Empirical Modeling of Spatial 3D Flow Characteristics Using a Remote-Controlled ADCP System: Monitoring a Spring Flood

Abstract

The use of acoustic Doppler current profilers (ADCP) for measuring streamflow and discharge is becoming increasingly widespread. The spatial distribution of flow patterns is useful data in studying riverine habitats and geomorphology. Until now, most flow mapping has focused on measurements along a series of transects in a channel. Here, we set out to measure, model and analyze the 3D flow characteristics of a natural river over a continuous areal extent, quantifying flow velocity, 3D flow directions, volumes, water depth and their changes over time. We achieved multidimensional spatial flow measurements by deploying an ADCP on a remotely-controlled boat, combined with kinematic GNSS positioning and locally-monitored water level data. We processed this data into a 3D point cloud of accurately positioned individual 3D flow measurements that allows the visual analysis of flow velocities, directions and channel morphology in 3D space. We demonstrate how this allows monitoring changes of flow patterns with a time series of flow point clouds measured over the period of a spring flood in Finnish Lapland. Furthermore, interpolating the raw point cloud onto a 3D matrix allows us to quantify volumetric flow while reducing noise in the data. We can now quantify the volumes of water moving at certain velocities in a given reach and their location in 3D space, allowing, for instance, the monitoring of the high-velocity core and its changes over time