Snow cover over the Qinghai-Tibetan Plateau (QTP) plays an important role in climate, hydrological, and ecological systems. Currently, passive microwave remote sensing is the most efficient way to monitor snow depth on global and regional scales; however, it presents a serious overestimation of snow cover over the QTP and has difficulty describing patchy snow cover over the QTP because of its coarse spatial resolution. In this study, a new spatial dynamic method is developed by introducing ground emissivity and assimilating the snow cover fraction (SCF) and land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive snow depth at an enhanced spatial resolution. In this method, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) brightness temperature and MODIS LST are used to calculate ground emissivity. Additionally, the microwave emission model of layered snowpacks (MEMLS) is applied to simulate brightness temperature with varying ground emissivities to determine the key coefficients in the snow depth retrieval algorithm. The results show that the frozen ground emissivity presents large spatial heterogeneity over the QTP, which leads to the variation of coefficients in the snow depth retrieval algorithm. The overestimation of snow depth is rectified by introducing the ground emissivity factor at 18 and 36 GHz. Compared with in situ observations, the snow cover accuracy of the new method is 93.9%, which is better than the 60.2% accuracy of the existing method (old method) which does not consider ground emissivity. The bias and root-mean-square error (RMSE) of snow depth are 1.03 cm and 7.05 cm, respectively, for the new method; these values are much lower than the values of 6.02 cm and 9.75 cm, respectively, for the old method. However, the snow cover accuracy with depths between 1 and 3 cm is below 60%, and snow depths greater than 25 cm are underestimated in Himalayan mountainous areas. In the future, the snow cover identification algorithm should be improved to identify shallow snow cover over the QTP, and topography should be considered in the snow depth retrieval algorithm to improve snow depth accuracy in mountainous areas.

Climate warming is altering historical patterns of snow accumulation and ablation, hence threatening natural water resources. We evaluated the impact of climate warming on snowmelt rates using the GlobSnow v2.0 and the second Modern‐Era Retrospective analysis for Research and Applications data sets over the Northern Hemisphere (NH) during the past 38 years (1980–2017). Higher ablation rates were found in the locations with deeper snow water equivalent (SWE) because high snow melt rates occurred in late spring and early summer in deep snowpack regions. In addition, due to the reduction of SWE in deep snowpack regions, moderate and high snow ablation rates showed a decreasing trend. Therefore, slower snowmelt rates were found over the entire NH in a warmer climate in general. Based on projections of SWE in Representative Concentration Pathways 2.6, 4.5, and 8.5 climate scenarios, slower snowmelt rates in the NH may continue to happen in the future.

Decline in snow mass threatens the regional economy that critically depends on meltwater. However, the economic scale of snow mass loss is hardly understood, and its role in the vulnerability of future economic development is unclear. We investigate the current reserves of snow cover and the value of its loss. The result showed that the total annual snow mass in western China declines at a rate of 3.3 × 10⁹ Pg per decade (p < 0.05), which accounts for approximately 0.46% of the mean of annual snow mass (7.2 × 10¹¹ Pg). Snow mass loss over the past 40 years in western China turns into an average loss value of CN¥0.1 billion (in the present value) every year ($1 = CN¥7). If the trend continues at the current rate, the accumulated loss value would rise to CN¥63 billion by 2040. Furthermore, subject to the combinations of RCPs and SSPs scenario, the future economic value of snow mass loss in western China appears to accelerate driven by both declining snowmelt resources and socioeconomic development demand. RCP26-SSP1 is the pathway among all to have the least economic cost in replacing the snowmelt loss, and the cost would be quadrupled in RCP80-SSP3 scenario by 2100. At a basin scale, the declining snow mass would turn the regional economy to be more vulnerable except Junggar and Ili endorheic basin. The Ertis river and Qaidam endorheic basins display to be most vulnerable. It highlights that the snow value can be economically important in the regions of west China and should be considered more properly in water resources management.

Historical patterns of snow cover and snowmelt are shifting due to climate warming and perhaps some human activities, threatening natural water resources and the ecological environment. Passive microwave remote sensing provides quantitative data for snow mass evaluation. Here, we evaluated the long-term impact of climate warming on snowmelt rates, using snow water equivalent (SWE) datasets derived from passive microwave remotely sensed data over China’s three main stable snow cover regions during the past 40 years (1981–2020). The results showed that higher ablation rates in spring were found in locations with a deeper SWE because of high snowmelt rates that occurred in late spring and early summer in areas with a deeper snowpack. Annual maximum SWE (snow water equivalent) has declined across two out of the three main mountains of China’s snow cover regions over the past 40 years under climate warming. The maximum and mean snowmelt rate was ca. 30 and 3 mm/day, respectively, over the three regions. Further, due to SWE being reduced in these deep snowpack areas, moderate and high rates of snowmelt showed trends of decline after 2000. Accordingly, an earlier snow onset day (average 0.6~0.7 day/a) and slower snowmelt rates characterized the mountainous areas across the three main snow cover regions. The slower snowmelt rate is also closely related to vegetation improvement over the three main stable snow cover regions. Therefore, not only vegetation in spring but also streamflow and other ecological processes could be affected by the pronounced changes in SWE and snowmelt rates. These findings strengthen our understanding of how to better assess ecological and environmental changes towards the sustainable use of freshwater resources in spring and earlier summer months in snow-rich alpine regions.