The hiding-exposure effect revisited: A method to calculate the mobility of bimodal sediment mixtures

McCarron, Connor J.; Van Landeghem, Katrien J.J.; Baas, Jaco H.; Amoudry, Laurent O.; Malarkey, Jonathan. 2019 The hiding-exposure effect revisited: A method to calculate the mobility of bimodal sediment mixtures. Marine Geology, 410. 22-31.

Before downloading, please read NORA policies.
Available under License Creative Commons Attribution 4.0.

Download (5MB) | Preview


Predicting seabed mobility is hampered by the limited accuracy of sediment transport models when the bed is composed of mixed sediments. The hiding-exposure (HE) effect modifies the threshold of motion of individual grain classes in sediment mixtures and its strength is dependent on the grain size distribution. However, an appropriate method of predicting this effect for bimodal sediment mixtures remains to be developed. The prototypical example of a bimodal mixture is that consisting of a well-sorted sand and gravel for the fine and coarse fractions respectively. Through a comprehensive series of laboratory experiments, the HE effect has been quantified for a full range of sand-gravel mixtures from pure sand to pure gravel, the choice of which has been underpinned by an integrated study of offshore geophysical and sedimentological data found in coastal and shelf seas. In the sand–gravel mixtures used in the present study the critical shear stress needed to mobilise the sand and gravel fractions increased by up to 75% and decreased by up to 64%, respectively, compared to that needed to mobilise well-sorted sediment of similar size. The HE effect was found to be dependent on the percentage of gravel (coarse mode) present in the bimodal mixture, whereby the effect for the mixture is the weighted sum of the HE effect for the fine and coarse modes.

Item Type: Publication - Article
Digital Object Identifier (DOI):
ISSN: 00253227
Date made live: 19 Feb 2019 10:02 +0 (UTC)

Actions (login required)

View Item View Item

Document Downloads

Downloads for past 30 days

Downloads per month over past year

More statistics for this item...