## 5 Discussion
### Evaluation of Hydrological Performance of the GPM-Era SPPs
This study is associated with large uncertainties from precipitation input, parameter calibration, and hydrological model structure. This work found that the gauge-based gridded precipitation data set might not capture the spatial pattern of precipitation, which mainly results from the limited number of rain gauges (seven stations) in such a large watershed (110,350 km\({}^{2}\)). Despite of the unrepresentativeness of the gauge-based gridded precipitation data set, the rain-gauge-based GXAJ model run provides satisfactory streamflow simulations at the five streamflow stations in the watershed. This good hydrological performance mainly results from the compensation effect of model calibration. Hydrological models are, to some degree, tolerant of the biased forcing data via model calibration, and hence the models might reproduce reasonable streamflow processes at the hydrological stations where local streamflow data are used for calibration. However, the calibrated model parameter set likely fails to represent real basin features and further lowers the model's predictive capability in internal basins or sub-basins [19]. Future work will entail investigating the impact of the spatially unrepresentative precipitation input on hydrological modeling performance, by calibrating the model only at the outlet station and analyzing the simulated streamflow at the stations within the watershed. Meanwhile, it is necessary to construct an enhanced precipitation data set for model calibration by means of bias-correction, such as using ground precipitation data for magnitude correction and using SPPs for spatial pattern correction. The unrepresentativeness of the gauge-based precipitation data might also be caused by the simple interpolation method (inverse-distance weighting). Using complicated interpolation methods, such as the revised Kriging method that accounts for elevation, is desirable. However, this method requires sufficient sample size of rain gauges to establish the reliable relationship between precipitation and topographic features. Therefore, it emphasizes that local authorities must establish a denser rainfall observation network. This measure will not only facilitate the validation of satellite-derived precipitation but also help improve the accuracy of streamflow simulations/forecasting for effective flood warning and disaster mitigation. Moreover, this study employed the SCE-UA algorithm to optimize the hydrological model and selected the parameter set with the highest NSE for flood simulations. This procedure ignores the uncertainty from parameter estimation, in particular the effect of parameter equifinality. Thus, the generalized likelihood uncertainty estimation (GLUE) method should be applied to quantify parameter uncertainty in future research. Furthermore, parameter calibration is also influenced by the length of observed discharge data. In this study, the discharge data in a three-year length (from 1 January 2014 to 31 December 2016) were obtained from DMH, and IMERG and GSMaP provided precipitation estimates since mid-March 2014. For this reason, the streamflow data in a 21-month length (from 1 April to 31 December 2015) were used for model calibration, and one-year discharge data (from 1 January 2016 to 31 December 2016) for model validation. The model parameters calibrated using discharge data in such a short length might not accurately capture the hydrological features of the basin. Consequently, the calibrated model is likely to generate considerable biases in streamflow simulations. Since the GPM technical community continuously releases the up-to-date precipitation data, we will actively contact DMH and try to extend the length of streamflow and weather data, which will facilitate model calibration and evaluation of GPM-era SPPs for a longer time period. In addition, this study only used one hydrological model to assess the hydrological feasibility of SPPs and did not consider the uncertainty from model structure.