Data and code for: "Predicting head loss and hydraulic roughness of channel-spanning large wood jams"

Log jams enhance hydraulic and geomorphic diversity in river corridors. Channel-spanning log jams induce backwatering while also increasing local flow heterogeneity, promoting sediment deposition, and improving aquatic habitat diversity. Recognizing the benefits of log jams, river scientists, managers, and engineers are increasingly adding jams to restoration projects with little guidance on predicting the hydraulic effects of jams. Understanding and predicting the head loss induced by log jams in natural systems with variable channel dimensions requires an alternative approach to a traditional backwater calculation. We paired historical flume studies and field data from natural log jams to develop and evaluate a model to predict dimensionless head loss through jams for sub-bankfull flows. 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. Using field-verified head loss values, we calculated Darcy-Weisbach friction factor and Manning’s roughness coefficients for a range of unit discharges. Roughness values 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.