Figure 1 - uploaded by Heidi Nepf
Content may be subject to copyright.
Source publication
This chapter describes the mean and turbulent flow structure near aquatic plants and coral. The first section describes flow at the scale of individual blades and branches. The second section describes the flow at the scale of multiple blades and branches, i.e. the canopy scale. Both waves and uni-directional current are described.
Contexts in source publication
Context 1
... chapter describes the mean and turbulent flow structure near aquatic plants and coral. The first section describes flow at the scale of individual blades and branches. The second section describes the flow at the scale of multiple blades and branches, i.e . the canopy scale. Both waves and unidirectional current are described. Introduction : Scales of Morphology and Flow The presence of coral, seagrass, kelp and other macrophytes influences the velocity field across several scales, ranging from individual elements, such as branches, blades, and polyps, to the community of elements, i.e . the reef, meadow, and forest. For simplicity we will call all of these communities a canopy. Significant spatial variation in the velocity field exists at the element-scale due to the wakes generated by individual elements and branches. Velocity heterogeneity is also introduced at larger scale due to spatial variation in canopy density and morphology. Flow structure at the different scales is relevant to different processes. For example, the uptake of nutrients by an individual blade depends on the boundary layer that forms on that blade, i.e. on the blade-scale flow ( e.g . Koch 1994, Hurd 2000). Similarly, the capture of pollen is mediated by the flow structure generated around individual stigmas ( e.g . Ackerman 1997, 2000). In contrast, the flushing of larvae and pollen from a seagrass meadow or kelp forest depends on the flow structure at the scale of the meadow or forest, i.e . the canopy-scale (e.g. Jackson and Winant, 1983, Gaylord et al. 2004). Spatial heterogeneity in the canopy-scale parameters can lead to complex flow systems, e.g . as found in a coastal marsh. Within a coastal marsh a branching network of channels cuts through regions of dense, largely emergent vegetation. While the channels provide most of the flow conveyance, the vegetated zones provide most of the ecosystem function and particle trapping. Thus, to describe marsh function one must describe the transport into and circulation within the vegetated regions. The added drag provided by the marsh grass greatly reduces the flow speeds in the canopy relative to zones without vegetation. Because the vegetative drag increases with vegetation density (stems m -2 ), the circulation pattern within the marsh is largely dictated by spatial variability in vegetation density. In addition to dictating the mean flow pattern, the presence of vegetation also impacts turbulence structure. In unobstructed (open-channel) flow turbulent length-scales are set by flow depth and channel width, typically 1/10 the confining dimension, e.g . 10-100 cm in tidal channels. However, the distributed drag within the canopy damps large-scale eddies, just as it damps the mean flow, and the turbulent length-scales are reduced to stem-diameter and stem-spacing (typically 1 to 10 cm). This reduction in eddy-scale reduces the turbulent diffusivity relative to comparable flow in open water. This chapter describes the mean and turbulent flow structure at the scale of individual blades and branches (Section 2), as well as at the canopy-scale (Section 3). Section 3.1 introduces the mathematical methods for describing the geometry of a canopy and incorporating element-scale heterogeneity into the flow equation, known as the double-average method. A description of unidirectional flow through submerged and emergent canopies is given in Section 3.2. Finally, Section 3.3 describes the interaction of waves with submerged canopies. Flow at the scales of individual elements is dominated by the no-slip condition that applies at the surface of every blade, branch and stem. Specifically, the velocity on every surface must match exactly the velocity of that surface ( i.e . have no slip). For simplicity, we begin by considering rigid, rooted elements. Since the velocity of the element is zero by definition, the velocity of the fluid directly adjacent to the element is also zero. Moving perpendicularly (or in the normal direction n ⊥ ) away from the surface, the velocity, u , increases from zero to match that of the ambient flow between elements. Thus, there is a region near the element surface with a strong spatial velocity € gradient (shear). This is called the boundary layer. Because the velocity near the element surface is small (approaching zero), but the shear (velocity gradient) is high, the viscous damping, ρ μ ∂ u/ ∂ n ⊥ , dominates the flow inertia, u 2 , and the region is constrained to be laminar. Here is the fluid density and μ is the absolute viscosity. This region is called a viscous boundary layer. For example, consider flow along a flat plate (Fig 1). This is often used as a model for flow adjacent to canopy elements that are elongated in the direction of flow, such as stream-wise oriented blades and leaves. A viscous boundary layer (denoted by grey shading) forms at the leading edge of the plate, and grows with stream-wise distance along the plate. The edge of the boundary layer is defined as the point above the plate at which the local streamwise velocity, u , reaches 99% of the free-stream velocity, U . For a laminar boundary layer, this distance, δ , has been described analytically by Blasius (e.g. White (2008)). Let x be the distance from the leading edge (Fig 1), ...
