CoBAGNPS: A Toolbox to Estimate Sediment Removal Efficiency of WASCoBs–Pipe Risers and Blind Inlets


  •  Anand K. Gupta    
  •  Ramesh P. Rudra    
  •  Bahram Gharabaghi    
  •  Prasad Daggupati    
  •  Gary Parkin    
  •  Pradeep K Goel    
  •  Rituraj Shukla    

Abstract

Water and Sediment Control Basin (WASCoB) is an important BMP constructed along concentrated flow-paths (gullies etc.) to control the movement of water and sediment within a watershed. A WASCoB constitutes of a berm, surface inlets, and a drainage pipe to route water into a ditch. Direct runoff ponded behind the berm is routed through surface inlets into an underground drainage pipe. Therefore, surface inlets are an exceedingly important constituent of a WASCoB. Further pipe risers and blind inlets are the two most common type of surface inlets used. Therefore, maximum sediment removal efficiency of WASCoBs at a watershed-scale can be attained by the appropriate selection of a surface inlet, since the efficiency of a WASCoB is greatly impacted by the quantity of runoff and sediment leaving the surface inlet. In this study a toolbox was developed viz., CoBAGNPS to compute the sediment removal efficiency of pipe risers and blind inlets. A watershed-scale model (AGNPS) was integrated within the toolbox. Output files of the AGNPS model are fed as input files into the toolbox where a sediment routing module is programmed separately for pipe risers and blind inlets to obtain the sediment removal efficiency for each type of surface inlet. Further, the sediment routing module programmed for blind inlets integrates the AGNPS model with the HYDRUS 1-D model. The toolbox developed was applied to the Gully Creek watershed in Ontario, and the sediment load routed through pipe risers and blind inlets were compared.



This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1927-0488
  • ISSN(Online): 1927-0496
  • Started: 2011
  • Frequency: semiannual

Journal Metrics

Google-based Impact Factor (2016): 6.22
h-index (November 2017): 12
i10-index (November 2017): 19
h5-index (November 2017): 11
h5-median (November 2017): 12

Learn More

Contact