Volume 15, Issue 4 e2427
RESEARCH ARTICLE
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Flow behaviour in a multi-layered vegetated floodplain region of a compound channel

Jyotirmoy Barman

Jyotirmoy Barman

Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, India

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Bimlesh Kumar

Corresponding Author

Bimlesh Kumar

Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, India

Correspondence

Bimlesh Kumar, Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, India.

Email: [email protected]

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First published: 29 April 2022
Citations: 7

Significance of the present work Analysis of velocity indicates the damping of velocity in downstream of channel in multi-layered vegetation compared with single layered.Analysis of turbulent intensities and turbulent kinetic energy indicates dominance in the slope and main channel region.Integral scale analysis shows the dominance of uniform triple-layered vegetation over non-uniform triple-layered vegetation.Octant analysis shows the importance of vegetation in determining different bursting events in the cross-section of a compound channel.

Abstract

Riparian vegetation plays a crucial role in determining the flow behaviour in the channel. The effect of flow on the slope and main channel varies based on the size, type, and density of floodplain vegetation in a compound channel. Though real field vegetation distribution is non-uniform, most studies mainly concentrate on uniformly distributed vegetation with fixed vegetation height. This paper attempts to address this issue through laboratory studies as it was not explored properly. Flow properties like velocity, Reynolds shear stress, turbulence intensities, and turbulent kinetic energy behave differently in heterogeneous vegetated channel compared with the homogeneous vegetated channel. These flow properties in the slopes and main channel section are more pronounced for uniformly distributed vegetation than non-uniform distribution. The multi-layered/varying vegetation height showed higher velocity, turbulent intensity, and turbulent kinetic energy than single-layered/constant vegetation height on slopes and main channel sections. Integral scales (Taylor and Euler) analysis showed greater magnitude for uniform multi-layered vegetation than non-uniform multi-layered vegetation. Further, the study of octant analysis reveals that internal outward and internal inward interaction events dominate the slope floodplain interaction section, which is not seen in no-vegetation cases. These studies give a better perspective to understand flow in heterogenous vegetation and move closer to real field scenarios.

1 INTRODUCTION

Aquatic vegetation affects flow behaviour and sediment transport as it obstructs its natural flow. Most rivers are covered with vegetation in the bed and the banks. Understanding the flow and turbulent behaviour in a vegetated channel is important for studying and managing fluvial processes. Aquatic vegetation increases the flow resistance in a channel and reduces the conveyance capacity, leading to vegetation removal to increase the flow passage (Kouwen, 1992; Masterman & Thorne, 1992; Wu et al., 1999). Recently, a case study on Dongting lake, China, concluded that aquatic vegetation decreases suspended sedimentation, ultimately increasing sediment deposition thickness and reducing the conveyance capacity over one flood season (Zhang et al., 2020). Aquatic vegetation provides food sources and habitats for fishes and other aquatic beings (Edgar, 1990; Kemp et al., 2000). Vegetation also increases bank stability, decreases erosion, decreases floods, aesthetic values, and filters pollutants. Aquatic vegetation study also becomes important while constructing hydraulic structures like dams. Substantial effects like change in vegetation distribution can be seen in the downstream vegetation after constructing dams (Bejarano et al., 2011; New & Xie, 2008).

Studies related to vegetation have been widely explored in the past. Various experimental, numerical, and field studies were performed to understand the flow behaviour in water bodies covered with vegetation. However, laboratory studies mainly on vegetation were homogenous (Carollo et al., 2002; Caroppi et al., 2021; Huai et al., 2019; Ikeda & Kanazawa, 1996; Järvelä, 2002; Kouwen et al., 1969; Nepf & Ghisalberti, 2008; Termini, 2019; Wang et al., 2021). Even experiments showing the vegetation effect in a compound channel mainly was limited to homogenous vegetation (Dupuis et al., 2017; Hamidifar & Omid, 2013; Mehrabani et al., 2020; Nezu & Onitsuka, 2001; Proust & Nikora, 2019; Rameshwaran & Shiono, 2007; Thornton et al., 2000). Though studies related to heterogeneous vegetation have been fast-growing since the last decade, it is limited compared with homogeneous vegetation. Field investigations give the best visualization of the role of heterogeneous vegetation on the nature of flow (Lacy & Wyllie-echeverria, 2011; Przyborowski & Łoboda, 2021; Sukhodolov & Sukhodolova, 2010; Sukhodolov et al., 2017). However, these studies did not emphasize the difference between heterogeneous vegetation with homogeneous ones. In this situation, laboratory studies help to simplify and understand the complex flow situations regarding heterogeneous vegetation. Researchers tried to explain flow behaviour using different heterogeneous conditions like heterogeneity in spacing, height, and vegetation types. Devi et al. (2016) and Li et al. (2022) attained heterogeneity in spacing by mixing the vegetation densities in the test section. Chembolu et al. (2019) attained heterogeneity by changing the vegetation type in the test section. They studied the flow behaviour in mixed vegetation and compared it with a single type of vegetation. However, these researches kept the vegetation height uniform throughout the test section. Heterogeneity in height is obtained either by naturally growing vegetation in the laboratory (Shucksmith et al., 2010; Stephan & Gutknecht, 2002) or experimented with different vegetation layers (Hamed et al., 2017; Horstman et al., 2018; Li et al., 2014). Flow-through different layers or vegetation heights produce more inflection points than single-layered vegetation (Li et al., 2014; Liu et al., 2010). Li et al. (2014) also concluded that the resistance offered by a combination of submerged and emergent vegetation is more than a single vegetation layer. Hamed et al. (2017) compared flow behaviour between uniform vegetation and double-layered vegetation. They found that there is greater turbulent exchange in double-layered vegetation in the canopy interface compared with homogenous vegetation. Horstman et al. (2018) tried to replicate a real mangrove vegetation distribution in the laboratory and found that there was a reduction of strength in heterogeneous canopy layer as compared with homogeneous type. Different numerical and analytical models of heterogeneous vegetation also indicate the difference in treatments concerning homogeneous vegetation (Anjum & Tanaka, 2020a, 2020b; Huai et al., 2014).

