6. Conclusions

The overall objective of this study was to evaluate the hydrological application potential of TRMM, IMERG, CMADS, and CFSR in the YRSR. The major findings of this study are summarized as follows.
(1) At the basin-scale, the TRMM, IMERG, CMADS, and CFSR have higher detection accuracy in the warm season, and the PBIAS and CC values of each precipitation product are characterized by small warm season and large cold season values. Among the four precipitation products, IMERG had the smallest deviation (average RMSE = 13.71 mm), while CFSR had the best correlation (average CC = 0.73)
(2) At the grid scale, among the four precipitation products, CMADS has the best performance for precipitation observation, with PBIAS of -27.22%2.48 %, CC of 0.430.58, and RMSE of 2.684.96 (mm/d), followed by IMERG, CFSR, and TRMM. CFSR has the best performance for precipitation events, with POD of 0.900.98, FAR of 0.290.51, and CSI of 0.480.69, followed by CMADS, IMERG and TRMM.
(3) Taken together, IMERG has the best performance, followed by CMADS, CFSR, and TRMM. TRMM severely overestimated high rainfall of > 10 mm/day. CFSR obviously overestimated moderate precipitation events of 110 mm/d, while CMADS underestimated the precipitation events of 120 mm/d.
(4) Models using the GO as input resulted in satisfactory performance during 20082013, and precipitation products have poor simulation results. The results of simulation using CMADS significantly underestimated the runoff during the dry season, but the performance in the validation periods (R 2 = 0.78, NSE = 0.53 at TNH; R 2 = 0.64, NSE = 0.53 at JM) was best among those scenarios analyzed. The runoff simulated using TRMM and CFSR is significantly overestimated, especially when using TRMM. Although the model using IMERG as input yielded unsatisfactory performance during 20142016, it did not affect the use of IMERG as a potential data source for YRSR.
(5) After bias correction, the quality of CFSR improves significantly with increases to R 2 and NSE of 0.25 and 0.31 at TNH, respectively. SWAT model driven by the combination of GO and CMADS precipitation was the best across all scenarios. The simulation results at TNH yielded satisfactory performance (R 2 = 0.77, NSE = 0.72). Although the simulation results at JM yielded an unsatisfactory performance, they were close to being deemed satisfactory (R 2 = 0.53, NSE = 0.48).
In summary, although the satellite and reanalysis precipitation products represented by TRMM and CFSR have been widely used in hydrological modeling, the quality of these products could be significantly improved when applied to alpine basins. In contrast, IMERG has a better performance in observing solid precipitation due to the more advanced GPM microwave imager sensor and the dual-frequency precipitation radar mounted on the GPM satellites (Yang et al ., 2020). The findings of this assessment provide valuable reference and feedback for satellite and reanalysis precipitation product development for use in alpine basins. In addition, snowfall is the main form of precipitation in the YRSR from September to May, however, such an assessment was not fulfilled due to the lack of snowfall observation site, a task that warrants investigation and inclusion in future research.