Representative point‐integrated suspended sediment sampling in rivers

Gitto, A.; Venditti, Jeremy G.; Kostaschuk, Ray; Church, Michael

doi:10.1002/2016wr019187

Cited by 21 publications

(26 citation statements)

References 30 publications

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“…All physical sediment samples were treated with copper sulfate to reduce biological growth in attempt to minimize particle cohesion and processed in a laboratory setting following the methodology outlined in Folk (1980). The in-beam samples were run through a LISST-100 (Sequoia Scientific, USA) laser grain-size analyzer using a random shape model which provides volumetric GSD in 32 logarithmically spaced bins from 2 to 356 microns and then filtered to obtain M. We investigated potential bias in our grain size sampling methods due to flocculation by comparing our physical samples to in situ observations of concentration and grain size made by Gitto et al (2017) at the site using a LISST-SL, which is an isokinetic laser grain size analyzer also made by Sequoia Scientific. The in situ results were entirely consistent with our laboratory measured grain sizes.…”

Section: Physical Sediment Observationsmentioning

confidence: 99%

Application of Multifrequency Acoustic Inversions Using Three Horizontally Profiling ADCPs

Haught

Venditti

Wright

2020

Water Resources Research

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An array of three horizontally mounted acoustic Doppler current profilers (ADCPs) deployed in the Fraser River at Mission British Columbia was used to calculate suspended sediment concentration (M), mean grain size (D g ), and geometric standard deviation σ g of the grain size distribution (GSD) using acoustic signal inversion. We examine two different multifrequency methods, an explicit and implicit inversion, by systematically allowing M, D g , and GSD standard deviation (σ g ) to vary in the inversions. This is the first application of these methods to commercially produced ADCPs, and we further develop methods that use both viscous and scattering attenuation. We couple these inversions with bottle samples collected within the ensonified volume that were analyzed for suspended sediment concentration and GSD characteristics. Concentrations ranged from 20 to 350 mg/L with GSD characteristics dominated by silt to fine sand. We estimate the necessary calibration parameters needed for complete acoustic inversions and find that M and D g are both dependent on the calibration constant, which could be a source of error in multifrequency inversions using uncalibrated ADCPs. Comparisons between the inversion results and samples show that the implicit method tends to perform best at all flows for estimating M while providing realistic estimates of grain size at high flows only. The explicit method performed well at high flows, but poorly at low flows, for M and D g . Estimates of σ g using both inversion methods indicate frequency dependence. We show that the daily averaged acoustically derived M and D g capture the hysteretic nature of sediment transport in the Fraser River.

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Section: Physical Sediment Observationsmentioning

confidence: 99%

Application of Multifrequency Acoustic Inversions Using Three Horizontally Profiling ADCPs

Haught

Venditti

Wright

2020

Water Resources Research

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“…in which w s (L T −1 ) is the particle settling velocity, κ is the dimensionless von Karman constant of 0.41, u * (L T −1 ) is the bed shear velocity, and β is a dimensionless factor that accounts for differences between the turbulent diffusivity of sediment and the parabolic eddy viscosity model used to derive the Rouse equation (e.g., Graf and Cellino, 2002). Although more sophisticated models exist, some of which abandon the Rouse theory entirely in favor of a more rigorous turbulence model (Mellor and Yamada, 1982), the Rouse equation remains a useful and tractable approach for modeling and field application (Graf and Cellino, 2002;van Rijn, 1984;Wright and Parker, 2004b). The Rouse equation was derived assuming an equilibrium suspension whereby the upwards volumetric flux of sediment per unit area due to turbulence (F z ; L T −1 ) is balanced by a downwards gravitational settling flux (Cw s ) ( Fig.…”

Section: Introductionmentioning

confidence: 99%

“…β < 1 in Eq. (2) has been attributed to damping of turbulence due to sediment-induced stratification, which alters the eddy viscosity (Einstein, 1955;Graf and Cellino, 2002;Wright and Parker, 2004a), and to flocculation, which increases the settling velocity of finegrained sediment (e.g., Bouchez et al, 2011;Droppo and Existing relations for the factor β (Eq. 2) as a function of (a) shear velocity normalized by setting velocity u * /w s (van Rijn, 1984;Graf and Celino, 2002;Wright and Parker, 2004b), (b) near-bed concentration normalized by slope C a /S (Wright and Parker, 2004b), and (c) w s /u * (H /D) 0.6 as proposed by Santini et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Entrainment and suspension of sand and gravel