The study of flow in heterogeneous vegetation can be extended to a compound or partially vegetated channel for the fulfilment of moving closer to a real or practical field. During the high flood season, the floodplain gets submerged, where vegetation contributes to the flow behaviour in the channel. A case study on a tributary of Conodoguinet Creek, Pennsylvania, USA, concludes that there is more erosion and bank migration in the non-forested area than the forested area (Allmendinger et al., 2005). Previous studies on the compound or partially vegetated channels mainly focused on the homogeneous vegetated floodplain (Caroppi et al., 2021; Dupuis et al., 2017; Proust & Nikora, 2019; Yang et al., 2007). The analytical approach in compound channel was also investigated by many researchers (Pu, 2019; Pu et al., 2020; Shiono & Knight, 1991; Tang & Knight, 2008). However, these researches did not consider the variation of vegetation height in the floodplain of the compound channel. Ahmad et al. (2020) and Tang et al. (2021) explained flow dynamics in double-layered vegetation on a compound and partially vegetated channel respectively. However, their studies did not incorporate the variation of double-layered vegetation with single-layered or uniformly distributed vegetation. Laboratory experiments regarding heterogeneously vegetated floodplain compound channels are mostly unexplored due to the sheer complexity of handling vegetation with varying heights and densities. In addition to that, researchers have to also deal with flow interaction between the floodplain and main channel region where constant momentum exchange takes place, making its study more interesting. Previous researchers complimented this floodplain-main channel interaction on a homogeneous floodplain but did not explore it in the heterogeneously vegetated region (Proust & Nikora, 2019; Shiono & Knight, 1991; Tominago & Nezu, 1991; Truong et al., 2019). The momentum exchange depends on vegetation density, height, submergence level, and vegetation type. This changed vegetation distribution can affect the flooding and erosion pattern, making its study important.

In the present paper, the authors attempted to observe the flow behaviour in natural heterogeneous vegetation in the floodplain region of the compound channel. Heterogeneity in spacing and height are used to study the flow nature and its impacts on the slopes and main channel. Despite the fact that many researches have been done on vegetation in compound channels, detailed investigation of flow behaviour in multi-layered vegetation in the floodplain zone has been scarce. This research also attempts to address the flow nature differences between single-layered and multi-layered vegetation in a compound vegetated channel, which have never been investigated before. The study considers multi-layered (three-layered) vegetation, which differs from previous studies where mostly double-layered vegetation was considered (Anjum & Tanaka, 2020a, 2020b; Li et al., 2014; Huai et al., 2014; Tang et al., 2021). Although vegetation height and distribution are more random in the real field, the author used three layers of vegetation to reduce complexity during analysis. This fact can be regarded a study constraint because heterogeneous vegetation distribution is extremely unpredictable, both in terms of height and space. Only one vegetation type is included in this study to keep the variable to height and space. In the field, however, vegetation of various types (leafy, bladed, cylindrical etc.) might be found in a specific region. This study on heterogenous vegetated compound channel attempts to move a step forward towards achieving a real field view.

2 METHODOLOGY

Experimental runs on laboratory flume provide a clear display of flow nature in a channel. The conditions of an experimental setup can be altered according to the need, which gives an advantage over a field study. The following sections provide the methods and procedure of the experiments.

