# Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment
## 5 Conclusions  
Flood inundation models have an important role in flood forecasting and there has been scientific interest in combining direct observation of flooding from remote sources with hydrodynamic models in order to improve predictions since there is continuing decline in the number of operational gauging stations, and also because river basins are inaccessible for ground measurement [25]. This study investigates the integration of SAR data with a 2D hydraulic model for reducing uncertainty in model predictions, as it calibrates model roughness parameters with observed data extracted by Synthetic Aperture Radar (SAR) images and DEM with the aim of improving real-time flood management. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Statistical Performance Measurements of Water-Depths Estimation 2 December 2013**} \\ \hline \hline \(c\) & \(R\)SME (m) & \(N\)SE & \(R^{2}\) & \(R\)SMEu (m) & \(FB\) & \(FA2\) \\ \hline \(c\) & & & & & & \\ _0.04 for channel and floodplains_ & **0.40** & **0.19** & 0.51 & 0.44 & 0.26 & 0.79 \\ _0.08 for channel and floodplains_ & **0.46** & \(\mathbf{-0.16}\) & 0.52 & 0.42 & 0.43 & 0.63 \\ _0.1 for channel and floodplains_ & **0.61** & \(\mathbf{-0.89}\) & 0.58 & 0.68 & \(\mathbf{-0.41}\) & 0.77 \\ _0.033 for channel and 0.06 for floodplains_ & **0.34** & **0.42** & 0.57 & 0.50 & \(\mathbf{-0.05}\) & 0.84 \\ _0.025 for channel and 0.01 for floodplains_ & **0.40** & **0.15** & 0.52 & 0.43 & 0.28 & 0.78 \\ _variable in time and space_ & **0.26** & **0.65** & 0.71 & 0.45 & \(\mathbf{-0.09}\) & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of hydraulic simulation model calibration performance for 2 December 2013 in terms of water depth: the relative value of the Manning coefficients \(c\), Root Mean Square Error \(RMSE\), Nash-Sutcliffe Efficiency Coefficient (\(NSE\)), coefficient of determination \(R^{2}\), Unsystematic \(R\)SME \(R\)SMEu, fraction \(bias\)\(FB\), prediction within a factor of two of observation \(FA2\). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Statistical Performance Measurements of Water Depths Estimation 3 December 2013**} \\ \hline \hline  & \(R\)SME (m) & \(N\)SE & \(R^{2}\) & \(R\)SMEu (m) & \(FB\) & \(FA2\) \\ \hline \(c\) & & & & & & \\ _0.04 for channel and floodplains_ & **0.32** & **0.55** & 0.6 & 0.38 & 0.16 & 0.79 \\ _0.08 for channel and floodplains_ & **0.33** & **0.42** & 0.7 & 0.48 & \(\mathbf{-0.30}\) & 0.74 \\ _0.1 for channel and floodplains_ & **0.43** & \(\mathbf{-0.15}\) & 0.6 & 0.51 & \(\mathbf{-0.42}\) & 0.58 \\ _0.033 for channel and 0.06 for floodplains_ & **0.21** & **0.70** & 0.74 & 0.43 & \(\mathbf{-0.10}\) & 0.81 \\ _0.025 for channel and 0.01 for floodplains_ & **0.33** & **0.51** & 0.51 & 0.36 & 0.18 & 0.77 \\ _variable in time and space_ & **0.22** & **0.72** & 0.8 & 0.41 & 0.04 & 0.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of hydraulic simulation model calibration performance for 3 December 2013: the relative value of the Manning coefficients \(c\), Root Mean Square Error \(RMSE\), Nash- Sutcliffe Efficiency Coefficient (\(NSE\)), coefficient of determination \(R^{2}\), Unsystematic \(R\)SME \(R\)SMEu, fraction \(bias\)\(FB\), prediction within a factor of two of observation \(FA2\). By processing SAR data, it was possible to produce binary maps of flood extent, following the method proposed by [18], which can then be used with a digital elevation model (DEM) to produce flood-water-depth maps in flat areas with complex topography for model calibration and validation. To assess the usefulness of the multitemporal SAR observations and to complement their information content for complete flood damage assessment, six sets of Manning coefficient values were utilized in the FLORA-2D hydrodynamic model for calibration purposes on the 3-4 November 2010 on the Bradano River (Italy).