Since the mid-19th century, the rise of industrial agriculture and growing population has significantly altered nutrient cycling. These changes are from multiple sources, such as chemical fertilizers, livestock waste, and human waste. Excess nutrients have led to a suite of water quality problems that damage human and animal health, ecology, and economics. In this thesis, I begin to quantify the "Nutrient Landscape", a term I use to refer to the set of processes and properties that drive cycling of nitrogen and phosphorus throughout our modern environment.To understand the "Nutrient Landscape", I first develop algorithms utilizing broadly available data to estimate nutrient inputs from seven distinct sources across the U.S. portion of the Laurentian Great Lakes Basin at 30 meter resolution. Chapter I's mapping effort, referred to as the Spatially Explicit Nutrient Source Estimate map (SENSEmap), provides new information for management and modeling, as well as a classification system to categorize watersheds based on their nutrient source composition. Second, I examine the groundwater component of the "Nutrient Landscape" by exploring a dataset of over 300,000 nitrate samples from drinking water wells using Classification and Regression Tree (CART) analysis to determine drivers of elevated concentration. This analysis revealed high nitrate concentrations result from a combination of hazardous land use and vulnerable geology. The data products and findings in this thesis provide a quantitative framework for informing management strategies and driving the next generation of nutrient modeling.
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