## 4 Results
### Runoff Simulation Results Obtained by the M/C Signal Method  
Figure 4a depicts the \(R\) and \(NS\) statistics of each category for the whole simulation time. The results indicate that among the 79 stations, the percentage of stations with \(R\) and \(NS\) reaching 0.5 is 92.4% and 59.5%. While the percentage of stations with \(R\geq 0.8\) is 46.8%, with 27.8% in the range of 0.8-0.9 and 19.0% reached 0.9. The \(NS\) is slightly worse, all stations are greater than 0, including 15.2% in the range of 0.8-0.9 and 2.5% reached 0.9. Figure 4b demonstrates the statistics for the 9 basins. In terms of individual station analysis, the best simulation results in the YZR were obtained at the Tuotuo River station as \(R\) is 0.93 and \(NS\) is 0.85, followed by the Zhimenda station on the source of the Yangtze River with \(R\) reaching 0.91 and \(NS\) above 0.82. \(R\) scores in the SWR are all greater than 0.61 and the mean value of \(NS\) reached 0.87 with the best simulation results observed at the Gengzhang station on the tributary of the Yarlung Tsangpo River. In the YR, runoff of the Qiaotou station at the source of the Yellow River and the Xiaochuan station in the upper reaches of the Yellow River were best simulated with \(R\) of 0.85 and \(NS\) above 0.72. The mean values of \(R\) and \(NS\) in the SLR reached 0.72 and 0.51, respectively, with the Balinqiao station on the tributary of Liao River showing the best performance. The stations in the IR have the best overall simulated results with all of the \(R\) scores exceeding 0.8 and \(NS\) exceeding 0.78, among which the Xidaqiao station on the Tarim River tributary shows the highest \(R\) and \(NS\) of 0.97 and 0.94, respectively. The simulated results of the PR are also quite good with the best \(R\) and \(NS\) of 0.89 and 0.79 at the Pingle station on the tributary of the Xi River. The performance of the other three basins (the HR, Hai and SER) are slightly less well, as shown in Figure 4b.  
In comparison, the runoff model got the best performance at the IR, followed by the SWR, while the performance of the Hai is worse than others due to low streamflow, a relatively narrow river channel and a lack of discernible \(T_{b}\) difference between pixels \(M\) and \(C\). As shown in Figure 5, \(R\) score of each station during the validation period shows a decreasing trend from the west to east, which is similar with the spatial distribution of \(NS\).  
Figure 6 compares the observed and simulated monthly runoff processes at several representative stations. During the calibration and validation period, the simulated runoff \(Q\_sim\) obtained by the \(M/C\) signal method at the Yambajing, Gengzhang, Xidaqiao, Yangcun, Pingle and Zhimenda station basically matched very well with the observed runoff \(Q\_obs\). Generally, the performance during the calibration period is better than that of the validation period. The simulated results in dry seasons are significantly better at all stations, while the flood peaks were underestimated for some of the stations. This phenomenon was also observed in the research of [PERSON] et al. [51]. For specific reasons, the flooded area of  
some stations will exceed the coverage extent of a single pixel \(M\) (i.e., 3.125 km \(\times\) 3.125 km) when flood approaches. However, the monitored \(T_{b}\) value by the satellite sensors does not decrease accordingly, resulting in an underestimation of simulated runoff during flood seasons.