The mean settling velocities of suspended sediments in turbulence have been examined. The settling velocities in a flume are directly measured by using an acoustic Doppler velocimeter. The results indicate the same trend as previous work in homogeneous isotropic turbulence. In addition to the flume experiment, the numerical experiments were conducted in the velocity field of homogeneous isotropic turbulence simulated by Kraichnan’s technique. The experimental and numerical results show that at high turbulence intensity the relative settling velocity increases with the increasing relative turbulence intensity regardless of the Stokes number. At intermediate turbulence intensity, it seems that the settling data bifurcate, i.e., the particles at the large Stokes number tend to be slowed, whereas the settling velocity of particles is increased at the small Stokes number.

Acquiring discharge estimates is crucial in hydrological studies, extreme event analyses, and water resources management. This study demonstrates the improved fluvial acoustic tomography system (FATS), a promising approach for automatically measuring streamflows with high temporal resolution for a long period. Unlike past hydroacoustic systems, the FATS enables continuous capture of the depth- and range-averaged water velocity with a single transducer set. Streamflow measurements were performed on November 26, 2015, in a mountainous river (depth: 0.8 m under low-flow conditions, width: 115 m). This paper presents a 127-day record of the flow. The average stream velocities and streamflows are automatically estimated from the acoustic data and water level data. The real-time flow data can be monitored by the Internet access. The continuous FATS estimates indicate that the streamflow does not change smoothly with the river stage. Analyses reveal that the streamflow may not be estimated accurately from a smooth rating curve (RC). The relative biases of RC reach $\sim \pm 20\%$ even under higher flow conditions. The FATS captures short-wave irregularities in the streamflow time-series.