Inter‐basin Comparison of Wood Flux Using Random Forest Modelling and Repeated Wood Extractions in Unmonitored Catchments

Predicting wood flux (i.e., wood piece number per time interval) or discharge (metre cubes of wood per second) in rivers is crucial for adequate integrated river management that balances risk assessment and ecological improvement. To enhance our understanding of the transport mechanisms of wood and assess their effects in various geographical contexts, it is necessary to conduct inter‐basin comparative studies and preliminary modelling. The wood flux of two river basins was analysed using video monitoring and random forest predictions based on hydrological drivers. The wood flux dynamics of the Ain and Allier rivers were both compared and contrasted. Although there was shared wood transport hysteresis, hourly wood flux, relative critical flow discharges of wood transport and certain hydrological factors exhibited differences between the two river basins. As a next step, the two random forest models, which were trained previously, were applied to predict wood flux and then wood discharge in a third river (the Rhône), in order to estimate a wood volume export, which can be compared with the wood volumes extracted over a series of a few monthly periods in the Génissiat reservoir. By using the random forest modelling, it is possible to estimate the volume of wood on the Rhône river. Despite the absence of any training data, there is a noticeable correlation, however, the estimated volumes were significantly overestimated. To resolve this issue, a correction factor was applied, accounting for disparities in wood recruitment dynamics between the Rhône basin and the training river basins. It was found that high flow events are underestimated, emphasizing the importance of incorporating local annotations and additional parameters in training the random forest model. Accurately predicting wood flux is important for integrated watershed management, but field observations are still lacking for validation and process‐based understanding.