2.1 Experimental set up and procedure

Experiments were carried out on a laboratory flume of 17.2 m long, 1 m wide, and 0.72 m deep (Figure 1). At the upstream of the flume, a tank of length 2.8 m, width 1.5 m, and depth 1.5 m were provided to regulate straight flow into the main channel. Uniform sand of 1.1 mm median grain diameter and geometric standard deviation 1.03 were utilized for bed sediment. The water was gradually released with the help of a valve from the overhead storage tank to the inlet tank and ultimately to the main flume. Since it is a recirculating flume, the water is drained into an underground tank and pumped to the upstream overhead storage tank. Flow depth and discharge in the flume were regulated with the help of the tailgate and rectangular notch situated downstream of the flume. A compound channel of 9 m length was constructed in the rectangular flume from 13 m upstream of the flume to 4 m in downstream, as shown in Figure 1. The compound channel was divided into three regions: floodplain, sloping, and the main channel region (Figure 1). The slope was made rigid with an aluminium sheet and kept at a fixed angle of 31°. Vegetation was distributed in the floodplain region staggered from 11 m to 5 m in the flume (Figure 2). The experimental cases and vegetation distribution are discussed in detail in subsequent sections.

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Experimental flume present at IIT Guwahati
Details are in the caption following the image
Vegetation set up of (a) set 1 and (b) set 2. (c) Snapshot of vetiver grass (Chrysopogon zizanioides) from site (d) velocity reading at 9.5 m, 7.5 m and 5.5 m test sections for set 1 and set 2

2.1.1 Vegetation collection

The vegetation used in various experimental cases is described in Table 1 and Figure 2c accumulated from the field. For the present study, Vetiver grass (Chrysopogon zizanioides) was used in the floodplain region of the compound channel. Vetiver grass can grow up to 5 ft tall, and the roots grow from 7 ft to 13 ft in depth. The experiments considered varying vegetation heights of 3 cm, 6 cm, and 9 cm. The average breadth of each vegetation patch considered in the present study is 3.5 cm for all vegetation cases. The root system of the vetiver is very strong, which is why it is used for controlling bank erosion in many parts of the world. Various studies concerning Vetiver grass and their uses in bank protection have been discussed by different researchers (Hamidifar et al., 2018; Islam et al., 2013; Jaspers-Focks & Algera, 2006). Vetiver grass was used in this study mainly for two reasons: (1) It has an erect and stiff stem that helps to distinguish the flow when it encounters different vegetation heights, and (2) it is readily available in India, which helps in the procurement of the vegetation.

TABLE 1. Description of different experimental cases
Set Discharge (L/s) Experimental cases Notation of experimental cases Vegetation distribution type
20 No vegetation No vegetation Nil
35 No vegetation No vegetation Nil
1 20 6 cm uniform vegetation U 6 cm uniform Uniform homogeneous
20 3-6-9 cm submerged U 3-6-9 cm submerged Uniform heterogeneous
35 6 cm uniform vegetation U 6 cm uniform Uniform homogeneous
35 3-6-9 cm submerged U 3-6-9 cm submerged Uniform heterogeneous
2 20 6 cm uniform vegetation NU 6 cm uniform Non-uniform homogeneous
20 3-6-9 cm submerged NU 3-6-9 cm submerged Non-uniform heterogeneous
35 6 cm uniform vegetation NU 6 cm uniform Non-uniform homogeneous
35 3-6-9 cm submerged NU 3-6-9 cm submerged Non-uniform heterogeneous

2.1.2 Experimental cases

Two sets of experiments were conducted on the compound channel. In the first set, the vegetation zone was uniformly distributed, whereas, in the second set, it was non-uniformly distributed (Figure 2). No-vegetation cases where the flow was allowed to move without vegetation in the floodplain zone were performed for both the discharges. In the first set, the spacing between two vegetation patches was 10 cm centre to centre (c/c) from 11 m (upstream) to 5 m (downstream) in the flume. It consists of two experimental cases according to the height of vegetation in the floodplain: a single-layer and a multi-layer submerged vegetation case. In the single layer, all vegetation patch heights were 6 cm (Figure 3b). The multi-layered vegetation comprises three layers of vegetation height of 3 cm, 6 cm, and 9 cm. This varying height vegetation was distributed throughout the vegetated floodplain zone to achieve heterogeneity, as shown in Figure 3c. For convenience, the experiments in the uniform setup are denoted as U 6 cm uniform for uniform single-layer vegetation and U 3-6-9 cm submerged for uniform multi-layered vegetation in the paper. The second set of experiments was divided into three vegetation zones: sparsely vegetated, medium densely, and densely vegetated floodplain as shown in Figure 2b. Flow moves from sparsely vegetated portion upstream to denser region downstream of the flume. This was done to achieve non-uniformity in spacing in the vegetated floodplain. The spacing between two vegetation patches in sparsely, medium densely, and densely vegetated zones were 20 cm, 15 cm, and 10 cm, respectively. The cases performed for the first set were repeated for the second set, that is, single-layered and multi-layer submerged vegetation. The experiments in the non-uniform setup are denoted as NU 6 cm uniform for non-uniform single-layered vegetation and NU 3-6-9 cm submerged for non-uniform multi-layered vegetation. The average flow depth in the floodplain and main channel region of the compound channel was taken as 10 cm and 20 cm, respectively. The flow depth in the channel is kept constant for different discharges and vegetation cases by regulating the tailgate present in the downstream of the flume (Figure 1). The flow in the channel was in subcritical condition for both discharges for all the experiments. A detailed explanation of the experiments and distribution of vegetation is shown in Table 1 and Figure 2.

