## 3 Results
### Case 2: Results Using Rural SAR Data and Pre-Computed FRP Maps
The results for Blackett Close are shown in Table 4. The flood detection accuracies were similar to those for Cases 1 and 2, though for 13 February 2014, the false positive rate was slightly lower, leading to higher precision. It is also worth assessing how much the SAR data helped to improve the Blackett Close classification accuracies compared to using model data alone. This test was carried out by omitting the SAR data when the waterline height threshold maps were combined, so that the threshold was determined solely by the Flood Foresight model output. The resulting accuracies are shown in brackets in Table 4. Averaging results from both dates, there was a slight fall in urban flood detection rate but also a rise in precision. It seems that, in this case where the model was being driven by sufficient gauge data, the effect of including the rural SAR data was fairly marginal. For Tewkesbury, Figure 12a shows the correspondence between the Flood Foresight maximum flood extent (modified by SAR data in the urban areas) and aerial photo flood extent, in urban and adjacent rural areas of Tewkesbury. The maximum extent represents the total extent of flooding throughout the flooding incident. The waterline height threshold \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image + Model Output** & **Flood Detection Rate (Recall) (\%)** & **Precision (\%)** & **Critical Success Index (CSI) (\%)** \\ \hline Blackett 13 February 2014 & & & \\ image + 13 February 2014 18:00 & 100 (93) & 91 (100) & 91 (93) \\ timestep model extent & & & \\ \hline Blackett 14 February 2014 & & & \\ image + 14 February 14 18:00 & 98 (93) & 100 (100) & 98 (93) \\ timestep model extent & & & \\ \hline Tewkesbury 25 July 2007 image & 74 (38) & 90 (97) & 69 (38) \\ + model maximum extent & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Urban flood detection accuracy using rural SAR WLOs, FRP maps, and dynamic Flood Foresight model output (figures in brackets are for model output only). Figure 11: Flood Foresight monitoring output centred on Blackett Close Staines (**a**) at 12 February 2014 18:00 near flood peak and (**b**) at 21 February 2014 18:00 when waters have receded. The blue shades indicate flooding to various depths, with darker blue being deeper flooding. maps were combined using the same weightings used in the Staines case. Urban flood detection results are shown in Table 4. The flood detection accuracy (74%) was slightly lower than the 88% achieved for Cases 1 and 2. An assessment was again made of how much the SAR data helped to improve the classification accuracy compared to using model data alone, by omitting the SAR data when the waterline height threshold maps were combined. The resulting flood detection accuracy using only model data (shown in brackets in Table 4) was 38%. This means that, in this case, the SAR WLOs provided a significant improvement compared to using model data alone. The underestimation of urban flooding using only model data is likely to be because substantial surface water flooding was present in Tewkesbury during the event. This would have been present in the rural SAR data but not in the model output, as this predicts fluvial flooding only. In this event a good deal of rain fell in the vicinity of Tewkesbury itself, as well in the upper reaches of the Severn and Avon catchments, and there were extremely high flows in small local responsive catchments, such as the Isbourne just 20 km north-east of Tewkesbury [54]. In addition, Flood Foresight is a national-scale model being tested in the localized case of Tewkesbury that experienced a very small area of urban flooding. As a river model, it will mostly show water on the floodplain and is less likely to include urban flooding in its output. That Flood Foresight has modelled the rural flooding well in this case is shown in Figure 12b.