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The bedload transport rate is the quantified amount of sediment being transported in the active layer of the riverbed. Traditional measuring methods involving laborious mechanical equipment are unable to capture the spatial and temporal fluctuations of bedload transport rate and measurements done with these methods have large uncertainties caused by the disturbance of the hydraulic conditions of the riverbed by the equipment itself. Computer vision-based particle image velocimetry methods have been previously successfully applied to quantify bedload transport rates from video data sets in laboratory conditions, but not in ice-covered and open channel field conditions. The aims of this study are to 1) to apply image velocimetry methods to underwater video data sets to quantify seasonal bedload transport rates in different types of flow conditions 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 (~70N latitude) and draining towards the Arctic Sea. The data has been gathered over 2021-2023 during winter (ice-covered, low flow), spring (open channel, high flow), and autumn (open channel, low flow) seasons to cover different possible sediment transport conditions. 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 in sub-arctic river conditions.
Välimäki et al. (Fri,) studied this question.