Electronic Thesis and Dissertation Repository


Master of Science




Adam Yates


Human activities have transformed the landscape and altered natural habitats through intensive land uses including agriculture and urbanization. Identifying land use drivers of tributary nutrient concentrations and describing the magnitude and direction of their relationship are critical activities to improvement management of water quality in basins draining into the Great Lakes. The overarching goal of my thesis was to quantify the cumulative influence of spatial patterns in land use and land cover on variation of nutrient concentrations in tributaries of the Great Lakes. Biweekly water chemistry samples were collected in 29 streams located in southern Ontario between May and November, 2012. Agriculture, urbanization and the population served per km2 by a municipal sewage treatment plant were quantified for each stream at multiple spatial scales. Ordinary least squares regression analysis models identified relationships between nutrient parameters (NO3-+NO2-, NH3, TKN, TN, SRP, TDP, and TP) and land use descriptors. Significant associations were identified for all nutrient parameters with the exception of TDP. NO3-+NO2- was driven by a combination of urban and agriculture land use in the catchment. NH3, TKN, and SRP were related to agriculture and sewage treatment plants (STPs). TN and TP were only associated with STP population served per km2. Model predictive performance was evaluated under three scenarios; data comparability, spatial robustness and temporal robustness (dry, moderate and wet climate scenarios). Overall, assessment of model performance indicated that data sampling and collection protocol may limit prediction accuracy. My results show that human activities are significant drivers of stream nutrient concentrations and that nitrogen forms can be predicted, on average, in70% of evaluation streams under most scenarios. My findings demonstrate the utility of land use as a predictive tool for managing stream nutrient concentrations. The nitrogen models generated in my study could be used to enable planners and managers to better understand the potential implications of future land management decisions on water quality.