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dc.contributor.authorHrubecky, Jacob
dc.contributor.authorDarlington, Robert
dc.contributor.authorSherer, Will
dc.contributor.authorFranczyk, Xavier
dc.date.accessioned2019-03-01T16:57:20Z
dc.date.available2019-03-01T16:57:20Z
dc.date.issued2017
dc.identifier.urihttp://digital.library.wisc.edu/1793/78993
dc.descriptionIncludes Maps, Figures, Charts, Graphs, Appendices and Bibliography.en_US
dc.description.abstractThe research question focuses on the connection of land use and nonpoint nutrient loading, Phosphorus and Nitrogen, through testing water samples at Lake Mendota’s four river inputs. This project aims to trace the cause of Mendota’s extreme eutrophication back to certain land uses in the respected watersheds of the four water inputs into the lake. Water was collected at the points before lake entrance at Yahara River, Dorn Creek, Sixmile Creek, Dorn Creek, and Pheasant Branch Creek. Water was examined for levels of Phosphorus and Nitrogen with test kits and was recorded for a four week testing span in October and November. Testing the water samples came in the form of concentration, this was then converted with each river’s input rate with use of the USGS daily flows, resulting in a measure of mg/sec. Sub-watersheds then had to be divided for land analyses. This research uses the boundaries provided by the Wisconsin DNR which conveniently divides each river into its own area of land in the watershed. With data of nutrient inputs, and land cover for each testing area, a Pearson’s correlation coefficient was used for data analyses. In order of amount of the nutrient loading into the lake, the largest was the Yahara, followed by Sixmile, Dorn and Pheasant Branch as the smallest. By looking at each of these locations and their results, with the land cover, correlations could be made. For phosphorus input, continuous corn had a strong positive correlation of 0.811. Zero is no correlation and 1 is a strong positive correlation for scale of the result. Pastures, Emergent wetland and meadows also had a high coefficient value in phosphorus. Nitrogen had a similar trend but with slightly smaller numbers for their correlation value. With contrast, high and low intensity development had a negative correlation with nutrient loading. The largest land cover, dairy rotation, didn’t have any strong correlation with any of the nutrients. The data collected and analyzed could be improved upon in the future with a longer temporal extent of the testing. Taking samples in the entire growing period from spring to fall would help to draw stronger conclusions on the correlations of data and land use. Using more precise tools would also help to make conclusions and testing data stronger. Overall, the research question was answered with certain land cover types having a strong correlation to high nutrient loading, while others have negative correlations, proving the idea of different land cover types having different effects on Lake Mendota eutrophication.en_US
dc.language.isoen_USen_US
dc.subjectLake Mendotaen_US
dc.subjectPhosphorusen_US
dc.subjectNitrogenen_US
dc.subjectEutrophicationen_US
dc.subjectWater qualityen_US
dc.titleLake Mendota: Tracking Nonpoint Phosphorus and Nitrogen Loadingen_US
dc.typeField projecten_US


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