Field, laboratory and numerical approaches to studying flow through mangrove pneumatophores

Friday, 19 December 2014
Vivien P Chua, National University of Singapore, Singapore, Singapore
The circulation of water in riverine mangrove swamps is expected to be influenced by mangrove roots, which in turn affect the nutrients, pollutants and sediments transport in these systems. Field studies were carried out in mangrove areas along the coastline of Singapore where Avicennia marina and Sonneratia alba pneumatophore species are found. Geometrical properties, such as height, diameter and spatial density of the mangrove roots were assessed through the use of photogrammetric methods. Samples of these roots were harvested from mangrove swamps and their material properties, such as bending strength and Young’s modulus were determined in the laboratory. It was found that the pneumatophores under hydrodynamic loadings in a mangrove environment could be regarded as fairly rigid. Artificial root models of pneumatophores were fabricated from downscaling based on field observations of mangroves. Flume experiments were performed and measurements of mean flow velocities, Reynolds stress and turbulent kinetic energy were made. The boundary layer formed over the vegetation patch is fully developed after x = 6 m with a linear mean velocity profile. High shear stresses and turbulent kinetic energy were observed at the interface between the top portion of the roots and the upper flow. The experimental data was employed to calibrate and validate three-dimensional simulations of flow in pneumatophores. The simulations were performed with the Delft3D-FLOW model, where the vegetation effect is introduced by adding a depth-distributed resistance force and modifying the k-ε turbulence model. The model-predicted profiles for mean velocity, turbulent kinetic energy and concentration were compared with experimental data. The model calibration is performed by adjusting the horizontal and vertical eddy viscosities and diffusivities. A skill assessment of the model is performed using statistical measures that include the Pearson correlation coefficient (r), the mean absolute error (MAE), and the root-mean-squared error (RMSE).