Context 2
... chapter describes the mean and turbulent flow structure near aquatic plants and coral. The first section describes flow at the scale of individual blades and branches. The second section describes the flow at the scale of multiple blades and branches, i.e . the canopy scale. Both waves and unidirectional current are described. Introduction : Scales of Morphology and Flow The presence of coral, seagrass, kelp and other macrophytes influences the velocity field across several scales, ranging from individual elements, such as branches, blades, and polyps, to the community of elements, i.e . the reef, meadow, and forest. For simplicity we will call all of these communities a canopy. Significant spatial variation in the velocity field exists at the element-scale due to the wakes generated by individual elements and branches. Velocity heterogeneity is also introduced at larger scale due to spatial variation in canopy density and morphology. Flow structure at the different scales is relevant to different processes. For example, the uptake of nutrients by an individual blade depends on the boundary layer that forms on that blade, i.e. on the blade-scale flow ( e.g . Koch 1994, Hurd 2000). Similarly, the capture of pollen is mediated by the flow structure generated around individual stigmas ( e.g . Ackerman 1997, 2000). In contrast, the flushing of larvae and pollen from a seagrass meadow or kelp forest depends on the flow structure at the scale of the meadow or forest, i.e . the canopy-scale (e.g. Jackson and Winant, 1983, Gaylord et al. 2004). Spatial heterogeneity in the canopy-scale parameters can lead to complex flow systems, e.g . as found in a coastal marsh. Within a coastal marsh a branching network of channels cuts through regions of dense, largely emergent vegetation. While the channels provide most of the flow conveyance, the vegetated zones provide most of the ecosystem function and particle trapping. Thus, to describe marsh function one must describe the transport into and circulation within the vegetated regions. The added drag provided by the marsh grass greatly reduces the flow speeds in the canopy relative to zones without vegetation. Because the vegetative drag increases with vegetation density (stems m -2 ), the circulation pattern within the marsh is largely dictated by spatial variability in vegetation density. In addition to dictating the mean flow pattern, the presence of vegetation also impacts turbulence structure. In unobstructed (open-channel) flow turbulent length-scales are set by flow depth and channel width, typically 1/10 the confining dimension, e.g . 10-100 cm in tidal channels. However, the distributed drag within the canopy damps large-scale eddies, just as it damps the mean flow, and the turbulent length-scales are reduced to stem-diameter and stem-spacing (typically 1 to 10 cm). This reduction in eddy-scale reduces the turbulent diffusivity relative to comparable flow in open water. This chapter describes the mean and turbulent flow structure at the scale of individual blades and branches (Section 2), as well as at the canopy-scale (Section 3). Section 3.1 introduces the mathematical methods for describing the geometry of a canopy and incorporating element-scale heterogeneity into the flow equation, known as the double-average method. A description of unidirectional flow through submerged and emergent canopies is given in Section 3.2. Finally, Section 3.3 describes the interaction of waves with submerged canopies. Flow at the scales of individual elements is dominated by the no-slip condition that applies at the surface of every blade, branch and stem. Specifically, the velocity on every surface must match exactly the velocity of that surface ( i.e . have no slip). For simplicity, we begin by considering rigid, rooted elements. Since the velocity of the element is zero by definition, the velocity of the fluid directly adjacent to the element is also zero. Moving perpendicularly (or in the normal direction n ⊥ ) away from the surface, the velocity, u , increases from zero to match that of the ambient flow between elements. Thus, there is a region near the element surface with a strong spatial velocity € gradient (shear). This is called the boundary layer. Because the velocity near the element surface is small (approaching zero), but the shear (velocity gradient) is high, the viscous damping, ρ μ ∂ u/ ∂ n ⊥ , dominates the flow inertia, u 2 , and the region is constrained to be laminar. Here is the fluid density and μ is the absolute viscosity. This region is called a viscous boundary layer. For example, consider flow along a flat plate (Fig 1). This is often used as a model for flow adjacent to canopy elements that are elongated in the direction of flow, such as stream-wise oriented blades and leaves. A viscous boundary layer (denoted by grey shading) forms at the leading edge of the plate, and grows with stream-wise distance along the plate. The edge of the boundary layer is defined as the point above the plate at which the local streamwise velocity, u , reaches 99% of the free-stream velocity, U . For a laminar boundary layer, this distance, δ , has been described analytically by Blasius (e.g. White (2008)). Let x be the distance from the leading edge (Fig 1), ...
