Using Remote Sensing to Identify Changes in Land Use and Sources of Fecal Bacteria to Support a Watershed Transport Model

Water

Sean Butler³  Tim Webster³  Anna Redden¹  Jennie Rand²  Nathan Crowell³ 

[1] Acadia Centre for Estuarine Research, Acadia University, 23 Westwood Avenue, Wolfville, Nova Scotia, NS B4P 2R6, Canada; E-Mail:;Ivan Curry School of Engineering, Acadia University, Wolfville, Nova Scotia, NS B4P 2R6, Canada; E-Mail:;Applied Geomatics Research Group, Centre of Geographic Sciences (COGS), Nova Scotia Community College, 50 Elliot Road RR#1 Lawrencetown, Nova Scotia, NS B0S 1M0, Canada; E-Mails:

关键词: remote sensing;    MIKE 11;    Escherichia coli;    shellfish;    water quality;   

DOI  :  10.3390/w6071925

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.