## 5 Discussion
### Feasibility of Representing 3D Flow in 3D Space
For instance, [PERSON] and [PERSON] [23] found a two-day measurement period to be adequate for minimizing hydrological non-stationarity effects, whereas [PERSON] _et al._[13] found a five-hour measurement time to be acceptable for capturing large-scale 3D flow features. The fairly rapid survey during the spring flood conditions means that the time-integrated snapshots we achieved here should give a good overview of the overall situation. Given that an instant survey of the flow field of the entirety of a given reach is impossible, a compromise between survey density and time has to be struck with the steadiness of the flow conditions in mind. The time-integrated point cloud may be regraded as a better representation of the overall three-dimensional flow field than each individual point is of the mean flow at that particular position, meaning that, while individual measurement points may erroneous, the synthesis of all points is nevertheless able to represent the big picture. ADCP measurements are well known to include noise (see [PERSON] and [PERSON] [38] for a detailed break-down of all error sources involved in ADCP measurements), both when considering single measurement samples and classical transects, but the averages of the measured values have been shown to give a very good approximation of the actual situation measured [9]. [PERSON] [24] found the uncertainty of depth-averaged velocities to be smaller than the uncertainty of single-cell measurements. Each individual measurement cell on each sample represents the average of the return signals measured. The assumption is that flow within each cell is horizontally homogeneous, so any deviation from this assumption caused by small eddies or turbulence results in noise. In order to get truly accurate velocity measurements, stationary measurements with several tens of seconds of measuring time need to be employed [9, 23, 39]. This, however, is not possible in moving vessel measurements. In the same manner, the raw point clouds are likely to include noise, that is erroneous values on the individual point level, but the overall representation of the hydrological conditions of the time-integrated measurement period should be fairly good. [PERSON] _et al._[13] argued that spatially-interpolated data may better represent the flow field compared to a series of transects. Visual interpretation of the raw point cloud (_cf._ Figure 1 and 5) certainly allows us to get insight into the flow pattern, with zones of high and low velocities, as well as back flows clearly distinguishable (_cf._ Figure 5). The scale of features to be mapped, such as eddies or vortices in particular situations, needs to be included in the above-mentioned compromise of area and resolution. In a relatively small river, such as the Pulmanki, measuring 3D flow at a dense resolution can be feasible, and smaller features can be captured that way than in a larger river. On the other hand, in larger rivers, smaller features are unlikely to be of great interest, so a coarser resolution can still allow the mapping of site-specific features of importance in reasonable time, so that the time-integration does not overstretch the steady flow assumption. One aspect that remains to be investigated in the future is the extent to which areas that look noisy in the raw point clouds are actually caused by measurement errors or whether they represent a real situation that is locally non-uniform. For now, we reason that noise in the raw data is likely to result from instrument noise inherent in ADCP measurements, possibly in combination with imprecision in the compass and IMU, as discussed above. Positioning errors are smaller than the distance between measurement samples, so they are unlikely to cause noise in the flow data. One important aspect is the inability of the measurement method to represent the whole water body at the same instant in time. On the other hand, a flow field measured as a transect is also a time-integrated measurement.