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Bed material abrasion is a major control on the partitioning of basin‐scale sediment fluxes between coarse and fine material. While abrasion is traditionally treated as an exponential function of transport distance and a lithology‐specific abrasion coefficient, experimental studies have demonstrated greater complexity in the abrasion process: the rate of abrasion varies with clast angularity, transport rate, and grain size. Yet, few studies have attempted to assess the importance of these complexities in a field setting. Here, we develop a new method for rapidly quantifying baseline abrasion rate in the field via Schmidt Hammer Rock Strength. We use this method, along with measurements of gravel bar lithology, to quantify abrasion in the Suiattle River, a basin in the North Cascades of Washington State in which sediment supply to the channel is dominated by recurrent debris flows from a tributary draining Glacier Peak stratovolcano. Rapid downstream strengthening of river bar sediment and a preferential loss of weak, low‐density vesicular volcanic clasts relative to non‐vesicular ones suggest that abrasion is extremely effective in this system. The standard exponential model for downstream abrasion, using single‐lithology abrasion rates fails to reproduce observed downstream patterns in lithology and clast strength. Incorporating heterogeneity in source material strength as well as transport rate‐dependent abrasion largely resolves this failure. Further work is needed to develop a comprehensive quantitative framework for the dependence of bed material abrasion on grain size and transport rate.

Mountain rivers often receive sediment in the form of episodic, discrete pulses from a variety of natural and anthropogenic processes. Once emplaced in the river, the movement of this sediment depends on flow, grain size distribution, and channel and network geometry. Here, we simulate downstream bed elevation changes that result from discrete inputs of sediment (∼10,000 m³), differing in volume and grain size distribution, under medium and high flow conditions. We specifically focus on comparing bed responses between mixed and uniform grain size sediment pulses. This work builds on a Lagrangian, bed‐material sediment transport model and applies it to a 27 km reach of the mainstem Nisqually River, Washington, USA. We compare observed bed elevation change and accumulation rates in a downstream lake to simulation results. Then we investigate the magnitude, timing, and persistence of downstream changes due to the introduction of synthetic sediment pulses by comparing the results against a baseline condition (without pulse). Our findings suggest that bed response is primarily influenced by the sediment‐pulse grain size and distribution. Intermediate mixed‐size pulses (∼50% of the median bed gravel size) are likely to have the largest downstream impact because finer sizes translate quickly and coarser sizes (median bed gravel size and larger) disperse slowly. Furthermore, a mixed‐size pulse, with a smaller median grain size than the bed, increases bed mobility more than a uniform‐size pulse. This work has important implications for river management, as it allows us to better understand fluvial geomorphic responses to variations in sediment supply.

A mountain watershed network model is presented for use in decadal to centurial estimation of source‐to‐sink sediment dynamics. The model requires limited input parameters and can be effectively applied over spatial scales relevant to management of reservoirs, lakes, streams, and watersheds (1–100 km²). The model operates over a connected stream network of Strahler‐ordered segments. The model is driven by streamflow from a physically based hydrology model and hillslope sediment supply from a stochastic mass wasting algorithm. For each daily time step, segment‐scale sediment mass balance is computed using bedload and suspended load transport equations. Sediment transport is partitioned between grain size fractions for bedload as gravel and sand, and for suspended load as sand and mud. Bedload and suspended load can deposit and re‐entrain at each segment. We demonstrated the model in the Elwha River Basin, upstream of the former Glines Canyon dam, over the dam's historic 84‐year lifespan. The model predicted the lifetime reservoir sedimentation volume within the uncertainty range of the measured volume (13.7–18.5 million m³) for 25 of 28 model instances. Gravel, sand, and mud fraction volumes were predicted within measurement uncertainty ranges for 18 model instances. The network model improved the prediction of sediment yields compared to at‐a‐station sediment transport capacity relations. The network model also provided spatially and temporally distributed information that allowed for inquiry and understanding of the physical system beyond the sediment yields at the outlet. This work advances cross‐disciplinary and application‐oriented watershed sediment yield modeling approaches.

River channel beds aggrade and incise through time in response to temporal variation in the upstream supply of water and sediment. However, we lack a thorough understanding of which of these is the dominant driver of channel bed elevation change. This lack hampers flood hazard prediction, as changes to the bed elevation can either augment or reduce flood heights. Here, we explore the drivers of channel change using multidecadal time series of river bed elevation at 49 United States Geological Survey (USGS) gage sites in the uplands of Washington State, USA. We find that channel bed elevations at many of the gages change remarkably little over >80 years, while others are highly unstable. Despite regionally synchronous decadal fluctuations in flood intensity, there is a lack of regional synchrony of channel response at the decadal scale. At the monthly scale, the magnitude of antecedent high flow events between gage measurements does not predict either the direction or magnitude of shift in channel bed elevation. That variations in flood magnitude are insufficient to explain changes in bed elevation suggests that fluctuations in sediment supply, rather than variation in peak flows, are the primary driver of change to river bed elevation. In this region, channels downstream from glaciers have statistically significantly greater variability in bed elevation compared to those lacking upstream glaciers. Together, these findings suggest that aggradation and incision signals in this region predominately reflect fluctuations in sediment supply, commonly associated with glaciogenic sources, rather than response to high flow events.

Basin‐scale predictive geomorphic models for river characteristics, particularly grain size, can aid in salmonid habitat identification. However, these basin‐scale methods are largely untested with actual habitat usage data. Here, we develop and test an approach for predicting grain size distributions from high resolution LiDAR (Light Detection and Ranging)‐derived topographic data for a 77 km²watershed along the central California Coast. This approach improves on previous efforts in that it predicts the full grain size distribution and incorporates an empirically calibrated shear stress partitioning factor. The predicted grain size distributions are used to calculate the fraction of the bed area movable by spawning fish. We then compare the ‘movable fraction’ with 7 years of observed spawning data. We find that predicted movable fraction explains the paucity of spawning in the upper reaches of the study drainage, but does not explain variation along the mainstem. In search of another morphologic characteristic that may help explain the variation within the mainstem, we measure riffle density, a proxy for physical habitat complexity. We find that field surveys of riffle density explain 64% of the variation in spawning in these mainstem reaches, suggesting that within reaches of appropriate sized gravel, spawning density is related to riffle density. Because riffle density varies systematically with channel width, predicting riffle spacing is straightforward with LiDAR data. Taken together, these findings demonstrate the efficacy of basin‐scale spawning habitat predictions made using high‐resolution digital elevation models. Copyright © 2016 John Wiley & Sons, Ltd.