Inter-Granular Relationships and Characterization of Bed Structures for Fluvial Sediment in Gravel-Bed Rivers Using Computed Tomography

Friday, 18 December 2015
Poster Hall (Moscone South)
Hal Voepel, University of Southampton, Geography and Environment, Southampton, SO14, United Kingdom, Sharif I Ahmed, University of Southampton, Micro-Vis CT Imaging Centre, Southampton, United Kingdom, Rebecca A Hodge, University of Durham, Department of Geography, Durham, United Kingdom, Julian Leyland, University of Southampton, Southampton, United Kingdom and David Ayres Sear, University of Southampton, Geography and Environment, Southampton, United Kingdom
Uncertainty in bedload estimates for gravel bed rivers is largely driven by our inability to characterize arrangement, orientation and resultant forces of fluvial sediment in river beds. Water working of grains leads to structural differences between areas of the bed through particle sorting, packing, imbrication, mortaring and degree of bed armoring. In this study, non-destructive, micro-focus X-ray computed tomography (CT) imaging in 3D is used to visualize, quantify and assess the internal geometry of sections of a flume bed that have been extracted keeping their fabric intact. Flume experiments were conducted at 1:1 scaling of our prototype river. From the volume, center of mass, points of contact, and protrusion of individual grains derived from 3D scan data we estimate 3D static force properties at the grain-scale such as pivoting angles, buoyancy and gravity forces, and local grain exposure. By aggregating representative samples of grain-scale properties of localized interacting sediment into overall metrics, we derive inter-granular relationships to compare and contrast bed structure and stability at a macro-scale. This is the first time bed stability has been studied in 3D using CT scanned images of sediment from the bed surface to depths well into the subsurface. The derived metrics and inter-granular relationships and characterization of bed structures will lead to improved bedload estimates with reduced uncertainty.