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Dr. Gronewold’s research spans hydrological science topics ranging from basin-scale water budget simulation and forecasting (with an emphasis on the North American Great Lakes) to quantifying spatiotemporal variability in nearshore water quality and water quality measurements. Dr. Gronewold is particularly interested in incorporating probability theory and Bayesian inference into conventional hydrology and engineering science problems to propagate data and model parameter uncertainty and variability into explicit expressions of forecast uncertainty, and to provide a basis for risk-based management decisions.
Dr. Gronewold’s research spans hydrological science topics ranging from basin-scale water budget simulation and forecasting (with an emphasis on the North American Great Lakes) to quantifying spatiotemporal variability in nearshore water quality and water quality measurements. Dr. Gronewold is particularly interested in incorporating probability theory and Bayesian inference into conventional hydrology and engineering science problems to propagate data and model parameter uncertainty and variability into explicit expressions of forecast uncertainty, and to provide a basis for risk-based management decisions.
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