## 5 Discussion  
### Influencing Factors of Runoff Simulation Based on the M/C Signal Method  
Comparing to the performance of \(M/C\) signal method in simulating runoff at global scale, this method shows to be more capable and effective in typical river basins in Mainland China. For example, in the analysis of [PERSON] et al. (2016) for the simulated runoff  
Figure 8: Distribution of drought event frequency for the 79 stations during 1996–2016. Figure 9: Distribution of Dd and Ds for the most severe drought events for each station during 1996–2016. from several rivers across the globe, the percentage of \(R\geq 0.5\) is only 10.6% and the percentage of \(NS<0\) reached 53.6%. Furthermore, the outcomes of [PERSON] et al. (2018) at the global scale show that only 2.5% of stations had \(R\geq 0.8\). In this study, \(R\) score for 92.4% of the stations is greater than 0.5 and \(NS\) of all stations is above 0. Error analysis also indicate that both random error and systematic error are within accepted range (please see Figures S1 and S2). Especially in the Qinghai-Tibetan Plateau area, the performance for stations such as the Gengzhang, Zhimenda, Yangcun is more prominent. To further clarify the factors influencing the \(M/C\) signal method, 7 topographic and hydro-meteorological factors, including elevation, river width, mean discharge, catchment area, vegetation cover, LULC and climate, were selected and discussed based on the \(R\) score of each station during the 1999-2008 (or 1996-2005) time period. **Elevation:** In Figures 10a and 11b, the values of elevation and \(R\) score show a certain positive correlation. The higher the elevation, the better the \(R\) score obtained from the simulation. The performance is clearly better for stations with elevation greater than 1000 m, where \(R\) all exceeded 0.61. These stations are mostly located in the Qinghai-Tibet Plateau and the Yunnan-Guizhou Plateau. **River width:** According to the equation \(Q=v\cdot A\), the variation of runoff \(Q\) depends not only on the runoff velocity \(v\), but also the river cross-sectional area \(A\), which is related to the river width, water depth and cross-sectional geometry. As a result, there can exist a relationship between \(R\) score and river width \(W\)(Kal  
pixel \(M\), larger hydroelectric power plants and dams upstream and downstream may still affect. In addition, during the flood seasons overflowing from pixel \(M\) is very likely to occur, which can also introduce uncertainties to the simulated results. **Vegetation cover:** Vegetation cover indicates the percentage of tree canopy cover for each pixel and can be used to assess how vegetation cover affects the \(T_{b}\) value of the pixel. The VCF product used in this study has three attributes: percent tree, percent non-tree and percent non-vegetated, which represent forest land, non-forest land and bare land, respectively. From Figure 10e, it can be noticed that 57.0% of the stations are dominated by non-tree, while 35.4% by bare land. The spatial distribution of \(R\) score and percent tree is shown in Figure 11a. All the stations dominated by forest land have \(R\) scores greater than 0.5 and \(R\) scores at 93.3% of non-forest land dominated stations all exceeded 0.5. The proportion of bare land dominated stations with \(R\) scores lower than 0.5 is slightly larger than the other two. **LULC types:** As illustrated in Figures 11c and 12a, six LULC types were observed at the 79 stations, including grassland, urban, bare land, forest land, crop and forest + grass. From the box line diagram of the \(R\) score under different LULC types, it can be found that stations with grassland or bare land tend to acquire the best \(R\) scores, both with a mean value above 0.8.