Using Wave Statistics to Drive a Simple Sediment Transport Model

Because both contaminant and nutrient cycles in the Laurentian Great Lakes depend on particle behavior and movement, sediment transport is a critical component of many of the water quality models being developed to understand and manage this important resource. To avoid complicated models that cannot be supported by the available field data, we have used observation-based, empirical analysis as the basis for developing methods of predicting sediment resuspension from relatively simple measurements of the surface wave field. Our modeling is based on data obtained from instrumented tripods designed to measure near-bottom hydrodynamic and sedimentological conditions for extended periods of time. Because of the long duration of the deployments, it usually is impractical to both sample and record the data at the high frequency that would be needed to resolve the effects of individual surface waves. Instead, we have used a system of burst sampling, in which we sample the sensors at high frequency during a period of time that is repeated at an interval appropriate for the deployment duration. Rather than record the individual samples during the burst, we record only statistics obtained from the individual samples. Our results show that simple representations of the surface wave field obtained from the burst statistics can be used to model sediment transport in wave-dominated environments. We also show that once the model parameters are determined, the forcing wave conditions can be derived from other sources, including wind-driven wave models, with comparable success.