As the largest inland saltwater lake in China, Qinghai Lake plays an important role in regional sustainable development and ecological environment protection. In this study, we adopted a spatial downscaling model for mapping lake water at 10 m resolution through integrating Sentinel-2 and Landsat data, which was applied to map the water extent of Qinghai Lake from 1991 to 2020. This was further combined with the Hydroweb water level dataset to establish an area-level relationship to acquire the 30-year water level and water volume. Then, the driving factors of its water dynamics were analyzed based on the grey system theory. It was found that the lake area, water level, and water volume decreased from 1991 to 2004, but then showed an increasing trend afterwards. The lake area ranges from 4199.23 to 4494.99 km². The water level decreased with a speed of ~0.05 m/a before 2004 and then increased with a speed of 0.22 m/a thereafter. Correspondingly, the water volume declined by 5.29 km³ in the first 13 years, and rapidly increased by 15.57 km³ thereafter. The correlation between climatic factors and the water volume of Qinghai Lake is significant. Precipitation has the greatest positive impact on the water volume variation with the relational grade of 0.912, while evaporation has a negative impact.

Monitoring of river discharge is a key process for water resources management, soil and water conservation, climate change, water cycling, flood or drought warning, agriculture and transportation, especially for the sustainable development of rivers and their surrounding ecological environment. Continuous and comprehensive discharge monitoring was usually impossible before, due to sparse gauges and gauge deactivation. Satellite remote sensing provides an advanced approach for estimating and monitoring river discharge at regional or even global scales. River morphology is generally considered to be a direct factor that affects the accuracy of remote sensing estimation, but the specific indicators and the extent to which it affects the estimation accuracy have not yet been explored, especially for medium to small rivers (width < 100 m). In this paper, six sites with hydrological gauges in the upper Heihe River Basin (HRB) of northwestern China and the Murray Darling Basin (MDB) of southeastern Australia were selected as the study cases. River discharge was estimated from Landsat imagery using the C/M method accordingly. River gradient, sinuosity, and width were obtained from Digital Elevation Model data for each site. Global Surface Water Dataset (GSWD) was also employed for indicating the dynamic status of river morphology. A series of methods were applied to analyze the influence of river morphology on estimation accuracy qualitatively and quantitatively, based on which we established inference about the theory of selecting satellite virtual gauges (SVGs). The results confirm the feasibility of the C/M method for discharge estimation, with the accuracy affected by multiple river morphological indicators. Among them, river width was found to be the most significant one. Moreover, water occurrence and water extent extracted from GSWD also have impact on the discharge estimation accuracy. Another independent river section in MDB was set as an example to demonstrate the reasonability of the established theory. It is anticipated that this study would promote the application of remote sensing for discharge estimation by providing practical guidance for establishing appropriate SVGs.