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Wood jams in rivers and on floodplains play an essential role in shaping valley bottoms, and their dynamics regulate the ecology and morphology of river systems. We present the Wood Jam Dynamics Database and Assessment Model (WooDDAM) to improve understanding and management of natural and anthropogenic wood jams in rivers. WooDDAM is comprised of a field data collection protocol, an open database of wood jam characteristics and dynamics, machine learning statistical models for predicting wood jam dynamics during high flows, and an online user interface to facilitate collaborative data collection and use. Here, we provide the background and guidance necessary to utilize WooDDAM to make predictions of and contribute to the database describing wood jam dynamics. We present tests of interoperator variability to justify database variable selection. To refine model predictions and improve predictive power, we encourage users to follow simple resurvey procedures and submit observations of wood jam dynamics. WooDDAM provides a management and monitoring tool for the retention or reintroduction of wood jams in rivers and facilitates further research into the interactions between wood jam dynamics and fluvial or ecological processes.

The Colorado Front Range flood of September 2013 induced a diverse range of geomorphic changes along numerous stream corridors, providing an opportunity to assess responses to a large flood in a semiarid landscape. We defined six classes of geomorphic change related to peak unit stream power and valley confinement for 531 stream reaches over 226 km, spanning a gradient of channel scales and slope. Geomorphic change was generally driven by erosion of channel margins in confined reaches and by a combination of deposition and erosion in unconfined reaches. The magnitude of geomorphic change typically increased with unit stream power ( ω ), with greater responses observed in unconfined channels. Cumulative logit modeling indicated that total stream power or unit stream power, unit stream power gradient, and valley confinement are significant predictors of geomorphic response for this flood event. Based on this dataset, thresholds for geomorphic adjustment were defined. For channel slopes ω > 230 W/m 2 (16 lb/ft-s; at least 10% of the investigated sites experienced substantial channel widening) and a credible potential for avulsions, braiding, and loss of adjacent road embankments associated with ω > 480 W/m 2 (33 lb/ft-s; at least 10% of the investigated sites experienced such geomorphic change). Infrequent to numerous eroded banks were very likely with ω > 700 W/m 2 (48 lb/ft-s), with substantial channel widening or major geomorphic change shifting from credible to likely. Importantly, in reaches where there were large reductions in ω as the valley form shifted from confined to relatively unconfined, large amounts of deposition-induced, reach-scale geomorphic change occurred in some locations at relatively low ω . Additionally, alluvial channels with slopes > 3% had greater resistance to geomorphic change, likely caused by armoring by larger bed material and increased flow resistance from enhanced bedforms. Finally, we describe how these results can potentially be used by practitioners for assessing the risk of geomorphic change when evaluating current or planned conditions.

We investigated the influence on flow resistance of flow structure and turbulence at the reach scale in a mountain channel using 3-D velocity measurements and geostatistical analysis to understand the complexity of the flow structure in a reach with limited bed irregularities. The increase in flow resistance at low flows in a plane-bed reach was not fully explained by grain resistance, therefore detailed 3-D velocity measurements were made to investigate spatial variability in velocity and turbulence components and potential controls on flow resistance. One plane-bed reach was surveyed over two stages in Fraser Experimental Forest, Colorado, using a combination of a total station, LiDAR (Light Detection and Ranging), and a SonTek Flowtracker handheld ADV (acoustic Doppler velocimeter). LiDAR was used to capture bank and channel geometry at low flows, whereas the water surface and bed data were collected with the total station at all flows. We used the standard deviation of bed elevation (σb) within a moving window as an index of roughness height (ks) and calculated the relative submergence of the bed at different stages as h/ks, where h is the local flow depth. ADV measurements were collected on a grid with a 0.3 m to 0.5 m spacing. Geostatistical analysis of the velocity data indicated that the flow was highly three-dimensional and varied based on stage, demonstrating that even small irregularities in the bed have a significant influence on the flow characteristics. The streamwise component was the largest at both low and high flow, but varied more throughout the reach at low flow. At high flow, the greatest streamwise velocities were located within the thalweg. Areas of upwelling and downwelling also varied based on stage, with this component being strongly influenced by small changes in the morphology at high flow, and by protuberant grains at low flows. The cross-stream velocity and turbulence components were controlled by the flow structure and less by the roughness of the bed. The turbulence intensity is significant when considering hydraulics for predicting sediment transport and for habitat assessment. The plane-bed flow patterns were compared to results from a pool-riffle reach in the North Fork Cache La Poudre River, a step-pool reach in East St. Louis Creek, and a step-pool reach in Italy on the Rio Cordon. The comparison of the 3-D velocities among channel types showed that the plane-bed reach has higher streamwise velocities, but similar values of cross-stream and transverse velocities. Streamwise turbulence intensities were similar in both the plane-bed and step-pool reach in East St. Louis Creek. The analysis revealed that the connection between the flow characteristics and the channel was not only related to the gross morphology and location of larger clasts, but also to the development of shear layers from the convergence of flow. Therefore, it is essential to understand how water moving in one direction can alter the characteristics of another component of flow and how this interaction is connected to the bed morphology.

