Author(s): David Froehlich

Linked Author(s): David Froehlich

Keywords: Sand-bed river; Flow resistance; Bedforms; Neural network

Abstract: Resistance to flow in the main channel of a river – that is, the combined forces that tend to oppose or retard fluid motion – is related to the cross-section shape, streambed texture (material size and gradation), riverbank irregularities and vegetative cover, horizontal curvature (meandering), and sediment-transport processes that extract energy from the mean flow. In streams with beds covered by gravel, cobbles, and boulders, impedance from friction produced by the coarse bed particles predominates. However, in alluvial rivers whose beds consist almost entirely of cohesionless shifting sand, flow resistance is also greatly influenced by drag forces caused by features known as bedforms created by the interaction of moving water with the bottom sediment. The overall bed geometry that exists in a sand-bed river – that is, the bed configuration – is composed of individual bedforms that can change with time in response to variations in flow conditions and random interactions between the sediment and the water. A wide range of configurations can develop in a stream as the bed material is fashioned into various shapes (ripples, dunes, antidunes, and bars) on scales that can be orders of magnitude larger than the grain sizes. As a result, the hydraulic roughness in sand-bed streams is a dynamic parameter that depends strongly on the flow (velocity and depth), the fluid (mass density and viscosity), and the bed sediment properties (particle size distribution, angularity, and mass density). The dynamics of bed configurations and the effect of the bedforms on river flow are among the most challenging aspects of fluvial hydraulics to quantify. The big unknown is the resistance coefficient. Two basic approaches have been used to account for the influence of bedforms on flow resistance. The first method divides the total resistance into the sum of two components: one related to impedance generated by the channel boundary without bedforms (grain resistance) and the other related to the opposing force produced by the bedforms (form resistance). The second tactic predicts the total resistance based on the overall flow and sediment parameters. The second approach is followed in this study. Nearly 900 onsite measurements of reach-averaged flow resistance in small to large sand-bed rivers are evaluated employing a neural network model. The assembled data exclude streams in which the average water depth was less than 0.3 meters, and the average topwidth less than 30 meters. The model predicts a discharge coefficient from the known values of unit flow rate (the total discharge divided by the cross-section topwidth), the median size of the bed sediment, and the water temperature; and an initial estimate of the average cross-section water depth, which is adjusted until reaching a balance between the known and calculated unit discharge.

DOI: https://doi.org/10.3850/IAHR-39WC252171192022488

Year: 2022