| description abstract | Measurement of river discharge during flooding events has especially been a challenging and dangerous task in the southwestern US, where flows can be flashy, laden with sediment, and at high velocity. Small unoccupied aircraft systems (sUAS) can be deployed to access unsafe field sites and capture imagery for measuring surface flow velocity and discharge. This paper compares flow discharge estimation at eight field sites—located at or near USGS gauging stations—using time-averaged surface velocities and the turbulence dissipation rate (TDR) derived from large-scale particle image velocimetry (LSPIV) analysis of sUAS videos with conventional measurement techniques conducted by professional USGS hydrographers. Sites characteristics include both natural and engineered channels. The conventional measured discharges were treated as the reference discharges for evaluating the accuracy of the LSPIV discharge estimates. This study evaluated four approaches to estimate the depth-averaged or cross-sectional averaged velocity: constant-velocity index, logarithmic law, power-law, and the entropy method. Results showed the discharges can be accurately calculated by using any of these methods, and that choice of method depended on width to depth ratios. Accurate measurement of water quantity is of vital importance to water resource managers, forecasters, and the public. Often, such as during floods, conditions at the river can be very dangerous to the crews responsible for such measurements. Small unoccupied aircraft systems (or drones) are proving to be an excellent tool for quantifying river flows using methods that do not involve directly entering flooding rivers. By using video collected from drones, we show that it is possible for practitioners to accurately measure flow discharge during in rivers and canals. We evaluate four methods for completing the task, and offer suggestions based on our findings. Although more research is needed to perfect the methods, we find that it is possible to accurately measure river flows using video from sUAS, and thus potentially improve safety for those put in harm’s way. | |
