## 5 Discussion
### Influencing Factors of Runoff Simulation Based on the M/C Signal Method
**Climate types:** Figure 12b shows that among the four climate types: arid, temperate, cold and polar, most stations with good performance exist in the polar climate area with both of the mean and median of \(R\) reached 0.87, followed by the arid climate and temperate climate with the mean \(R\) of 0.81 and 0.78, respectively. Stations with the cold climate tend to have poor performance, as Figure 11d shows. Considering that climate type can influence river discharge and LULC type, further analysis shows that in 77% of these stations located in cold climate, the LULC type is grassland and 85% of them have a mean discharge of lower than 500 m\({}^{3}\)/s. Figure 11: Spatial distribution maps of \(R\) and \(NS\): (**a**) percent tree, (**b**) elevation, (**c**) LULC types and (**d**) climate types. To obtain the factors that have the greatest influence on runoff modelling, the multiple linear regression analysis was conducted. The results indicate that elevation, climate type and mean discharge significantly affected the simulation results (please see Table S1). These three factors are dominant. The remaining factors such as river width, land use/cover, vegetation cover and catchment area have less influence on the performance of the \(M/C\) signal method. The finding is generally consistent with the studies of [PERSON] et al. (2018) and [PERSON] et al. (2019). Among these factors, elevation is the most significant, followed by mean discharge and then climate type. Most of the stations with good performance are located in high altitude areas such as the Qinghai-Tibet Plateau and the Yunnan-Guizhou Plateau. As the source and upstream areas of many rivers in Asia, these areas are less influenced by human activities. Moreover, they usually have smaller river widths and low soil moisture in the floodplain ([PERSON] et al., 2019), which interfere relatively less with the \(T_{b}\) observations, thus creating more favorable conditions for runoff simulation when using the \(M/C\) signal method. In summary, the simulated runoff tends to match with the observed runoff in the plateau area with elevation above 1000 m, and mean discharge of 100-1500 m\({}^{3}\)/s. The \(M/C\) signal method based on passive microwave remote sensing \(T_{b}\) is proven to have broad applicability in China's plateau region. In regions such as the Huai River Plain and the middle and lower reaches of the YZR, the large runoff and wide river channel usually lead to slightly inferior performance of the \(M/C\) signal method. Furthermore, the fitting method, the length of data series, uncertainty of the method (e.g., in the selection of pixel \(C\), in the producing of \(T_{b}\) data), the presence of other water bodies near the stations and the river channel shape can also affect. For example, after precipitation, the water volume of depressions around some stations accumulates, which interferes with the accuracy of acquiring the \(T_{b}\) value of pixel \(C\), subsequently affecting the simulation results ([PERSON] et al., 2019; [PERSON] et al., 2019). Another example is that for rivers with wide and shallow channels or with floodplains on both sides, river width can change significantly, making the \(T_{b}\) values within the pixel \(M\) more different from the pixel \(C\), which is more conducive to simulating runoff based on the \(M/C\) signals ([PERSON] et al., 2019; [PERSON] et al., 2019).