Details are in the caption following the image
Snapshots of (a) no-vegetation, (b) 6 cm uniform vegetation and (c) 3-6-9 cm submerged vegetation

2.2 Data collection

An acoustic Doppler velocimeter (ADV) developed by Nortek was used to measure the instantaneous velocities at 9.5 m, 7.5 m, and 5.5 m section of vegetation zone of the compound channel for both sets (Figure 2a,b). Readings were taken at six regions across the cross sections, as shown in Figure 2d. The notations at each section are denoted as follows: FP 20 means floodplain at 20 cm from the left wall of the flume, FP 35 is floodplain at 35 cm from the left wall, SFPI denotes slope floodplain interaction, SM denotes slope mid, SE is slope end, and MC denotes the main channel of the compound channel. The Nortek ADV used in the experiment is a downward probe ADV which can capture velocity data 5 cm from the central transmitter. That is why all data were collected near the channel bed to 5 cm below the free surface. This is a limitation of ADV where flow behaviour in the channel cannot be analysed 5 cm below the free surface. The ADV signal output also consists of doppler noise, signal aliasing, and other disturbances. The output is also affected by velocity shear in the near-bed of the channel. The accuracy of ADV data for the present study was checked by taking 15 readings of velocity data at z / h = 0.1. It was found that the standard deviation of streamwise velocity ( u) and streamwise intensity ( u u ¯ ) at z / h = 0.1 was 2.42 * 10 3 and 6.24 * 10 3 , respectively, which is low. The ADV collected data with 100 Hz frequency for 120 s. The total samples acquired for a single reading are 12,000. Garcia et al. (2005) assumed implicitly that 6000 samples were enough to describe turbulence. In presence of vegetation, Dupuis et al. (2017) considered sample time of 2 min for single velocity reading. Moreover, Horstman et al. (2018) and Tang et al. (2021) considered sample time of 1 min for velocity data in mixed vegetation (submerged and emergent) cases. Horstman et al. (2018) observed that 60 s or 1 min is long enough for obtaining consistent flow characteristics. The measured instantaneous velocity was u, v, and w in X (streamwise), Y (transverse), and Z (vertical) directions, respectively. Evaluation of turbulence was done by decomposing the instantaneous velocity samples u, v, and w into time-averaged velocity and fluctuating component of the velocity as u = u ¯ + u , v = v ¯ + v , and w = w ¯ + w , respectively, where u ¯ , v ¯ , and w ¯ , and u , v , and w are the time-averaged velocities and velocity fluctuations in streamwise, transverse, and vertical direction, respectively. This process is called Reynolds decomposition. The time-averaged velocities were calculated as
u ¯ = 1 n i = 1 n u i , v ¯ = 1 n i = 1 n v i and w ¯ = 1 n i = 1 n w i . (1)
The data acquired from ADV consist of noise, which needs to be filtered. Various researchers gave different methods of filtering, like Goring and Nikora (2002), Wahl (2003), Cea et al. (2007), and so forth. In the present study, a modified code based on the code developed by Islam and Zhu (2013) was used. They used the bivariate Kernel density estimation to separate the spikes in the data. The removal of the spikes is followed by replacement with linear interpolation method. They found that their algorithm works fine on cases with high contamination, which was not the case in other methods mentioned above. In the present study, this method is apt as highly contaminated data might be present due to vegetation.

