Uncertainty in Calibrating Flood Propagation Models with Flood Boundaries Derived from Synthetic Aperture Radar Imagery

An instantaneous synthetic aperture radar (SAR) derived flood extent map helps retrieving the distributed conveyance parameters in one-dimensional flood routing models. These models are generally calibrated based on the sole use of ground data. This research aims to use earth observation (EO) data in order to establish a significant parameter retrieval strategy, providing an alternative model calibration technique. Owing to model structural errors, parameter equifinality and the fuzziness of the available radar and ground data used for calibration, there are some uncertainties with respect to the model predictions. In order to assess these uncertainties in a statistical framework, Monte Carlo simulations of a well-documented flood event in the Alzette river floodplain, Luxembourg are used to explore the parameter space of roughness coefficients. It is shown that many parameter sets perform equally well. The subsequent generalized likelihood uncertainty estimation (GLUE) methodology is used to compare both calibration strategies. Due to the coarse resolution of the available radar scenes and the difficulty in defining an appropriate radar backscattering threshold value during the inundation delineation, the exact flood extent is relatively uncertain. Hence, it is recommended to use a fuzzy-rule based calibration procedure with the available instantaneous flood boundaries derived from ERS and Envisat radar scenes. The uncertainty bounds of the flood extension predictions are assessed for the two types of calibration procedures based on ground survey data and earth observation data respectively. It is shown that both techniques provide similar performances. By combining EO data with ground based data in the calibration procedure, the parameter space will be constrained providing more reliable flood extension predictions i.e. with narrower uncertainty bounds. This study shows that earth observation data are very useful for hydraulic model calibration and that their combined use with ground data provides more accurate inundation simulations. * Corresponding author.