Graduation Year

2011

Document Type

Dissertation

Degree

Ph.D.

Degree Granting Department

Mathematics and Statistics

Major Professor

Chris P. Tsokos

Keywords

chlorophyll a, eutrophication, Hillsborough Bay, regression, three-parameter lognormal distribution, water quality

Abstract

Nutrient pollution has been identified as a significant threat to U.S. coastal and estuarine water quality. Though coastal and estuarine waters need nutrients to maintain a healthy, productive ecosystem, excess nutrients can lead to eutrophication. There are significant potential negative consequences associated with eutrophication, including loss of habitat, loss of economic activity, and direct threats to human health. Hillsborough Bay experienced eutrophication in the 1960s and 1970s due to a rapidly growing population and associated increases in nutrient pollution. These eutrophic conditions led to more frequent phytoplankton and macroalgae blooms and declines in seagrasses. To address these problems, a series of actions were taken including legislation limiting nutrient concentrations from domestic wastewater treatment plants, development of water quality and nutrient loading targets, and establishment of seagrass restoration and protection goals. Since the 1970s, water quality improvements and increasing seagrass acreages have been documented throughout Tampa Bay. In the current study, a series of analyses and tools are developed to obtain a more in depth understanding of water quality in Hillsborough Bay. The first tool is a linked hydrodynamic and water quality model (Environmental Fluid Dynamics Code) of Hillsborough Bay which can be employed to predict water quality responses to proposed management actions. In the second part of the study, a series of water quality indices were evaluated. The most appropriate index for determining overall water quality in Hillsborough Bay was identified. Chlorophyll a is one of the constituents in the water quality index and is currently used to evaluate annual water quality conditions in Hillsborough Bay. Therefore, the statistical distribution that describes chlorophyll a concentrations in Hillsborough Bay was identified and robust confidence intervals were developed to better understand the uncertainty associated with chlorophyll a measurements. Previous work linked chlorophyll a concentrations in Hillsborough Bay to explanatory variables based on monthly estimates. These relationships were used to develop water quality targets for the system. In this study, the previously developed relationship was revisited, resulting in an improved statistical model that is more robust. This improved model can also be used to evaluate the previously proposed targets and to better predict future changes due to climate change, sea level rise, and management actions. Lastly, a new method was developed to estimate atmospheric temperature in the contiguous United States.

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