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TitleUsing remote sensing to identify changes in land use and sources of fecal bacteria to support a watershed transport model
Publication TypeJournal Article
Year of Publication2014
AuthorsButler, S, Webster, T, Redden, A, Rand, J, Crowell, N, Livingstone, W
Start Page1925-1944
Pagination20 p.; 9 Fig.; 1 Tab.;
Date Published07/2014
PublisherMDPI AG
Place PublishedBasel, Switzerland
Publication LanguageEnglish
Keywordsescherichia coli, remote sensing, shellfish, water quality

The contamination of shellfish harvesting areas by fecal bacteria in the Annapolis Basin of Nova Scotia, Canada, is a recurring problem which has consequences for industry, government, and local communities. This study contributes to the development of an integrated water quality forecasting system to improve the efficiency and effectiveness of industry management. The proposed integrated forecasting framework is composed of a database containing contamination sources, hydrodynamics of the Annapolis Basin, Escherichia coli (E. coli) loadings and watershed hydrology scenarios, coupled with environmental conditions of the region (e.g., temperature, precipitation, evaporation, and ultraviolet light). For integration into this framework, this study presents a viable methodology for assessing the contribution of fecal bacteria originating from a watershed. The proposed methodology investigated the application of high resolution remote sensing, coupled with the commercially available product, MIKE 11, to monitor watershed land use and its impact on water quality. Remote sensing proved to be an extremely useful tool in the identification of sources of fecal bacteria contamination, as well as the detection of land use change over time. Validation of the MIKE 11 model produced very good agreement (R2 = 0.88, E = 0.85) between predicted and observed river flows, while model calibration of E. coli concentrations showed fair agreement (R2 = 0.51 and E = 0.38) between predicted and observed values. A proper evaluation of the MIKE 11 model was constrained due to limited water sampling. However, the model was very effective in predicting times of high contamination for use in the integrated forecasting framework, especially during substantial precipitation events. [authors abstract]



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