3 RESULTS AND DISCUSSIONS

3.1 Velocity

Floodplain vegetation density influences the flow behaviour in the entire cross section of the channel. This effect could be seen in the main channel of the compound channel, as seen in Figure 4. Velocity for uniform set up in the main channel does not show any particular consistent trend throughout the flow depth (Figure 4a,c). It is almost equal for z / h > 0.4. The introduction of different vegetation density zones showed a trend where velocity profile at 5.5 m main channel section is highest whereas at 9.5 m, it shows lowest magnitude velocity. It is seen that there is more than 20% increase of depth-averaged streamwise velocity in the main channel section from 9.5 m section to 5.5 m section. The role of vegetation density can also be seen in Figure 5, where velocity profiles at various uniform and non-uniform setup sections are shown. The 5.5 m cross section was chosen to compare as vegetation density in that region (7 m to 5 m) is the same for both the setup. It is seen that in all the sections from SFPI to MC section, velocity in uniform vegetation set up is higher compared with non-uniform vegetation set up. However, the velocity at the MC section for uniform setup starts to diverge or increase from non-uniform set up at z / h > 0.5. The velocity is almost the same for both vegetation setup at z / h = 0 0.5. The flow in the vegetation zone in a non-uniform setup travels from a sparsely vegetated zone to a densely vegetated zone, whereas in uniform case, the whole vegetation zone is densely populated from 11 m to 5 m (Figure 2b). This decreases the velocity in the floodplain zone of uniform cases, increasing the velocity in the slope and main channel sections. Figure 5 also shows the importance of vegetation height to divert the flow towards the main channel. The velocity in the U 3-6-9 cm submerged case is greater than the U 6 cm uniform case at SFPI, SM, and SE throughout most of the flow depth. In the MC section, velocity in both U 6 cm uniform and U 3-6-9 cm submerged are almost comparable. The reason for greater velocity in slope and main channel sections for multi-layered vegetation (3-6-9 cm submerged) for most cases is the contribution of 9-cm-length vegetation. This shows that though 33% of the vegetation is 3 cm in length, the resistance in flow is mainly provided by 9-cm-length vegetation. The 9-cm vegetation provides more frontal area and flow obstruction. This ultimately decreases the flow velocity in the floodplain region and increases in the slope and main channel sections. This point can be seen in Figure 5, where the velocity of NU 3-6-9 cm submerged case is less than the NU 6 cm uniform case at the SFPI section and becomes larger at SE and MC sections.

Details are in the caption following the image
Velocity in the main channel of U 6 cm uniform and NU uniform 6 cm case from upstream (9.5 m) to downstream (5.5 m) for (a, b) 20 lps and (c, d) 35 lps
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Velocity profile in different locations of different vegetation cases for 35 lps at 5.5 m cross section

3.2 Reynolds shear stress

The Reynolds shear stresses (RSS) help to get a better glimpse of the momentum transfer between different layers in streamwise, spanwise, and vertical directions. There is a constant momentum transfer between the floodplain and the main channel region in the compound channel because of differences in velocities and flow depth across the cross section (Dupuis et al., 2017; Proust & Nikora, 2019). Figures 6 and 7 show streamwise and transverse RSS ( uˊwˊ ¯ and u v ¯ ) for different vegetation cases at 5.5 m cross section for 35 lps. It can be seen that in all the cases, irrespective of discharge and uniformity condition, the RSS is more pronounced in the slope and the main channel sections. For higher flow depths of z / h > 0.4, the streamwise RSS in uniform set up (U 6 cm uniform and U 3-6-9 cm submerged) is greater than in non-uniform cases, primarily at SE and MC sections (Figure 6e,f). However, no particular trend is observed for z / h < 0.4 at both SE and MC sections. The reason might be because of flow irregularities in the lower depth of the channel. The uniform setup increases the velocity in the slope and main channel sections, as seen in Figure 4. As a result, the difference in velocity between the floodplain and main channel increases, ultimately increasing the momentum exchange compared with the non-uniform setup. The pointwise RSS profile of SE and MC (Figure 6e,f) shows that the RSS increases from z / h ~ 0.75 to z / h = 0.2 , after which it decreases till channel bed. The maximum magnitude of RSS in multi-layered vegetation (U 3-6-9 cm submerged and NU 3-6-9 cm submerged) reaches more than 0.0003 m2/s2, which is greater than the homogenous vegetation height (U 6 cm uniform and NU 6 cm uniform). Negative streamwise RSS is observed for all cases, mainly in the floodplain and SFPI regions. However, the magnitude of negative RSS at the SFPI section of NU 3-6-9 cm submerged case is higher than all other cases. This point shows the presence of a strong circulation system in the region. The SFPI and SM sections are also subjected to high transverse RSS ( u v ¯ ) for all cases, as seen in Figure 7. The white portion in slopes shows the extreme magnitude of uˊvˊ ¯ and displays the importance of transverse velocity fluctuations (v ˊ) in overall flow in a compound channel. It is also seen that the magnitude of transverse RSS ( uˊvˊ ¯ ) is also higher than streamwise RSS ( uˊwˊ ¯ ) especially on the slopes.

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(a–d) Contouring of streamwise RSS uw ¯ and (e, f) pointwise RSS at SE and MC for 35 lps at 5.5 m cross section
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Contouring of transverse RSS uv ¯ case, respectively, for 35 lps at 5.5 m cross section

