Flow resistance in mountain streams is important for assessing flooding hazard and quantifying sediment transport and bedrock incision in upland landscapes. In such settings, flow resistance is sensitive to grain‐scale roughness, which has traditionally been characterized by particle size distributions derived from laborious point counts of streambed sediment. Developing a general framework for rapid quantification of resistance in mountain streams is still a challenge. Here we present a semi‐automated workflow that combines millimeter‐ to centimeter‐scale structure‐from‐motion (SfM) photogrammetry surveys of bed topography and computational fluid dynamics (CFD) simulations to better evaluate surface roughness and rapidly quantify flow resistance in mountain streams. The workflow was applied to three field sites of gravel, cobble, and boulder‐bedded channels with a wide range of grain size, sorting, and shape. Large‐eddy simulations with body‐fitted meshes generated from SfM photogrammetry‐derived surfaces were performed to quantify flow resistance. The analysis of bed microtopography using a second‐order structure function identified three scaling regimes that corresponded to important roughness length scales and surface complexity contributing to flow resistance. The standard deviation σz of detrended streambed elevation normalized by water depth, as a proxy for the vertical roughness length scale, emerges as the primary control on flow resistance and is furthermore tied to the characteristic length scale of rough surface‐generated vortices. Horizontal length scales and surface complexity are secondary controls on flow resistance. A new resistance predictor linking water depth and vertical roughness scale, i.e.  H/σz, is proposed based on the comparison between σz and the characteristic length scale of vortex shedding. In addition, representing streambeds using digital elevation models (DEM) is appropriate for well‐sorted streambeds, but not for poorly sorted ones under shallow and medium flow depth conditions due to the missing local overhanging features captured by fully 3D meshes which modulate local pressure gradient and thus bulk flow separation and pressure distribution. An appraisal of the mesh resolution effect on flow resistance shows that the SfM photogrammetry data resolution and the optimal CFD mesh size should be about 1/7 to 1/14 of the standard deviation of bed elevation. © 2019 John Wiley & Sons, Ltd.

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Geomorphic transport laws are mathematical relationships that describe the mass flux of material over a landscape. The formulation of such laws is central to the modern field of landscape evolution modeling (Dietrich et al., 2003;Temme et al., 2017;Tucker & Hancock, 2010). Landscape evolution models have been used in constraining sediment fluxes, understanding the developmental history of landscapes, and projecting the evolution of landscapes into the future (Dietrich et al., 2003;Temme et al., 2017;Tucker & Hancock, 2010). Laws have been formulated to describe fluvial erosion in both transport and detachment limited settings, and to simulate the processes related to hillslope diffusion (Braun et al., 2001;Dietrich et al., 2003;Roering et al., 2001), among others. The earliest diffusion transport law was the linear diffusion model, derived from equations describing chemical diffusion (Culling, 1960;Roering et al., 1999). This transport law initiated landscape modeling of hillslope evolution (Culling, 1960;Roering et al., 1999). The linear diffusion model states that the flux of hillslope material is directly proportional to topographic gradient (Culling, 1960;Hirano, 1968). However, this model failed to replicate observations of hillslope curvature in steep landscape positions (Roering et al., 1999). In response to this