Impact of flexible vegetation on hydraulic characteristics in compound channels with converging floodplains Open Access

boundary shear stress, compound channel, convergence, depth-averaged velocity, flexible vegetation, relative depth

Floodplains in natural rivers are curved and narrow, rather than following a straight path and the presence of vegetation and varying depth of flow affects the flow parameters of the channels. The purpose of this study is to investigate the effects of the convergence of the channel and relative depth of flow on the depth-averaged velocity (DAV) and boundary shear stress (BSS) of a converging compound channel. The effect of vegetation on DAV and BSS was also deliberated. To examine the effect of vegetation, synthetic grass 8 cm in height was used as a flexible vegetation to depict the natural conditions of the rivers. The DAV was calculated using an acoustic Doppler velocimeter (ADV) and the measurement of BSS was done using the Preston tube technique. After analysing the data, it was revealed that as the convergence of floodplains increases, maximum DAV increases. The presence of vegetation reduces the velocity specifically at lower flow depths. BSS increases with the convergence as well as vegetation. This research provides valuable data and results which will be helpful in better flood management, river restoration, and designing effective hydraulic structures.

HIGHLIGHTS

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The study examines the influence of channel convergence across various flow depths on depth-averaged velocity and boundary shear stress within a compound channel.
Synthetic grass (flexible vegetation) is utilized to mimic natural vegetation, offering insights into vegetation's influence on flow parameters.
The study's findings provide valuable insights for enhancing flood management, river restoration, and hydraulic structure design.

NOTATIONS

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The following symbols are used in this article:

B: width of compound channel
b: width of main channel
h: height of main channel
H: depth of flow
β: relative flow depth [(H – h)/H]
θ: converging angle
U_d: depth-averaged velocity
Q: discharge of compound channel

INTRODUCTION

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The acceleration of global warming brought on by human activities has hastened climate change, which has impacted precipitation patterns and made extreme weather events more frequent. These changes are increasing the likelihood of floods in many locations, putting already vulnerable people at risk. Among all the natural disasters that a country faces, river floods are the most frequent and often devastating (Alam & Muzzammil 2011). With the substantial changes in the global climate, growing urbanization, and land use, the dynamics of flooding are changing in ways that are complicated and unprecedented. Understanding river hydraulics is a crucial initial phase in the examination and management of floods. Determining flow discharge in rivers is a crucial aspect of designing river engineering projects (Azamathulla & Zahiri 2012). When assessing flood damages, it is essential to analyse the flow parameters in both the main channel and the floodplains (Pandey et al. 2022). By delving into these flow parameters, researchers and flood management authorities can gain a deeper understanding of the mechanisms at play, facilitating a more accurate assessment of the potential damages and aiding in the development of effective mitigation strategies.

Due to increased urbanization and changes in land use patterns, many natural watercourses differ from the uniform prismatic shape, although prismatic compound channels with uniform cross-sections have been the subject of several classic hydraulic research (Khatua et al. 2012; Proust et al. 2013). To handle these systems effectively, one must have a clearer understanding of their inherent non-prismatic complexity. The previous studies (Bousmar et al. 2004; Proust et al. 2006; Rezaei & Knight 2011; Yonesi et al. 2013; Das & Khatua 2018) reveal that flow parameters in compound channels with non-prismatic floodplains are considerably more complicated than that in prismatic channels. The investigation of flow in prismatic channels is a well-explored topic in current research. In the design phase, crucial parameters such as turbulent characteristics, conveyance capacity, mean flow pattern, distribution of depth-averaged velocity (DAV), secondary flow features, and boundary shear stress (BSS) play a significant role. Hydraulic engineers often rely on experimental research to gain insights into the complex flow parameters involved when examining turbulent flows in open channels. This complexity escalates when the floodplain is non-prismatic inducing mass transfer. The flow velocity decreases on spreading floodplains, while it increases on converging floodplains due to the convergence of channel shape, as observed in studies conducted by Rezaei (2006). Rezaei & Knight (2011) conducted experiments on compound channels with symmetrically narrowing floodplains, emphasizing the geometric transfer of momentum and the resulting additional head losses.

Many natural rivers display a two-stage geometry, and floodplains feature diverse roughness elements, including various sizes of gravel, vegetation, and combinations of these elements. Analysing the flow parameters becomes more challenging as the flow enters its floodplains.

