## 5 Discussion  
### Feasibility of Representing 3D Flow in 3D Space  
The results show that it is possible to create a three-dimensional model of measured flow velocity and flow direction as a point cloud. The raw point clouds of measured data (Figure 1) give us a synoptic view of a time-averaged flow situation in 3D. When interpreting these data, it is important to consider the time and space dimensions involved. The whole point cloud is a time-integrated representation of a given situation, while at the same time, each individual measured point is a snapshot of the flow at that particular position and one particular instant. Therefore, the model implicitly assumes that the flow situation is constant throughout the measurement period. This is on top of the assumption of locally horizontally homogeneous flow implicit in all ADCP measurements, due to the way that measurements of three or four divergent beams are integrated into one value [38]. Turbulence, eddies and hysteresis are ignored in this case, because it is impossible to measure the entire water body over the desired spatial extent at once. This assumption of constant flow is the same in discharge measurements using ADCP, even though, in the case of a single transect, the time-integration is smaller. [PERSON] _et al._[16] found the number of transect measurements to control the accuracy with which moving vessel ADCP measurements are able to model the spatial distribution of flow, while noting that the time-averaged flow velocities obtained from fixed-vessel measurements could not be reproduced by four spatially interpolated transects. They did not determine how many transects would be required to give an accurate representation of the spatial distribution of the mean flow. [PERSON] _et al._[16] analyzed transects, so the spatial distribution that they refer to is along a cross-section, rather than areal or in 3D. Nevertheless, the zig-zag pattern employed in the present study, spatially averaged into a 3D matrix, replicates an averaged series of transect measurements that, in the real world, are rarely spatially concordant. In the case of this study, we monitored the water levels and temperatures throughout the measurement period. These parameters give an indication of the stability of the flow situation throughout the measurement period. The mean temperatures and water levels for each measurement are listed in Table 2 along with their corresponding standard deviations. The largest standard deviation of water level was 0.007 m and occurred on 17 May, as the water level was rising rapidly, thereafter declining towards zero. This means that the assumption of a constant flow situation became increasingly valid during the field measurement period. Temperature also gives an indication of how constant the situation remained. The largest standard deviation of temperature during one measurement period was 0.16 \({}^{\circ}\)C. The ADCP measures water temperature at the sensor with each measurement sample, so any changes in temperature, as it affects water density and the effect of this on the propagation of sound, is already included in the flow measurements. The duration of each measurement for the meander bend AOI was around two hours, and the measurement for the whole 3.7-km reach with a sparser survey pattern was around five hours. The duration of a survey is an important consideration, relative to the prevailing flow conditions at the time. During any rapid change in the hydrograph, such as the rising stage of a flood, it is important to adjust the measurement density, so that the reach under investigation can be surveyed in a few hours, whereas during summer low flow conditions, for instance, it may be perfectly acceptable to extend a survey over several days, allowing the freedom to measure a longer reach and/or at a higher density.