X-ray computed tomography reveals that grain protrusion controls critical shear stress for entrainment of fluvial gravels

Hodge, Rebecca; Voepel, Hal; Leyland, Julian; Sear, David; Ahmed, Sharif

doi:10.1130/g46883.1

Cited by 18 publications

(30 citation statements)

References 26 publications

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“…Hodge et al. (2020) found that clast protrusion ( P ) was the only one among several measures of the bed that correlated significantly with τ c *. Adopting σ = 10 mm (Figure 6) as an approximation of their measure of protrusion for the post‐R3‐developed surface, P / D 50b ≈ σ / D 50b = 2.0, and τ c * = 0.036 from the relation of Hodge et al.…”

Section: Discussionmentioning

confidence: 99%

“…Adopting σ = 10 mm (Figure 6) as an approximation of their measure of protrusion for the post‐R3‐developed surface, P / D 50b ≈ σ / D 50b = 2.0, and τ c * = 0.036 from the relation of Hodge et al. (2020). This value, derived from the relatively large value of σ , is lower than the values displayed in Figure 4, implying that general surface roughness accounts for only part of the resistance to flow and reluctance of bed particles to move.…”

Section: Discussionmentioning

confidence: 99%

“…Masteller and Finnegan (2017) found that bed stability varied with coarse-grain protrusion. Hodge et al (2020) used X-ray tomography to measure complete surface grain geometries, and found that protrusion is more important than pocket geometry in controlling thresholds for individual grains. Their measurements included only stones that were free to move (i.e., not constrained by other stones).…”

Section: Second Hypothesis: Transport Rates Evolve With Armor Developmentioning

confidence: 99%

“…This variability has been attributed to many interrelated factors, including bed surface coarsening (e.g., Andrews & Parker, 1987; Gessler, 1970; Parker et al., 1982; Parker & Klingeman, 1982; Tait & Willetts, 1992), sand or finer gravel on the bed (Perret et al., 2018; Venditti et al, 2010a, 2010b; Wilcock & Crowe, 2003), protrusion of grains into the flow relative to the rest of the bed (e.g., Fenton & Abbott, 1977; Hodge et al., 2020; Kirchner et al., 1990; Masteller & Finnegan, 2017; Perret et al., 2020; Roberts et al., 2020), intergranular friction and imbrication (Johnston, 1922; Yager et al., 2018), the geometry of pockets from which grains are entrained (Buffington et al., 1992; Johnston & Andrews, 1998; Kirchner et al., 1990; Masteller & Finnegan, 2017), influence of bed surface structures (e.g., Brayshaw, 1984, 1985; Brayshaw et al., 1983; Church et al., 1998; Cin, 1968; Laronne & Carson, 1976), bedform evolution (e.g., Recking et al., 2009; Saletti et al., 2015), and sediment availability (e.g., Andrews & Parker, 1987; Hassan & Church, 2000; Mao et al., 2011; Parker et al., 1982; Recking, 2012). These factors cause spatial and temporal changes in the local threshold for motion (e.g., Hodge et al., 2020; Johnson, 2016; Masteller et al., 2019; Turowski et al., 2011; Yager et al., 2018). It has been shown that the threshold of motion increases as grain protrusion decreases (Masteller & Finnegan, 2017), although they have argued that pocket geometry is controlling thresholds for individual grains (Hodge et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

“…These factors cause spatial and temporal changes in the local threshold for motion (e.g., Hodge et al., 2020; Johnson, 2016; Masteller et al., 2019; Turowski et al., 2011; Yager et al., 2018). It has been shown that the threshold of motion increases as grain protrusion decreases (Masteller & Finnegan, 2017), although they have argued that pocket geometry is controlling thresholds for individual grains (Hodge et al., 2020). Given this very large number of controls, the gravel threshold of motion can be thought of as a lumped parameter that is controlled by the “bed state,” a somewhat loosely defined term to describe the ensemble of factors that control bed mobility and transport rates.…”

Section: Introductionmentioning

confidence: 99%

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Experimental Insights Into the Threshold of Motion in Alluvial Channels: Sediment Supply and Streambed State

