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Characterising the structure and fluvial drag of emergent vegetation

Characterising the structure and fluvial drag of emergent vegetation
Characterising the structure and fluvial drag of emergent vegetation
Plants function as large-scale, flexible obstacles that exert additional drag on surface water flows, affecting local scale turbulence and the structure of the boundary layer. Hence, vegetation plays a significant role in controlling flood water and modulating geomorphic change. This makes it an important, but often under-considered, component when undertaking flood or erosion control actions or designing river restorations strategies. Vegetative drag varies depending on flow conditions and the associated vegetation structure and temporary reconfiguration of the plant. Whilst several approaches have been developed to describe this relationship, they have been limited due to the difficulty of accurately and precisely characterising the vegetation itself, especially during flow. In practice, vegetative drag is commonly expressed through bulk parameters that are typically derived from lookup tables. Terrestrial Laser Scanning (TLS) has the ability to capture the surface of in situ objects as 3D point clouds, at high resolution (mm), precision and accuracy, even when submerged in water. In this study, the potential for characterising the 3D structure of vegetation from high resolution TLS data is explored. Novel methods capable of converting unstructured TLS 3D point clouds to structured 2D and 3D grid arrays are developed enabling the accurate representation of plant structure. These methods are adapted and combined with physical modelling experiments to investigate a series of structurally variable plants at a range of flow scenarios, including the collection of precise hydraulic measurements and capturing of plant deformation during flow. Models capable of predicting vegetation’s fluvial drag from the combination of bulk porous plant structure and extent are developed. Small scale flow characteristics in the vicinity of plant elements, including adjustments of the velocity profile, the extent and intensity of the wake region, the development of secondary flow and adjustments of the turbulent kinetic energy, are quantified and associated with the explicitly characterised plant structure. The results show a promising potential for transferring the methods to field studies. They highlight the potential of employing vegetation in natural flood management applications and can help to inform decisions regarding the choice of plant types to be used for the reforestation of floodplains as well as the optimal plant spacing for a satisfactory control of flood water during floods.
University of Southampton
Vasilopoulos, Grigorios
300d6991-00cb-409a-9412-021eeb96fa49
Vasilopoulos, Grigorios
300d6991-00cb-409a-9412-021eeb96fa49
Leyland, Julian
6b1bb9b9-f3d5-4f40-8dd3-232139510e15
Nield, Joanna
173be2c5-b953-481a-abc4-c095e5e4b790

Vasilopoulos, Grigorios (2017) Characterising the structure and fluvial drag of emergent vegetation. University of Southampton, Doctoral Thesis, 268pp.

Record type: Thesis (Doctoral)

Abstract

Plants function as large-scale, flexible obstacles that exert additional drag on surface water flows, affecting local scale turbulence and the structure of the boundary layer. Hence, vegetation plays a significant role in controlling flood water and modulating geomorphic change. This makes it an important, but often under-considered, component when undertaking flood or erosion control actions or designing river restorations strategies. Vegetative drag varies depending on flow conditions and the associated vegetation structure and temporary reconfiguration of the plant. Whilst several approaches have been developed to describe this relationship, they have been limited due to the difficulty of accurately and precisely characterising the vegetation itself, especially during flow. In practice, vegetative drag is commonly expressed through bulk parameters that are typically derived from lookup tables. Terrestrial Laser Scanning (TLS) has the ability to capture the surface of in situ objects as 3D point clouds, at high resolution (mm), precision and accuracy, even when submerged in water. In this study, the potential for characterising the 3D structure of vegetation from high resolution TLS data is explored. Novel methods capable of converting unstructured TLS 3D point clouds to structured 2D and 3D grid arrays are developed enabling the accurate representation of plant structure. These methods are adapted and combined with physical modelling experiments to investigate a series of structurally variable plants at a range of flow scenarios, including the collection of precise hydraulic measurements and capturing of plant deformation during flow. Models capable of predicting vegetation’s fluvial drag from the combination of bulk porous plant structure and extent are developed. Small scale flow characteristics in the vicinity of plant elements, including adjustments of the velocity profile, the extent and intensity of the wake region, the development of secondary flow and adjustments of the turbulent kinetic energy, are quantified and associated with the explicitly characterised plant structure. The results show a promising potential for transferring the methods to field studies. They highlight the potential of employing vegetation in natural flood management applications and can help to inform decisions regarding the choice of plant types to be used for the reforestation of floodplains as well as the optimal plant spacing for a satisfactory control of flood water during floods.

Text
Characterising the Structure and Fluvial Drag or Emergent Vegetation - Accepted Manuscript
Available under License University of Southampton Thesis Licence.
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Published date: October 2017

Identifiers

Local EPrints ID: 415780
URI: http://eprints.soton.ac.uk/id/eprint/415780
PURE UUID: c5bb3b5d-c121-4e35-81fa-387672e7264b
ORCID for Julian Leyland: ORCID iD orcid.org/0000-0002-3419-9949
ORCID for Joanna Nield: ORCID iD orcid.org/0000-0002-2657-0525

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Date deposited: 23 Nov 2017 17:30
Last modified: 16 Mar 2024 03:56

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Contributors

Author: Grigorios Vasilopoulos
Thesis advisor: Julian Leyland ORCID iD
Thesis advisor: Joanna Nield ORCID iD

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