Anette Eltner

Climate change will be pronounced in sub‐arctic and arctic regions. Consequently, a shorter ice‐cover period, increased precipitation and changes in the timing and magnitude of discharge are expected. These hydroclimatic changes can have an impact on sediment transport in northern rivers. Studying spatial and temporal variation of the processes in low‐flow open‐water and ice‐covered conditions is crucial to improve the prediction of future changes. The investigation of under‐ice conditions has been challenging and new approaches for their measurement are much needed. We analyse the spatial sediment transport in a meandering subarctic river during different flow conditions, i.e., low flow open‐channel during autumn and ice‐covered during winter. We use one‐dimensional (1D) morphodynamic models with image‐based sediment transportation data sets. We also simulate sediment transport with different ice thicknesses to better understand the overall bedload transport in a subarctic river in mid‐winter conditions. Simulations for the studied meander bend showed a considerable flow‐driven decrease of sediment transport in ice‐covered conditions when compared to open‐water conditions. Erosion was more pronounced in open‐water conditions and deposition was the prevailing process in ice‐covered conditions. When an additional increase in ice thickness was simulated, bedload increased, because the thicker ice narrowed the river channel substantially and thus leading to an increase in flow velocity and shear forces. Instead of solely relying on the sediment samples, the additional consideration of an image‐based sediment data set enabled a more reliable model calibration in ice‐covered conditions. This encourages further usage of image‐based methods for sediment transport estimations, especially in ice‐covered conditions. In the future, changes in river‐ice can be expected to decrease under‐ice sediment load but increase annual open‐water sediment loads. The increasing summative sediment transport of small arctic and subarctic rivers can have significant consequences on the downstream waterbodies.

Recently developed cameras in the low-cost sector exhibit lens distortion patterns that cannot be handled well with established models of radial lens distortion. This study presents an approach that divides the image sensor and distortion modeling into two concentric zones for the application of an extended radial lens distortion model. The mathematical model is explained in detail and it was validated on image data from a DJI Mavic Pro UAV camera. First, the special distortion pattern of the camera was examined by decomposing and analyzing the residuals. Then, a novel bi-radial model was introduced to describe the pattern. Eventually, the new model was integrated in a bundle adjustment software package. Practical tests revealed that the residuals of the bundle adjustment could be reduced by 63% with respect to the standard Brown model. On the basis of external reference measurements, an overall reduction in the residual errors of 40% was shown.