Rocky Mountain (56th Annual) and Cordilleran (100th Annual) Joint Meeting (May 3–5, 2004)

Paper No. 11
Presentation Time: 11:20 AM


STROM, Kyle B., CEE, Iowa Institute of Hydroscience & Engineering, Hydraulics Lab, Iowa City, IA 52242, PAPANICOLAOU, Athanasios, Department of Civil Engineering, The Univ of Iowa, IIHR, Iowa City, IA 52242 and EVANGELOPOULOS, Nicholas, Dept. of Bus. Comp. Info. Sci, Univ of North Texas, Denton, TX 76203,

This research aims to advance current knowledge on cluster formation and evolution by tackling some of the aspects associated with cluster microtopography and the effects of clusters on bedload transport. The specific objectives of the study are: (1) to identify the bed shear stress range in which clusters form and disintegrate; (2) to quantitatively describe the spacing characteristics and orientation of clusters with respect to flow characteristics; (3) to quantify the effects clusters have on the mean bedload rate; and (4) to assess the effects of clusters on the pulsating nature of bedload. In order to meet the objectives of this study, two main experimental scenarios, namely test series A and B (twenty experiments overall) are considered in a laboratory flume under well-controlled conditions. Series A tests are performed to address objectives (1) and (2) while series B is designed to meet objectives (3) and (4). Results show that cluster microforms develop in uniform sediment at 1.25 to 2 times the Shields parameter of an individual particle and start disintegrating at about 2.25 times the Shields parameter. It is found that during an unsteady flow event, effects of clusters on bedload transport rate can be classified in three different phases: a sink phase where clusters absorb incoming sediment, a neutral phase where clusters do not affect bedload, and a source phase where clusters release particles. Clusters also increase the magnitude of the fluctuations in bedload transport rate, showing that clusters amplify the unsteady nature of bedload transport. A fourth order autoregressive ARIMA model is employed to describe the time series of bedload and provide a predictive formula for predicting bedload at different periods. Finally, a change-point analysis enhanced with a Binary Segmentation procedure is performed to identify the abrupt changes in the bedload statistic characteristics due to the effects of clusters and detect the different phases in bedload time series using probability theory. The analysis verifies the experimental findings that three phases are detected in the bedload rate time series structure namely, sink, neutral and source.