Articles | Volume 4, issue 3
https://doi.org/10.5194/esurf-4-685-2016
https://doi.org/10.5194/esurf-4-685-2016
Research article
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29 Aug 2016
Research article | Highlight paper |  | 29 Aug 2016

Gravel threshold of motion: a state function of sediment transport disequilibrium?

Joel P. L. Johnson

Abstract. In most sediment transport models, a threshold variable dictates the shear stress at which non-negligible bedload transport begins. Previous work has demonstrated that nondimensional transport thresholds (τc*) vary with many factors related not only to grain size and shape, but also with characteristics of the local bed surface and sediment transport rate (qs). I propose a new model in which qs-dependent τc*, notated as τc(qs)*, evolves as a power-law function of net erosion or deposition. In the model, net entrainment is assumed to progressively remove more mobile particles while leaving behind more stable grains, gradually increasing τc(qs)* and reducing transport rates. Net deposition tends to fill in topographic lows, progressively leading to less stable distributions of surface grains, decreasing τc(qs)* and increasing transport rates. Model parameters are calibrated based on laboratory flume experiments that explore transport disequilibrium. The τc(qs)* equation is then incorporated into a simple morphodynamic model. The evolution of τc(qs)* is a negative feedback on morphologic change, while also allowing reaches to equilibrate to sediment supply at different slopes. Finally, τc(qs)* is interpreted to be an important but nonunique state variable for morphodynamics, in a manner consistent with state variables such as temperature in thermodynamics.

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Short summary
Accurately predicting gravel transport rates in mountain rivers is difficult because of feedbacks with channel morphology. River bed surfaces evolve during floods, influencing transport rates. I propose that the threshold of gravel motion is a state variable for channel reach evolution. I develop a new model to predict how transport thresholds evolve as a function of transport rate, and then use laboratory flume experiments to calibrate and validate the model.