## 3 Results
### Hydrological Evaluation of Discharge Simulation Using Two SPPs  
The HEC-HMS model was used to calibrate and validate the 3 h of rainfall events from (1 September 2014) to (31 August 2017), at the level of the Zat basin, using the rainfall and runoff data from the Taferi gauge station and satellite precipitation products. The four episodes that we chose to present are the most representative and complete of the data series. The hydrological calibration and validation were carried out according to two different scenarios. The obtained calibration and validation results are very satisfactory; the Nash-Sutcliffe coefficients obtained for calibration and validation are on average 88.20% and 57.50%, respectively (Tables 4 and 5). The episode calibrations were performed by manual adjustment of the parameters in a way that does not lead to the deviation of the parameters from their real physical meaning, and which allows for a better understanding of each calibration parameter. This method requires a lot of time and effort to understand the behavior of each parameter and approximate it to the natural condition of the event occurrence. On the other hand, the objective function optimization method is simple and practical for function-based investigations but may ignore the real physical meaning of the parameters. Therefore, this paper combines the two methods to adjust the model parameters. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**TRMM**} & \multicolumn{4}{c}{**GPM**} \\ \hline  & **3 h** & **Daily** & **Monthly** & **Yearly** & **3 h** & **Daily** & **Monthly** & **Yearly** \\ \hline
**POD** & 0.13 & 0.39 & 0.89 & 1 & 0.58 & 0.82 & 1 & 1 \\
**FAR** & 0.79 & 0.66 & 0.08 & 0 & 0.83 & 0.81 & 0.07 & 0 \\
**CSI** & 0.08 & 0.22 & 0.82 & 1 & 0.14 & 0.18 & 0.92 & 1 \\
**FBI** & 0.65 & 1.17 & 0.97 & 1 & 3.63 & 4.37 & 1.07 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Contingency statistical metrics results of 3B42 V7 and IMERG V5 precipitation estimates at multiple time scales from 2014 to 2017. In this paper, eleven parameters were calibrated by maintaining the maximum and minimum intervals of calibration parameters based on the literature. The intervals of the calibrated parameter used are illustrated in Table 4.  
Scenario 1 calibration: simulation and calibration using precipitation from both satellite products with observed fluxes, by adjusting the model parameters values until the model results acceptably match the observed data. Scenario 2 validation: due to the limited sample size, the model was validated using the leave-one-out resampling approach; for the n flood events, each event i is successively removed, in order to find the relationship between the root-soil moisture measured by the time domain reflectometry \"TDR\" tool and the two models' most sensitive calibration parameters (Curve Number \"CN\", and time of concentration \"TC\"). A new CN was then re-estimated (Calculate CN) using the remaining n-1 episode. The CN calibration and the Tc parameters for an event i are set to the median of the calibrated parameters for the n-1 episodes. The calculated CN values obtained by this procedure are then used to model flood event i, and the simulated discharge is compared to the observed discharge. The validation results for the 15 events are presented in Table 6, indicating better model performance when using the SCS-CN model and taking into account soil moisture, with Nash coefficients between 0.51 and 0.82, using the leave-one-out procedure [43].