Erickson, Kevin Anders2013-04-112013-04-112011-02https://hdl.handle.net/11299/147451University of Minnesota M.S. thesis. February 2011. Major: Civil Engineering. Advisor: William Arnold. 1 computer file (PDF); viii, 68 pages, appendices A-C.Understanding the water quality within a watershed is vital to the proper management of that watershed. This requires monitoring frequencies that observe variations in water quality over a variety of time scales, and analytical techniques that effectively interpret the resulting data sets. The following study assesses the use of high frequency, in situ sensors and discreet grab sampling along with Principal Component Analysis (PCA) for watershed management. PCA of the water quality parameters produced graphical representations of the data that captured between 73.0% and 88.6% of the variability in the data sets analyzed. These figures were useful for showing changes in water quality over different time periods and for describing the relationships between different water quality parameters in the data sets. The monitoring scheme used was effective for tracing changes in water quality over a wide range of temporal scales, and showed the importance of flow to the water quality of Minnehaha Creek. This type of monitoring has economic and analytical limitations, however, that must be addressed to make it viable on a watershed management scale.en-USThesis or Dissertation