In the context of climate change, precipitation and runoff in the arid inland basins of northwest China have undergone significant changes. The Qaidam Basin (QB) is a typical highland arid inland area. Understanding the spatial and temporal variations in surface water and groundwater chemistry and isotopes, as well as their causes, is crucial for future water resource management and ecological protection. Samples of river, lake, and groundwater, as well as others, were collected and tested in five typical watersheds in the summer and winter. The hydrochemistry and isotopic spatiotemporal differences in various water bodies were studied using the significant difference method, water vapor flux models, hydrochemistry, isotopes, and other methods for cause analyses. The results indicate the following: (1) There are differences in hydrochemistry between the southern and northern basins because the southern basin is more influenced by the dissolution of salt rocks and evaporation, whereas the northern basin is mainly affected by carbonate weathering. (2) The enrichment of δD and δ¹⁸O in the northern basin gradually increases from west to east, while in the southern basin, it is the opposite. This is because the southern basin receives a larger contribution of water vapor from the mid-latitude westerlies, while the northern basin primarily relies on local evaporation as its water vapor source. (3) Significant differences are observed in the total dissolved solids (TDS) and hydrochemical types of river water and groundwater between the summer and winter due to higher rates of rock weathering and evaporation in the summer. (4) The more pronounced seasonal differences in hydrogen and oxygen stable isotopes in the southern basin are due to higher rates of internal water vapor circulation in the summer. (5) The similarity in characteristics between river water and groundwater is the result of strong exchanges between river water and groundwater from piedmonts to terminals. The spatiotemporal heterogeneity of terminal lakes is attributed to the accumulation of salts and groundwater replenishment from other sources.

Dissolved oxygen (DO) concentration is a widely used and effective indicator for assessing water quality and pollution in aquatic environments. Continuous and large-scale inversion of water environments using remote sensing imagery has become a hot topic in water environmental research. Remote sensing technology has been extensively applied in water quality monitoring, but its limited sampling frequency necessitates the development of a high-frequency dynamic water quality monitoring model. In this study, we utilized Lake Chaohu as a case study. Firstly, we constructed a dynamic water quality inversion model for monitoring DO concentrations using machine learning methods, with Himawari-8 (H8) satellite imagery as input data and DO concentrations in Lake Chaohu as output data. Secondly, the developed DO concentration inversion model was employed to estimate the overall grid-based DO concentration in the Lake Chaohu region for the years 2019 to 2021. Lastly, Pearson correlation analysis and significance tests were performed to examine the correlation and significance between the estimated grid-based DO concentration and the ERA5 reanalysis dataset. The results demonstrate that the Random Forest (RF) model performs best in DO concentration inversion, with a high R² score of 0.84, and low RMSE and MAE values of 0.69 and 0.54, respectively. Compared to other models, the RF model improves average performance with a 38% increase in R², 13% decrease in RMSE, and 33% decrease in MAE. The model accurately predicts DO concentrations. Furthermore, the inversion results reveal seasonal differences in DO concentrations in Lake Chaohu from 2019 to 2021, with higher concentrations in spring and winter, and lower concentrations in summer and autumn. The average DO concentrations in the northwest, central-south, and northeast regions of Lake Chaohu are 10.12 mg/L, 9.98 mg/L, and 9.96 mg/L, respectively, with higher concentrations in the northwest region. Pearson correlation analysis indicates a significant correlation (p < 0.01) between DO concentrations and temperature, surface pressure, latent heat flux from the atmosphere to the surface, and latent heat flux from the surface to the atmosphere, with correlation coefficients of −0.615, 0.583, −0.480, and 0.444, respectively. The results verify the feasibility of using synchronous satellites for real-time inversion of DO concentrations, providing a more efficient, economical, and accurate means for real-time monitoring of DO concentrations. This study has practical value in improving the efficiency and accuracy of water environmental monitoring.