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Abstract 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. 

Abstract. We developed a new rule-based, cellular-automaton algorithm for predicting the hazard extent, sediment transport, and topographic change associated with the runout of a landslide. This algorithm, which we call MassWastingRunout (MWR), is coded in Python and implemented as a component for the package Landlab. MWR combines the functionality of simple runout algorithms used in landscape evolution and watershed sediment yield models with the predictive detail typical of runout models used for landslide inundation hazard mapping. An initial digital elevation model (DEM), a regolith depth map, and the location polygon of the landslide source area are the only inputs required to run MWR to model the entire runout process. Runout relies on the principle of mass conservation and a set of topographic rules and empirical formulas that govern erosion and deposition. For the purpose of facilitating rapid calibration to a site, MWR includes a calibration utility that uses an adaptive Bayesian Markov chain Monte Carlo algorithm to automatically calibrate the model to match observed runout extent, deposition, and erosion. Additionally, the calibration utility produces empirical probability density functions of each calibration parameter that can be used to inform probabilistic implementation of MWR. Here we use a series of synthetic terrains to demonstrate basic model response to topographic convergence and slope, test calibrated model performance relative to several observed landslides, and briefly demonstrate how MWR can be used to develop a probabilistic runout hazard map. A calibrated runout model may allow for region-specific and more insightful predictions of landslide impact on landscape morphology and watershed-scale sediment dynamics and should be further investigated in future modeling studies. 

We use salinity observations from drifters and moorings at the Quinault River mouth to investigate mixing and stratification in a surf-zone-trapped river plume. We quantify mixing based on the rate of change of salinity DS/Dt in the drifters’ quasi-Lagrangian reference frame. We estimate a constant value of the vertical eddy diffusivity of salt of Kz=(2.2 +/- 0.6) x 10^-3 m^2 s^-1, based on the relationship between vertically integrated DS/Dt and stratification, with values as high as 1 x 10^-2 m^2 s^-1 when stratification is low. Mixing, quantified as DS/Dt, is directly correlated to surf-zone stratification, and is therefore modulated by changes in stratification caused by tidal variability in freshwater volume flux. High DS/Dt is observed when the near-surface stratification is high and salinity gradients are collocated with wave-breaking turbulence. We observe a transition from low stratification and low DS/Dt at low tidal stage to high stratification and high DS/Dt at high tidal stage. Observed wave-breaking turbulence does not change significantly with stratification, tidal stage, or offshore wave height; as a result, we observe no relationship between plume mixing and offshore wave height for the range of conditions sampled. Thus, plume mixing in the surf zone is altered by changes in stratification; these are due to tidal variability in freshwater flux from the river and not wave conditions, presumably because depth-limited wave breaking causes sufficient turbulence for mixing to occur during all observed conditions.