Patch-scale representation of vegetation within hydraulic models

Marjoribanks, T.I.; Hardy, R.J.; Lane, S.N.; Tancock, M.J.

Abstract

Submerged aquatic vegetation affects flow, sediment and ecological processes within rivers. Quantifying these effects is key to effective river management. Despite a wealth of research into vegetated flows, the detailed flow characteristics around real plants in natural channels are still poorly understood. Here we present a new methodology for representing vegetation patches within computational fluid dynamics (CFD) models of vegetated channels. Vegetation is represented using a Mass Flux Scaling Algorithm (MFSA) and drag term within the Reynolds-Averaged Navier-Stokes Equations, which account for the mass and momentum effects of the vegetation respectively. The model is applied using three different grid resolutions (0.2, 0.1 & 0.05 m) using time-averaged solution methods and compared to field data. The results show that the model reproduces the complex spatial flow heterogeneity within the channel and that increasing the resolution leads to enhanced model accuracy. Future applications of the model to the prediction of channel roughness, sedimentation and key eco-hydraulic variables are presented, likely to be valuable for informing effective river management.

Citation

Marjoribanks, T., Hardy, R., Lane, S., & Tancock, M. (2017). Patch-scale representation of vegetation within hydraulic models. Earth Surface Processes and Landforms, 42(5), 699-710. https://doi.org/10.1002/esp.4015

• Journal Article Type: Article
• Acceptance Date: Jul 28, 2016
• Online Publication Date: Sep 15, 2016
• Publication Date: Apr 1, 2017
• Deposit Date: Dec 3, 2015
• Publicly Available Date: Aug 10, 2016
• Journal: Earth Surface Processes and Landforms
• Print ISSN: 0197-9337
• Electronic ISSN: 1096-9837
• Publisher: British Society for Geomorphology
• Peer Reviewed: Peer Reviewed
• Volume: 42
• Issue: 5
• Pages: 699-710
• DOI: https://doi.org/10.1002/esp.4015
• Public URL: https://durham-repository.worktribe.com/output/1394669