Global warming affects the hydrological characteristics of the cryosphere. In arid and semi-arid regions where precipitation is scarce, glaciers and snowmelt water assume important recharge sources for downstream rivers. Therefore, the simulation of snowmelt water runoff in mountainous areas is of great significance in hydrological research. In this paper, taking the Hutubi River Basin in the Tianshan Mountains as the study area, we used the “MODIS Daily Cloud-free Snow Cover 500 m Dataset over China” (MODIS_CGF_SCE) to carry out the Snowmelt Runoff Model (SRM) simulation and evaluated the simulation accuracy. The results showed that: (1) The SRM preferably simulated the characteristics of the average daily flow variation of the Hutubi River from May to October, from 2003–2009. The monthly total runoff was maximum in July and minimum in October. Extreme precipitation events influenced the formation of flood peaks, and the interannual variation trend of total runoff from May to October was increased. (2) The mean value of the volume difference (${\mathrm{D}}_{\mathrm{V}}$) during the model validation period was 8.85%, and the coefficient of determination (${\mathrm{R}}^2$) was 0.73. In general, the SRM underestimates the runoff of the Hutubi River, and the simulation accuracy is more accurate in the normal water period than in the high-water period. (3) By analyzing MODIS_CGF_SCE from 2003 to 2009, areas above 3200 m elevation in the Hutubi River Basin were classified as permanent snow areas, and areas below 3200 m were classified as seasonal snow areas. In October, the snow area in the Hutubi River Basin gradually increased, and the increase in snow cover in the permanent snow area was greater than that in the seasonal snow area. The snowmelt period was from March to May in the seasonal snow area and from May to early July in the permanent snow area, and the minimum snow cover was 0.7%.

The expansion of urban areas and unsustainable land use associated with human activities have brought about a decline in habitat quality (HQ), especially in arid regions with fragile ecosystems. A precise prediction of land use and habitat quality changes across different scenarios is crucial for the sustainable maintenance of ecological diversity. In this article, the InVEST model was employed to assess both the quality and degradation levels of habitats in the Turpan–Hami Basin (THB) spanning 1990~2020. Additionally, the InVEST-PLUS coupling model was employed to forecast habitat conditions under three different scenarios in 2050. Specifically, it involved the comparison of land use changes and spatial distribution of HQ across natural development (ND) scenarios, town development (UD) scenarios, and ecological protection (EP) scenarios, along with the analysis of hot spots of HQ spanning 1990~2050. The outcomes revealed the following: (1) The primary land use in the THB was categorized as unused land, alongside notable expansions in cultivated land, grassland, and built-up land. Conversely, there was a considerable decline observed in forests, water bodies, and unused land spanning 1990~2020. (2) The HQ within the THB exhibited evident spatial clustering characteristics. Between 1990 and 2020, areas with low HQ accounted for over 85%, areas with unchanged HQ constituted 88.19%, areas experiencing deteriorated HQ comprised approximately 5.02%, and areas displaying improved HQ encompassed around 6.79%. (3) Through the comparison of HQ for the ND, UD, and EP scenarios in 2050, it was observed that the average HQ under the EP scenario ranked highest, exhibiting the lowest degree of degradation on average. This indicates that the EP scenario is most advantageous for preserving HQ. Conclusively, this research provides valuable viewpoints for making decisions aimed at enhancing HQ in ecologically fragile arid regions.

Accurate information on snow cover extent plays a crucial role in understanding regional and global climate change, as well as the water cycle, and supports the sustainable development of socioeconomic systems. Remote sensing technology is a vital tool for monitoring snow cover’ extent, but accurate identification of shallow snow cover on the Tibetan Plateau has remained challenging. Focusing on the Three-Rivers Headwater Region (THR), this study addressed this issue by developing a snow cover discrimination model (SCDM) using a random forests (RF) algorithm. Using daily observed snow depth (SD) data from 15 stations in the THR during the period 2001–2013, a comprehensive analysis was conducted, considering various factors influencing regional snow cover distribution, such as land surface reflectance, land surface temperature (LST), Normalized Difference Snow Index (NDSI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Forest Snow Index (NDFSI). The key results were as follows: (1) Optimal model performance was achieved with the parameters Ntree, Mtry, and ratio set to 1000, 2, and 19, respectively. The SCDM outperformed other snow cover products in both pixel-scale and local spatial-scale discrimination. (2) Spectral information of snow cover proved to be the most influential auxiliary variable in discrimination, and the combined inclusion of NDVI and LST improved model performance. (3) The SCDM achieved accuracy of 99.04% for thick snow cover (SD > 4 cm) and 98.54% for shallow snow cover (SD ≤ 4 cm), significantly (p < 0.01) surpassing the traditional dynamic threshold method. This study can offer valuable reference for monitoring snow cover dynamics in regions with limited data availability.

The Tarim River Basin, a core region for economic development and ecological security in China’s inland arid areas, faces the pressing challenge of synergistically improving land use efficiency to resolve human-land conflicts under water resource constraints and achieve sustainable development. Based on the “economic-social-ecological” benefit coordination theory, this study constructs a land use efficiency evaluation system with 16 indicators and integrates the coupling coordination degree model and gravity model to quantitatively analyze the spatiotemporal differentiation patterns and coupling mechanisms of land use efficiency in the basin from 1990 to 2020. Results show that economic and social benefits of land use increased during this period, exhibiting a “high-north, low-south” spatial pattern, while ecological benefits remained relatively high but declined gradually. The coupling coordination degree of subsystem benefits displayed significant spatial heterogeneity, with an overall upward trend, where composite factors emerged as the primary constraint. Spatially, land use efficiency coupling coordination evolved from “core polarization” to “axial expansion” and finally “networked synergy,” with stronger linkages concentrated in oasis irrigation districts. These findings provide theoretical support for ecological conservation, water management, and policy-making in southern Xinjiang, offering pathways to synergize the “economic-social-ecological” system and promote sustainable development in arid regions.

The local or overall mass balance of a glacier is significantly influenced by the spatial heterogeneity of its overlying debris thickness. Accurately estimating the debris thickness of glaciers is essential for understanding their hydrological processes and the impact of climate change. This study focuses on the Koxkar Glacier in the Tian Shan Mountains, using debris thickness data to compare the accuracy of three commonly used approaches for estimating the spatial distribution of debris thickness. The three measurement approaches include two empirical relationships between the land surface temperature (LST) and debris thickness approaches, empirical relationship approach 1 and empirical relationship approach 2, and the energy balance of debris approach. The analysis also explores the potential influence of topographic factors on the debris distribution. By incorporating temperature data from the debris profiles, this study examines the applicability of each approach and identifies areas for possible improvement. The results indicate that (1) all three debris thickness estimation approaches effectively capture the distribution characteristics of glacial debris, although empirical relationship approach 2 outperforms the others in describing the spatial patterns; (2) the accuracy of each approach varies depending on the debris thickness, with the energy balance of debris approach being most accurate for debris less than 50 cm thick, while empirical relationship approach 1 performs better for debris thicker than 50 cm and empirical relationship approach 2 demonstrates the highest overall accuracy; and (3) topographic factors, particularly the elevation, significantly influence the accuracy of debris thickness estimates. Furthermore, the empirical relationships between the LST and debris thickness require field data and focus solely on the surface temperature, neglecting other influencing factors. The energy balance of debris approach is constrained by its linear assumption of the temperature profile, which is only valid within a specific range of debris thickness; beyond this range, it significantly underestimates the values. These findings provide evidence-based support for improving remote-sensing methods for debris thickness estimation.