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https://doi.org/10.1016/j.ecss.2009.05.015
Copy DOIJournal: Estuarine, Coastal and Shelf Science | Publication Date: May 28, 2009 |
Citations: 24 |
Understanding suspended particulate matter (SPM) dynamics in coastal waters is crucial to assess changes in coastal sediment budgets and biogeochemical fluxes. SPM dynamics are subject to various physical and biological factors and processes such as, e.g. tidal currents and aggregation which can be enhanced by extracellular polymeric substances (EPS) that are produced by algae and bacteria. It is still unclear how the different factors and processes interact and together determine SPM dynamics. To unravel the interacting processes and factors, we propose a new distribution-based modeling approach. Based on the derivation of explicit equations for size distribution characteristics of SPM such as the average radius, we derived a model of reduced complexity characterized by low initialization and parameterization effort as well as low computational cost. The proposed 0D model includes the processes of aggregation and fragmentation due to shear, aggregation due to differential settling, deposition, resuspension and tidal exchange, and describes the evolution of the SPM concentration in the water column linked by the settling velocity to the change of the mass average radius of the aggregate distribution. A systematic parameter variation for critical bottom shear stress of erosion, the size of resuspended aggregates, the fractal dimension, the collision efficiency, and the aggregate strength has been performed and compared to observations in the back-barrier basin of Spiekeroog Island in the German Wadden Sea. This analysis confirms the hypothesis that in winter biological influences on SPM dynamics are smaller compared to summer. This is mainly reflected by a significant shift in the various parameters. We hence conclude that biological control mechanisms have a much more quantitative relevance for SPM dynamics than currently represented by state-of-the-art SPM transport models.
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