Context 3
... x . As the viscous boundary layer grows thicker along the plate, it becomes more sensitive to perturbations caused by turbulent oscillations in the outer flow (outside the viscous boundary layer) and/or by irregularities in the surface texture of the plate. When the viscous layer is very thin, i.e . close to the leading edge, these perturbations are damped out by viscosity. However, as the viscous sub-layer grows, the perturbations within the layer can be stronger (have more inertia), and eventually are strong enough to overcome the viscous damping. At this point the boundary layer transitions to a turbulent boundary layer with a viscous sub-layer, δ s (Figure 1). The distance from the leading edge at which this transition occurs depends on the surface texture and the conditions of the flow outside the boundary layer. The transition typically occurs around Re x = 5 x 10 5 . However, if the plate is very smooth, and the free stream is very quiet, the transition can be delayed to as high as 3 x 10 6 (White, 2008). Alternatively, the distance is shorter when the free stream is highly turbulent and for rough surfaces, as micro-separation around the roughness will facilitate the transition to a turbulent boundary layer. Once the boundary layer is turbulent, the viscous sub-layer will have a constant thickness set by the friction velocity, u * , which describes the stress on the surface, τ = ρ u *2 . Experiments and scaling indicate that the viscous sub-layer is between δ s = 5 ν /u * and 10 ν /u * ( e.g . Kundu and Cohen 2002, Boudreau and Jorgensen 2001). Over this distance adjacent to the plate the flow is essentially laminar. Meanwhile, the outer edge of the boundary layer continues to grow with distance along the plate. White (2008) recommends the following growth rate, based on a power law description of the boundary layer ...
Similar publications
Citations
... For submerged vegetation, typically referred to as a meadow or canopy, the corresponding drag depends mainly on the depth of submergence, stem density and bending angle (Nepf, 2011;2012a;. If this drag dominates the bed induced drag, a shear layer will develop near the top of the canopy. ...
Coastal flood risk is expected to increase due to climate change and population growth. Much of our coastlines is protected by “grey” infrastructure such as a dike. Dike maintenance and strengthening requires ever increasing capital and space, putting their economic viability in question. To combat this trend, more sustainable alternatives are explored, also known as Nature based Solutions. A promising option has shown to be tidal marshes. Tidal marshes are coastal wetlands with high ecological and economic value. Also, they protect dikes through wave attenuation and in case of a dike breach reduce its development. However, the effectiveness of a tidal marsh on reducing dike breach development rates highly depends on the stability of the tidal marsh itself. Not much is known about the stability of a tidal marsh under dike breach conditions, which are accompanied with flow velocities that can reach 4–5 m s ⁻¹ . In this study we tested the vegetation response and erodibility of a mature tidal marsh, in-situ , under high flow velocities ( > 0.5 m s ⁻¹ ). Our results confirm that tidal marshes similar to the one tested in this study are highly erosion resistant with low erodibility. More research is necessary to confirm this for tidal marshes with different soil and vegetation properties. For tidal marshes similar to what is tested thus far, erosion under dike breach conditions is negligible and other erosion mechanisms such as headcut erosion probably dominate the erosion process.
