Quantification of river bedform variability and complexity is important for sediment transport modeling as well as for characterization of river morphology. Alluvial bedforms are shown to exhibit highly nonlinear dynamics across a range of scales, affect local bed roughness, and vary with local hydraulic, hydrologic, and geomorphic properties. This paper examines sediment sorting on the crest and trough of gravel bedforms and relates it to bed elevation statistics. The data analysed here are the spatial and temporal series of bed elevation, grain size distribution of surface and subsurface bed materials, and sediment transport rates from flume experiments. We describe surface topography through bedform variability in height and wavelength and multiscale analysis of bed elevations as a function of discharge. We further relate bedform migration to preferential distribution of coarse and fine sediments on the troughs and crests, respectively, measuring directly surface and subsurface grain size distributions, and indirectly the small scale roughness variations as estimated from high resolution topographic scans.
|Original language||English (US)|
|Number of pages||32|
|State||Published - Dec 2012|
Bibliographical noteFunding Information:
A c k n o w l e d g m e n t s . This research was supported by the National Center for Earth-surface Dynamics (NCED), a Science and Technology Center funded by NSF under agreement EAR-0120914 as well as by NSF grants EAR-0824084 and EAR-0835789. The experiments performed for this study are the follow up of previous experiments (known as StreamLab06) conducted at the St. Anthony Falls Laboratory as part of an NCED program to examine physical-biological aspects of sediment transport (http://www.nced. umn.edu). The authors are thankful to Jeff Marr, Craig Hill, and Sara Johnson for providing help in running the experiments. The authors are also thankful to the editors and reviewers whose suggestions and constructive comments substantially improved our presentation and refined our interpretations. Computer resources were provided by the Minnesota Supercomputing Institute, Digital Technology Center at the University of Minnesota.
- grain sorting
- power spectral density
- sediment transport