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Lake Michigan Mass Balance
Sediment/Contaminant Transport Model
A [3-dimensional] version of the sediment transport model, such as SEDZL, will be used to simulate the movement of sediment particles in both the water column and sediment bed, including settling, resuspension, flocculation, transport, and deposition. SEDZL will be linked to hydrodynamic output from the associated contaminants in the lake. SEDZL will be linked to hydrodynamic output from the POM, and will be based upon the same 3-D water column grid. State variables will include 3 particle classes (plankton/biotic solids, cohesive fine-grained sediment/detritus, and coarse-grained solids) and PCBs. SEDZL sill simulate the 1982-83 and 1994-95 periods for which hydrodynamic forecasts will be available, as well as intensive confirmation data provided by sediment trap and radionuclide monitoring. Further confirmation data for 1994-95 will be provided by remote sensing, transmissometer arrays, and water intake monitoring. Sediment bed properties, particle resuspension rate parameters, flocculation parameters and settling properties necessary for the model will be determined by field measurements to be performed on Lake Michigan sediments, and by results of experiments conducted with other sediments from the Great Lakes. Allochthonous sediment loadings will be estimated for tributary export, shoreline erosion, and atmospheric particle deposition. Autochthonous production will be provided from the eutrophication/sorbent dynamics model, and input as loadings to the sediment transport model.
The sediment transport model is applied to predict the transport of particles in the lake, which predominantly carry hydrophobic contaminants from near-shore locations such as tributary mouths, to deposition zones usually in deep water. The transport of sediment an associated contaminants is a complex interaction of the properties of sediment particles and the sediment bed, circulation, bathymetry, an turbulent shear stresses applied by waves and current. Moving from shore to deep water, regimes of sediment transport are encountered, resulting in distinct distributions of grain size, bed thickness, sedimentation rate, and contaminant concentrations in the lake sediments. Contaminants move along this gradient associated primarily with the fine-grained sediments, yet their transport is influenced by the entire particle assemblage. In terms of resuspension and deposition, most sediment transport is associated with the sequence of short, infrequent events such as storms. SEDZL simulates the interactions and dynamics of sediment transport, and offers predictive capabilities beyond that obtainable by a calibrated-transport approach. Advantages include compatibility with the hydrodynamic simulation, high spatial resolution consistent with the spatial variability of the resuspension process, and verified process descriptions for the dynamics of sediment resuspension and deposition under event conditions which are the most difficult to model. SEDZL predictions have been confirmed mostly in tributary systems; in large water bodies, simulations have been conducted for events, with only limited confirmation. Thus, significant development is still required for credible application of SEDZL in the lake Michigan mass balance model. Sediment and contaminant transport model predictions will require extensive confirmation against Enhanced Monitoring Program [EMP] data to ensure model credibility.
The alternative approach to treating sediment transport is descriptive, where direct calibration of Total Suspended Solids (TSS) and associated particle tracers is used to specify settling and resuspension fluxes. The descriptive approach ensures a model and event-responsive nature of sediment transport described above introduce too many degrees of freedom to allow model calibration to the data being generated by the EMP. This approach relies entirely upon fitting suspended constituent data, which will be too sparse (both in space and time) to allow accurate description of sediment transport fluxes. The second major disadvantage of descriptive transport, is that the resulting model has no forecasting basis other than replaying the calibration. Attempts to go beyond the calibration are, in general, weak emulations of predictive transport approaches.
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