## 4 Results and Discussion
### Hydrological Evaluation of Satellite Precipitation Estimates  
In the previous section, we compared the GPM-era satellite precipitation products against the rain gauge observations; the next step was to evaluate the hydrological utility of these precipitation datasets. In this section, since the streamflow data after 2015 were not available at the Tangnaihai hydrological station, the hydrological evaluation of four satellite precipitation estimates was performed for the whole year of 2015. We also calculated statistical indices of precipitation estimates over the upper Yellow River basin in 2015 (Table 5). By analyzing this indices, we can conclude that the error characteristics of satellite precipitation during 2005 are consistent with prior comparison results. For example, the CC values of daily IMERG-UC, IMERG-C, GSMaP-MVK, and GSMaP-Gauge estimates were 0.57, 0.61, 0.52, and 0.75 over the upper Yellow River basin during 2005, and 0.67, 0.70, 0.52, and 0.77 over the TP during the periods of April 2014 to March 2017, respectively. For the basin-scale evaluation, statistical values with the basin-averaged data were better than those with the grid-scale evaluation. This is expected because random errors would decrease with spatial scale averaging. Thus, both grid-scale and basin-scale analysis confirm that the performance of satellite precipitation in the upper Yellow River basin was similar to that on the TP. Next, the VIC model was calibrated and validated with observed precipitation and streamflow for the periods of 2009-2011 and 2012-2014 over the upper Yellow River basin. Figure 9 shows the CGDPA-simulated and observed streamflow at the daily scale. Comparing the observed and simulated streamflow, the values of NSE and RB were 0.61% and \(-\)2.56% during the calibration period, and NSE increased to 0.73 and RB of 0.96% in the validation period. It can be seen that the simulated streamflow generally agrees with observations very well, although overestimation and underestimation of peak floods existed in some cases. Improved results were obtained at the monthly scale for both the calibration and validation periods (NSE of 0.88 and 0.87, respectively). After the model was benchmarked by the in situ data, the VIC model was then driven by gauge- and satellite-based precipitation datasets for the period from 1 January 2015 to 31 December 2015, without any further adjustment of parameters. The simulated and observed hydrographs are shown in Figure 10, and the statistical comparisons are summarized in Table 6. As shown, the CGDPA had a worse performance in 2015, with NSE of 0.41 and a runoff overestimation of 26.39%. The observed mean daily discharge from 2009 to 2014 was 711.61 m\({}^{3}\)/s, whereas that in 2015 was 480.82 m\({}^{3}\)/s. Differences in hydrological features during the two periods may potentially influence the simulation performance. Using the same parameters enabled us to compare the performance of simulated streamflow from different precipitation inputs. For the GPM-era satellite precipitation products, the GSMaP-Gauge showed the best performance in the streamflow simulation; the IMERG-C took second place; and the two purely satellite-derived estimates demonstrated poor performance due to the large precipitation bias, especially for GSMaP-MVK with 151.97% runoff overestimation at a daily scale. Interestingly, the simulated streamflow with GSMaP-Gauge inputs had lightly better performance than the CGDPA (e.g., 0.53 versus 0.41 for NSE). We considered that gauge corrections involved in the GSMaP-Gauge products remarkably improved the skill of streamflow simulation. For the monthly comparisons,