ARS | Publication request: River Bed Sediment Classification Using ADCP

Submitted to: Proceedings of the World Environmental and Water Resources Congress Conference
Publication Type: Proceedings
Publication Acceptance Date: February 1, 2009
Publication Date: May 25, 2009
Citation: Shields Jr, F.D. 2009. River Bed Sediment Classification Using ADCP. In: Starrett, S., Ed., Proceedings of the World Environmental and Water Resources Congress: Great Rivers. American Society of Civil Engineers, Reston, VA. CD-ROM.

Interpretive Summary: Physical characteristics of riverine aquatic ecosystems vary continuously in time and space, and measuring the habitat quality of a given river segment usually requires collection of a large amount of data. An existing, widely used, commercially-available device for measuring river depths and velocities using acoustic echoes from the bed and from particles suspended in the water was used to classify bed sediment types by recording the strength of echoes from the bed. Mean signal strength from soft, muddy beds wasconsistently 10 to 20 dB lower than mean signal strength from noncohesive (gravel or sand) beds, and sand beds tended to have complex signatures with large variances. These results will be useful for advancing the use of widely-available, relatively low cost technology for monitoring the size and type of river bottom sediments.

Technical Abstract: Description of physical aquatic habitat in rivers often includes data describing distributions of water depth, velocity and bed material type. Water depth and velocity in streams deeper than about 1 m may be continuously mapped using an acoustic Doppler current profiler from a moving boat. Herein we examine the potential of using the echo signal strength from the bed as an indicator of bed material type. ADCP data were collected from fixed points in five rivers and one oxbow lake. Bed material samples were collected concurrently with Doppler data collection. Mean signal strength from soft, muddy beds was consistently 10 to 20 dB lower than mean signal strength from noncohesive (gravel or sand) beds. Sand beds tended to have complex signatures with large variances.