SIMPLE PREDICTIONS OF NON-POINT SOURCE POLLUTION IN AGRICULTURAL WATERSHEDS USING GIS TECHNIQUES

A common cause of poor surface water quality in the Midwestern portion of the United States is contamination from agricultural runoff, which can result in elevated turbidity and nutrient concentrations in streams and lakes. Water bodies showing high levels of contamination can be classified as impaired, and resources can be directed towards improving the water quality, but it is often difficult for regulators to identify which water bodies are most in need of protection, since limited personnel availability and funding constraints prevent sampling of many water bodies. Sophisticated non-point source pollution runoff models are available, but these models often require complex inputs and skilled personnel to implement. In this research, we seek to derive simple but robust regression equations between watershed characteristics and probable surface water quality to allow users to quickly identify watersheds that would benefit from more sophisticated modeling or sampling. Surface water quality indicators, including nitrate, phosphate, dissolved oxygen, turbidity, bacteria count, pH, electrical conductivity, temperature, and biotic index, were acquired at two times, after a dry fall and the following wetter spring, in 64 sub-watersheds within the Lower Grand River watershed. This watershed was chosen because it is similar in land use, geologic properties, and topography to many other watersheds in the lower Midwest. For each sub-watershed, publically available geographical information system (GIS) data including land use/land cover, soil texture, depth to bedrock, and depth to the water table. A digital elevation model (DEM) was used to calculate sub-watershed area, total stream length, watershed shape/relief ratio, topographic complexity, mean elevation, and slope. Correlations were calculated between each of the water quality indicators and the watershed characteristics, and predictive relationships were determined for the each water quality indicator. Results showed that the factors affecting water quality changed between the study periods, with forested area and average slope having the greatest impact on water quality during the drier season, and agriculture, urban area and precipitation having the greatest impact during the wetter season.