## 4 Results
### Hydrological Model Validation with Observed and GPM IMERG-E Precipitation  
The calibrated CN are correlated with the ESA-CCI soil moisture data in order to estimate the initial soil moisture condition of the model. This correlation is good with a coefficient equal to r = 0.96 when the SCS-CN model is calibrated with rain gauge data (Figure 3). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Events**} & \multicolumn{6}{c}{**Calibration**} & \multicolumn{6}{c}{**Validation**} \\ \cline{2-13}  & \multicolumn{2}{c}{**Nash**} & \multicolumn{2}{c}{**BiasQ (\%)**} & \multicolumn{2}{c}{**BiasV (\%)**} & \multicolumn{2}{c}{**Nash**} & \multicolumn{2}{c}{**BiasQ (\%)**} & \multicolumn{2}{c}{**BiasV (\%)**} \\ \cline{2-13}  & **Observed** & **GPM** & **Observed** & **GPM** & **Observed** & **GPM** & **Observed** & **GPM** & **Observed** & **GPM** & **Observed** & **GPM** \\ \hline  
7 & 1.8 & 0.4 & 0.9 & 19.6 & –31.0 & 22.5 & –6.0 \\ \hline Mean & 0.8 & 0.7 & –6.8 & –20.8 & –0.6 & 3.9 & 0.6 & 0.7 & 16.8 & –24.2 & 20.4 & –0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Calibration and validation results. Figure 2: Floods simulated with observed precipitation. For the data based on the IMERG-E satellite precipitation, the correlation between the CN and the ESA-CCI data are also strong and equal to 0.95 (Figure 3). This correlation confirms the good relationship between the CN parameter of the production function and ancillary source of soil moisture conditions, as demonstrated in similar studies [18; 55; 64]. The validation will therefore be based on ESA-CCI data. The equation based on the correlation allowed us to calculate the validation CN of each event in turn by the leave-one-out method. This method involves correlating the calibrated CN values of the sixteen events with ESA-CCI soil moisture to determine the CN of the event that remains. Table 6 presents the results of the efficiency criterions used to evaluate the agreement between observed and simulated hydrographs. For the validated events, the average Nash values is equal to 0.58. The mean relative bias on peak discharge and volume are 16.8% and 20.4%, respectively. Concerning the events modeled with satellite rainfall, the model presents slightly better performances with a NS raised to 0.71. The relative bias on peak discharge and volume are \(-\)24.2% and \(-\)0.2%, respectively. We were able to validate 13 out of 17 events simulated with rain gauge data (Figure 2) and 15 events for satellite precipitation data (Figure 4). It should be noted that this validation experiment combines the uncertainties stemming from the CN/ESA-CCI soil moisture relationship but also the uncertainties on precipitation estimates of the IMERG-E product. For non-validated events from observed data (i.e., with very low negative Nash values reported as N.A. in Table 6), a detailed analysis of the results reveals that for these events, the difference in CN values between calibration and validation is three time larger than the difference in CN values for the other events. Therefore, this indicates that for some events a slight change in the CN values can have a strong negative impact on the results. This finding denotes the importance of the soil moisture data to adequately estimate the antecedent soil moisture conditions, for the validation of flood events in a semi-arid environment [18; 51; 65]. For the floods simulated with satellite precipitation data, the events of the 1 May 2011 and 2 May 2011 present higher values of CNs in calibration compared to other events. For these two events, the cumulative rainfall recorded which is very low (2.36 mm and 4.44 mm, respectively) and the high CN values obtained reflect the overestimation of rainfall by IMERG-E. It would be required then for these two events to increase the value of the CN in order to adequately reproduce the observed hydrograph.