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Ensemble data assimilation in flood forecasting depends strongly on thedensity, frequency and statistics of errors associated with the observationnetwork. This work focuses on the assimilation of 2D flood extent data,expressed in terms of wet surface ratio, in addition to the in-situ water leveldata. The objective is to improve the representation of the flood plaindynamics with a TELEMAC-2D model and an Ensemble Kalman Filter (EnKF). The EnKFcontrol vector is composed of friction coefficients and corrective parametersto the input forcing. It is augmented with the water level state averaged overselected subdomains of the floodplain. This work focuses on the 2019 floodevent that occurred over the Garonne Marmandaise catchment. The merits ofassimilating SAR-derived flood plain data complementary to in-situ water levelobservations are shown in the control parameter and observation spaces with 1Dand 2D assessment metrics. It was also shown that the assimilation of Wetsurface Ratio in the flood plain complementary to in-situ data in the river bedbrings significative improvement when a corrective term on flood plainhydraulic state is included in the control vector. Yet, it has barely no impactin the river bed that is sufficiently well described by in-situ data. Wehighlighted that the correction of the hydraulic state in the flood plainsignificantly improved the flood dynamics, especially during the recession.This proof-of-concept study paves the way towards near-real-time floodforecast, making the most of remote sensing-derived flood observations.

Enhancing Flood Forecasting with Dual State-Parameter Estimation and Ensemble-based SAR Data Assimilation