  Owing to advances in data assimilation, notably Ensemble Kalman Filter
(EnKF), flood simulation and forecast capabilities have greatly improved in
recent years. The motivation of the research work is to reduce comprehensively
the uncertainties in the model parameters, forcing and hydraulic state, and
consequently improve the overall flood reanalysis and forecast capability,
especially in the floodplain. It aims at assimilating SAR-derived (typically
from Sentinel-1 mission) flood extent observations, expressed in terms of wet
surface ratio. The non-Gaussianity of the observation errors associated with
the SAR flood observations violates a major hypothesis regarding the EnKF and
jeopardizes the optimality of the filter analysis. Therefore, a special
treatment of such non-Gaussianity with a Gaussian anamorphosis process is thus
proposed. This strategy was validated and applied over the Garonne Marmandaise
catchment (Southwest of France) represented with the TELEMAC-2D hydrodynamic
model, focusing on a major flood event that occurred in December 2019. The
assimilation of the SAR-derived wet surface ratio observations, in complement
to the in-situ water surface elevations, is illustrated to consequentially
improve the flood representation.
