Mesoscale Dispersive Flux in the Lower Reaches of the Elizabeth River Estuary

Congress has mandated under Section 303(d) of the Clean Water Act that Total Maximum Daily Loads (TMDL\u27s) be established for impaired water bodies to further identify and regulate their waste assimilative capacity. To establish a TMDL, the sources of waste loading or pollutants must be identified, and a method of determining how each should be limited or removed must be developed. Using a water quality model is a typical method for determining these limits, and since new or changed limits will likely impact local businesses and government budgets, the model must be accurate. For most tidal systems larger than small coastal basins (meso-scale and larger), these models are ineffective without performing an in-situ assessment of how pollution is specifically dispersed, which is often deemed impractical. In order to meet the robust requirements of the TMDL process, more practical methods for performing these assessments is needed. This thesis describes and evaluates a method for assessing meso-scale dispersive flux in the Elizabeth River estuary, Virginia using field-measured salinity data and the U.S. Environmental Protection Agency (USEPA) Water Quality Analysis Simulation Program (WASP) model. First, a longitudinal, one-dimensional model of the lower reaches of the Elizabeth River is developed using Arc View GIS software by dividing the study site (approximately 1000 meters wide, 3200 meters long, and 15 meters deep), into two segments. The WASP model is then calibrated with dispersion coefficients calculated by two mass-balance methods using vertically-averaged, high-tide salinity data measured over a five-day period. Freshwater flow for the model is estimated from data provided by the U.S. Norfolk District Army Corps of Engineers (USACE), the Hampton Roads Sanitation District (HRSD), and point discharge data available on the USEPA Envirofacts Internet website. The salinity values predicted by the WASP model are then statistically compared to the measured salinity data. The results show dispersion coefficients estimated by the methods selected produce salinity predictions with a 95% level of confidence, while the historical dispersion coefficient values do not. The study recommends further research in different estuary settings to validate the methodology used and to update the Elizabeth River historical dispersion coefficient values