## 3 Methods and Data
### Data Processing  
All ADCP data were exported to MATLAB format. The GNSS data were post-processed using VRS correction data from the Finnish virtual reference station network ([[http://www.vrsnet.fi](http://www.vrsnet.fi)]([http://www.vrsnet.fi](http://www.vrsnet.fi))). Baselines were calculated, and points with a positional accuracy better than 0.05 m were retained and exported as CSV (Table 1), including UTC timestamp information. Further processing was carried out in R [35]. Figure 6 outlines the conceptual processing flow. The GNSS position data were merged with the ADCP flow data using the GPS timestamps. The continuous  
Figure 5: Point cloud of raw measured flow data of the whole study reach (3.7 km) on 18 May 2013 looking downstream. The left-hand plot shows flow directions in degrees from the north and the right-hand plot shows flow velocities in m/s. A back-flow eddy location is clearly detectable in the plot of flow directions. The location of the meander bend AOI is indicated by a box, and the approximate locations of the water level loggers are indicated by three points. The actual water level loggers were located about 2 m from the shore at low water, but for ease of visualization, approximate locations are shown here. water level measurements were used to create a localized time-specific water surface model. The water surface model is created by fitting a plane through the water surface elevations at the three level logger locations at the time of measurement:  
\[ax+by+cz=d \tag{1}\]  
where a, b and c are the level logger points; x, y and z are their respective coordinates and d is the distance from a level plane. Given the locations of the water level loggers (Figure 5) with the upstream and downstream logger near the inside of the meander bend and the middle logger near the outside of the bend at the bend apex, we consider that this surface accounts for the possible superelevation of the water surface that is expected to be found in meander bends. In reality, though, at this particular meander bend, this effect is smaller than the vertical accuracy of the GNSS. The flow data were registered to this water surface in order to avoid small remaining inaccuracies in the vertical component of the positioning. Whereas an accuracy of 5 cm or better horizontally is more than adequate for the purpose of 3D flow measurements in a river, in the vertical, this can leave a notable roughness to the water surface that was not present in the field. This artificial surface roughness would propagate to all measurement points in the water column. The water surface of the Pulmanki River is very  
Figure 6: Conceptual process of creating a point cloud of flow data from ADCP, virtual reference station (VRS)-GNSS and water level measurements. A simplified conceptual view of the original ADCP sample structure is shown, where one measurement sample [ i ] consists of a series of superimposed flow measurement cells [ j ], each of which contains flow parameters. This is contrasted with the processed point cloud structure, where each cell becomes its own unit with spatial information _i.e._, a 3D spatial point, as well as additionally computed flow parameters (direction from north, velocity), calculated from the ENUvalues of the original cells, that is the velocity magnitude to the east, the north and up. Here, the individual point elements are shown superimposed in the “processed ADCP point cloud structure”, only to show how their positions relate to the original measurement structure. calm, even during high water, with at most small ripples caused by wind (_cf._ Figure 3). For measuring purposes, the water surface can be considered flat. We therefore consider the Levelogger-based water surface to be more accurate as vertical positioning than each measurement's individual GNSS-measured elevation. The variation in the water surface elevations during the measurement period was considerably smaller than the vertical precision of the VRS-GNSS (Table 2).