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.