Predicting Head Loss and Hydraulic Roughness of Channel‐Spanning Large Wood Jams

Abstract Log jams enhance hydraulic and geomorphic diversity in river corridors. Channel‐spanning log jams induce backwatering, increase local flow heterogeneity, promote sediment deposition, and improve aquatic habitat diversity. Despite their increasing popularity in river restoration, predicting their hydraulic effects remains a challenge. We developed a model to predict dimensionless head loss through log jams for sub‐bankfull flows as traditional backwater methods are limited in variable natural channels. We developed the model from historical flume studies and tested the model application on field data from natural jams. As solid volume fraction increased, we found that dimensionless head loss also increased. Field application of our model successfully predicted head loss in naturally occurring log jams. Roughness values (Manning's and Darcy‐Weisbach ) varied but generally decreased with increased unit discharge. Our approach for determining head loss and roughness allows for better prediction and design of the localized hydraulic impacts of log jams.