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High resolution topographic data are necessary to understand benthic habitat, quantify processes at the water-sediment interface, and support computational fluid dynamics models for both surface and hyporheic hydraulics. In riverine systems, these data are typically collected using traditional surveying methods (total station, DGPS, etc.), airborne or terrestrial laser scanning, and photogrammetry. Recently, handheld surveying equipment has been rapidly acquiring popularity in part due to its processing capacity, price, size, and versatility. One such device is the iPhone LiDAR, which could have a good balance between precision and ease of use and is a potential replacement for conventional measuring tools. Here, we evaluated the accuracy of the LiDAR sensor and a Structure from Motion (SfM) method based on photos collected using the iPhone Cameras. We compared the LiDAR and SfM elevations to those from a high-precision laser scanner for an experimental rough water-worked gravelbed channel with boulder-like structures. We observed that both the LiDAR and SfM methods captured the overall streambed morphology and detected large (Hs
15 cm) and macro (5cm  Hs < 15cm) scales of topographic variations (Hs, roughness). The SfM technique also captured small scale (Hs <5cm) roughness whereas the LiDAR consistently simplified it with errors of 3.7 mm. 

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. 

Abstract Steep, boulder bed streams often contain sediment patches, which are areas of the bed with relatively well‐defined boundaries that are occupied by distinct grain size distributions (GSD). In sediment mixtures, the underlying GSD affects the critical Shields stress for a given grain size, which is commonly predicted using hiding functions. Hiding functions may vary with reach‐wide bed GSD, but the effect of local GSD on relative sediment mobility between sediment patches is poorly understood. We explore the effects of patch‐scale GSD on sediment mobility using tracer particles combined with local shear stresses to develop hiding functions for different patch classes within a steep stream. Hiding functions for all tested patch classes were similar, which indicates that the same hiding function can be used for different patches. However, the critical Shields stress for a given grain size generally decreased with lower patch median grain size (D₅₀) suggesting that patches control the relative mobility of each size through both the underlying GSD and local shear stresses. The effects of the underlying GSD partly depend on grain protrusion, which we measured for all grain sizes present on each patch class. Protrusion was generally greater for larger grains regardless of patch class, but for a given grain size, protrusion was increased with smaller patchD₅₀. For a given grain size, higher protrusion results in greater applied fluid forces and reduced resisting forces to partly explain our lower critical Shields stresses in finer patches. Patches therefore can importantly modulate relative sediment mobility through bed structure and may need to be included in reach‐scale sediment transport and channel stability estimates.