et al. 2020

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Abstract. The entrainment and suspension of sand and gravel are important for the evolution of rivers, deltas, coastal areas, and submarine fans. The prediction of a vertical profile of suspended sediment concentration typically consists of assessing (1) the concentration near the bed using an entrainment relation and (2) the upward vertical distribution of sediment in the water column. Considerable uncertainty exists in regard to both of these steps, especially the near-bed concentration. Most entrainment relations have been tested against limited grain-size-specific data, and no relations have been evaluated for gravel suspension, which can be important in bedrock and mountain rivers. To address these issues, we compiled a database with suspended sediment data from natural rivers and flume experiments, taking advantage of the increasing availability of high-resolution grain size measurements. We evaluated 12 dimensionless parameters that may determine entrainment and suspension relations and applied multivariate regression analysis. A best-fit two-parameter equation (r2=0.79) shows that near-bed entrainment, evaluated at 10 % of the flow depth, decreases with the ratio of settling velocity to skin-friction shear velocity (wsi/u∗skin), as in previous relations, and increases with Froude number (Fr), possibly due to its role in determining bedload-layer concentrations. We used the Rouse equation to predict concentration upward from the reference level and evaluated the coefficient βi, which accounts for differences in the turbulent diffusivity of sediment from the parabolic eddy viscosity model used in the Rouse derivation. The best-fit relation for βi (r2=0.40) indicates greater relative sediment diffusivities for rivers with greater flow resistance, possibly due to bedform-induced turbulence, and larger wsi/u∗skin; the latter dependence is nonlinear and therefore different from standard Rouse theory. In addition, we used empirical relations for gravel saltation to show that our relation for near-bed concentration also provides good predictions for coarse-grained sediment. The new relations extend the calibrated parameter space over a wider range in sediment sizes and flow conditions compared to previous work and result in 95 % of concentration data throughout the water column predicted within a factor of 9.

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“…We revisited the problem of sediment entrainment and suspension of cohesionless bed sediment by compiling a large database of sediment-size specific data for bed-sediment mixtures, testing existing relations against the database, and proposing improved relations for Es and P. Our database is a significant improvement compared to data used in past studies due to development of high resolution grain-size measurements using laser diffraction, which is now commonly used in field and laboratory studies (Lupker et al, 2011;Gitto et al, 2017;Santini et al, 2019). These grain-size measurements allow a single concentration profile to be separated into many grain-size specific concentration profiles and for the parameterization to be tested over a wide range of parameter space.…”

Section: Introductionmentioning

confidence: 99%

Entrainment and suspension of sand and gravel

Leeuw¹,

Lamb²,

Parker³

et al. 2019

Preprint

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Abstract. Entrainment and suspension of sand and gravel is important for the evolution of rivers, deltas, coastal areas and submarine fans. The prediction of a vertical profile of suspended sediment concentration typically consists of assessing (1) the concentration near the bed using an entrainment relation and (2) the upward vertical distribution of sediment in the water column. Considerable uncertainty exists in regard to both of these steps, and especially the near-bed concentration. Most entrainment theories have been tested against limited grain-size specific data, and no relations have been evaluated for gravel suspension, which can be important in bedrock and mountain rivers, as well as powerful turbidity currents. To address these issues, we compiled a database with suspended sediment data from natural rivers and flume experiments, taking advantage of the increasing availability of high-resolution grain-size measurements. We evaluated 14 dimensionless parameters that may determine entrainment and suspension relations, and applied multivariate regression analysis. A best-fit two-parameter equation (r2 = 0.79) shows that near-bed entrainment, evaluated at 10 % of the flow depth, increases with the ratio of skin-friction shear velocity to settling velocity (u*skin / wsi), as in previous relations, and with Froude number (Fr), possibly due to its role in determining bedload-layer concentrations. We used the Rouse equation to predict concentration upward from the reference level, and evaluated the coefficient βi, which accounts for differences between turbulent diffusivities of sediment and momentum. The best-fit relation for βi (r2 = 0.40) indicates greater relative sediment diffusivities for rivers with greater flow resistance, possibly due to bed-form induced turbulence, and smaller u*skin / wsi; the latter effect makes the dependence of Rouse number on u*skin / wsi nonlinear, and therefore different from standard Rousean theory. In addition, we used empirical relations for gravel saltation to show that our relation for near-bed concentration also provides good predictions for coarse-grained sediment. The new relations are a significant improvement compared to previous work, extend the calibrated parameter space over a wider range in sediment sizes and flow conditions, and result in 95 % of concentration data predicted within a factor of nine.

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Representative point‐integrated suspended sediment sampling in rivers

Cited by 21 publications

References 30 publications

Application of Multifrequency Acoustic Inversions Using Three Horizontally Profiling ADCPs

Application of Multifrequency Acoustic Inversions Using Three Horizontally Profiling ADCPs

Entrainment and suspension of sand and gravel

Entrainment and suspension of sand and gravel

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