Abstract:
<p>This doctoral dissertation presents the derivation and application of a series of water quality models and modeling strategies which provide critical guidance to water quality-based management decisions. Each model focuses on identifying and explicitly acknowledging uncertainty and variability in terrestrial and aquatic environments, and in water quality sampling and analysis procedures. While the modeling tools I have developed can be used to assist management decisions in waters with a wide range of designated uses, my research focuses on developing tools which can be integrated into a probabilistic or Bayesian network model supporting total maximum daily load (TMDL) assessments of impaired shellfish harvesting waters. Notable products of my research include a novel approach to assessing fecal indicator bacteria (FIB)-based water quality standards for impaired resource waters and new standards based on distributional parameters of the in situ FIB concentration probability distribution (as opposed to the current approach of using most probable number (MPN) or colony-forming unit (CFU) values). In addition, I develop a model explicitly acknowledging the probabilistic basis for calculating MPN and CFU values to determine whether a change in North Carolina Department of Environment and Natural Resources Shellfish Sanitation Section (NCDENR-SSS) standard operating procedure from a multiple tube fermentation (MTF)-based procedure to a membrane filtration (MF) procedure might cause a change in the observed frequency of water quality standard violations. This comparison is based on an innovative theoretical model of the MPN probability distribution for any observed CFU estimate from the same water quality sample, and is applied to recent water quality samples collected and analyzed by NCDENR-SSS for fecal coliform concentration using both MTF and MF analysis tests. I also develop the graphical model structure for a Bayesian network model relating FIB fate and transport processes with water quality-based management decisions, and encode a simplified version of the model in commercially available Bayesian network software. Finally, I present a Bayesian strategy for calibrating bacterial water quality models which improves model performance by explicitly acknowledging the probabilistic relationship between in situ FIB concentrations and common concentration estimating procedures.</p>
