Observations of dune interactions from DEMs using through-water Structure from Motion

Abstract

The role of three-dimensionality (3D) in modulating both flow and sediment transport remains poorly understood. 3D bed elevation measurements are difficult to obtain due to irregular dune shapes and submergence. Using photogrammetric tools for topographic reconstruction has become popular in surface studies, yet water refraction makes through-water image capturing of submerged dunes still challenging. We show that throughwater images and Structure from Motion (SfM) tools can produce accurate Digital Elevation Models (DEMs) of a measured dune field, with a high level of detail. It is a low-cost and non-intrusive alternative for submerged bed elevation measurements. We provide a detailed SfM workflow, introduce and discuss the use of camera coordinates and underwater ground control points, and processing steps needed to obtain high accuracy DEMs. Our method results in DEMs with mm resolution in controlled laboratory conditions. New data for 23 dune field DEMs from 6 experiments were obtained and are presented. The dune field DEM time series provide insights on dune interaction processes. In our experiments, merging and defect creation were most common. Merging decreases the number of bedforms, and defect creation is responsible for more bedforms. Dunes continue to interact and jostle after achieving equilibrium. Simple crests are much more frequently observed than irregular, sinuous or double crests. Two-dimensional (2D) crest shapes were observed, although crest shapes were generally of 3D nature. Observed dune migration rates are not steady and vary in time and space. Bedform splitting speeds up dune migration, whilst neighbouring dunes tend to accelerate or decelerate. We identify a cascade of processes and show that 3D DEMs at high frequency are required to resolve observed rapid deformations and interaction of dunes. The identified dune interaction processes are inherent to sediment flux (variability), whereas the temporal component of the underlying sediment transport processes is still not well understood in relation to average transport.

Scale‐Dependent Bedform Migration and Deformation in the Physical and Spectral Domains

“…Simultaneous splitting and merging are shown in M2 and S2. This phenomena are assumed to be closely related to cross‐stream bedform propagation, that is, lateral linking (Kocurek et al., 2010; van Scheltinga et al., 2020). Figure 16c quantifies spanwise directionality, defined as the ratio of spanwise to streamwise velocity v b / u b , obtained by the BIV technique.…”

“…The coupling among migrating bedforms, shear stress, and turbulent structures is highlighted in (i) the streamwise velocity power spectrum showing slow temporal modulation of the near wall velocity (Khosronejad & Sotiropoulos, 2014; Singh et al., 2010; Sotiropoulos & Khosronejad, 2016), and (ii) in the coherent vortical structures resulting from flow separation over two‐ and three‐dimensional crests (Best, 2005; Maddux, 2003b; Parsons et al., 2005; Swanson et al., 2018; van Scheltinga et al., 2020). Those flow structures are found to significantly influence the geometry of downstream bedforms, suggesting a cyclical modulation exists among flow structures, local bed shear stress, sediment transport, and bedform morphology (Jerolmack & Mohrig, 2005a; Kocurek et al., 2010; van Scheltinga et al., 2020). Such interaction has a visible impact on the spatial heterogeneity of sediment deposits, as discussed in Galeazzi et al.…”

“…A systematic identification and statistical characterization of bedform morphology and kinematics have been a great challenge due to the multi‐scale interaction between the evolving bathymetry and the turbulent flow (Bennett & Best, 1995; Best, 2005; Best & Kostaschuk, 2002; Kwoll et al., 2017, 2016; Maddux et al., 2003a, 2003b; Nelson et al., 1995; Jerolmack & Mohrig, 2005a). The coupling among migrating bedforms, shear stress, and turbulent structures is highlighted in (i) the streamwise velocity power spectrum showing slow temporal modulation of the near wall velocity (Khosronejad & Sotiropoulos, 2014; Singh et al., 2010; Sotiropoulos & Khosronejad, 2016), and (ii) in the coherent vortical structures resulting from flow separation over two‐ and three‐dimensional crests (Best, 2005; Maddux, 2003b; Parsons et al., 2005; Swanson et al., 2018; van Scheltinga et al., 2020). Those flow structures are found to significantly influence the geometry of downstream bedforms, suggesting a cyclical modulation exists among flow structures, local bed shear stress, sediment transport, and bedform morphology (Jerolmack & Mohrig, 2005a; Kocurek et al., 2010; van Scheltinga et al., 2020).…”

Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides

“…Despite this, the existing studies have analyzed the susceptibility of landslides using a single time-related data source without considering whether the date of the data source is consistent with the study date [57,58]. Therefore, it is necessary to understand the temporal evolution of influencing factors (e.g., topography) and the influencing mechanisms of the factors on the susceptibility assessment [59,60]. The changes in influencing factors are significant in earthquake-and landslide-prone areas [61], such as the study area.…”

Influence of Dunes on Channel‐Scale Flow and Sediment Transport in a Sand Bed Braided River

“…To account for these effects, new approaches for predicting dune orientation are needed that will provide a basis for deriving improved parameterizations of the deviation between the depth-averaged and near-bed flow. However, this task is difficult to accomplish a priori (e.g., Weij, 2012), as dune orientation is often a product of the interplay between bed slope, spatially nonhomogeneous sediment transport rates (Rubin, 2012;Terwisscha van Scheltinga et al, 2020;Unsworth et al, 2016;Weij, 2012), and the interaction that dunes have on the mean flow field.…”