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Dr. Gronewold’s research spans hydrological science topics, including basin-scale water budget simulation and forecasting, with a particular emphasis on the North American Great Lakes. He focuses on quantifying spatiotemporal variability in nearshore water quality measurements and is particularly interested in incorporating probability theory and Bayesian inference into conventional hydrology and engineering science problems. This approach allows him to propagate data and model parameter uncertainty while providing explicit expressions for forecast uncertainty, which serves as a basis for risk-based management decisions.
Department of Electrical Engineering and Computer Science