Vegetation, including submerged, emergent, and floating types, significantly influences the flow characteristics within a channel. The introduction of vegetation elements adds complexity to the flow structure in non-prismatic compound channels. Brito et al. (2016) and Yang et al. (2007) conducted experimental studies to explore how vegetation impacts the three-dimensional flow properties. The presence of vegetation in floodplains not only alters the hydraulic capacity of compound channels but also impacts river ecosystems, influencing flow attributes like velocity distribution, turbulence patterns, vortices, and the exchange of mass and momentum between vegetated and non-vegetated zones (Nezu & Sanjou 2008; Uotani et al. 2014). Numerical simulations have been employed by numerous scholars, including Zhang et al. (2017) and Koftis & Prinos (2018) to investigate the characteristics of turbulent flow patterns within vegetated channels. Most of the research on vegetated floodplains has primarily concentrated on straight compound channels, while there is limited literature on compound channels with vegetation on diverging and converging floodplains (Mehrabani et al. 2020; Rahim et al. 2023). In previous studies, rigid vegetation models have been used by several researchers in their studies of compound channels (Rahim et al. 2022; Rao et al. 2022; Zhang & Hu 2023). However, it is important to note that in the natural river systems, flexible vegetation is prevalent. The fundamental distinction between rigid and flexible vegetation types can have significant implications for our understanding of how compound channels interact with their surrounding environments. It is crucial to investigate the impact and dynamics of flexible vegetation within compound channels. This article aims to bridge the gap between theoretical research and the actual conditions present in natural river ecosystems. Moreover, flexible artificial plants have been used to simulate floodplain vegetation in compound channels, and flume experiments have produced results similar to field observations (Huai et al. 2013; Liu et al. 2016). Previous studies have identified several unexplored aspects and gaps regarding vegetated floodplains in non-prismatic compound channels.

This article investigates the flow parameters in a converging compound channel with vegetative floodplains, the focus is on comprehending the flow physics under converging floodplains, considering flexible vegetation at various flow depths.

EXPERIMENTAL SET-UP

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Experiments were conducted at the Hydraulics Laboratory of the Department of Civil Engineering, Delhi Technological University, using a symmetrical compound channel of dimensions 12 m long, 1 m wide, and 0.8 m deep. The cross-sectional width of the main channel (b) was 0.5 m and the depth (h) was 0.25 m. The converging segment of the channel had a convergence angle (θ) of 4°. The whole set-up was constructed with brick masonry and plastered with concrete to provide a smooth surface finish. The compound channel comprised a 6 m long prismatic section and a 3.6 m long non-prismatic section and a downstream portion as presented in Figure 1. To represent the flexible vegetation in the floodplains, the flume's bottom was covered with synthetic grass, with an average height of 8 cm as presented in Figure 2.

Figure 1

(a) Plan view of experimental set-up without vegetation. (b) Plan view of experimental set-up with vegetation.

Figure 2

(a) Laboratory set-up of converging channel without vegetation. (b) Laboratory set-up of converging channel with vegetation.

METHODOLOGY

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The flume was operated at six different relative flow depths (β = 0.20, 0.30, 0.35, 0.40, 0.45, 0.50). The discharge in the channel varied with the relative depth.

(1)

where H represents the water height at a specific section and ℎ indicates the height of water in the main channel.

The relation between discharge (Q) and relative depth (β) is presented in Figure 3. Totally five sections termed Section 1, Section 2, Section 3, Section 4, and Section 5 were selected for the study as represented in Figure 1. Section 1 and Section 5 denote the start and end of channel convergence, while Section 2, Section 3, and Section 4, positioned equidistantly, serve as intermediate sections. The longitudinal distance between every section is constant. Velocity and shear stress were measured at every non-prismatic section for each relative depth. Detailed information about measuring of velocity and shear stress is given in further sections.

Figure 3

Discharge vs. relative depth.

FLOW CONDITIONS AND MEASUREMENTS

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The water was drawn from an underneath sump and pumped to an overhead tank in the testing channel. A volumetric tank fitted with a v-notch was exercised for measuring the discharge from the experimental compound channel. The v-notch was calibrated for this specific purpose, and the water was subsequently returned to the sump located underneath. To control the water surface profile and enforce a particular depth of flow in the flume portion, a tailgate was installed at the downstream end of the flume.

A point gauge with a precision of 0.10 mm was utilized to measure the water surface profile at distances of 1.0 and 0.30 m in the prismatic length and non-prismatic length of the compound channel, respectively. The point velocity at the pre-defined points along the wetted perimeter was determined using an acoustic Doppler velocimeter (ADV) at vertical intervals of 2.5 cm and horizontal intervals of 10 cm. The data obtained from the ADV underwent filtering using the ADV Horizon software.

