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Comparison of Modeled Results for Kansas City Middle Blue River Green Infrastructure Pilot Project
Simon, M., J. Lee, D. O'Bannon, R. Pitt, J. Wright, AND D. Bambic. Comparison of Modeled Results for Kansas City Middle Blue River Green Infrastructure Pilot Project. Presented at World Environmental & Water Resources Congress 2013, Cincinnati, OH, May 19 - 23, 2013.
To compare modeled results for a green infrastructure pilot project.
The Water Services Department (WSD) in Kansas City, Missouri (KCMO) has conducted extensive modeling and economic studies of its combined sewer system (CSS) over the last several years. A number of green infrastructure (GI) solutions were identified and constructed to reduce discharges to the CSS, including rain gardens, bioretention, and infiltration cells. Limiting factors for selecting and placing GI included slope (eliminating areas where slope exceeds five percent), location of utilities, soils and geology, rights-of-way width, obstructions, such as driveways or parking lots, and owner acceptance. After completing the GI construction in a pilot area, USEPA ran SUSTAIN to compare predictions to actual performance/effectiveness of the GI project. SUSTAIN is a decision support system to facilitate selection and placement of Best Management Practices (BMPs) and Low Impact Development (LID) techniques at strategic locations in urban watersheds. It was developed to assist stormwater management professionals in developing implementation plans for flow and pollution control to protect source waters and meet water quality goals. USEPA collected sewershed scale flow data from 2009 to 2012. This period covers the time before the CSS was upgraded to repair leaks; after leak repair; and then after GI was installed. Before the GI implementation, SUSTAIN predicted the sewershed flow response to predict volume reduction versus cost for various combinations of GIs. This work investigates the reliability of these predictions by comparing sewershed response after the instillation of GIs. The soundness of the model prediction is a function of the range of the system parameters (e.g., slope, soil infiltration, etc.), spatial variation for GI structures, and the method for aggregating GIs in the model.