Assimilation of probabilistic flood maps from SAR data into ahydrologic-hydraulic forecasting model: a proof of concept

Abstract. Coupled hydrologic and hydraulic models represent powerful tools for simulating streamflow and water levels along the riverbed and in the floodplain. However, input data, model parameters, initial conditions and model structure represent sources of uncertainty that affect the reliability and accuracy of flood forecasts. Assimilation of satellite-based Synthetic Aperture Radar observations into a flood forecasting model are generally used to reduce such uncertainties. In this context, we evaluate how sequential assimilation of flood extent derived from synthetic aperture radar data can help in improving flood forecasts. In particular, we carried out twin experiments based on a synthetically generated data-set with controlled uncertainty. To this end, two assimilation methods are explored and compared: the Sequential Importance Sampling (standard method) and its enhanced method where a tempering coefficient is used to inflate the posterior probability (adapted method) and to reduce degeneracy. The experimental results show that the assimilation of SAR probabilistic flood maps significantly improves the predictions of streamflow and water elevation, thereby confirming the effectiveness of the data assimilation framework. In addition, the assimilation method significantly reduces the spatially averaged root mean square error of water levels with respect to the case without assimilation. The critical success index of predicted flood extent maps is significantly increased by the assimilation. While the standard method proves to be more accurate in estimating the water levels and streamflow at the assimilation time step, the adapted method enables a more persistent improvement of the forecasts. However, although the use of a tempering coefficient reduces the degeneracy problem, the accuracy of model simulation is lower at the assimilation time step.