Bed shear stress estimation on an open intertidal flat using in situ measurements

•The most widely used theories are tested to obtain total bed shear stresses.•Wind enhances both the wave and current induced bed shear stresses.•Near-bed vertical velocity fluctuations are contaminated by surface waves.•A solution is proposed to obtain current bed shear stress from in situ ADV data.

Accurate estimations for the bed shear stress are essential to predict the erosion and deposition processes in estuaries and coasts. This study used high-frequency in situ measurements of water depths and near-bed velocities to estimate bed shear stress on an open intertidal flat in the Yangtze Delta, China. To determine the current-induced bed shear stress (τc) the in situ near-bed velocities were first decomposed from the turbulent velocity into separate wave orbital velocities using two approaches: a moving average (MA) and energy spectrum analysis (ESA). τc was then calculated and evaluated using the log-profile (LP), turbulent kinetic energy (TKE), modified TKE (TKEw), Reynolds stress (RS), and inertial dissipation (ID) methods. Wave-induced bed shear stress (τw) was estimated using classic linear wave theory. The total bed shear stress (τcw) was determined based on the Grant-Madsen wave-current interaction model (WCI). The results demonstrate that when the ratio of significant wave height to water depth (Hs/h) is greater than 0.25, τcw is significantly overestimated because the vertical velocity fluctuations are contaminated by the surface waves generated by high winds. In addition, wind enhances the total bed shear stress as a result of the increases in both τw and τc generated by the greater wave height and reinforcing of vertical turbulence, respectively. From a comparison of these various methods, the TKEw method associated with ESA decomposition was found to be the best approach because: (1) this method generates the highest mean index of agreement; (2) it uses vertical velocities that are less affected by Doppler noise; and (3) it is less sensitive to the near-bed stratification structure and uncertainty in bed location and roughness.