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Abstract Grain force‐balance models utilize grain protrusion and in‐situ resistance force data to evaluate the likely distributions of gravel‐bed sediment entrainment thresholds, specifically dimensionless critical shear stress (τ*_c). These methods can give insight into the spatial variability of particle mobilities both within a channel, and between different gravel‐beds, but are yet to be evaluated across multiple sites with varying texture and fabric. We evaluate two published force‐balance approaches: (a) a Monte Carlo style sampling approach using grain size and topography distributions from field measurements; and (b) an automated point cloud segmentation and analysis approach with an updated set of force‐balance equations, Pro+. We compare the workflows, assumptions and inputs for each approach, apply them to an extensive UK‐wide data set comprising 45 upland riverbeds, and evaluate the estimatedτ*_cdistributions. We find that mobility thresholds estimated from both methods are variable, with medianτ*_cranging from 0.05 to 0.15, and are consistent with published values of approximately 0.02–0.1. Uncertainties in grain sampling strategy or point cloud segmentation quality lead to markedly different grain size distributions between approaches, but their resulting influences onτ*_cdistributions are small relative to the range of estimatedτ*_c. Sensitivity analyses onτ*_cdistributions for grain‐size fractions also show that bed mobilities are sensitive to the roughness height of the velocity profile. We highlight uncertainties associated with these approaches, suggest areas for further targeted comparisons between methods, and provide guidance for the application of grain force‐balance models for estimating entrainment thresholds and bed stability in gravel‐bed rivers. 

Abstract Understanding when gravel moves in river beds is essential for a range of different applications but is still surprisingly hard to predict. Here we consider how our ability to predict critical shear stress (τ_c) is being improved by recent advances in two areas: (1) identifying the onset of bedload transport; and (2) quantifying grain‐scale gravel bed structure. This paper addresses these areas through both an in‐depth review and a comparison of new datasets of gravel structure collected using three different methods. We focus on advances in these two areas because of the need to understand how the conditions for sediment entrainment vary spatially and temporally, and because spatial and temporal changes in grain‐scale structure are likely to be a major driver of changes inτ_c. We use data collected from a small gravel‐bed stream using direct field‐based measurements, terrestrial laser scanning (TLS) and computed tomography (CT) scanning, which is the first time that these methods have been directly compared. Using each method, we measure structure‐relevant metrics including grain size distribution, grain protrusion and fine matrix content. We find that all three methods produce consistent measures of grain size, but that there is less agreement between measurements of grain protrusion and fine matrix content. 

Abstract The dimensionless critical shear stress (τ*_c) needed for the onset of sediment motion is important for a range of studies from river restoration projects to landscape evolution calculations. Many studies simply assume aτ*_cvalue within the large range of scatter observed in gravel‐bedded rivers because direct field estimates are difficult to obtain. Informed choices of reach‐scaleτ*_cvalues could instead be obtained from force balance calculations that include particle‐scale bed structure and flow conditions. Particle‐scale bed structure is also difficult to measure, precluding wide adoption of such force‐balanceτ*_cvalues. Recent studies have demonstrated that bed grain size distributions (GSD) can be determined from detailed point clouds (e.g. using G3Point open‐source software). We build on these point cloud methods to introduce Pro+, software that estimates particle‐scale protrusion distributions andτ*_cfor each grain size and for the entire bed using a force‐balance model. We validated G3Point and Pro+ using two laboratory flume experiments with different grain size distributions and bed topographies. Commonly used definitions of protrusion may not produce representativeτ*_cdistributions, and Pro+ includes new protrusion definitions to better include flow and bed structure influences on particle mobility. The combined G3Point/Pro+ provided accurate grain size, protrusion andτ*_cdistributions with simple GSD calibration. The largest source of error in protrusion andτ*_cdistributions were from incorrect grain boundaries and grain locations in G3Point, and calibration of grain software beyond comparing GSD is likely needed. Pro+ can be coupled with grain identifying software and relatively easily obtainable data to provide informed estimates ofτ*_c. These could replace arbitrary choices ofτ*_cand potentially improve channel stability and sediment transport estimates.