Classifying seabed sediment type using simulated tidal-induced bed shear stress

Ward, S.L. and Neill, S.P. and Van Landeghem, K.J.J. and Scourse, J.D. (2015) Classifying seabed sediment type using simulated tidal-induced bed shear stress. Marine Geology, 367. pp. 94-104. DOI: 10.1016/j.margeo.2015.05.010

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An ability to estimate the large-scale spatial variability of seabed sediment type in the absence of extensive observational data is valuable for many applications. In some physical (e.g., morphodynamic) models, knowledge of seabed sediment type is important for inputting spatially-varying bed roughness, and in biological studies, an ability to estimate the distribution of seabed sediment benefits habitat mapping (e.g., scallop dredging). Although shelf sea sediment motion is complex, driven by a combination of tidal currents, waves, and wind-driven currents, in many tidally energetic seas, such as the Irish Sea, long-term seabed sediment transport is dominated by tidal currents. We compare observations of seabed sediment grain size from 242 Irish Sea seabed samples with simulated tidal-induced bed shear stress from a three-dimensional tidal model (ROMS) to quantitatively define the relationship between observed grain size and simulated bed shear stress. With focus on the median grain size of well-sorted seabed sediment samples, we present predictive maps of the distribution of seabed sediment classes in the Irish Sea, ranging from mud to gravel. When compared with the distribution of well-sorted sediment classifications (mud, sand and gravel) from the British Geological Survey digital seabed sediment map of Irish Sea sediments (DigSBS250), this �grain size tidal current proxy� (GSTCP) correctly estimates the observed seabed sediment classification in over 73% of the area.

Item Type: Article
Subjects: Research Publications
Departments: College of Natural Sciences > School of Ocean Sciences
Date Deposited: 28 Jul 2015 03:45
Last Modified: 23 Sep 2015 02:46
ISSN: 0025-3227
URI: http://e.bangor.ac.uk/id/eprint/4883
Identification Number: DOI: 10.1016/j.margeo.2015.05.010
Publisher: Elsevier
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