Classifying seabed sediment type using simulated tidal-induced bed shear stress
Van Landeghem, KJJ
© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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.
Funding was provided by the Natural Environment Research Council (NERC) through grant NE/I527853/1 (Ph.D. studentship to SLW). The authors are grateful for access to the seabed sediment sample data and would like to acknowledge colleagues collecting and preparing these data through the projects HABMAP, SWISS, IMAGIN, ADFISH, and various projects led by the JNCC, as well as Hilmar Hinz, Lee Murray and Gwladys Lambert for work undertaken on a project funded by the Isle of Man Government (Department of Environment, Food and Agriculture). The author acknowledges modelling support from Patrick Timko and Reza Hashemi. The digital seabed sediment map (DigSBS250) was kindly made available by the BGS. The model simulations were undertaken on High Performance Computing (HPC) Wales, a collaboration between Welsh universities, the Welsh Government and Fujitsu.
This is the author accepted manuscript. The final version is freely available from Elsevier via the DOI in this record.
Vol. 367, pp. 94 - 104