Bed Load Transport for Small Streams: Case Study of Kurau River
Bedload transport is an essential component of river dynamics and estimation of bedload transport rate is important for practical computations of river morphological variations because the transport of sediment through river channels has major effects on public safety, water resources management and environmental sustainability. Numerous well-known bedload equations are derived from limited flume experiments or field conditions. These time-consuming equations, based on the relationship between the reliability and representativeness of the data utilized in defining variables and constants, require complex parameters to estimate bedload transport. Thus, a new simple equation based on a balance between simplicity and accuracy is necessary for using in small rivers. In this study the easily accessible data including flow discharge, water depth, slope, and surface grain diameter d50 from the three small rivers in Malaysia used to predict bedload transport. Genetic programming (GP) and artificial neural network (ANN) models that are particularly useful in data interpretation without any restriction to an extensive database are presented as complementary tools for modelling bed load transport in small streams. The ability of GP and ANN as precipitation predictive tools showed to be acceptable. The developed models demonstrate higher performance with an overall accuracy of 97% for ANN and 93% for GP compared with other traditional methods and empirical equations.
A three-dimensional numerical model was applied to study the bed morphology and bedload transport of the junction of Ara and Kurau rivers for short term event and for high flow with 100 ARI. SSIIM2 a 3D, k-epsilon turbulence computational fluid dynamics model with an adaptive, non-orthogonal and unstructured grid has been used for modelling the hydrodynamic of confluence. The numerical model was tested against field data from Ara-Kurau confluence. Satisfactory agreement was found between computed and measured bedload and bed elevation in the field. The study indicates that numerical models became a useful tool for predicting the bedload transport rate in such complex dynamic environment. The results have demonstrated that the short term hydrologic variability can considerably influence the morphodynamics of Ara-Kurau channel confluence and for the different flow conditions the bedload transported near to edge of shear layer. The coincidence of the shear layer that was generated the considerable turbulence indicated that the increasing turbulence levels contribute substantially to the required increase in bedload transport capacity. The simulation results showed the grain size distribution on the bar at the downstream junction corner is remarkably constant and the particle size in the upstream part of the bar is more affected by the changes in flow conditions than the downstream end where the median diameters not varied during the period.