Harris C.K.,Virginia Institute of Marine Science |
Rinehimer J.P.,Virginia Institute of Marine Science |
Rinehimer J.P.,University of Washington |
Kim S.-C.,Engineer Research Development Center
Proceedings of the International Conference on Estuarine and Coastal Modeling | Year: 2012
Estimates of near-bed turbulence and suspended sediment concentrations depend critically on bed shear stress, which varies with seafloor roughness, current velocities, and, often, wave properties. This project sought, in a computationally efficient manner, to improve representation of the bottom boundary layer within the Chesapeake Bay Program's models of water quality and sediment transport. Maps of sediment bed grain size distributions were compiled for the Chesapeake Bay, and used to provide input into a high resolution, one-dimensional (vertical) bottom boundary layer model that estimated both bed roughness and bed shear stresses. The one-dimensional model required as input grain size distributions, wave properties, and near-bed current velocities. It was run for a range of these model inputs and the estimated bed roughness, total bed stress, and skin friction shear stress values were used to compile a numerical look-up table. The full three-dimensional model could then quickly estimate bed shear stresses by accessing the look-up table for given values of grain size distribution, near-bed current velocity, and wave properties. Observations, especially from locations within the bay impacted by wave processes, were rare, but comparison of estimated values of shear stress to observations from three locations within Chesapeake Bay showed that the methodology provided reasonable estimates. Calculations were sensitive to the one-dimensional model's assumption for a minimum value of hydraulic roughness used in regions where clay and silt predominated. In such locations, the model assumed biogenic elements dominated seafloor roughness, but had little guidance from field observations regarding the behavior of these roughness elements under varying flow conditions. © 2013 American Society of Civil Engineers.
Fang S.,University of Alberta |
Gertner G.Z.,Urbana University |
Anderson A.B.,Engineer Research Development Center |
Howard H.R.,Engineer Research Development Center |
And 2 more authors.
Journal of Environmental Management | Year: 2010
Vehicle use during military training activities results in soil disturbance and vegetation loss. The capacity of lands to sustain training is a function of the sensitivity of lands to vehicle use and the pattern of land use. The sensitivity of land to vehicle use has been extensively studied. Less well understood are the spatial patterns of vehicle disturbance. Since disturbance from off-road vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) was used to predict the pattern of vehicle disturbance across a training facility. An uncertainty analysis of the model predictions assessed the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty. For the most part, this analysis showed that mapping and modeling process errors contributed more than 95% of the total uncertainty of predicted disturbance, while satellite imagery error contributed less than 5% of the uncertainty. When the total uncertainty was larger than a threshold, modeling error contributed 60% to 90% of the prediction uncertainty. Otherwise, mapping error contributed about 10% to 50% of the total uncertainty. These uncertainty sources were further partitioned spatially based on other sources of uncertainties associated with vehicle moment, landscape characterization, satellite imagery, etc. © 2009 Elsevier Ltd.