Learning with Spaceborne LiDAR for Enhancement of Bare-Earth Digital Elevation Models from Global Data

River depth is crucial in flood modelling, yet often unavailable. Estimation methods can fill this gap but have errors that can affect flood modelling outputs. Our study quantified flood-prediction uncertainty due to these errors. Between Uniform Flow and Conceptual Multivariate Regression formulas, river depths from the former have higher uncertainty than the latter. Among parameters used in these equations, river width corresponds to the largest uncertainty, followed by the flow and slope.

During flooding, the sources of water that inundate a location can influence impacts such as pollution. However, methods to trace water sources in flood events are currently only available in complex, computationally expensive hydraulic models. We propose a simplified method which can be added to efficient, reduced-complexity model codes, enabling an improved understanding of flood dynamics and its impacts. We demonstrate its application for three sites at a range of spatial and temporal scales.