... In accordance with Nepf and Vivoni, 33 penetration depth dp is calculated as the distance from the vegetation-main channel interface to 10% of the peak Reynolds stress. Figure 4(b) shows the penetration depth dp at different cross sections in case 1, with dashed lines representing the values in case 5. Penetration depth at cross section P3 in case 1 is similar to that at cross section P in case 5 due to the same density, while the other cross sections have greater penetration depths than cross section P. According to Nepf,34 in submerged vegetation, penetration depth is inversely proportional to vegetation density, and this is also observed in heterogeneous vegetation. In case 1, the penetration depth dp at P1 is greater than that at P3, indicating that increasing density reduces penetration depth and inhibits momentum exchange. ...
A large amount of vegetation in nature exists in the form of heterogeneous vegetation patches, and variations in vegetation characteristics significantly affect water flow structures. The objective of this study is to investigate the effect of alternating sparse and dense patches on turbulence characteristics. Multiple sets of heterogeneous vegetation scenarios were designed for numerical simulation analysis, and a comparison was made with homogeneous vegetation. Results indicate that compared with that of homogeneous vegetation, the arrangement of heterogeneous vegetation alters the distribution of flow velocities in the vegetation zone and the main channel, promoting material exchange between these regions. The vegetation density difference between sparse and dense vegetation patches in heterogeneous vegetation effect on the main channel increases with larger vegetation density differences, but the magnitude of the effect is limited and generally remains within 10% of the vegetation width. In the balance equation of turbulent kinetic energy, the terms are influenced differently by changes in vegetation density. As vegetation density difference increases, the convective term gradually increases, while the production and diffusion terms exhibit a "lower in the middle, higher at both ends" pattern. The dissipation term demonstrates a reduction effect at low-density differences, gradually increases with larger differences, and ultimately exhibits an amplification effect. Furthermore, this study determines that using data from mid-height to represent the entire cross section for heterogeneous vegetation may result in a maximum error of up to 11%.
... Analysing data from the Linx II study in headwater streams across the United States, Grant et al. (2018) imposed a physical upper limit of the N removal rate, governed by turbulent mass transfer and thus indirectly by flow velocity (Anlanger et al., 2021). However, flow velocity (m/s) is rather constant among the sites in the present study (i.e., varying for most records by a factor of 2 around 1 m/s) suggesting that in our case N-uptake in biofilms is mainly limited by biological processes (Nepf, 2011). These biological processes include the balance between nitrification and denitrification (Hall et al., 2009;Ribot et al. 2012Ribot et al. , 2017, the general health and composition of the involved organisms, their activity, ability to grow, to reproduce (Besemer, 2016;Krsmanovic et al., 2021) and their adaption to pollution (Wijeyekoon et al., 2004). ...
Surface waters are under increasing pressure due to human activities, such as nutrient emissions from wastewater treatment plants (WWTPs). Using the retention of nitrogen (N) released from WWTPs as a proxy, we assessed the contribution of biofilms grown on inorganic and organic substrates to the self-cleaning capacity of second-order streams within the biosphere reserve Vosges du Nord/Palatinate forest (France/Germany). The uptake of N from anthropogenic sources, which is enriched with the heavy isotope 15N, into biofilms was assessed up- and downstream of WWTPs after five weeks of substrate deployment. Biofilms at downstream sites showed a significant positive linear relationship between δ15N and the relative contribution of wastewater to the streams' discharge. Furthermore, δ15N substantially increased in areas affected by WWTP effluent (∼8.5‰ and ∼7‰ for inorganic and organic substrate-associated biofilms, respectively) and afterwards declined with increasing distance to the WWTP effluent, approaching levels of upstream sections. The present study highlights that biofilms contribute to nutrient retention and likely the self-cleaning capacity of streams. This function seems, however, to be limited by the fact that biofilms are restricted in their capacity to process excessive N loads with large differences between individual reaches (e.g., δ15N: -3.25 to 12.81‰), influenced by surrounding conditions (e.g., land use) and modulated through climatic factors and thus impacted by climate change. Consequently, the impact of WWTPs located close to the source of a stream are dampened by the biofilms' capacity to retain N only to a minor share and suggest substantial N loads being transported downstream.
