Browsing by Author "Reckhow, Kenneth H"

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.

Urbanization causes myriad changes in watershed processes, ultimately disrupting the structure and function of stream ecosystems. Urban development introduces contaminants (human waste, pesticides, industrial chemicals). Impervious surfaces and artificial drainage systems speed the delivery of contaminants to streams, while bypassing soil filtration and local riparian processes that can mitigate the impacts of these contaminants, and disrupting the timing and volume of hydrologic patterns. Aquatic habitats where biota live are degraded by sedimentation, channel incision, floodplain disconnection, substrate alteration and elimination of reach diversity. These compounding changes ultimately lead to alteration of invertebrate community structure and function. Because the effects of urbanization on stream ecosystems are complex, multilayered, and interacting, modeling these effects presents many unique challenges, including: addressing and quantifying processes at multiple scales, representing major interrelated simultaneously acting dynamics at the system level, incorporating uncertainty resulting from imperfect knowledge, imperfect data, and environmental variability, and integrating multiple sources of available information about the system into the modeling construct. These challenges can be addressed by using a Bayesian modeling approach. Specifically, the use of multilevel hierarchical models and Bayesian network models allows the modeler to harness the hierarchical nature of the U.S. Geological Survey (USGS) Effect of Urbanization on Stream Ecosystems (EUSE) dataset to predict invertebrate response at both basin and regional levels, concisely represent and parameterize this system of complicated cause and effect relationships and uncertainties, calculate the full probabilistic function of all variables efficiently as the product of more manageable conditional probabilities, and includes both expert knowledge and data. Utilizing this Bayesian framework, this dissertation develops a series of statistically rigorous and ecologically interpretable models predicting the effect of urbanization on invertebrates, as well as a unique, systematic methodology that creates an informed expert prior and then updates this prior with available data using conjugate Dirichlet-multinomial distribution forms. The resulting models elucidate differences between regional responses to urbanization (particularly due to background agriculture and precipitation) and address the influences of multiple urban induced stressors acting simultaneously from a new system-level perspective. These Bayesian modeling approaches quantify previously unexplained regional differences in biotic response to urbanization, capture multiple interacting environmental and ecological processes affected by urbanization, and ultimately link urbanization effects on stream biota to a management context such that these models describe and quantify how changes in drivers lead to changes in regulatory endpoint (the Biological Condition Gradient; BCG).

Natural steroidal estrogen hormones in swine wastes generated from Concentrated Animal Feeding Operations (CAFOs) have become a potential pollutant to many aquatic environments due to their adverse impacts on the reproductive biology of aquatic organisms. In North Carolina, the swine CAFO industry is a major agricultural economic enterprise that is responsible for the generation of large volumes of waste. However, there is limited scientific understanding regarding the concentration, fate, and transport of the estrogenic compounds from these swine facilities into terrestrial and aquatic environments. To address this issue, my research involved the development and application of integrated Bayesian networks (BNs) models that can be used to better characterize and assess the generation, fate, and transport of site-specific swine CAFO-derived estrogen compounds. The developed model can be used as decision support tool towards estrogen risk assessment. Modularized and melded BN approaches were used to capture the predictive and casual relationships of the estrogen budget and its movement within and between the three major systems of a swine farrowing CAFO. These systems include the animal barns, the anaerobic waste lagoon, and the spray fields. For the animal barn system, a facility-wide estrogen budget was developed to assess the operation-specific estrogen excretion, using an object-oriented BN (OOBN) approach. The developed OOBN model provides a means to estimate and predict estrogen fluxes from the whole swine facility in the context of both estrogen type and animal operating unit. It also accounts for the uncertainties in the data and in our understanding of the system. Next, mass balance melding BN models were developed to predict the natural estrogen fates and budgets in two lagoon compartments, the slurry and the sludge storage. This involved utilizing mass balance equations to account for the mechanisms of flushing, sorption, transformation, settling, and burial reactions of estrogen compounds in the slurry and sludge storages. As an alternative approach, a regression based BN melding approach was developed to both characterize estrogen fate and budgets as a result of the sequential transformation processes between natural estrogen compounds and to assess the seasonal effects on the estrogen budgets in the two different lagoon compartments. Finally, a dynamic BN model was developed to characterize rainfall-driven estrogen runoff processes from the spray fields. The dynamic BN models were used to assess the potential risk of estrogen runoff to adjacent waterways. In addition, the dynamic model was used to quantify the effects of manure application rates, rainfall frequency, the time of rainfall and irrigation, crop types, on-farm best management practices, seasonal variability, and successive rainfall and manure application events on estrogen runoff.

The model results indicate that the farrowing barn is the biggest contributor of total estrogen as compared to the breeding and gestation operating barns. Once the estrogen reaches the anaerobic lagoon, settling and burial reactions were shown to be the most significant factors influencing estrogen levels in the slurry and sludge, respectively. The estrogen budgets in the lagoon were also found to vary by season, with higher slurry and sludge estrogen levels in the spring as compared to the summer. The risk of estrogen runoff was predicted to be lower in the summer as compared to the spring, primarily due to the spray field crop management plans adopted. The results also indicated that Bermuda grass performed more favorably when compared to soybean, when it came to retaining surface water runoff in the field. Model predictions indicated that there is a low risk of estrogen runoff losses from the spray fields under multiple irrigation and rainfall events, unless the time interval between irrigation was less than 10 days and/or in the event of a prolonged high magnitude rainstorm event. Overall, the estrone was the most persistent form of natural estrogens in the three major systems of the swine farrowing CAFO.

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.

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.