INTEGRATED ASSESSMENT OF ANTHROPOGENIC, CLIMATE, AND POLICY INDUCED CHANGES OF PHOSPHORUS EXPORT IN THE UNITED STATES LAURENTIAN GREAT LAKES WATERSHEDS

Anthropogenic activities have increased phosphorus (P) loading in tributaries to the Laurentian Great Lakes resulting in eutrophication in small bays to most notably, Lake Erie. Changes to surface water quality from P loading have resulted in billions of dollars in damage and threaten the health of the world’s largest freshwater resource. To understand the factors affecting P delivery with projected increasing urban lands and biofuels expansion, two spatially explicit models were coupled. The coupled models predict that the majority of the basin will experience a significant increase in urban area P sources while the agriculture intensity and forest sources of P will decrease. Changes in P loading across the basin will be highly variable spatially. Additionally, the impacts of climate change on high precipitation events across the Great Lakes were examined. Using historical regression relationships on phosphorus concentrations, key Great Lakes tributaries were found to have future changes including decreasing total loads and increases to high-flow loading events. The urbanized Cuyahoga watersheds exhibits the most vulnerability to these climate-induced changes with increases in total loading and storm loading , while the forested Au Sable watershed exhibits greater resilience. Finally, the monitoring network currently in place for sampling the amount of phosphorus entering the U.S. Great Lakes was examined with a focus on the challenges to monitoring. Based on these interviews, the research identified three issues that policy makers interested in maintaining an effective phosphorus monitoring network in the Great Lakes should consider: first, that the policy objectives driving different monitoring programs vary, which results in different patterns of sampling design and frequency; second, that these differences complicate efforts to encourage collaboration; and third, that methods of funding sampling programs vary from agency to agency, further complicating efforts to generate sufficient long-term data to improve our understanding of phosphorus into the Great Lakes. The dissertation combines these three areas of research to present the potential future impacts of P loading in the Great Lakes as anthropogenic activities, climate and monitoring changes. These manuscripts report new experimental data for future sources, loading and climate impacts on phosphorus.

Estimation of vertical groundwater fluxes into a streambed through continuous temperature profile monitoring and the relationship of groundwater fluxes to coaster brook trout spawning habitat

We hypothesized that the spatial distribution of groundwater inflows through river bottom sediments is a critical factor associated with the selection of coaster brook trout (a life history variant of Salvelinus fontinalis,) spawning sites. An 80-m reach of the Salmon Trout River, in the Huron Mountains of the upper peninsula of Michigan, was selected to test the hypothesis based on long-term documentation of coaster brook trout spawning at this site. Throughout this site, the river is relatively similar along its length with regard to stream channel and substrate features. A monitoring well system consisting of an array of 27 wells was installed to measure subsurface temperatures underneath the riverbed over a 13-month period. The monitoring well locations were separated into areas where spawning has and has not been observed. Over 200,000 total temperature measurements were collected from 5 depths within each of the 27 monitoring wells. Temperatures within the substrate at the spawning area were generally cooler and less variable than river temperatures. Substrate temperatures in the non-spawning area were generally warmer, more variable, and closely tracked temporal variations in river temperatures. Temperature data were inverted to obtain subsurface groundwater velocities using a numerical approximation of the heat transfer equation. Approximately 45,000 estimates of groundwater velocities were obtained. Estimated velocities in the spawning and non-spawning areas confirmed that groundwater velocities in the spawning area were primarily in the upward direction, and were generally greater in magnitude than velocities in the non-spawning area. In the non-spawning area there was a greater occurrence of velocities in the downward direction, and velocity estimates were generally lesser in magnitude than in the spawning area. Both the temperature and velocity results confirm the hypothesis that spawning sites correspond to areas of significant groundwater influx to the river bed.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.