# Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment
## 4 Results and Discussion
### Validation Performance Measures  
Model validation is an extension of model calibration and is necessary for evaluating a model's robustness under different operating conditions [60]. In this light, the selected sets of Manning coefficient values were used in the hydraulic model to formulate predictions for the 2-3 December 2013 Bradano River flood event. We could have excluded simulation runs showing poor performance during calibration, e.g., Manning coefficient value equal to 0.1, but we chose to show all of the results to demonstrate the robustness of the proposed approach. Hence, in Tables 6 and 7, model performance in terms of flood extent detection are shown respectively for 2 December and 3 December 2013 timeframes, as we have done previously during calibration process. In general, Tables 6 and 7 show an increasing variation and a decrease in performance in comparison with calibration performance results (see Tables 2 and 3). As described in Section 2, the December 2013 event was of a great magnitude, in terms of precipitation intensity, and produced more damage with respect its predecessor in November 2010. Therefore, the uncertainty and sensitivity of the model increased with the increased magnitude of the event, as shown in Figures 6 and 7. Moreover, in this case, the first SAR images (2 December) were acquired shortly after the peak flow and the second was in the advanced recession stage (3 December). During the initial recession, the routing of water was supported mainly by both spatial distribution of floodplain Manning coefficients and by downstream conditions and was less affected by the roughness of the river channel, generally less variable in time and space. Therefore, the preferred pre-peak observations are less sensitive to spatial roughness variation so the model performance is more uniform, as seen in Tables 2 and 3. Figure 4: Comparison between the 6 hydrodynamic simulations characterized by a different set of Manning coefficient values and the SAR- and DEM-derived water-depth map for 3 November 2010. Figure 5: Comparison between the 6 hydrodynamic simulations characterized by a different set of Manning coefficient values and the SAR- and DEM-derived water depth map for 4 November 2010. Figure 6: Comparison between the 6 hydrodynamic simulations characterized by a different set of Manning coefficient values and the SAR- and DEM-derived water depth map for 2 December 2013. Figure 7: Comparison between the 6 hydrodynamics simulations characterized by a different set of Manning coefficient values and the SAR- and DEM-derived water-depth map for 3 December 2013. It highlights that a tight sequence of images could improve model predictions. Combining observations in this way appears to focus the location of the roughness parameter more clearly than is possible using single images. Indeed, as listed in Tables 8 and 9, values greater than 0.65 of the estimated \(NSE\) efficiency coefficient and the low value of the \(RMSE\) (varying between 0.04 and 0.07) for simulation with variable spatio-temporal Manning coefficients shows, in all of the considered time steps, that this model-predicted scenario was in substantial agreement with the SAR observations performed on subsequent days in the validation period. In both timeframes (i.e., 2 and 3 December) most of the simulation model errors with variable roughness were unsystematic, as shown by the comparison of \(RSME\) and \(RSMEu\). The tendency to overestimate was confirmed by a positive \(FB\) value, although the response was spatially variable and areas of under-prediction also occurred. Over-prediction areas tend to affect related floodplain areas that drain rapidly along particular drainage stretches or where backward effects cannot be derived by combining SAR and DEM in flat areas with complex topography.