Non-invasive measurement of fluvial bedload transport velocity

A new method for measurement of bedload transport velocity using an acoustic Doppler current profiler (aDcp) is evaluated. Conventional bedload sampling involves physical samplers that are notoriously inaccurate and of limited use for characterizing the spatial and temporal distribution of bedload. The new technique utilizes the bias in aDcp bottom tracking due to movement on the river bed. This bias can be determined by comparing the boat velocity by differential global positioning system (DGPS) and by bottom tracking. The evaluation of the method had four components: field demonstration, laboratory calibration, development of an error model to separate the bedload velocity signal from the noise in the data, and use of the method in the field to characterize the spatial distribution of bedload transport velocity. The field demonstration involved concurrent aDcp and physical sampler measurements of bedload transport at stationary sampling stations in the gravel-bed reach of Fraser River. Mean bedload transport velocities measured using an aDcp were shown to correlate with mean bedload transport rates estimated with the physical samplers (r²=0.93, n=9). The laboratory calibration involved the creation of a synthetic bedload by dragging small cobbles over an artificial river-bed in a towing tank. It was shown that, despite high variability in the measurements that was due to instrument noise, the aDcp can separately estimate the mean magnitude and direction of the synthetic bedload velocity. However, due to excessive noise in individual beam velocities that did not appear to be present in the field data, the bedload velocity in the direction of transport was underpredicted by 79% on average. The error model is a new numerical method to probabilistically deconvolve the bedload velocity signal and the noise in the data. For data from Fraser River and from Norrish Creek, the probability density functions of the highly positively-skewed bedload velocity signal and the acoustic noise were resolved. The bedload velocity signal could be modelled as either a compound Poisson-gamma distribution or a gamma distribution. The acoustic noise was normally distributed and comparable to typical noise levels for aDcp water velocity measurements. Finally, field measurements from a moving boat in a sand-bed reach and a gravel-bed reach of Fraser River were used to characterize the spatial distribution of bedload transport velocity. The bedload velocity spatial distribution was shown to be significantly correlated with the spatial distributions of near-bed water velocity and depth averaged water velocity. Smoothing was achieved by both block averaging and kriging, which revealed coherent patterns in the bedload velocity spatial distribution.Applied Science, Faculty ofCivil Engineering, Department ofGraduat