The influence of rating curve uncertainty on flood inundation predictions

The uncertainty of rating curves is well explored and understood in current literature. However, most estimations and methods are usually accompanied by a warning not to extrapolate the rating curve beyond a certain range. This is very often impossible for flooding events. Nevertheless, the uncertainty in using these rating curves for flood inundation models is usually ignored. In this paper we investigate the effect of uncertainty of rating curves on flood inundation predictions. The rating curve has been interpolated with two different equations, which are commonly used. The first method is based on a polynomial representation and the second method interpolates data points with the help of the Manning equation. A set of rating curves which represent the system equally well has been derived via the Generalized Likelihood Uncertainty Estimation (GLUE) and the Multicomponent Mapping (M) methodology. The multiple rating curves have been used as upstream boundary of the one dimensional unsteady flow routing model HEC-RAS. The Manning roughness, as well as the model input, have been considered as uncertain and varied within a Monte Carlo framework. The model has been evaluated on inundation information retrieved from three different remote sensing sources. It has been shown that the boundary condition and thus the rating curve parameters have a significant impact on inundation predictions. The impact of the boundary condition depends on the hydrograph characteristics. Under some circumstances the quantification of the input uncertainty is more important than inundation model parameters.