Browsing by Subject "Eutrophication"

Coastal eutrophication is a complex process that is caused largely by anthropogenic nutrient enrichment. Estuaries are particularly susceptible to nutrient impairment, owing to their intimate connection with the contributing watersheds. Estuaries experiencing accelerating eutrophication are subject to a loss of key ecological functions and services. This doctoral dissertation presents the development and implementation of an integrated approach toward assessing the water quality in the Neuse Estuary following the implementation of the total maximum daily load (TMDL) program in the Neuse River basin. In order to accomplish this task, I have developed a series of water quality models and modeling strategies that can be effectively used in assessing nutrient based eutrophication. Two watershed-level nutrient loading models that operate on a different temporal scale are developed and used to quantify nitrogen loading to the Neuse Estuary over time. The models are used to probabilistically assess the success of the adopted mitigation measures in achieving the 30 % load reduction goal stipulated by the TMDL. Additionally, a novel structure learning approach is adopted to develop a Bayesian Network (BN) model that describes chlorophyll dynamics in the Upper Neuse Estuary. The developed BN model is compared to pre-TMDL models to assess any changes in the role that nutrient loading and physical forcings play in modulating chlorophyll levels in that section of the estuary. Finally, a set of empirical models are developed to assess the water quality monitoring program in the estuary, while also exploring the possibility of incorporating remotely sensed satellite data in an effort to augment the existing in-situ monitoring programs.

Estuaries interfacing with the land, atmosphere and open oceans can be influenced in a variety of ways by anthropogenic activities. Centuries of overexploitation, habitat transformation, and pollution have degraded estuarine ecological health. Key concerns of public and environmental managers of estuaries include water quality, particularly the enrichment of nutrients, increased chlorophyll a concentrations, increased hypoxia/anoxia, and increased Harmful Algal Blooms (HABs). One reason for the increased nitrogen loading over the past two decades is the proliferation of concentrated animal feeding operations (CAFOs) in coastal areas. This dissertation documents a study of estuarine eutrophication modeling, including modeling of major source of nitrogen in the watershed, the use of the Bayesian Networks (BNs) for modeling eutrophication dynamics in an estuary, a documentation of potential problems of using BNs, and a continuous BN model for addressing these problems.

Environmental models have emerged as great tools to transform data into useful information for managers and policy makers. Environmental models contain uncertainty due to natural ecosystems variability, current knowledge of environmental processes, modeling structure, computational restrictions, and problems with data/observations due to measurement error or missingness. Many methodologies capable of quantifying uncertainty have been developed in the scientic literature. Examples of such methods are BNs, which utilize conditional probability tables to describe the relationships among variables. This doctoral dissertation demonstrates how BNs, as probabilistic models, can be used to model eutrophication in estuarine ecosystems and to explore the effects of plausible future climatic and nutrient pollution management scenarios on water quality indicators. The results show interaction among various predictors and their impact on ecosystem health. The synergistic eftects between nutrient concentrations and climate variability caution future management actions.

BNs have several distinct strengths such as the ability to update knowledge based on Bayes' theorem, modularity, accommodation of various knowledge sources and data types, suitability to both data-rich and data-poor systems, and incorporation of uncertainty. Further, BNs' graphical representation facilitates communicating models and results with environmental managers and decision-makers. However, BNs have certain drawbacks as well. For example, they can only handle continuous variables under severe restrictions (1- Each continuous variable be assigned a (linear) conditional Normal distribution; 2- No discrete variable have continuous parents). The solution, thus far, to address this constraint has been discretizing variables. I designed an experiment to evaluate and compare the impact of common discretization methods on BNs. The results indicate that the choice of discretization method severely impacts the model results; however, I was unable to provide any criteria to select an optimal discretization method.

Finally, I propose a continuous variable Bayesian Network methodology and demonstrate its application for water quality modeling in estuarine ecosystems. The proposed method retains advantageous characteristics of BNs, while it avoids the drawbacks of discretization by specifying the relationships among the nodes using statistical and conditional probability models. The Bayesian nature of the proposed model enables prompt investigation of observed patterns, as new conditions unfold. The network structure presents the underlying ecological ecosystem processes and provides a basis for science communication. I demonstrate model development and temporal updating using the New River Estuary, NC data set and spatial updating using the Neuse River Estuary, NC data set.

Phosphorus load reduction and dreissenid invasion are the two most important factors that influence europhication dynamics in the Great Lakes. The 1978 amendments to the Great Lakes Water Quality Agreement (GLWQA) between the United States and Canada established target phosphorus loads for the lakes, leading to reductions in external phosphorus loading to the Great Lakes. With diminished phosphorus levels, further nutrient management was a minor concern until the proliferation of invasive dreissenid mussels. Dreissenid mussels were first documented in the Laurentian Great Lakes in the late 1980s. Zebra mussels (Dreissena polymorpha) spread quickly into shallow, hard-substrate areas; quagga mussels (Dreissena rostriformis bugensis) spread more slowly and are currently colonizing deep, offshore areas. These mussels have the potential to modify biogeochemical processes and food web structure, altering nutrient cycling and availability. Following the mussel invasion, cyanobacterial blooms and nuisance benthic algal growth have reappeared in many nearshore areas of the Great Lakes.

This dissertation characterizes long-term patterns of phosphorus loading and mussel populations for Saginaw Bay, and estimates the effects of load reductions and dreissenid invasion on several aspects of pelagic water quality, focusing on phosphorus flux and cycling in Saginaw Bay. Bayesian approaches were used to quantify the impacts of load reduction and mussel invasion, while at the same time addressing model parameter uncertainty and prediction uncertainty associated with long-term observational data. Annual total phosphorus load estimates suggest a decreasing trend until the late 1970s to early 1980s, reflecting the effectiveness of point source controls implemented pursuant to GLWQA. Despite the decrease, however, the annual loads have not likely met the 440 tonne yr-1 target established for Saginaw Bay. In 1990 zebra mussels were discovered in the bay and by 1992 they were widespread and peaked with densities of >30,000 m-2. Following the peak, mean densities dropped and modeling results predict that the density will reach equilibria at ~600 m-2. When mussels appeared, the proportion of tributary phosphorus retained in Saginaw Bay increased from ~0.5 to ~0.7, reducing phosphorus export to the main body of Lake Huron. The combined effects of increased phosphorus retention and decreased phosphorus loading have caused an ~60% decrease in phosphorus export from Saginaw Bay to Lake Huron. The analysis of long-term patterns of pelagic water quality highlights the sustained effects of mussel invasion on altering water quality parameters in Saginaw Bay; there was a consistent decrease in chlorophyll concentrations by ~46%, and total phosphorus concentrations by ~25%, and an increase in secchi depths by ~15% over ~20 year invasion of mussels. A comparison of chlorophyll-phospohrus relationship between pre- and post-invasion periods suggest the reduced chlorophyll yield for a given phosphorus concentration after the mussel invasion. Further, decreases in both total phosphorus and chlorophyll concentrations were found in the majority of 24 mussel-invaded US lakes in addition to Saginaw Bay, and modeling results predict less chlorophyll yields per unit phosphorus level that ranges from oligo- to mesotrophic conditions. All lines of evidence presented in the dissertation point to the important roles of load reductions and invasive mussels affecting eutrophication dynamics in lake ecosystems.