Résumé
Synthetic Aperture Radar (SAR) satellites are capable of all-weather day and night observations that can discriminate between land and smooth open water surfaces over large scales. Because of this there has been much interest in the use of SAR satellite data to improve our understanding of water processes, in particular for fluvial flood inundation mechanisms. Past studies prove that integrating SAR derived data with hydraulic models can improve simulations of flooding. However while much of this work focusses on improving model channel roughness values or inflows in ungauged catchments, improvement of model bathymetry is often overlooked. The provision of good bathymetric data is critical to the performance of hydraulic models but there are only a small number of ways to obtain bathymetry information where no direct measurements exist. Spatially distributed river depths are also rarely available. We present a methodology for calibration of model average channel depth and roughness parameters concurrently using SAR images of flood extent and a Sub-Grid model utilising hydraulic geometry concepts. The methodology uses real data from the European Space Agency's archive of ENVISAT Wide Swath Mode images of the River Severn between Worcester and Tewkesbury during flood peaks between 2007 and 2010. Historic ENVISAT WSM images are currently free and easy to access from archive but the methodology can be applied with any available SAR data. The approach makes use of the SAR image processing algorithm of Giustarini et al. (2013) to generate binary flood maps. A unique feature of the calibration methodology is to also use parameter "identifiability" to locate the parameters with higher accuracy from a pre-assigned range (adopting the DYNIA method proposed by Wagener et al., 2003). [1] https://gpod.eo.esa.int/services/ [2] Giustarini. 2013. "A Change Detection Approach to Flood Mapping in Urban Areas Using TerraSAR-X". IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 4. [3] Wagener. 2003. "Towards reduced uncertainty in conceptual rainfall-runoff modelling: Dynamic identifiability analysis". Hydrol. Process. 17, 455-476.