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A Diagnostic Decision Support System for BMP Selection in Small Urban Watershed

Author

Listed:
  • Yan Wang

    (University of Maryland)

  • Hubert J. Montas

    (University of Maryland)

  • Kaye L. Brubaker

    (University of Maryland)

  • Paul T. Leisnham

    (University of Maryland)

  • Adel Shirmohammadi

    (University of Maryland)

  • Victoria Chanse

    (University of Maryland)

  • Amanda K. Rockler

    (University of Maryland)

Abstract

Best Management Practices (BMPs) have become the most effective way to mitigate non-point source pollution (NPS) issues. Much attention has been paid to NPS in rural areas, where agricultural activities increase nutrients, toxics, and sediments in surface water. Stormwater from urban areas is also a major contributor to NPS pollution. For watersheds bearing various soil types and land uses, a single type of BMP cannot be the panacea to all stormwater problems. To solve these problems, a Diagnostic Decision Support System (DDSS) was developed in this research. The DDSS can identify and locate the most critical NPS areas (hotspots) within a watershed in high spatial resolution. The DDSS can provide a series of spatially distributed small-scale BMPs which are effective in treating the NPS and are suitable for the physical environment. The BMPs, varying in types and locations, are recommended at HRU (Hydrologic Response Unit) level. The DDSS was tested in Watts Branch, a small urban watershed of the Anacostia River in metropolitan Washington D.C., USA. The process-based hydrologic model, Soil and Water Assessment Tool (SWAT), was used to simulate watershed responses. The simulation results were then used by the DDSS for BMP recommendation. Hotspots of different NPS were successfully located and prescribed with spatially distributed BMPs. The DDSS serves as a useful tool to better understand urban watersheds and to make proper stormwater management plans.

Suggested Citation

  • Yan Wang & Hubert J. Montas & Kaye L. Brubaker & Paul T. Leisnham & Adel Shirmohammadi & Victoria Chanse & Amanda K. Rockler, 2017. "A Diagnostic Decision Support System for BMP Selection in Small Urban Watershed," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(5), pages 1649-1664, March.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:5:d:10.1007_s11269-017-1605-x
    DOI: 10.1007/s11269-017-1605-x
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    References listed on IDEAS

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    1. Hansen, LeRoy T. & Hellerstein, Daniel, 2006. "Better Targeting, Better Outcomes," Economic Brief 34099, United States Department of Agriculture, Economic Research Service.
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    Cited by:

    1. Xiaoyan Gong & Jianmin Bian & Yu Wang & Zhuo Jia & Hanli Wan, 2019. "Evaluating and Predicting the Effects of Land Use Changes on Water Quality Using SWAT and CA–Markov Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4923-4938, November.
    2. Sara Lucía Jiménez Ariza & José Alejandro Martínez & Andrés Felipe Muñoz & Juan Pablo Quijano & Juan Pablo Rodríguez & Luis Alejandro Camacho & Mario Díaz-Granados, 2019. "A Multicriteria Planning Framework to Locate and Select Sustainable Urban Drainage Systems (SUDS) in Consolidated Urban Areas," Sustainability, MDPI, vol. 11(8), pages 1-33, April.
    3. Ali Nasiri Khiavi & Seyed Hamidreza Sadeghi & Mehdi Vafakhah, 2024. "Comparative Prioritization of Sub-Watersheds in Flood Generation Using Co-Management Best-Worst Method and Game Theory Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(12), pages 4431-4453, September.

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