  The challenges in operational flood forecasting lie in producing reliable
forecasts given constrained computational resources and within processing times
that are compatible with near-real-time forecasting. Flood hydrodynamic models
exploit observed data from gauge networks, e.g. water surface elevation (WSE)
and/or discharge that describe the forcing time-series at the upstream and
lateral boundary conditions of the model. A chained hydrologic-hydraulic model
is thus interesting to allow extended lead time forecasts and overcome the
limits of forecast when using only observed gauge measurements. This research
work focuses on comprehensively reducing the uncertainties in the model
parameters, hydraulic state and especially the forcing data in order to improve
the overall flood reanalysis and forecast performance. It aims at assimilating
two main complementary EO data sources, namely in-situ WSE and SAR-derived
flood extent observations.
