## 5 Conclusions  
A number of conclusions can be drawn from the above results.  
1. Simply by using the rural SAR WLOs alone as in Case 1, a high urban flood detection accuracy (94%) and low false positive rate (9%) were achieved. However, this simple method cannot prevent urban areas that are low but defended from flooding from being detected as flooded.  
2. The Case 1 method using SAR backscatter only in rural areas was compared to the method of [42], which used SAR returns in both the rural and urban areas to detect urban flooding. It was found that the present method using SAR solely in rural areas was more accurate than that of [[42]].
3. The Case 2 method using rural SAR data and precomputed FRP maps should in theory have an advantage over the simple Case 1, as the flood return-period maps may contain information on defended regions in the urban area. There were no examples of this in the test areas, and consequently, the results for Case 1 and Case 2 were very similar. However, the potential advantage of using FRP maps was illustrated by simulating a defended region. In addition, the high accuracy obtained using the Case 2 method confirmed the findings of [PERSON] et al. (2017) [43], who merged FRP maps with flood inundation maps derived from RADARSAT-2.
4. Where the dynamic Flood Foresight model flood extents were combined with the rural SAR and FRP data (Case 3), then, for the Tewkesbury example, the rural SAR WLOs were able to provide a significant improvement compared to using model data alone, because there was significant surface-water flooding that was not reflected in the fluvially modelled flood extents. For the Blackett Close example, the classification improvement achieved by combining the rural SAR WLOs with the Flood Foresight model output was fairly marginal. However, it is interesting that, for these two examples, the results were almost no worse (indeed, for Tewkesbury, rather better), than if no dynamic model flood extents were used and the urban flood extent was predicted simply using rural SAR data and precomputed FRP maps (Case 2).  
In summary, given the availability of VHR SAR and DSM data, the urban flood detection method merging model-derived FRP maps of the urban area with SAR returns in adjacent rural areas gave a high detection accuracy and was more accurate than that using SAR returns in both rural and urban areas. It would allow urban flood extents to be obtained in near real-time, and these could be used for emergency flood incident management and as observations for assimilation into flood forecasting models. The method could probably be extended to work with high resolution Sentinel-1 data, though further work is needed to confirm this. For the dynamical Flood Foresight model outputs, it is likely to be advantageous to use assimilation to combine the SAR and model WLO maps.  
Conceptualization, D.C.M. and J.B.; Methodology, D.C.M. and J.B.; Software, D.C.M., B.R.-R. and R.S.; Validation, D.C.M., B.R.-R. and R.S.; Formal Analysis, D.C.M. and J.B.; Investigation, D.C.M., B.R.-R. and R.S.; Writing--Original Draft Preparation, D.C.M. and J.B.; Writing--Review and Editing, D.C.M., J.B., S.L.D., S.V.-C. and H.L.C.; Supervision, S.L.D.; Project Administration, S.L.D.; Funding Acquisition, S.L.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded under the UK EPSRC grant EP/P002331/1 \"Data Assimilation for the Resilient City (DARE)\".  
The authors are grateful to the UK Satellite Applications Catapult Centre for providing the CSK images under the CORSAIR Project. They are also grateful to the EA for provision of the LiDAR data.  
The authors declare no conflict of interest.