Dongsu Kim

Near-Transducer Errors in ADCP Measurements: Experimental Findings

Acoustic Doppler current profilers (ADCPs) are not able to accurately determine velocity near their transducers and near the bed. These limitations have restricted the use of ADCPs to flow depths that are large enough to allow acquisition of few directly measured velocity data that can be subsequently used to accurately estimate vertical velocity profiles and flow discharge in cross sections. While the causes that make ADCPs unable to collect data in the near-bed region are relatively well documented, the causes of near-transducer errors have not yet been fully understood and are only partly documented. We present results from an experimental study aimed at characterizing the systematic errors due to the combined effect of acoustic interference and instrument-induced flow disturbance near a Janus-configured ADCP. The study comprises: (1) concurrent measurements with an ADCP and an acoustic Doppler velocimeter (ADV) under the ADCP; (2) measurements of the flow disturbance produced by the ADCP in the vertical and horizontal planes; and (3) ADV measurements along the path of the acoustic beams ensonified by the ADCP during a measurement. Results suggest that ADCPs bias low the velocity profiles with respect to the undisturbed velocity profiles, mostly because of the flow disturbance induced by the ADCP, with acoustic effects playing a secondary role. For the range of flows we studied, both undisturbed and disturbed profiles exhibit similar shapes when plotted in dimensionless form, with the bulk flow velocity and the ADCP diameter (D) as characteristic scales. The differences between the undisturbed and the ADCP-disturbed profiles extend up to a distance of about 1.5D from the ADCP, except for the profiles measured at locations where the flow depth is close to D for which the boundary layer induced by the ADCP interacts with the one induced by the flume bed.

Acoustic mapping velocimetry

Knowledge of sediment dynamics in rivers is of great importance for various practical purposes. Despite its high relevance in riverine environment processes, the monitoring of sediment rates remains a major and challenging task for both suspended and bed load estimation. While the measurement of suspended load is currently an active area of testing with nonintrusive technologies (optical and acoustic), bed load measurement does not mark a similar progress. This paper describes an innovative combination of measurement techniques and analysis protocols that establishes the proof-of-concept for a promising technique, labeled herein Acoustic Mapping Velocimetry (AMV). The technique estimates bed load rates in rivers developing bed forms using a nonintrusive measurements approach. The raw information for AMV is collected with acoustic multibeam technology that in turn provides maps of the bathymetry over longitudinal swaths. As long as the acoustic maps can be acquired relatively quickly and the repetition rate for the mapping is commensurate with the movement of the bed forms, successive acoustic maps capture the progression of the bed form movement. Two-dimensional velocity maps associated with the bed form migration are obtained by implementing algorithms typically used in particle image velocimetry to acoustic maps converted in gray-level images. Furthermore, use of the obtained acoustic and velocity maps in conjunction with analytical formulations (e.g., Exner equation) enables estimation of multidirectional bed load rates over the whole imaged area. This paper presents a validation study of the AMV technique using a set of laboratory experiments.

A GIS-based relational data model for multi-dimensional representation of river hydrodynamics and morphodynamics

The emerging capabilities of the geo-based information systems to integrate spatial and temporal attributes of in-situ measurements is a long-waited solution to efficiently organize, visualize, and analyze the vast amount of data produced by the new generations of river instruments. This paper describes the construct of a river data model linked to a relational database that can be populated with both measured and simulated river data to facilitate descriptions of river features and processes using hydraulic/hydrologic terminology. The proposed model, labeled Arc River, is built in close connection with the existing Arc Hydro data model developed for water-related features to ensure the connection of the river characteristics with their floodplains and watersheds. This paper illustrates Arc River data model capabilities in conjunction with Acoustic Doppler Current Profiler measurements to demonstrate that essential river morphodynamics and hydrodynamics aspects can be described using data on the flow and its boundaries. Represent river data in a curvilinear coordinate system to support river channel oriented spatial analyses.Represent multidimensional river features through points, lines, polygons, and volumes.Represent simulated gridded data for river channels that can be readily coupled with observed data.Represent spatio-temporal evolution of dynamic river objects using Eulerian or Lagrangian observational frameworks.Efficiently store and retrieve data acquired in-situ along with the ancillary metadata.