Hydraulic Bottom Friction and Aerodynamic Roughness Coefficients for Mangroves in Southwest Florida, USA

Abstract

Mangroves are a natural feature that enhance the resilience of natural and built coastal environments worldwide. They mitigate the impacts of hurricanes by dissipating energy from storm surges and waves, as well as reducing wind speeds. To incorporate mangroves into storm surge simulations, surface roughness parameters that accurately capture mangrove effects are required. These effects are typically parameterized using Manning’s n bottom friction coefficient for overland flow and aerodynamic roughness length (z0) for wind speed reduction. This paper presents the suggested values for these surface roughness parameters based on field observation and a novel voxel-based processing method for laser scanning point clouds. The recommended Manning’s n and z0 values for mangroves in southwest Florida are 0.138 and 2.34 m, respectively. The data were also used to retrain a previously developed random forest model to predict these surface roughness parameters based on point cloud statistics. The addition of the mangrove sites to the training data produced mixed results, improving the predictions of z0 while weakening the predictions of Manning’s n. The paper concludes that machine learning models developed to predict environmental attributes using small datasets with predictor features containing subjective estimates are sensitive to the uncertainty in the field observations.