Over the past three decades, both the frequency and magnitude of floods have increased globally, with the 2021–2022 eastern Australian floods underscoring the vulnerability of conventional flood management approaches and the need for effective nature-based alternatives. Natural Flood Management (NFM) offers a sustainable framework for mitigating flood risk. The most common way that NFM is delivered on-the-ground is by increasing the vegetative roughness of river channels, riparian zones and catchments. However, the estimation of vegetation-induced hydraulic roughness, commonly represented by the Manning’s roughness coefficient (n), is almost always reliant on visual guide, look-up tables and expert judgement, introducing substantial uncertainty into flood modelling. This study quantifies Manning’s n values across the full spectrum of Specht vegetation classes, ranging from groundcover to rainforest. The Specht classification scheme is widely used in Australia for vegetation classification. Depth-dependent Manning’s n curves are developed to capture how hydraulic roughness evolves with increasing flow depth and velocity across different vegetation classes.
The study advances the application of Terrestrial Laser Scanning (TLS) to estimate Manning’s n through robust, voxel-based quantification of vegetation surface area. The resulting Manning’s n values enhance the representation of riparian vegetation by explicitly accounting for structural attributes including vegetation height, growth form, and canopy cover. These findings offer transferable roughness estimates for riparian environments classified under the Specht system and provide improved parameterisation for a wide range of applications, including NFM modelling and on-ground river rehabilitation riparian vegetation management and replanting programs.