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Identifying critical source areas of nonpoint source pollution with SWAT and GWLF

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  • Niraula, Rewati
  • Kalin, Latif
  • Srivastava, Puneet
  • Anderson, Christopher J.

Abstract

Identification of critical source areas (CSAs) (areas contributing most of the pollutants in a watershed) is important for cost-effective implementation of best management practices. Identification of such areas is often done through watershed modeling. Various watershed models are available for this purpose, however it is not clear if the choice (and complexity) of a model would lead to differences in locations of CSAs. The objective of this study was to use two models of different complexity for identifying CSAs. The relatively complex Soil and Water Assessment Tool (SWAT) and the simpler Generalized Watershed Loading Function (GWLF) were used to identify CSAs of sediment and nutrients in the Saugahatchee Creek watershed in east central Alabama. Models were calibrated and validated for streamflow, sediment, total nitrogen (TN) and total phosphorus (TP) at a monthly time scale. While both models performed well for streamflow, SWAT performed slightly better than GWLF for sediment, TN and TP. Sub-watersheds dominated by urban land use were among those producing the highest amount of sediment, TN and TP loads, and thus identified as CSAs. Sub-watersheds with some amount of agricultural crops were also identified as CSAs of TP and TN. A few hay/pasture dominated sub-watersheds were identified as CSAs of TN. The identified land use source areas were also supported by field collected water quality data. A combined index was used to identify the sub-watersheds (CSAs) that need to be targeted for overall reduction of sediment, TN and TP. While many CSAs identified by SWAT and GWLF were the same, some CSAs were different. Therefore, this study concludes that model choice will affect the location of some CSAs.

Suggested Citation

  • Niraula, Rewati & Kalin, Latif & Srivastava, Puneet & Anderson, Christopher J., 2013. "Identifying critical source areas of nonpoint source pollution with SWAT and GWLF," Ecological Modelling, Elsevier, vol. 268(C), pages 123-133.
    • Handle: RePEc:eee:ecomod:v:268:y:2013:i:c:p:123-133
    DOI: 10.1016/j.ecolmodel.2013.08.007
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    References listed on IDEAS

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    1. Shang, Xiao & Wang, Xinze & Zhang, Dalei & Chen, Weidong & Chen, Xuechu & Kong, Hainan, 2012. "An improved SWAT-based computational framework for identifying critical source areas for agricultural pollution at the lake basin scale," Ecological Modelling, Elsevier, vol. 226(C), pages 1-10.
    2. Panagopoulos, Y. & Makropoulos, C. & Baltas, E. & Mimikou, M., 2011. "SWAT parameterization for the identification of critical diffuse pollution source areas under data limitations," Ecological Modelling, Elsevier, vol. 222(19), pages 3500-3512.
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    3. Ramesh P. Rudra & Balew A. Mekonnen & Rituraj Shukla & Narayan Kumar Shrestha & Pradeep K. Goel & Prasad Daggupati & Asim Biswas, 2020. "Currents Status, Challenges, and Future Directions in Identifying Critical Source Areas for Non-Point Source Pollution in Canadian Conditions," Agriculture, MDPI, vol. 10(10), pages 1-25, October.
    4. Caroline Petit & Audrey Vincent & Philippe Fleury & Amandine Durpoix & Fabienne Barataud, 2016. "Protecting Water from Agricultural Diffuse Pollutions: Between Action Territories and Hydrogeological Demarcation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 295-313, January.
    5. Caroline Petit & Audrey Vincent & Philippe Fleury & Amandine Durpoix & Fabienne Barataud, 2016. "Protecting Water from Agricultural Diffuse Pollutions: Between Action Territories and Hydrogeological Demarcation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 295-313, January.
    6. Leh, Mansoor D.K. & Sharpley, Andrew N. & Singh, Gurdeep & Matlock, Marty D., 2018. "Assessing the impact of the MRBI program in a data limited Arkansas watershed using the SWAT model," Agricultural Water Management, Elsevier, vol. 202(C), pages 202-219.
    7. Zhang, J.L. & Li, Y.P. & Wang, C.X. & Huang, G.H., 2015. "An inexact simulation-based stochastic optimization method for identifying effluent trading strategies of agricultural nonpoint sources," Agricultural Water Management, Elsevier, vol. 152(C), pages 72-90.
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