3.3 Turbulent intensities

The turbulent intensities are given by the square root of the average of the diagonal elements of the Reynolds stress matrix. The streamwise and transverse intensities are given as σ u = u u ¯ and σ v = v v ¯ , respectively. Figures 8 and 9 show the streamwise and transverse intensities, respectively, at a 5.5 m cross section for 35 lps. It is evident from the figures that both intensities are greater in uniform set up at the slopes and main channel sections as compared with non-uniform vegetation setup. The maximum streamwise and transverse intensities in the SE and MC sections are greater than 0.08 m/s for a uniform setup, whereas it is within 0.08 m/s for a non-uniform setup (Figures 8e,f and 9e,f). The magnitude of intensities is also seen to be maximum in the SFPI section as it acts as the boundary where momentum exchange occurs between the floodplain and main channel. It is also observed that the magnitude of both streamwise and transverse intensities of multi-layered vegetation (U 3-6-9 cm and NU 3-6-9 cm) at SE and MC sections is higher compared with single-layered vegetation (U 6 cm uniform and NU 6 cm uniform) throughout the flow depth. However, the percentage increase of depth-averaged intensities is different for uniform and non-uniform vegetation set up. There is a more than 15% increase of averaged streamwise intensities ( σ u ) at SE and MC sections in case of NU 3-6-9 cm submerged to NU 6 cm uniform whereas it is within 15% for U 3-6-9 cm submerged compared with U 6 cm uniform. Furthermore, in the case of average transverse intensity ( σ v ), it is more than 20% for non-uniform vegetation setup, whereas it is within 20% for uniform vegetation setup. The combined effect of varying vegetation density and height could result in greater intensity increase in the case of a non-uniform setup. On the other hand, only vegetation height acts as variable whereas vegetation spacing is constant throughout the vegetation zone in the uniform vegetation setup.

Details are in the caption following the image
(a–d) Contouring of streamwise intensity σ u and (e, f) pointwise σ u at SE and MC for 35 lps at 5.5 m cross section
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(a–d) Contouring of transverse intensity σ v and (e, f) pointwise σ v at SE and MC for 35 lps at 5.5 m cross section

3.4 Turbulent kinetic energy (TKE)

The total TKE is the sum of the squares of turbulent intensities in streamwise, transverse, and vertical directions, and it is formulated as k = 1 2 u u ¯ + v v ¯ + w w ¯ . The results of TKE resemble turbulent intensities to some extent, as shown in Figures 8 and 9. The magnitude of TKE is more pronounced in the slopes and main channel sections irrespective of vegetation setup (Figure 10). Like turbulent intensities, the magnitude of TKE in uniform set up is greater than that of non-uniform vegetation throughout the flow depth. The TKE magnitude in multi-layered vegetation cases for both uniform and non-uniform setup is also greater than single or constant vegetation height cases especially at slope and MC sections. However, there is more than a 30% increase of average TKE magnitude for multi-layered vegetation as compared with single-layered vegetation in non-uniform setup whereas it is within 30% for uniform vegetation setup. It is also observed in Figure 10 that TKE magnitude in the floodplain sections is comparatively less than sloping sections. It signifies that the velocity fluctuating components in different directions are more pronounced in the slopes than in the floodplain sections.

Details are in the caption following the image
(a–d) Contouring of TKE k and (e, f) pointwise TKE at SE and MC for 35 lps at 5.5 m cross section

3.5 Integral scales

One of the defining features of turbulence flow is the formation of eddies. The eddy size has a wide range of scales, and the largest scale is comparable with the flow depth of the channel. This section analyses the Taylor micro-scale and Euler's scale for U 3-6-9 cm submerged and NU 3-6-9 cm submerged cases.

3.5.1 Taylor scale

The Taylor micro-scale gives the length scale of inertial subrange where inertial effects mostly govern eddy motions. It gives the average size of eddies at a particular point, and it is given as
λ T = 15 ν u 2 ¯ ε 0.5 (2)
where ν is the kinematic viscosity of water and u′ is the velocity fluctuations in the streamwise direction. The value of dissipation rate ε is found as given by Krogstad and Antonia (1999):
ε = 15 ν u ¯ 2 δ u δt 2 ¯ (3)
Figure 11 shows the Taylor micro-scale of uniform and non-uniform vegetation floodplain for multi-layered vegetation cases at a 5.5 m cross section. It can be seen that in all cases, the Taylor scale governs the slope and main channel sections. The magnitude of the Taylor scale is larger in uniform multi-layered vegetation (U 3-6-9 cm submerged) compared with non-uniform multi-layered vegetation (NU 3-6-9 cm submerged) for both discharges. This trend was also previously seen in turbulence intensities and TKE analysis. It is found that there are more than 60% points above 0.004 m from slopes and main channel sections for U 3-6-9 cm submerged case at 35 lps. On the other hand, it is less than 20% points for NU 3-6-9 cm submerged case. The mean velocity of the section plays a key role in the Taylor scale, as seen in the dissipation rate equation (2). The flow in the U 3-6-9 cm submerged case is diverted more towards the slope and main channel region, ultimately affecting the average size of eddies.
Details are in the caption following the image
Contouring of Taylor scale of U 3-6-9 cm submerged and NU 3-6-9 cm submerged case, respectively, at 5.5 m cross section for (a, b) 20 lps and (c, d) 35 lps

