A comprehensive and reliable assessment of the water resources in China's transboundary river basins is vital for water resources management and peaceful development. In this study, we built machine learning (random forest, gradient boosting, and stacking) and traditional linear models to identify the relation between the runoff coefficient and its influencing factors, including topography, climate, land cover, and soil. The cross‐validation results show that the machine learning models greatly outperform the traditional linear model in predicting runoff coefficient. High‐resolution (0.1°) runoff coefficient and runoff maps for the China's transboundary river basins riparian countries were produced and compared with other estimates at the country level. The best water resources estimates achieved from the machine learning model are consistent with the Food and Agriculture Organization of the United Nations AQUASTAT database (root‐mean‐square error = 76.97 km³/year, normalized root‐mean‐square error = 12%) at the country level. This outperformed two currently available runoff products: the UNH/GRDC Global Composite Runoff Fields and the Global Streamflow Characteristics Dataset. The study also demonstrated that accurate precipitation data can improve runoff and water resources estimation accuracy and that climate and topographic factors have a controlling role in prediction, whereas the influences of land cover and soils are weak. Finally, China's transboundary water resources were calculated and thoroughly assessed at basin and country levels.

The knowledge of water storage variations in ungauged lakes is of fundamental importance to understanding the water balance on the Tibetan Plateau. In this paper, a simple framework was presented to monitor the fluctuation of inland water bodies by the combination of satellite altimetry measurements and optical satellite imagery without any in situ measurements. The fluctuation of water level, surface area, and water storage variations in Lake Qinghai were estimated to demonstrate this framework. Water levels retrieved from ICESat (Ice, Cloud, and and Elevation Satellite) elevation data and lake surface area derived from MODIS (Moderate Resolution Imaging Spectroradiometer) product were fitted by linear regression during the period from 2003 to 2009 when the overpass time for both of them was coincident. Based on this relationship, the time series of water levels from 1999 to 2002 were extended by using the water surface area extracted from Landsat TM/ETM+ images as inputs, and finally the variations of water volume in Lake Qinghai were estimated from 1999 to 2009. The overall errors of water levels retrieved by the simple method in our work were comparable with other globally available test results with r = 0.93, MAE = 0.07 m, and RMSE = 0.09 m. The annual average rate of increase was 0.11 m/yr, which was very close to the results obtained from in situ measurements. High accuracy was obtained in the estimation of surface areas. The MAE and RMSE were only 6 km2, and 8 km2, respectively, which were even lower than the MAE and RMAE of surface area extracted from Landsat TM images. The estimated water volume variations effectively captured the trend of annual variation of Lake Qinghai. Good agreement was achieved between the estimated and measured water volume variations with MAE = 0.4 billion m3, and RMSE = 0.5 billion m3, which only account for 0.7% of the total water volume of Lake Qinghai. This study demonstrates that it is feasible to monitor comprehensively the fluctuation of large water bodies based entirely on remote sensing data.

China's water stress and its pressure on food security is a widely recognized crisis. A framework is proposed and applied at the provincial level to evaluate China's water security for food production. It is based on a water stress index that accounts for the deficit between water supply and aggregate demand of 23 major field crops over the study period (1961–2015). The water demand is estimated based on method recommended by the Food and Agriculture Organization of the United Nations. The water supply is composed of rainfall and irrigation. Consequently, the water security of both rainfed and irrigated agriculture is evaluated in our research, and the respective contribution of rainfall and irrigation is distinguished. Suggestions regarding adjustment in irrigation investment and planting area are proposed for practical application. The framework also holds great potential to investigate the impact of climate change and the virtual water flow of crops.