... Thus, the approach may become inaccurate in dense forests where wake sheltering becomes more problematic. The average spacing of the trees was estimated to be 2.7 m. Based on rigid cylinder studies (Etminan et al., 2017;Nepf, 2011) and a study measuring velocity recovery in the wake of a real tree (Schleiss et al., 2014), this spacing is sparse enough to suggest minimal wake sheltering. However, the canopies are broader than the rigid cylinders and are porous media. ...
Flow resistance through riparian forests due to drag on trees is often expressed in hydraulic models with an increase in a boundary resistance factor such as Manning's n. However, when Manning's n is used as a proxy for vegetation drag, this parameter is dependent on flow conditions and a single, uniform value may be inadequate for simulating a broad range of flood magnitudes. To investigate this issue, flow resistance, and the commensurate Manning's n values through a riparian forest were computed using measured drag forces and estimates of the forest structure and tree morphology. The computed Manning's n values were applied to a 2D hydraulic model (TUFLOW) to simulate an observed flood and a range of design floods. Modelled peak flood levels for the observed flood were 0.16 m lower on average than that recorded at debris marks. There was little difference in modelled flood levels when using the computed Manning's n compared to a traditional, uniform Manning's n. Reassuringly, the traditional method appears adequate when reliable calibration data is available. Otherwise, the method developed here provides a useful alternative in cases where calibration data is limited or for testing reforestation as a nature‐based solution in river or flood management.
... Besides a number of chemical and biological processes, stream flow can be a key controlling factor of biofilm N uptake by mediating mass transfer across the concentration boundary layer at the streambed (Kim et al. 1992;Larned et al. 2004;Tomasek et al. 2018). Mass transfer can be a limiting step in nutrient uptake, particularly at low flow velocity, while at higher velocity uptake often becomes biologically limited (Nepf 2011). Moreover, hydraulics may indirectly affect N uptake and storage by altering biofilm architecture (Stoodley et al. 1998;Risse-Buhl et al. 2017) and biomass (Biggs and Thomsen 1995;Stevenson 1996) and probably also N transfer within the microbial biofilm food web (Weitere et al. 2018;Risse-Buhl et al. 2020a). ...
Epibenthic biofilms are important in regulating nitrogen (N) fluxes in stream ecosystems. The efficiency of the regulation is controlled by hydraulic and biological processes and their interactions. However, knowledge on the underlying physical and biological processes, their controlling parameters, and interactions in stream ecosystems is still limited. To analyze the relative importance of hydraulic and biological controls on biofilm N uptake, we measured turbulence, biofilm N uptake using a stable isotope tracer, and biofilm biomass in two gravel‐bed streams with contrasting nutrient concentrations for two seasons. We found high within‐stream variability in biofilm areal N uptake and uptake velocity, which exceeded variability between streams and seasons by 60% and 30%, respectively. Sixty‐four percent of the within‐stream variability in uptake velocity was explained by hydraulic mass transfer and biofilm characteristics, which were described in terms of the turbulent dissipation rate and the biofilm biomass, respectively. We show that surface renewal theory based on scales of the smallest turbulent eddies can be used to estimate transfer velocities at the sediment–water interface and can be extrapolated to larger scales by spatial averaging. Our results improved the mechanistic understanding of the processes regulating biofilm N uptake at small scale which contributes to the understanding of ecosystem functioning in low‐order streams and supports upscaling to larger spatiotemporal scales along stream networks.
... In the 3DSV and 3DFV simulations, the value of vegetation drag coefficient C D is required. The derivation of C D , using either direct measurement method or the calibration methods, is purely empirical and the selection of C D for real coastal cases is challenging as it is affected by different factors, including the vegetation density, vegetation morphology, wave-current interaction, as well as flow Reynolds number (Re) (e.g., Nepf, 2011;Hu et al. 2014;Chen et al. 2018;Mancheño et al. 2021). In this study, C D in Equation (7) was set to 1.0, which is a typical vegetation drag coefficient value at high Re (Ο(1 0 4 ) in this study) (e.g., White 1991;Armanini et al. 2005;Azechi et al., 2007;Luhar and Nepf 2013;Kalra et al. 2017;Moki et al. 2020). ...