summary In 15 mountain stream reaches containing instream wood, we characterized velocity and flow resistance at bankfull through low flows. These data were: (1) used to assess the accuracy of previously published velocity prediction techniques for high-gradient channels; and (2) were combined with field data from other studies to develop general methodologies for predicting velocity and flow resistance in alluvial and mixed alluvial-bedrock channels both with and without step-forming instream wood. Velocity and flow resistance were poorly predicted by variables characterizing grain size and relative grain submergence. Conversely, methods based on detrended standard deviation of bed elevations (rz) and relative bedform submergence (h/rz) explained up to 84% of the variance in the measured flow resistance coefficients and 97% of the variance in dimensionless velocity. With an average velocity of 0.44 m/s for the collected measurements, velocity was predicted with RMS (root mean square) error as low as 0.071 m/s (16% of average) when discharge and bedform geometry is known and 0.10 m/s (23%) when only bedform geometry is known. Additionally, an empirical relationship indicates V=u � ¼ h=r z, supporting previouslypublished laboratory findings using a field-based dataset in complex high-gradient channels. Interactions between instream wood and clasts result in substantially enhanced step heights and flow resistance. This compound effect defies description by grain size and relative grain submergence. However, rz and h/rz quantify variability due to both clasts in combination with wood and clasts alone, providing relatively accurate predictions for the tested dataset and indicating substantial predictive capabilities in channels where bedforms are the primary source of flow resistance. Published by Elsevier B.V.

Detailed hydraulic measurements were made in nine step-pool, five cascade and one plane-bed reach in Fraser Experimental Forest, Colorado to better understand at-a-station hydraulic geometry (AHG) relations in these channel types. Average values for AHG exponents, m (0·49), f (0·39), and b (0·16), were well within the range found by other researchers working in steep gradient channels. A principal component analysis (PCA) was used to compare the combined variations in all three exponents against five potential control variables: wood, D84, grain-size distribution (σ), coefficient of variation of pool volume, average roughness-area (projected wetted area) and bed gradient. The gradient and average roughness-area were found to be significantly related to the PCA axis scores, indicating that both driving and resisting forces influence the rates of change of velocity, depth and width with discharge. Further analysis of the exponents showed that reaches with m > b + f are most likely dominated by grain resistance and reaches below this value (m < b + f) are dominated by form resistance. Copyright © 2010 John Wiley & Sons, Ltd.

The Big Bay Dam embankment failure occurred on March 12, 2004, releasing 17,500,000 m3 (14,200 acre-ft) of water. In all, 104 structures were documented as being damaged or destroyed as a result of this failure. No human lives were lost. This paper documents data gathered and analyses performed on the hydraulics of the failure. High water levels from the failure were marked and measured. A HEC-RAS unsteady flow model was developed. Using observed breach geometry, HEC-RAS provided results that agreed with the measured high water marks from −0.02 to −0.90 m and 0.01 to 0.62 m with associated modeled flow depths ranging from 9.3 to 5.7 m (from 30 to 19 ft ). A peak breach flow of 4,160 m3 ∕s (147,000 ft3 ∕s) was predicted at the embankment. Breach peak flow prediction equations were found to substantially underpredict the peak flow indicated by HEC-RAS for this failure. HEC-RAS modeling utilizing predicted breach geometry and formation time also underpredicted the peak flow, but by a lesser amount. The Natio...

