The impact of intermittency on bed load sediment transport

This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint.

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Authors

Santiago J. Benavides , Eric A. Deal , Matthew Rushlow, Jeremy G. Venditti , Qiong Zhang, Ken Kamrin, J. Taylor Perron 

Abstract

Sediment transport by wind or water near the threshold of grain motion is dominated by rare transport events. This intermittency makes it difficult to calibrate sediment transport laws, or to define an unambiguous threshold for grain entrainment, both of which are crucial for predicting sediment transport rates. Intermittency in sediment transport has been observed in many contexts, but few studies have attempted to explain its origins or its impact on transport rates. Here we present a model that captures this intermittency and show that the noisy statistics of sediment transport contain useful information about the sediment entrainment threshold and the variations in driving fluid stress. Using a combination of laboratory experiments and analytical results from the study of stochastic systems we determine the threshold for grain entrainment in a novel way that is independent of any previous method. Furthermore, our analysis reveals a new property, the "bed sensitivity", which can be used to predict conditions under which transport will be intermittent. Our work suggests strategies for improving measurements and predictions of sediment flux and hints that the sediment transport law may change close to the threshold of motion.

DOI

https://doi.org/10.31223/X5PW3Q

Subjects

Dynamical Systems, Geomorphology, Statistical, Nonlinear, and Soft Matter Physics

Keywords

sediment transport, intermittency, stochastic, bed load, on-off intermittency, flume experiment

Dates

Published: 2021-02-09 02:29

Last Updated: 2021-03-06 07:25

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License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None

Data Availability (Reason not available):
The raw data is very large (a few TB) and we are working on a way to host it. Otherwise, the time-series will become available in the near future. If anybody requests the data, we will happily share it personally.

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