Vegetation drag is a fundamental quantity directly affecting results for both long- and short-term coastal marsh and geomorphological studies. The vegetation drag in coastal marshland has been modeled by various two-dimensional (2D) and three-dimensional (3D) numerical parameterizations. 2D parameterizations treat coastal marshes as bottom roughness elements, while 3D parameterizations resolve the vertically-variable vegetation drag through the water column. However, differences in tidal propagation arising from different drag parameterizations within a single model are largely unknown, and clear guidance on parameterization selection is still missing. In this study, we implemented four vegetation drag parameterizations into the Model for Prediction Across Scales-Ocean (MPAS-O), which include 1) a 2D parameterization using land-cover type-determined Manning’s n (2DLM); 2) a 2D parameterization using vegetation-determined Manning’s n (2DVM); 3) a 3D parameterization for stiff vegetation (3DSV); and 4) a 3D parameterization for flexible vegetation (3DFV). Estimates of the flow resistance effects from these parameterizations were compared using a series of idealized tidal propagation simulations. Given the same tidal condition, flooding depth and flooding distance are the largest in the 2DLM simulations and the smallest in the 3DSV simulations. 2DVM results are the closest to the 2DLM results. 3DFV results are the closest to the average of 2DVM, 3DSV, and 3DFV results. 2DVM and 3DSV results are the least and most sensitive to the vegetation aboveground biomass, respectively. Based on the input data requirement and computational efficiency of each parameterization, a comparison summary is provided to help inform parameterization selection for specific applications. The effects of these parameterizations on coastal geomorphology are further discussed, and the results demonstrate that estimates of the long-term evolution of coastal marshes and coastal morphology depend upon the selection of the vegetation drag parameterization.
... In the field, however, water depth and incoming wave height can differ between vegetation types and will reduce or increase wave attenuation, respectively (e.g., Möller et al. 1999;Möller 2006;Maza et al. 2015;Vuik et al. 2016). For simplicity, the drag coefficient was the same in all scenarios, although it is likely to vary between vegetation types as vegetation characteristics can influence the drag coefficient (Nepf 2011). More flexible vegetation, for example, can have a lower drag coefficient than rigid vegetation (van Veelen et al. 2020). ...
Salt marshes can protect coastlines against flooding by attenuating wave energy and enhancing shoreline stabilization. However, salt-marsh functioning is threatened by human influences and sea level rise. Although it is known that protection services are mediated by vegetation, little is known about the role of vegetation structure in salt-marsh accretion. We investigated the role of vegetation presence, vegetation type and structural vegetation characteristics in sedimentation and sediment grain size. We established 56 plots on a salt marsh on the Dutch Wadden island of Texel. Plots were divided over four vegetation types contrasting in vegetation structure and varied in elevation and distance to creeks. Vegetation presence was controlled by clipping in subplots. Within each plot, we measured seven vegetation characteristics, sedimentation and the sediment grain size distribution. Furthermore, we explored the effect of the natural variation in vegetation structure on wave attenuation with a simple model approach. For this, we developed vegetation scenarios based on the field measurements of stem height, diameter and density. We found that vegetation presence increased sedimentation on average by 42%. Sedimentation was highest in Salicornia vegetation and increased with stem height and branching level. Grain size also seemed to increase with branching level. Modelled wave attenuation was 7.5 times higher with natural vegetation compared to topography only, was strongest for Spartina vegetation and most sensitive to the natural variance in stem density. Our results can be used to improve predictions of salt-marsh accretion and the implementation of salt marshes in nature-based flood defences.
... Its variation with characteristic hydrodynamic parameters, i.e. Reynolds number (Re) 60 and Keulegan-Carpenter number (KC) has been extensively investigated (Nepf, 2011). CD is commonly derived by calibration method, i.e. calibrating the CD value to ensure the modelled WDV fits with the observation (e.g. ...
