Subwater particle image velocimetry and photogrammetry as solution for enhanced seasonal measurements of river dynamics (more precisely: bedload transport)

Abstract

Northern rivers are responding to global warming by changes in seasonal discharges, sediment transport rates and morphology. Very limited amount of studies about bedload transport rates have been carried out in the winter-season. Traditional methods of measuring bedload transport are limited by their proneness to user error, small spatial scales and uncertainties related to the equipment itself. Computer vision-based particle image velocimetry (PIV) and particle tracking velocimetry (PTV) methods have been successfully applied to measurements of water surface velocities and preliminary results show that they can be applied to underwater sediment transport velocity measurements. The aims of this study are to 1) to enhance the bedload calculations, by comparing traditional mechanical methods and computer vision-based particle image velocimetry methods applied to underwater video data sets. Additionally, topography created from underwater imagery is used to scale and  georeference the results with very good precision, and 2) understand the seasonal variation in bedload transport amounts based on both mechanical and image velocimetry methods.The study is based on field data, measured at sub-arctic Pulmanki river, located in northern Finland. The data has been gathered in 2021 autumn and winter, 2022 spring, 2022 autumn, to cover different possible sediment transport conditions, from low flow ice-covered to high flow open channel periods. The preliminary results are presented. They show that the method is promising in enhancing the understanding of sediment transport processes and the seasonal transported amounts.