Video monitoring of in-channel wood: from flux characterization and prediction to recommendations to equip stations

Wood flux (piece number per time interval) is a key parameter for understanding wood budgeting, determining the controlling factors, and managing flood risk in a river basin. Quantitative wood flux data is critically needed to improve the understanding of wood dynamics and estimate wood discharge in rivers. In this study, the streamside videography technique was applied to detect wood passage and measure instantaneous rates of wood transport. The goal was to better understand how wood flux responds to flood and wind events and then predict wood flux. In total, one exceptional wind and 7 flood events were monitored on the Ain River, France, and around than 24000 wood pieces were detected visually. It is confirmed that, in general, there is a threshold of wood motion in the river equal to 60% of bankfull discharge. However, in a flood following a windy day, no obvious threshold for wood motion was observed, confirms that wind is important for the preparation of wood for transport between floods. In two multi-peaks floods, around two-thirds of the total amount of wood was delivered on the first peak, which confirms the importance of the time between floods for predicting wood fluxes. Moreover, we found an empirical relation between wood frequency and wood discharge, which is used to estimate the total wood amount produced by each of the floods. The data set is then used to develop a random forest regression model to predict wood frequency as a function of three input variables that are derived from the flow hydrograph. The model calculates the total wood volume either during day or night based on the video monitoring technique for the first time, which expands its utility for wood budgeting in a watershed. A one-to-one link is then established between the fraction of detected pieces of wood and the dimensionless parameter “passing time ×framerate”, which provides a general guideline for the design of monitoring stations.