Coastal vegetation has been increasingly recognized as effective buffer against wind waves. Recent studies have advanced our understanding of wave dissipation process in vegetation (WDV). In intertidal environments, waves commonly propagate into vegetation fields with underlying tidal currents, which may alter WDV, but such influence is often overlooked. The key mechanism of WDV with co-existing currents are understudied, as previous studies have drawn contradictory conclusions on the effect of following currents on WDV. Subsequent laboratory experiments have partly explained the inconsistent conclusions, but relevant data are rarely available for theoretical or modelling development. Additionally, while the vegetation drag coefficient is a key factor influencing WDV, it is rarely reported for combined wave-current flows. This paper reports a unique dataset from two flume experiments, including 668 wave-only and wave with following/opposing current tests. A variety of data including wave height, drag coefficient, in-canopy velocity and acting force on mimic vegetation stem are recorded. This dataset is expected to assist future theoretical advancement on WDV, which may ultimately lead to more accurate prediction of wave dissipation capacity of real coastal wetlands. The dataset is available from figshare (https://doi.org/10.6084/m9.figshare.13026530.v2; Hu et al., 2020) with clear instructions for reuse. The current dataset will expand with additional WDV data from ongoing as well as planned future observation in real mangrove wetlands.
... This often is reasonable under a unidirectional current with rigid cylinders for high stem Reynolds number flow (Re d ! 100) (Nepf, 2011). The stem Reynolds number calculated in Xue et al. (2017) from the measured average velocity, V v , in the resistance layer is from 1,200 to 1,800. ...
Quantifying incipient sediment motion in vegetated open channel flow is pivotal for estimating bed load transport and the aquatic ecological environment in rivers. A new formula is developed to predict the critical flow velocity for incipient sediment motion in the presence of emergent vegetation, by incorporating the influence of vegetation drag that characterizes the effects of mean flow and turbulence on sediment movement. The proposed formula is shown to agree with existing experimental data. Moreover, the proposed formula is extended to scenarios with submerged vegetation, suggesting that the vegetation drag may be the inherent impact factor for incipient sediment motion in vegetated open channel flow.
... For simple stem morphologies, vegetation can be described at the canopy scale using the frontal area per volume ( = ) and the frontal area index ( ℎ), where is the number of the blades per bed area and is the characteristic stem or blade width [29]. A canopy can be categorized as either sparse ( ℎ ≪ 0.1) or dense ( ℎ ≫ 0.1) using measurements of the vegetation and canopy drag coefficient ( ), which is typically taken as ≈ 1 [46]. When ℎ ≈ 0.1, the canopy is considered transitional, with hydrodynamics influenced by both bottom roughness and differential flow drag at the top of the canopy [21]. ...
... For simple stem morphologies, vegetation can be described at the canopy scale using the frontal area per volume (a = md) and the frontal area index (ah), where m is the number of the blades per bed area and d is the characteristic stem or blade width [29]. A canopy can be categorized as either sparse (C D ah 0.1) or dense (C D ah 0.1) using measurements of the vegetation and canopy drag coefficient (C D ), which is typically taken as C D ≈ 1 [46]. When C D ah ≈ 0.1, the canopy is considered transitional, with hydrodynamics influenced by both bottom roughness and differential flow drag at the top of the canopy [21]. ...
Mean flow and turbulence measurements collected in a shallow Halodule wrightii shoal grass fringe highlighted significant heterogeneity in hydrodynamic effects over relatively small spatial scales. Experiments were conducted within the vegetation canopy (~4 cm above bottom) for relatively sparse (40% cover) and dense (70% cover) vegetation, with reference measurements collected near the bed above bare sediment. Significant benthic velocity shear was observed at all sample locations, with canopy shear layers that penetrated nearly to the bed at both vegetated sites. Turbulent shear production (P) was balanced by turbulent kinetic energy dissipation (ϵ) at all sample locations (P/ϵ≈1), suggesting that stem-generated turbulence played a minor role in the overall turbulence budget. While the more sparsely vegetated sample site was associated with enhanced channel-to-shore velocity attenuation (71.4 ± 1.0%) relative to flows above bare sediment (51.7 ± 2.2%), unexpectedly strong cross-shore currents were observed nearshore in the dense canopy (VNS), with magnitudes that were nearly twice as large as those measured in the main channel (VCH; VNS/VCH¯ = 1.81 ± 0.08). These results highlight the importance of flow steering and acceleration for within- and across-canopy transport, especially at the scale of individual vegetation patches, with important implications for nutrient and sediment fluxes. Importantly, this work represents one of the first hydrodynamic studies of shoal grass fringes in shallow coastal estuaries, as well as one of the only reports of turbulent mixing within H. wrightii canopies.