et al. 2020

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Multiple bed surface characteristics, including surface grain size distribution (GSD), grain protrusion and surface roughness, and development of coarse-grain clusters, respond to water and sediment supply in ways that generally enhance bed stability. As the sediment supply decreases, the gravel-bed surfaces coarsen and bed stability increases (Dietrich et al., 1989; Gessler, 1970; Nelson et al., 2009). This armoring (surface coarsening relative to the subsurface or the sediment supply) occurs when transport capacity exceeds sediment supply, resulting in an immobile armored bed surface. Armoring can also reflect a bed adjustment so that the GSD of sediment transported out of a reach matches the sediment supply GSD from upstream, producing a mobile armor (e.g., Parker & Klingeman, 1982; Temple & Wilcock, 2005). Early work on the organization of gravel beds showed that the process of bed coarsening is accompanied by the formation of structural features, including clustering of the relatively large and less mobile grains that further reduces the overall mobility of the bed surface sediments and increases roughness (e.g., Brayshaw, 1984; Brayshaw et al., 1983). A wide variety of bed structures is now known to occur that form coherent patterns of clasts in gravel-bed rivers (see review in Venditti et al., 2017). These features are larger than individual clasts and generally smaller than channel-scale features (e.g., bars or step-pool features). The bed features include clusters (e.g.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Second Hypothesis: Transport Rates Evolve With Armor Developmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Experimental Insights Into the Threshold of Motion in Alluvial Channels: Sediment Supply and Streambed State

et al. 2020

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Untangling the controls on bedload transport in a wood‐loaded river with RFID tracers and linear mixed modelling

Clark

Bennett

Ryan-Burkett

et al. 2022

Earth Surf Processes Landf

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Bedload transport is a fundamental process by which coarse sediment is transferred through landscapes by river networks and may be well described stochastically by distributions of grain step length and rest time obtained through tracer studies. To date, none of these published tracer studies have specifically investigated the influence of large wood in the river channel on sediment transport dynamics, limiting the applicability of stochastic sediment transport models in these settings. Large wood is a major component of many forested rivers and is increasing due to anthropogenic ‘Natural Flood Management’ (NFM) practices. This study aims to investigate and model the influence of large wood on grain‐scale bedload transport. We tagged 957 cobble to pebble sized particles (D50 = 73 mm) and 28 pieces of large wood (> 1 m in length) with radio frequency identification (RFID) tracers in an alpine mountain stream. We monitored the transport distance of tracers annually over 3 years, building distributions of tracer transport distances with which to compare with published distributions from wood free settings. We also applied linear mixed modelling (LMM), to tease out the influence of wood from other controls on likelihood of entrainment, deposition, and the transport distances of sediments. Tracer sediments accumulated both upstream and downstream of large wood pieces, with LMM analysis confirming a reduction in the probability of entrainment of tracers closer to wood in all 3 years. Upon remobilization, tracers entrained from positions closer to large wood had shorter subsequent transport distances in each year. In 2019, large wood also had a trapping effect, significantly reducing the transport distances of tracer particles entrained from upstream, that is forcing premature deposition of tracers. This study demonstrates the role of large wood in influencing bedload transport in alpine stream environments, with implications for both natural and anthropogenic addition of wood debris in fluvial environments.

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The influence of bedrock river morphology and alluvial cover on gravel entrainment. Part 2: Modelling critical shear stress

Hodge

Buechel

2022

Earth Surf Processes Landf

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The critical shear stress (τ c ) at which grains are entrained on a bedrock surface is important for determining how bedrock rivers evolve through changes in sediment cover and bedrock erosion. The difference in τ c for grains on bedrock and alluvial surfaces also determines whether a channel may be susceptible to runaway alluviation.Bedrock channel beds can have a wide variety of morphologies, but we do not fully understand how this variation affects τ c . Here we address how bedrock morphology alters the grain entrainment parameters of pivoting angle, grain exposure and roughness height z 0 , and thus τ c . In our companion article we used scaled, 3D printed replicas of seven bedrock surfaces to measure grain pivoting angles for four grain sizes.For three surfaces, pivoting angles were also measured with 25-100% sediment cover. In this second article, we combine these pivot angle data with measurements of grain exposure and surface roughness (standard deviation of elevations, σ z ) to predict τ c using a force-balance model. The bedrock topography produces substantial variation in τ c ; for a given grain diameter (D), a 3.6Â range of σ z across the surfaces without sediment cover produces up to a 5.1Â variation in τ c . For comparison, for any single surface, τ c varies by up to 2.5Â for a fourfold range in grain size. Comparison to previous models with less representation of grain-scale geometry shows that in our results grains move at lower values of dimensionless critical shear stress (τ* c ), and that τ* c decreases more quickly with increasing D/σ z . However, direct comparison is difficult because previous relationships are based on a hydraulic roughness length that cannot be easily predicted without hydraulic data. Our results propose a new relationship between D/σ z and τ* c , but further development and testing require datasets that combine measurements of flow, τ c and grain-scale geometry.

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X-ray computed tomography reveals that grain protrusion controls critical shear stress for entrainment of fluvial gravels

Cited by 18 publications

References 26 publications

Experimental Insights Into the Threshold of Motion in Alluvial Channels: Sediment Supply and Streambed State

Experimental Insights Into the Threshold of Motion in Alluvial Channels: Sediment Supply and Streambed State

Untangling the controls on bedload transport in a wood‐loaded river with RFID tracers and linear mixed modelling

The influence of bedrock river morphology and alluvial cover on gravel entrainment. Part 2: Modelling critical shear stress

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