Photographic guidance is presented to assist with the estimation of Manning’s n and Darcy-Weisbach f in high-gradient plane-bed, step-pool, and cascade channels. Reaches both with and without instream wood are included. These coefficients are necessary for the estimation of reachaverage velocity, energy loss, and discharge. Using data collected in 19 stream channels located in the State of Colorado and the Eastern Italian Alps, on slopes ranging from 2.4 to 21 percent, guidance is provided for low through bankfull flows. Guidance for low flow resistance estimation is additionally provided using data collected in 29 channels in the State of Washington, New Zealand, Chile, and Argentina. Bankfull n values range from 0.048 to 0.30 and low flow n values range from 0.057 to 0.96. Discussions of flow resistance mechanisms and quantitative prediction tools are also presented.

[1] Total flow resistance can be partitioned into its components of grain (ffgrain), form (ffstep), wood (ffwood), and spill (ffspill) resistance. Methods for partitioning flow resistance developed for low-gradient streams are commonly applied to high-gradient systems. We examined the most widely used methods for calculating each component of resistance, along with the limitations of these methods, using data gathered from 15 high-gradient (0.02 < S0 < 0.195) step-pool, cascade, and plane-bed reaches in Fraser Experimental Forest. We calculated grain resistance using three equations that relate relative submergence (R/Dm) to ffgrain as well as using an additive drag approach. The drag approach was also used for calculating ffwood and ffstep. The ffgrain contributed the smallest amount toward all reaches at all flows, although the value varied with the method used. The Parker and Peterson (1980) equation using D90 best represented ffgrain at high flows, whereas the Keulegan (1938) equation using D50 best characterized ffgrain at base flows, giving a lower bound for grain resistance. This suggests that ffgrain may be better represented if two grain sizes are used to calculate this component of resistance. The drag approach, which is used to calculate wood resistance, overestimated the significance of individual logs in the channel. The contribution of ffspill was reduced at higher flows when form drag around the step is accounted for at higher flows. We propose a method for evaluating the contribution of ffstep that accounts for form drag around the steps once they are submerged at higher flows. We evaluated the potential sources of error for the estimation of each component of resistance. Determination of the drag coefficient was one of the major sources of error when calculating drag around wood, steps, or boulders.

[1] Detailed channel and water surface surveys were conducted on 15 mountain stream reaches (9 step-pool, 5 cascade, and 1 plane-bed) using a tripod-mounted Light Detection and Ranging scanner and laser theodolite. Reach-average velocities were measured at varying discharges with dye tracers and fluorometers. Multiple regressions and analysis of variance tests were used to test hypothesized correlations between Darcy-Weisbach friction coefficient, f, and potential control variables. Gradient (S0) and relative grain submergence (Rh/D84) individually explained a low proportion of the variability in f (R2 = 0.18), where Rh is hydraulic radius, D84 is the 84th percentile of the cumulative grain size distribution, and R2 is equal to the coefficient of determination. Because channel type, grain size, and S0 are interrelated, we tested the hypothesis that f is highly correlated with all three of these variables or a combination of the above variables with flow period (a categorical variable) or dimensionless unit discharge (q*). Total resistance correlated strongly (adj-R2 = 0.74, 0.69, and 0.64) with S0, flow period, wood load (volume of wood/m2 of channel), q*, and channel type (step-pool, cascade, plane-bed). Total resistance differed between step-pool and plane-bed and between cascade and plane-bed reaches. Significant differences in f in step-pool and cascade reaches were found at the same values of flow and S0. The regression analyses indicate that discharge explains the most variability in f, followed by S0 when discharge is similar among channel reaches, but that Rh/D84 is not an appropriate variable in these steep mountain streams to represent variations in both resistance and discharge. Results also indicate that the forms of resistance among channel types are sufficiently different to change the relationship of the control variables with f in each channel type. These results can be used to further the development of predictive equations for high-gradient mountain streams.