Fall 2020 eNewsletter

Featured Research: Postdoctoral Research Fellow Update
Water Quality Modeling in the Great Lakes

Water quality research is an exercise in data extremes. Scientists engaged in this work must be comfortable with a handful of data points or a deluge of data points. Lakes and rivers are represented by sparse datasets because of the time, cost, and effort required to get on a boat, arrive at a sampling location, collect samples, transport them back to the laboratory, run the laboratory tests, and finally produce the result. At the same time, advancements in technology have enabled the deployment of in situ equipment that wirelessly beams lots of data from the lake back to researchers at ever shorter time intervals.

Postdoctoral Research Fellow Dr. Timothy Maguire. Photo Credit: Timothy Maguire.

Timothy Maguire is a Postdoctoral Research Fellow working with Drs. Casey Godwin (CIGLR) and Craig Stow (NOAA GLERL). His research involves using a variety of frequentist, Bayesian, GIS, and artificial intelligence/machine learning techniques to develop numerical water quality models in the Great Lakes. “My research looks at lakes and rivers throughout the Great Lakes basin, connects the two data extremes via statistical modeling, leverages the data available to fill in the missing data gaps, and uses this information to define changes in water quality. This will help us understand large scale management of the Great Lakes,” says Maguire.

Lake Erie represents a complex interconnection of water use and water quality. For several years, CIGLR and GLERL have been collecting water quality data throughout western Lake Erie at regular intervals throughout the summer-fall seasons when water quality issues peak. Starting with phosphorus concentrations, this research involves connecting the CIGLR/GLERL data to other regional datasets and developing a model that incorporates the physics of water movement in the lake to do three things: (1) We are using the data at observed sites to predict water quality at unobserved sites. This is an important step because Lake Erie is a story of both time and space. When and where phosphorus concentrations are high combine to determine what effects different useages have on the lake. (2) We are quantifying the impact on lake water quality derived from river water quality. Sources of nutrients and other water quality attributes are washed off the land around the lake, deposited from the atmosphere onto the lake, and delivered by rivers into the lake. Of these sources, rivers are the most influential, thus delineating the spatial reach, magnitude, and timing of their impact is an important goal. (3) Lastly, while we are starting with phosphorus, the model we are making is flexible, it can incorporate any water quality parameter and be used for any year.

In addition, Maguire is collaborating with community partners around the Great Lakes basin to bring citizen science data to life.

“I have been working with the Friends of the Rouge (FOTR), a community organization that has been collecting data on stream health for the River Rouge, which runs through urban Detroit, Michigan,” says Maguire.

Motivated and engaged volunteers for watershed organizations collect high quality data on river health, usually via surveys of invertebrates, which act as sensitive sentinels of water quality issues.

“My research goal is to help them make the most of their data by bringing data science and statistical modeling to their questions. For the FOTR, I described the population changes of the winter stonefly throughout their watershed and quantified the factors affecting that population. Currently, I am participating in a project attempting to measure the diversity and distribution of fish in the River Rouge. Outside of my research role with FOTR, I’ve also acted as their data science representative while collaborating with Ford Motor Company,” says Maguire.

As part of Ford’s community outreach, they dedicated their internal computer science department to two-day long “hack-a-thons”, where the FOTR data was analyzed.

“Combining these research projects and external data science events has benefited the FOTR decision making and connected my (sometimes rather abstract) modeling efforts with the real world of people living in an urbanized watershed,” says Maguire. “Interconnecting community organizations with concerned private industry and water quality research positions me for the next steps in finding holistic solutions to environmental issues.”