields an R of 0.912, RMSE of 2.65 cm, and MAE of 0.81 cm. The snow depth estimated by the SE-XGB algorithm exhibits excellent agreement with the measured data. This study validated the snow inversion accuracy of four major land cover types, showing that Bare Land had the highest accuracy. In contrast, the inversion accuracies for Cultivated Land, Urban Land, and Grass Land exhibit roughly comparable performance, yet all three demonstrate significantly lower accuracy than Bare Land. The validation regions selected for this study include northern Xinjiang and Northeast China. At the same time, the SE-XGB algorithm is verified in the northern Xinjiang Line 1 and line 2 of China snow survey. The SE-XGB algorithm exhibits enhanced accuracy in terms of RMSE and MAE, as well as significantly reduced production time, when compared to the down-scale snow depth inversion algorithm Spatial Dynamic Downscaling (SDD) algorithm employed by similar products. The SE-XGB algorithm employed in this study exhibits remarkable performance in discriminating snow and achieves high precision in retrieving snow depth. This leads to a more detailed spatial distribution of snow information, effectively eliminating the large area block structure, and providing valuable references for the development of global downscaled snow depth datasets.