5. Discussion
Precipitation is the key variable input for hydrological modeling and
the main source of error in simulation results (Duan et al .,
2019a; Ruan et al ., 2017). Currently, many satellite and
reanalysis precipitation products have been widely used in hydrological
simulation in areas lacking observation (Bhatta et al ., 2019;
Bitew and Gebremichael, 2010; Tang et al ., 2019). However, the
quality of precipitation products is obviously different in different
zones, owing to different climatic regions, seasonal cycles, and land
types (Wang et al ., 2020). Before further use of satellite and
reanalysis of precipitation products, it is necessary to evaluate the
quality of these precipitation products. The error in satellite
precipitation products usually stems from the weak relationship between
precipitation rate and remote sensing signals (Bitew and Gebremichael,
2010), satellite revisit time (B et al ., 2013), and retrieval
algorithm (Yan et al ., 2020). For reanalysis of precipitation
products, the uncertainties and errors mainly come from data sources,
interpolation algorithms, and data assimilation systems (Zhu et
al ., 2015).
According to the results, the accuracy of TRMM, IMERG, CMADS, and CFSR
in the warm season is higher than that in the cold season, and IMERG has
the best performance, followed by CMADS, CFSR, and TRMM. This is mainly
because snowfall is the main precipitation in winter in the YRSR (Huet al ., 2011). Although most satellite retrieval algorithms
perform quite well in rainfall estimation, the accuracy of snowfall
estimation is still not high, especially on snow-covered or frozen land
surfaces (Cai et al ., 2015; Noh et al ., 2009; Villariniet al ., 2009). Alijanian et al . (2017) and Condom et
al . (2011) reported that TRMM precipitation products cannot express the
spatio-temporal variability of precipitation over high-altitude, complex
terrain. IMERG, as a newer generation of TRMM precipitation products,
are more sensitive to the capture of solid precipitation events (Yanget al ., 2020), due to the more advanced GPM microwave Imager
sensor and the Dual-frequency Precipitation Radar onboard the GPM
satellites. Most scientific literatures reported that IMERG has a better
performance in alpine basin (Mou and Santo, 2018; Yuan et al .,
2015). In addition, high-latitude regions respond strongly to climate
change, which poses a huge challenge to satellite precipitation
observations (Mark et al ., 2016). Tian and Peters-Lidard (2010)
reported that the satellite precipitation products have large
uncertainty in high latitudes (beyond ± 40°). By contrast, the
reanalysis precipitation products is less affected by high-latitude and
high-altitude (Beck et al ., 2017; Serreze et al ., 2005;
Yong et al ., 2014). At the grid scale, among the four
precipitation products, CMADS has the best performance for precipitation
observation (Fig. 6 ), and CFSR has the best performance for
precipitation events (Fig. 7 ).
Due to the scarcity of in-situ gauged observation stations in the alpine
basin, it is not comprehensive to evaluate the performance of
precipitation products based on statistical methods. Hydrological
simulation verification is a supplementary method for the evaluation of
precipitation products (Deng et al ., 2019; Guoqiang et
al ., 2015). Using GO from even sparse in-situ gauged observation
stations resulted in better performance in runoff simulation than using
all four precipitation products, which is consistent with the previous
research results (Yuan et al ., 2015). Among the four
precipitation products, IMERG has the best performance in runoff
simulation, followed by CMADS, CFSR, and TRMM. TRMM seriously
overestimated runoff simulation with NSE values of -1.86 and -11.93 at
TNH and JM, respectively. This is mainly due to the poor quality of
precipitation products in near real time (Tekeli and Fouli, 2016). In
general, the simulation results of runoff at TNH are better than those
at JM. There are two main reasons: one is the high altitude in the basin
above JM and the large snow-covered, which will increase the microwave
reflectivity on the land surface, thereby mask the drop in microwave
signal due to scattering (Harpold et al ., 2017; Yong et
al ., 2014); Second, in-situ gauged observation stations in the YRSR are
mostly distributed downstream, and there are only two precipitation
stations in the basin above JM (Fig. 1 ). Compared with the
model driven by a single precipitation dataset, the model driven by the
combination of GO and satellite or reanalysis precipitation products has
better performance, especially Scenario S10 performed the best in all
scenarios (R 2 = 0.77, NSE = 0.72 at TNH;R 2 = 0.53, NSE = 0.48 at JM). This is probably
because CMADS precipitation products consider more gauge-based
precipitation than CFSR and IMERG (Meng et al ., 2016). These
results can provide reference data, and research ideas, for improved
hydrological modeling of alpine basins.