Abstract
Bed shear stress is a critical parameter in riverine environments, directly influencing sediment transport, erosion, deposition, and flow dynamics. However, direct measurement of bed shear stress in natural rivers is nearly impossible, and estimating bed shear stress by extrapolating the Reynolds stress profile, though known for accuracy, faces practical limitations due to the challenges of measuring turbulence in natural rivers. As alternatives, indirect methods, such as global bed shear stress estimation based on bed slope and hydraulic radius (assuming uniform flow) and local bed shear stress estimation through regression of velocity profiles using the log law, have been employed. The estimation of local bed shear stress using the log law has primarily been derived from laboratory flow conditions due to the limitations in observing detailed turbulence in field conditions. Consequently, the applicability of the log-law method under field conditions has not been sufficiently evaluated. This study aims to assess local bed shear stress using the log law through a dedicated field experiment in a specific river condition, comparing it with methods utilizing the Reynolds stress profile. The experiment was conducted in a straight river channel with a width of approximately 6.5 m, a depth of 0.61 m, and a mean flow velocity of 0.56 m/s. A total of 197 points of regularly distributed velocity and turbulence data for the given cross-section were acquired using micro-acoustic Doppler velocimeter measurements with a duration of 90 s. We found that the log-law method overestimated local bed shear stress by an overall factor of 2.3 compared to the method using Reynolds stress profiles. Thus, the experimental results conclusively indicate that local bed shear stress estimated from the log law in natural river conditions could be overestimated. However, additional research is needed under more diverse flow and geometric conditions to develop a correction formula for applying the log law in estimating local bed shear stress.