Articles | Volume 30, issue 1
https://doi.org/10.5194/hess-30-183-2026
https://doi.org/10.5194/hess-30-183-2026
Research article
 | 
14 Jan 2026
Research article |  | 14 Jan 2026

Quantifying uncertainty in flood predictions due to river bathymetry estimation

Martin Nguyen, Matthew D. Wilson, Emily M. Lane, James Brasington, and Rose A. Pearson

Data sets

Data for "Quantifying uncertainty in flood predictions due to river bathymetry estimation'' Martin Nguyen et al. https://doi.org/10.26021/canterburynz.27644997.v1

Download
Short summary
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.
Share