The lateral variations in BSS were additionally gauged using a Preston tube featuring a 4.77 mm outer diameter at the identical sections where velocity distributions were assessed as in Figure 4. For measuring the pressure difference, a digital manometer was used, and Patel's (1965) calibration equations were used for calculating shear stress values.

Figure 4

Grid points on cross-section of compound channel.

Depth-average velocity

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In this study, velocity measurements were performed using both Pitot tubes and a 16-MHz Micro-ADV. The ADV is unable to calculate velocity within 50 mm of the free surface and bed due to limitations of the instrument. To address this limitation, a standard Pitot tube with a 5 mm outer diameter was used in conjunction with a digital manometer to measure point velocity readings at specified locations within the upper and bottom 50 mm regions of the channel. Point velocities were measured at various points within pre-defined grids across the channel cross-sections to ensure comprehensive coverage of the entire flow area.

For every experimental configuration, velocity calculations were conducted at six different discharges, corresponding to six relative depths (β = 0.20, 0.30, 0.35, 0.4, 0.45, 0.50), at five different sections. The depth-averaged velocity (U_d) was computed using the following equation (Naghavi et al. 2023):

(2)

where U_d is the depth-averaged velocity; u(z) denotes local velocity at z depth; and H is the flow depth.

The parameter U_d is of crucial importance in compound channel flow studies. Accurate measurement of U_d and its distribution across the flow section is essential for understanding the behaviour of compound channels under varying relative depths.

Boundary shear stress

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Explorations into shear stress within open channel flow carry extensive implications, impacting sediment transport, channel migration, calculation of energy losses, and momentum transfer. The Preston tube technique stands out as a widely employed indirect approach for gauging shear stress in measurements. In this study, local BSS measurements were conducted using Preston's technique (1954) in conjunction with Patel's (1965) calibration curves. The relation between BSS and differential pressure was defined by Preston as follows:

(3)

where Δp is the differential pressure; F is an empirical function; d is the Preston tube outer diameter; ρ is the fluid density; ν is the fluid kinematic viscosity; and

is the boundary shear stress.

Patel (1965) developed precise calibration curves for the Preston tube based on two non-dimensional parameters, y* and x*, facilitating the conversion of pressure readings into BSS. The calibration equations are as follows:

(4)

(5)

(6)

(7)

(8)

where x* is logarithm of the dimensionless pressure difference and y* is logarithm of the dimensionless shear stress

Shear stress assessments were carried out in each of the five sections of the channel. Pressure readings were obtained using a Preston tube positioned at previously defined grid points within the channel, oriented towards the flow. The differential pressure was measured using a digital manometer connected to the Preston tube.

RESULTS AND ANALYSIS

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The flow is found to be non-uniform in the converging length of the channel. Subcritical flow conditions were attained under various conditions of the compound channel with a longitudinal bed slope of 0.001. The results of the DAV and BSS are discussed further in this section.

Depth-averaged velocity

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The distribution of velocity across all cross-sections is illustrated in Figure 5 for all six relative depths (β = 0.20, 0.30, 0.35, 0.40, 0.45, 0.50). The observed rise in maximum average depth velocity across the channel coincides with the convergence of floodplains. There is a considerable increase in the velocity of the second half of the converging reach. The flow becomes increasingly restricted as a result of converging floodplains thus reducing the channel width, which results in momentum transfer. The increased velocity indicates the concentration of flow, resulting in a more efficient conveyance of water through the channel. Foreseeing the effect of channel geometry on overall velocity patterns and hydraulic efficiency in non-prismatic channels with converging floodplains becomes essential when understanding this phenomenon. Naik et al. (2018) conducted an experiment of overbank flow in a converging channel, exploring three convergent angles (θ = 5°, θ = 9°, and θ = 12.38°). The study revealed that one of the results of the convergence on flow behaviour is an increase in velocity along the converging segment of the flume, aligning with the findings of the current research.

Figure 5

DAV distribution at all six relative depths (β = 0.2, 0.3, 0.35, 0.4, 0.45, 0.5) without vegetation.

DAV distribution at all six relative depths (β = 0.2, 0.3, 0.35, 0.4, 0.45, 0.5) without vegetation.

The observations of the study depicted in Figure 5 highlight that the highest velocity is typically concentrated in the central region of the main channel for the lower relative depths. The central concentration of maximum velocity in the main channel can be related to the fact that the highest flow velocities arise from diminished frictional resistance along this central axis. For higher relative depths, the maximum velocity shifts towards the floodplains.

