# Experiments on Longitudinal and Transverse Bedload Transport in Sine-Generated Meandering Channels
## 2 Experimental Setup and Measurements
As shown in Figure 1, bends B\({}_{1}\) and B\({}_{2}\) were paved with colored (nonwhite) grains, and the rest of the plume was paved with white grains with the same diameter, i.e., 1 mm. Using the channel centerline as a divider, the colors of grains paved on the left and right half-width parts of bends B\({}_{1}\) and B\({}_{2}\) were also different. Thus, the uniform-sized grains initially paved in bends B\({}_{1}\) and B\({}_{2}\) are distinguished by four different colors, named D\({}_{1}\) (green), D\({}_{2}\) (yellow), D\({}_{3}\) (red) and D\({}_{4}\) (blue). Grains D\({}_{1}\) and D\({}_{4}\) are initially paved on the concave side and grains D\({}_{2}\) and D\({}_{3}\) are initially paved on the convex side. The experiments were designed as closed systems, i.e., no sediment feed at the inlets. After the bed deformation reached equilibrium (no measurable bed deformation was observed), flow velocities and bed topography at each cross section were measured by Nortek micro-ADV (Vectrino+, Rijeka, Croatia) and a metal probe at 15 equally spaced verticals. The measuring range of the ADV was 0.01 m/s to 4 m/s, with an accuracy of \(\pm\)1 mm/s. The sampling frequency is 200 Hz. To monitor the temporal variation of bed elevation, plastic rulers were also pasted at both sides of each section of the two flumes. After this, the water in the flume was carefully drained, and the spatial distributions of the colored grains were estimated by the following procedure. The first step was to identify the sampling spots. For each section, there were 4 sampling plots and the width of each plot is approximately B/4. Therefore, the 4 plots almost covered the whole cross section. Thus, there were a total of 56 and 336 sampling plots in the \(\theta_{0}=30^{\circ}\) and \(110^{\circ}\) flumes, respectively. All of the grains remaining in each sampling plot were excavated to the flume bottom, dried, tiled over a white paper, and photographed. Then, the number of colored grains was counted by the Matlab Image Analysis Toolbox [14; 15]. The accuracy of the automatic image processing method was verified by comparing with manual recognition (by eyes). Table 2 shows the four test scenarios. In each scenario, a certain amount (5000 or 10,000) of mixed grains (D\({}_{1}\), D\({}_{2}\), D\({}_{3}\), and D\({}_{4}\)) was counted manually by eyes and tiled over a white paper. Then, we took a picture of the samples and counted the numbers of colored grains by the Matlab Image Analysis Toolbox. We repeated this procedure five times and adopted the average. Verification showed that the recognition rate was ranging from 91% to 96%, with an average of about 94.35%. The distinguishing of red and yellow grains was slightly better than blue and green grains. Besides, adding artificial light proved necessary when taking photos. In this way, we obtained the number ratios of colored particles to the mix. We can safely assume the volume ratios are the same as the number ratios. The volume of each sampler could be estimated with dried weight of sampler and its buck density. Thus, the volume of each colored particle in each sampler could be estimated. Comparing the initially paved volume and the sampled volume, we obtained the gain and loss of colored grains in each sampling plot. Finally, particle source for bar deposition could be estimated. In this way, we could obtain the gain and loss of color grains D\({}_{1}\), D\({}_{2}\), D\({}_{3}\), and D\({}_{4}\) along the meandering channels, and estimate the longitudinal and transverse bedload transport in the bends.