## 5 Discussion
These values of calibrated parameters can be considered as the best possible approximation of watershed hydrological features, and they should be adopted for all simulation runs using different satellite precipitation inputs. Nevertheless, this study recalibrated the XAJ model using the satellite products, instead of using the model parameters, based on the gauge-based gridded data. This is because the gauge-based precipitation data were derived from sparsely distributed rain gauge records and are therefore inadequate for characterizing the actual rainfall regimes for model calibration. A few studies [6; 21] found that recalibration can sometimes cause parameter values to exceed their reasonable ranges. To avoid this problem, we defined the searching space of each XAJ model parameter to be strictly within its physical range in the SCE-UA optimization algorithm. Thus, with the recalibrated model parameters, the streamflow simulations fed with the satellite precipitation data are likely to produce more accurate results than the case wherein the \"perfect\" parameter set is used, given that a few model parameters are flexible and sensitive to changes in precipitation inputs. Such characteristics raise the issue of parameter uncertainties, which significantly influence the evaluation of satellite precipitation products in hydrological simulations. This issue will be investigated thoroughly in the future. Precipitation resampling and interpolation were conducted in this study to make the different satellite and gauge-based data sets comparable. Although IMERG products are on a finer resolution (0.1\({}^{\circ}\)), this study found that 3B42V7 is better suited than IMERG. In this research, IMERG products were aggregated from a 0.1\({}^{\circ}\) spatial resolution to 0.25\({}^{\circ}\) for comparison with 3B42V7. This procedure may have produced some errors. Moreover, the gauged-based precipitation data were interpolated to 0.25\({}^{\circ}\) gridded precipitation data set for hydrological simulations without consideration of topological effects. This is likely to cause some errors and further effect streamflow simulations. In future works, the possible errors introduced by these procedures should be evaluated. In addition, more suitable rainfall-runoff models should be applied in this region, and the simulated streamflow from different models should be compared and analyzed to quantify the inherent uncertainties from model structures. Figure 11: Simulated daily hydrographs in May 2014 using the gauge-based precipitation data, the original 3B42V7 product, and the original IMERG precipitation estimates at Hkamti station. P (gauged) represents the gauge-based, basin-averaged daily precipitation; P (original 3B42V7) and P (original IMERG) represent the original basin-averaged 3B42V7 and IMERG daily precipitation. The other terms in the figure are the same as in Figure 8.  
Despite the poor correlation between the ground precipitation observations and the two satellite products (Table 2), the XAJ model driven by the satellite rainfall estimates was able to reproduce streamflow both in the original and in the corrected versions (Table 4). As discussed above, part of this is due to the model recalibration. Another reason could be also the high seasonality of the flood time series, which is quite easy for hydrological models to reproduce. To fairly evaluate the feasibility of satellite precipitation products in streamflow simulation, the seasonal cycle should be removed from the streamflow time series and the simulated streamflow anomalies should be validated against the observations. The work will be employed in the near future.