Figure 6 illustrates how the flow velocity across the channel is significantly reduced when there is the presence of flexible vegetation irrespective of the relative depth in all cross-sections. It can be observed from Figure 6 that the maximum DAV in the main channel is in the centre for lower flow depths, whereas, when the flow depth increases, the maximum centre velocity shifts towards floodplains. Maximum DAV in the channel with a vegetated floodplain is lower compared with a scenario without vegetation due to the frictional resistance by the vegetation. Moreover, at higher flow depths, the decrease in flow velocity in the main channel and floodplains due to vegetation is less pronounced compared with lower flow depths. This effect of the floodplain vegetation at higher relative depths was never shown in previous studies.

Figure 6

DAV distribution at all six relative depths (β = 0.20, 0.30, 0.35, 0.40, 0.45, 0.50) with vegetation.

DAV distribution at all six relative depths (β = 0.20, 0.30, 0.35, 0.40, 0.45, 0.50) with vegetation.

Boundary shear stress

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Figure 7 exhibits the experimental findings of the BSS distributions in all cross-sections without vegetation for the converging flood plain of angle 4° for all six relative depths (β = 0.20, 0.30, 0.35, 0.40, 0.45, and 0.50). The BSS distribution in all sections is logically symmetrical in these figures and progressively increases from β = 0.2 to β = 0.5. The minimum BSS is at the floodplains, whereas the maximum BSS is at the edge of the floodplain and main channel which is relatively higher than centreline shear stress along the channel as the floodplains converge. Notably, in the second half of the converging reach, there is a significant surge in BSS. This surge is attributed to the mass transfer from the floodplains into the main channel at the end of convergence.

Figure 7

BSS distribution at all six relative depths (β = 0.2, 0.3, 0.35, 0.4, 0.45, 0.5) without vegetation.

BSS distribution at all six relative depths (β = 0.2, 0.3, 0.35, 0.4, 0.45, 0.5) without vegetation.

Experimental results of the BSS distribution in all sections with vegetation for the converging flood plain of angle 4° for all relative depths 0.20, 0.30, 0.35, 0.40, 0.45, and 0.50, are presented in Figure 8. It shows similar distributions as without vegetation, but the flexible vegetation introduces additional roughness elements, causing an increase in flow resistance as water interacts with the vegetation. This increased resistance leads to higher BSS along the floodplains compared with the relatively smoother main channel.

Figure 8

BSS distribution at all six relative depths (β = 0.2, 0.3, 0.35, 0.4, 0.45, 0.5) with vegetation.

Flood disaster preparedness in Indian scenario

BSS distribution at all six relative depths (β = 0.2, 0.3, 0.35, 0.4, 0.45, 0.5) with vegetation.

CONCLUSIONS

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The present research investigated the flow dynamics of a compound channel with converging floodplains, focusing on the impact of relative depth and flexible vegetation on flow parameters such as averaged depth velocity and BSS. The key findings of this study are as follows:

1. Converging floodplains cause a significant increase in the DAV because of channel convergence and momentum transfer which results in more concentrated flows.
2. The maximum DAV is significantly dependent on relative depth. As the relative depth increases, the DAV also increases.
3. The flexible vegetation in the floodplains causes resistance to flow which results in a significant drop in the DAV in the entire cross-section.
4. The reduced resistance of flexible vegetation at higher relative depths causes a lateral shift of the zone with maximum DAV towards the floodplains.
5. The BSS increases along the converging reach of the channel due to the convergence of the channel and momentum transfer.
6. In the presence of flexible vegetation, the BSS distribution remains similar to scenarios without vegetation. However, the introduction of flexible vegetation increases flow resistance due to additional roughness elements, resulting in higher BSS along floodplains compared with the smoother main channel.

ACKNOWLEDGEMENTS

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The authors would like to express their gratitude for the support provided by the Department of Civil Engineering at Delhi Technological University, Delhi, India. The authors would also like to thank all the staff members in the hydraulic laboratory at the Delhi Technological University.

AUTHOR CONTRIBUTIONS

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All authors have read and agreed to the published version of the manuscript.

FUNDING

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This research did not receive any funding.

DATA AVAILABILITY STATEMENT

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Data cannot be made publicly available; readers should contact the corresponding author for details.

CONFLICT OF INTEREST

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The authors declare there is no conflict.

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