Abstract
A methodology for detecting nonlinear dynamics in bed load transport rate time series and testing the ability of distribution functions to characterize rate variations is outlined. The Kolmogorov entropy of a time series consisting of >10,000 bed load transport measurements made in a laboratory flume indicates that >80% of the variability can be explained by the sequential passage of bed forms as modeled by Hamamori's logarithmic cumulative distribution function, and the remainder may be attributable to deterministic uncertainty (chaos). Greater understanding of the mutual adjustments between the turbulent flow, sediment transport, and bed forms might reduce the level of uncertainty. Nevertheless, the Hamamori function affords an effective characterization of the distribution of at-a-point bed load transport rates in cases where the fine temporal details of the transport process can be ignored.
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