Abstract
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 (SAR) observations into a flood forecasting model is generally used to reduce such uncertainties. In this context, we have evaluated how sequential assimilation of flood extent derived from SAR data can help improve flood forecasts. In particular, we carried out twin experiments based on a synthetically generated dataset with controlled uncertainty. To this end, two assimilation methods are explored and compared: the sequential importance sampling method (standard method) and its enhanced method where a tempering coefficient is used to inflate the posterior probability (adapted method) and 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 than that of the standard method at the assimilation time step.
Highlights
Floods represent one of the major natural disasters with a global annual average loss of USD 104 billion (UNISDR, 2015)
The fact that several space agencies provide free access to high-resolution satellite Earth observation data paves the way for improving Earth Observation-based flood forecasting and reanalyses worldwide
This study represents a follow-up of the previous real-world case study by Hostache et al (2018) with the objective to further proceed in the evaluation of the proposed Data assimilation (DA) framework once the assumptions are effectively satisfied
Summary
Floods represent one of the major natural disasters with a global annual average loss of USD 104 billion (UNISDR, 2015). The extent of flood damages have risen over the last few years due to climate-driven changes and an increase in the asset values of floodplains (Blöschl et al, 2019). This emphasizes the need for reliable and cost-effective flood forecasting models to predict flood inundations in near real time. Hydrologic and hydraulic models represent useful tools for simulating flood extent, discharge, and water levels in the riverbed and on the floodplain. Both the models and the observations used as inputs for running, calibrating, and evaluating the models are affected by uncertainty. It optimally combines observations with the system state derived from a numerical model accounting for both model and observation errors
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