This paper describes a fully automatic processing chain that makes use of SAR images for retrieving water stage
information to be assimilated into a hydraulic forecasting model. This chain is composed of three steps: flood extent
delineation, water stage retrieval and data assimilation of stage information into a hydraulic model.
The flood-mapping step is addressed with a fully automatic algorithm, based on image statistics and applicable to all
existing SAR datasets. Uncertainty on the flood extent map is represented with an ensemble of flood extent maps,
obtained following a bootstrap methodology. Water stage observations are then retrieved by intersecting the flood
shoreline with the floodplain topography. The ensemble of flood extent maps allows extracting multiple water levels at
any river cross section of the hydraulic model, thereby taking into account the uncertainty associated with the floodmapping
step. Finally, data assimilation consists in integrating uncertain observations, i.e. SAR-derived water stages,
with uncertain hydraulic model simulations.
The proposed processing chain was applied to two case studies. For the test case of June 2008 on the Po River (Italy),
only low resolution but freely available satellite data were used. For the January 2011 flood on the Sure River
(Luxembourg), higher resolution data were used and obtained at a cost. The results show that with the assimilation of
SAR-derived water stages significant improvements can be achieved in the forecasting performance of the hydraulic