|Title||Effective Representation of River Geometry in Hydraulic Flood Forecast Models|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||Grimaldi, S, Li, Y, Walker, JP, Pauwels, VRN|
|Keywords||artificial neural networks (ANNs), calibration and evaluation, data allocation, data splitting, Emergency management, floods., hydrological models, model evaluation bias|
Bathymetric data are a critical input to hydraulic models. However, river depth and shape cannot be systematically observed remotely, and field data are both scarce and expensive to collect. In flood modeling, river roughness and geometry compensate for each other, with different parameter sets often being able to map model predictions equally well to the observed data, commonly known as equifinality. This study presents a numerical experiment to investigate an effective yet parsimonious representation of channel geometry that can be used for operational flood forecasting. The LISFLOOD-FP hydraulic model was used to simulate a hypothetical flood event in the Clarence catchment (Australia). A high-resolution model simulation based on accurate bathymetric field data was used to benchmark coarser model simulations based on simplified river geometries. These simplified river geometries were derived from a combination of globally available empirical formulations, remote sensing data, and a limited number of measurements. Model predictive discrepancy between simulations with field data and simplified geometries allowed an assessment of the geometry impact on inundation dynamics. In this study site, the channel geometrical representation for a reliable inundation forecast could be achieved using remote sensing-derived river width values combined with a few measurements of river depth sampled at strategic locations. Furthermore, this study showed that spatially distributed remote sensing-derived inundation levels at the very early stages of a flood event have the potential to support the effective diagnosis of errors in model implementations.