3.5.2 Euler scale

The Euler scale gives the size of macro-eddies in the channel. These macro-eddies are the energy-containing eddies that disintegrate into smaller eddies whose scales are given by Taylor and Kolmogorov scales. Euler time scale should be determined to find the Euler scale, at first, which is given as
E T = 0 R t dt (4)
where R(t) is the autocorrelation function and dt is the lag between two consecutive autocorrelation functions. The Eulerian length scale can now be determined as
E L = E T U (5)
where U is the average velocity at a specific point of the measuring section. The results are similar to the Taylor scale, where the macro-eddies govern the slope and main channel sections. There is not much variation of Eulerian time and length scale for multi-layered vegetation cases at 20 lps discharge in the floodplain sections (Figure 12a,b). In U 3-6-9 cm submerged case, the time scale range is 0.02–0.17 s, whereas, in the case of NU 3-6-9 cm submerged, its range is 0.02–0.14 s. However, at 35 lps discharge, the two cases have considerable variation in the floodplain sections. The range for U 3-6-9 cm submerged case is 0.04–0.75 s, whereas, in the case of NU 3-6-9 cm submerged, it lies between 0.01 and 0.34 s. The Euler time scale at the SFPI section is greater than other floodplain sections for all vegetation cases as it acts as a boundary for continuous momentum exchange. The slope and the main channel sections also show greater Euler time and length scale in the U 3-6-9 cm submerged case compared with the NU 3-6-9 cm submerged case for both the discharges. The Eulerian time scale range for U 3-6-9 cm case is 0.02–0.8 s, whereas for NU 3-6-9 cm case is 0.05–0.5 s for both discharges in the slope and MC sections. This point is also evident from Figure 12c, where the Euler length scale is more pronounced in the slope and main channel sections of U 3-6-9 cm submerged case compared with NU 3-6-9 cm submerged case.
Details are in the caption following the image
Contouring of Euler scale of U 3-6-9 cm submerged and NU 3-6-9 cm submerged case, respectively, 5.5 m cross section for (a, b) 20 lps and (c, d) 35 lps

3.6 Octant analysis

The octant analysis was developed by Keshavarzi and Gheisi (2006). It was an improved version of quadrant analysis (Kline et al., 1967), where it also considers the transverse velocity fluctuations as opposed to that in quadrant analysis. This transverse velocity ( v) plays a key role in obstructed flow, sinuous, and braided river (Keshavarzi et al., 2014; Khan et al., 2022; Taye & Kumar, 2021). Keylock et al. (2014) used the octant analysis to study bed load sediment entrainment. In the present study, the transverse/spanwise velocity plays a crucial role throughout the cross-section, as seen in Figure 7. Furthermore, the study of octant analysis in a vegetated compound channel is not explored properly. Recently, Kazem et al. (2021) studied octant analysis in a vegetated channel. However, the vegetation distribution is uniform throughout the region. The current research looks upon octant analysis in a multi-layered vegetated compound channel, which has not been explored previously. Based on the signs of streamwise (u′), transverse (v′), and vertical (w′) fluctuations, the different bursting events in octant analysis are divided as follows:
  1. Internal outward interaction or Class I-A (u′ > 0, w′ > 0, v′ > 0);
  2. Internal ejection or Class II-A (u′ < 0, w′ > 0, v′ < 0);
  3. Internal inward interaction or Class III-A (u′ < 0, w′ < 0, v′ < 0);
  4. Internal sweep or Class IV-A (u′ > 0, w′ < 0, v′ > 0);
  5. External outward interaction or Class I-B (u′ > 0, w′ > 0, v′ < 0);
  6. External ejection or Class II-B (u′ < 0, w′ > 0, v′ > 0);
  7. External inward interaction or Class III-B (u′ < 0, w′ < 0, v′ > 0);
  8. External sweep or Class IV-B (u′ > 0, w′ < 0, v′ < 0);
The occurrence probability of each event was given by Keshavarzi and Gheisi (2006) as follows:
P k = n k N (6)
N = k = 1 8 n k k = 1 , 2 , 3 . , 8 (7)
where P k is the occurrence probability of each event, n k is the number of events in each class, and N is the total number of bursting events.

