## 5 Discussion
### Drought Assessment Based on Remote Sensing  
Most previous drought studies based on meteorological data concluded that the southwestern region of China is drought-prone and most severely affected, while droughs in northeastern and southeastern China are usually characterized by short duration and low severity ([PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al., 2019). For example, the spatial and temporal variability of drought in China conducted by [PERSON] et al. (2019); [PERSON] et al. (2019) showed that the drought occurring in 2010-2011 was one of the most severe drought events in the past half century, sweeping through more than half of the non-arid regions in China. Large-scale droughs are usually concentrated  
Figure 12: Box plots of \(R\) for the influencing factors: (a) land use/cover (LULC) types and (b) climate types for the 79 stations.  
in the areas from the North China Plain to the lower reaches of the Yangtze River, as well as in the Southwest China, while the western part of the North China Plain, the Loess Plateau, the Sichuan Basin and the Yunnan-Guizhou Plateau show a significant drying trend. Based on meteorological data, [PERSON] et al. [69] investigated the spatial distribution of the Palmer drought severity index (PDSI) and SPI in 10 major basins of China. The results also show a visible increasing trend of SPI in the middle and upper reaches of the Yangtze River in the Yunnan-Sichuan region. These findings present considerable agreement with the analysis in Section 4.2, proving that drought assessment based on simulated runoff data from passive microwave remote sensing is feasible. In addition, we also compared the results of the drought based on observed and simulated runoff during the period of 1996-2008 (Figure S3). The results show that 60% of the stations are consistent in the trend test of SRI-3 with \(R\) reaching 0.6 or higher scores. Comparisons of Dd and Ds generated by observed and simulated runoff, respectively, also indicate that drought assessment based on simulated runoff is reliable.  
As mentioned in Section 5.1, passive microwave remote sensing is capable of runoff modelling, especially in areas with small river width and low discharge in the river channel, where are usually drought-prone regions. In fact, besides the runoff data used for calculating SRI in this study, meteorological data, soil moisture and vegetation data derived from multi-source satellites can also be used to calculate various drought indices as well as to provide data support and high-quality applications for precise drought research. At present, in meteorological and agricultural drought studies, remote sensing data such as multi-source satellite precipitation, soil moisture derived from records of passive or active satellite sensors are more widely used [65; 66; 67; 68]. With the development of satellite technology, multi-source remote sensing data are becoming more and more abundant. Previous studies show that relationship generated by the rating curve between \(M/C\) signals and \(Q\) at a downstream station can be applied to upstream areas [26], which means the relation curve can be transported into the data-gap regions in the upstream of a certain basin if well modified. It will bring more potential for drought investigation by generating long-term runoff time series at large scales or in data-scared areas. One of the focuses of future studies can be integrating the multi-source remote sensing data to explore the evolution of drought.