Progress on the calibration of channel geometry and friction parameters of the LISFLOOD-FP hydraulic model using time series of SAR flood images

Melissa Wood1,2, Jeffrey C Neal3, Renaud Hostache1, Giovanni Corato1, Paul D Bates4, Marco Chini1, Laura Giustarini1, Patrick Matgen1 and Thorsten Wagener5, (1)CRP Gabriel Lippmann, Belvaux, Luxembourg, (2)University Of Bristol, Bristol, United Kingdom, (3)University of Bristol, School of Geographical Sciences, Bristol, BS8, United Kingdom, (4)University of Bristol, School of Geographical Sciences, Bristol, United Kingdom, (5)University of Potsdam, Potsdam, Germany
Abstract:
The objective of this work is to calibrate channel depth and roughness parameters of the LISFLOOD-FP Sub-Grid 2D hydraulic model using SAR image-derived flood extent maps. The aim is to reduce uncertainty in flood model predictions for those rivers where channel geometry is unknown and/or cannot be easily measured. In particular we consider the effectiveness of using real SAR data for calibration and whether the number and timings of SAR acquisitions is of benefit to the final result. Terrain data are processed from 2m LiDAR images and inflows to the model are taken from gauged data.

As a test case we applied the method to the River Severn between Worcester and Tewkesbury. We firstly applied the automatic flood mapping algorithm of Giustarini[1] et al. (2013) to ENVISAT ASAR (wide swath mode) flood images; generating a series of flood maps. We then created an ensemble of flood extent maps with the hydraulic model (each model representing a unique parameter set). Where there is a favourable comparison between the modelled flood map and the SAR obtained flood map we may suggest an optimal parameter set. Applying the method to a sequence of SAR acquisitions provides insight into the advantages, disadvantages and limitations of using series of acquired images. To complete the investigation we simultaneously explore parameter ‘identifiabilty’ within a sequence of available satellite observations by adopting the DYNIA method proposed by Wagener[2] et al. (2003). We show where we might most easily detect the depth and roughness parameters within the SAR acquisition sequence.


[1] 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.

[2] Wagener. 2003. ‘Towards reduced uncertainty in conceptual rainfall-runoff modelling: Dynamic identifiability analysis’. Hydrol. Process. 17, 455–476.

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