An earlier paper provided an overview of the use of monitoring and environmental spatial data from ~70 small headwater watersheds to characterize key contributing factors leading to watershed-scale agrochemical runoff potential. This paper provides more detail on the multivariate analysis and visualization approaches. Data included daily monitoring across several seasons accompanied by weather, flow measurements, and crop observations. Numerous spatial environmental variables as well as combined and derived data (e.g., measures such as co-occurrence of shallow clay pan depth ranges with slopes in agricultural fields and over 200 PRZM model runoff model outputs) were aggregated at both watershed and individual field scales. Multivariate analysis involved identifying key response variables followed by correspondence analyses, non-metric multi-dimensional scaling, and various principal components techniques. Principal component 1 derived from field-by-field data overlaid closely with exposure metrics. A process was developed for visualizing and quantifying aggregated watershed results, enabling simple watershed-to-watershed comparisons.

Watershed characterization: Multivariate approaches for identifying key factors contributing to potential vulnerabilities for agrochemical runoff in small headwater Midwest watersheds

244th National Meeting (2012)

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