The objective of this research is to increase our ability to predict how variations in lake water levels will affect the suspension of sediment. Two specific goals for this project are listed below.
Evaluate an existing two-dimensional flow and sediment transport model with observations of suspended sediment, sediment morphology, and flow velocity at two locations in Lake Erie.
Examine the effect variations in the lake level will have on the suspension of sediment. Particular attention will be paid to locations where significant heavy-metal contamination of sediments has been shown to exist.

This investigation is motivated by recent concerns regarding a decrease in the Great Lake's water levels. The significant decadal scale fluctuations of water levels observed in the great lakes is a well documented phenomenon (Hunter and Croley, 1993). Following several years of high water levels, lake levels have fallen significantly in the past three years. For example, Lake Erie's water level has decreased by 1.5 m since 1996. One result of a falling water level is that less decay through the water column will occur for free surface gravity wind waves. The increased orbital amplitudes will cause an increase in near bed velocities. For example, the near bed water velocity, as predicted by linear wave theory, for a 1.5 m significant wave height and a 3 sec wave period in 6 m of water would be 0.2 m/s. In 4.5 m of water the predicted orbital velocity would be 0.4 m/s. An increase in near bed velocities will increase the amount of sediment set into motion and suspended.

Objective 1
Observations of sediment suspension, incipient motion, and wave and mean flow velocity will be made during two field deployments, each of two weeks duration in March 2003. We will sample in the vicinity of Cleveland harbor at either headlands or edgewater beaches. The water depths will have significant energy from combined wave and mean current processes, but will be (generally) outside the surfzone. The observations will be used to evaluate model performance as well as identify new physics related to the initiation of motion. We will identify regions, occurrences, and forcing conditions when the model/data comparisons are both favorable and unfavorable. The model skill will be quantified with time-averaged and time-varying statistics of bed stress, wave and current boundary layer thicknesses, and suspended sediment.

Objective 2
Following the model-data comparisons, the model will be used to predict the variations in the depth of closure with lake level variations. Historical sediment records will be used to specify the average water depth and small scale morphology in each local region (ie. Berkman et al., 1998). The averaging wave climate forcing over each kilometer scale region will be specified by the predictions from the Great Lakes Forecasting System model. Initially, we will look at characteristic climatological storms with 1 month, 1 year, and 10 year return periods. Independent model runs will predict the presence or absence of sediment motion in each coastal region.

Finally, we will examine the variation in the depth of closure relative to the water level variation for these three defined storms. This will be accomplished by predicting the wave climate for a variety of water levels with the GLFS model under the three climatological storms. We anticipate that this investigation will end with a prediction of variations in the depth of closure relative to the lake water level.

We anticipate benefits from this proposed research on both small large scales. At the smallest scales, we will provide useful data and model results on the incipient motion of sediment. Beyond the scientific merit of better understanding the incipient motion process, we anticipate that these results may be used by both scientists and planners for larger scale interests. The small scale modelling performed in this study may be used by coastal numerical modellers interested in improved parameterization of the bed stress and suspension of sediments in large scale circulation models such as the Great Lakes Forecasting System. The results may also be used to identify locations which should be sampled for heavy metal contamination in the Cleveland harbor region as they may experience sediment mobility if the lake water level continues to decrease. Combined these model-data results will highlight potential areas or hotspots for concern.