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Suspended sediment modelling in a shelf sea (North Sea)
Gerritsen, H.; Vos, R.J.; van der Kaaij, T.; Lane, A.; Boon, J.G. (2000). Suspended sediment modelling in a shelf sea (North Sea). Coast. Eng. 41(1-3): 317-352
In: Coastal Engineering: An International Journal for Coastal, Harbour and Offshore Engineers. Elsevier: Amsterdam; Lausanne; New York; Oxford; Shannon; Tokyo. ISSN 0378-3839, more
Peer reviewed article  

Available in Authors 
    VLIZ: Open Repository 222762 [ OMA ]


Authors  Top 
  • Gerritsen, H., more
  • Vos, R.J.
  • van der Kaaij, T., more
  • Lane, A., more
  • Boon, J.G.

    This paper extends the modelling of suspended particulate matter (SPM) on the local coastal scale (described in preceding papers) to SPM modelling on the scale of the North Sea, focusing on representing SPM patterns and their seasonal distribution. The modelling includes a sensitivity study, in which model results are assessed using surface SPM concentration patterns extracted from NOAA reflectance imagery, as well as North Sea Project in situ data. Over the past decade or so, first-order estimates of the net suspended load and its associated sources and sinks have been available and are generally substantiated. However, developments in the simulation of large-scale SPM behaviour are still severely restricted by the available descriptions of available sediment sources and sediment erosion and deposition processes. This paper indicates how remotely sensed reflectance images can provide additional information on the spatial distribution of (sea surface) suspended sediments. A primary objective of this paper is to examine sensitivities of SPM simulations in 2D (vertically averaged) and 3D models. A boundary-fitted coordinate modelling approach with intra-tidal resolution and synoptic meteorology is applied, as well as more schematic approaches. A related objective is to examine how both limited in situ observational data and reflectance imagery can be used to assess and improve such simulations. An integrated modelling-monitoring approach, using inverse and Goodness-of-Fit' (GoF) approaches applied to remotely sensed reflectance imagery, is used to derive a structured sensitivity analysis providing a quantified assessment of the strengths and weaknesses of modelling and input data. It is shown that, especially in the coastal zone where salinity stratification may occur, 3D modelling is required while much of the sensitivity analysis can be based on a 2D modelling approach. This quantification of the effects of uncertainties of inputs and erosion/deposition parameters improves understanding of the sediment distribution and budgets on the North Sea scale. It is concluded that whilst process studies are likely to contribute to improving erosion/deposition algorithms, and model developments will provide enhanced dynamical descriptions, accurate overall simulation will remain dependent on some (inverse) processes to reduce the uncertainty in sediment sources.

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