Figures 13-15 show the occurrence probability of different bursting events for no-vegetation, U 6 cm uniform vegetation, and NU 6 cm uniform vegetation cases, respectively. From the figures, it can be seen that Classes I-A, III-A, I-B, and III-B are less throughout the cross section in case of no-vegetation cases. The contribution is mainly provided by the ejection (Class II-A and Class II-B) and sweep (Class IV-A and Class IV-B) events. The contribution of sweep events throughout the cross section is more than 14%, whereas outward (Class I-A and Class I-B) and inward (Class III-A and Class III-B) interaction mainly lies within 12%. This shows that sweep and ejection events are mainly present in the channel in the no-vegetation case. This scenario changes after the introduction of vegetation in the floodplain region, as shown in Figures 14 and 15. Classes I-A (internal outward interaction) and III-A (internal inward interaction) are seen to govern mainly the SFPI and SM sections. It usually lies between 14% and 17%, which is not seen in the no-vegetation case. The contribution of Classes I-A and III-A in the U 6 cm uniform case is more widespread as compared with the NU 6 cm uniform case. The majority of the points at the SFPI section in the U 6 cm uniform case contribute more than 18% in Classes I-A and III-A, whereas it is within 18 % for the NU 6 cm uniform case. The ejection and sweep events mainly dominate the main channel section of the cross-section. The contribution of sweep events (Class IV-A and Class IV-B) surpasses the ejection events (Figures 14 and 15). However, there is not much distinction between both types of sweep events. Another thing noticed is that Classes I-B and III-B are less than other events irrespective of any cases.

Details are in the caption following the image
Occurrence probability of different bursting events for no-vegetation case at 5.5 m cross section for 35 lps
Details are in the caption following the image
Occurrence probability of different bursting events for U 6 cm uniform vegetation case at 5.5 m cross section for 35 lps
Details are in the caption following the image
Occurrence probability of different bursting events for NU 6 cm uniform vegetation case at 5.5 m cross section for 35 lps

Figures 16 and 17 show the occurrence probability of different bursting events for multi-layered vegetation cases at 35 lps discharge. The results are similar to that of uniform height set up (U 6 cm uniform and NU 6 cm uniform), where the slope and main channel region mainly contribute to various bursting events. However, the contribution of Classes I-A and III-A in the SFPI section of multi-layered cases (Figures 16 and 17) is more as compared with single-layered cases (Figures 14 and 15). As seen for single-layered vegetation in Figures 14 and 15, the percentage of sweep events (Class IV-A and Class IV-B) is also greater in the main channel section of multi-layered vegetation, as shown in Figures 16 and 17. Class II-A or internal ejection class is seen on the floodplain region of both U 3-6-9 cm and NU 3-6-9 cm submerged cases. Classes I-B and III-B are less in the slope and main channel region irrespective of discharge and vegetation setup, similar to Figures 14 and 15. However, it is seen to contribute in the floodplain region of U 3-6-9 cm submerged case, as seen in Figure 16.

Details are in the caption following the image
Occurrence probability of different bursting events for U 3-6-9 cm uniform vegetation case at 5.5 m cross section for 35 lps
Details are in the caption following the image
Occurrence probability of different bursting events for NU 3-6-9 cm uniform vegetation case at 5.5 m cross section for 35 lps

4 CONCLUSION

Laboratory experiments were performed to study the flow behaviour in a multi-layered vegetated compound channel. Different turbulence characteristics were evaluated and compared between different cases of single-layered (U 6 cm uniform and NU 6 cm uniform) and multi-layered vegetation cases (U 3-6-9 cm submerged and NU 3-6-9 cm submerged). It is seen that there is an increase of more than 20 % velocity in the main channel section as the flow progresses from sparsely vegetated (20 cm c/c) to densely vegetated (10 cm c/c) region in NU 6 cm uniform case. It was seen that flow characteristics like velocity, streamwise RSS, turbulent intensities, and TKE are more pronounced for uniform set up (U 6 cm uniform and U 3-6-9 cm submerged) compared with non-uniform set up (NU 6 cm uniform and NU 3-6-9 cm submerged) in the SE and MC sections. The integral scales (Taylor and Euler) analysis at the slope and main channel also showed that it is more in the U 3-6-9 cm submerged case than the NU 3-6-9 cm submerged case. The comparison between single-layered and multi-layered vegetation shows that flow velocity in the SM, SE, and MC sections of multi-layered vegetation is greater than single-layered vegetation. The magnitude of turbulent intensities and TKE is also greater for multi-layered vegetation to single-layered vegetation at SE and MC sections. The bursting phenomenon was analysed using octant analysis as transverse fluctuations (v') play an important role in overall flow behaviour. It was seen that Class I-A is mostly present at the SFPI section of the U 3-6-9 cm submerged case, whereas Class III-A was seen in the NU 3-6-9 cm submerged case. The sweep events (Class IV-A and Class IV-B) are mostly present in the main channel section compared with ejection events (Class II-A and Class II-B) for all vegetation set up. The contribution of Classes I-B and III-B is negligible whether the floodplain is covered in vegetation or not. The study mainly focused on the slope and main channel flow behaviour when it encounters multi-layered vegetation in the floodplain region. This research encourages the use of multi-layered vegetation in laboratory experiments because it is common in riparian vegetation. This study can be extended to emphasize the floodplain region, concentrating on the drag coefficient provided by multi-layered vegetation. It will also concentrate on experiments involving a mixture of submerged and emergent vegetation in the floodplain zone to achieve a realistic field view.

CONFLICT OF INTEREST

The authors have no conflicts of interest to declare.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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