Effective Representation of River Geometry in Hydraulic Flood Forecast Models

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. Plain Language Summary Floods are among the most frequent and destructive natural disasters worldwide. An accurate and reliable flood forecast can provide vital information for land management and emergency response. Flood forecasts are achieved using numerical models that are able to predict the depth, velocity, and arrival time of the flood wave at each point of the valley. The accuracy of these predictions is strongly related to the quality of the three-dimensional representation of the valley. In particular, information on river geometry (that is cross-section shape, depth, and width) is critical to the application of these numerical models. However, it is impossible to measure river geometry along the entire river length, especially in large basins. This study developed a method to represent river geometry using a limited amount of time and money. Specifically, this objective can be achieved using available satellite imagery complemented with a few measurements. Moreover, this study showed that flood forecast skill can be improved by combining information from satellite and numerical models. Albeit simple, the river geometry representation proposed in this study can support the accurate prediction of floodplain inundation. This method was described and tested using, as example, a hypothetical flood event in an Australian catchment.