## 6 Conclusions  
In this paper, we demonstrated that it is possible to analyze the areal 3D flow field at the reach scale by creating an areal three-dimensional model of 3D flow parameters in a natural environment as a point cloud, based exclusively on measured data. The Rcflow set-up, consisting of an ADCP coupled with a precision GNSS mounted on a remote-controlled boat, makes it possible to survey river reaches rapidly, including shallow areas that cannot be accessed by a larger boat. The raw flow data, while containing noise, gives a good synoptic time-integrated snapshot of a complex 3D flow field. In addition, a bathymetric model can be created to complement the flow data. Depending on the site-specific features to be investigated and the prevalent flow conditions, different measurement densities can be used to find a compromise between survey time, area and the scale of flow features to be captured. A simplified view of the point cloud can be achieved by abstracting the raw data into a regular 3D matrix that represents the water column as a regularly-spaced point cloud. These data can be easily visualized as either a point cloud of flow velocities or flow directions overlaid on a river bed model and underlying a water surface model in most any point cloud visualization software that is commonly used in the domain of laser scanning. Three-dimensional flow can be analyzed by visualizing vertical flow velocities, as well as horizontal or total flow velocities or flow directions. Such an analysis is best performed viewing the data in 3D visualization software. The density of the point cloud can make pattern detection of flow in this analysis quite challenging, especially when representing such complex and dense data on the 2D medium of paper without the ability to rotate or zoom the point cloud. For 2D analysis, a more traditional representation of 3D flow as a series of transects [15; 43] may be preferable. The interpolated 3D matrix can be interpreted as a dense series of transects and should lend itself to this type of analysis, allowing the additional freedom of choosing the direction of the transects at the visualization time (with no limitation on streamwise directions), rather than at the measurement time. In this paper, we presented a modeling methodology to create a spatially-accurate 3D representation of the 3D flow-field in the form of a point cloud. Naturally, these data can be used to derive other flow parameters than what is directly measured. For instance, [PERSON] _et al._[44] demonstrated how the data can be dissected into layers and used to calculate shear velocity and how this affects bed erosion and deposition. Simple bed change detection can be performed, as well. All of these applications rely on the absolute positional accuracy of the dataset achieved by combining RTK-GNSS with the ADCP. These data may also serve as measured reference boundary conditions for a 3D computational hydraulic model in a natural setting, such as employed in Kasvi [45]. [PERSON] _et al._[21] demonstrated how a dense series of transects can serve both as calibration and reference data for a 2D hydraulic model in a braided natural river. [PERSON] _et al._[46] used an ADCP in an areal sampling pattern in combination with multi-beam sonar to calibrate and validate a 2D morphodynamic model investigating the movement of suspended sediment. Such a comparison may reveal the strengths and weaknesses of this measurement-based model and theoretical models alike. We have developed this methodology in the hopes that it may prove useful and allow new insights for investigators in the fields of hydrology, fluvial geomorphology, river habitat studies and environmental monitoring. This study was funded by the Academy of Finland (RivCHANGE research project), the Geography Graduate School of Finland and the University of Turku Graduate School Doctoral program in Biology, Geography and Geology. Fieldwork was supported by the Kevo Subarctic Research Station, and the field assistance of [PERSON] is gratefully acknowledged.