  Flood simulation and forecast capability have been greatly improved thanks to
advances in data assimilation. Such an approach combines in-situ gauge
measurements with numerical hydrodynamic models to correct the hydraulic states
and reduce the uncertainties in the model parameters. However, these methods
depend strongly on the availability and quality of observations, thus
necessitating other data sources to improve the flood simulation and forecast
performances. Using Sentinel-1 images, a flood extent mapping method was
carried out by applying a Random Forest algorithm trained on past flood events
using manually delineated flood maps. The study area concerns a 50-km reach of
the Garonne Marmandaise catchment. Two recent flood events are simulated in
analysis and forecast modes, with a +24h lead time. This study demonstrates the
merits of using SAR-derived flood extent maps to validate and improve the
forecast results based on hydrodynamic numerical models with Telemac2D-EnKF.
Quantitative 1D and 2D metrics were computed to assess water level time-series
and flood extents between the simulations and observations. It was shown that
the free run experiment without DA under-estimates flooding. On the other hand,
the validation of DA results with respect to independent SAR-derived flood
extent allows to diagnose a model-observation bias that leads to over-flooding.
Once this bias is taken into account, DA provides a sequential correction of
area-based friction coefficients and inflow discharge, yielding a better flood
extent representation. This study paves the way towards a reliable solution for
flood forecasting over poorly gauged catchments, thanks to available remote
sensing datasets.
