Abstract

The City of Columbus, Ohio, completed a comprehensive Wet Weather Management Plan (WWMP) to mitigate hydraulic deficiencies in the City’s main trunk sewers and to perform a Long Term Control Plan (LTCP) to address combined sewer overflows (CSO) to the Scioto and Olentangy Rivers. The recommended solution includes a deep tunnel that will capture combined sewage overflows from the downtown CSO regulators. This combined flow tunnel will ensure that peak flow from the downtown combined sewer basins is captured up to a specific peak flow recurrence level. Due to the lack of long-term flow meter and downtown rainfall data, it was difficult to estimate peak flows for selected recurrence levels. Therefore, the design team proposed a procedure where available two to three-years of quality-checked flow meter data and concurrent 15-min rain gauge data between the years 2000 and 2003 was used to calibrate a SWMM model using the PCSWMM Genetic Algorithm Calibration (GAC). The long-term hourly rainfall data, collected by the National Weather Service at Port Columbus International Airport, in conjunction with the calibrated SWMM 4.4h model was then used to generate 56 y of flow records from each combined basin.

This chapter details how we applied the GAC procedure to calibrate the SWMM Runoff module parameters for each combined sewer basin in downtown Columbus. Calibration was performed on the largest 10 to 12 storm events recorded between the years 2000 and 2003. These storm events covered a wide-range of short, medium, and long durations with high rainfall intensities. Runoff parameters were controlled within suitable uncertainty ranges. The GAC was applied until the peak flow values of most of the high-intensity calibrated storms were within ±10% of the observed peak flow values. The hourly rainfall data from Port Columbus International Airport was then used with the calibrated Runoff modules to predict the highest peak flows that occurred over the 56-y recorded rain period. The Cunnane plotting position formula was then applied to the predicted peak flow data to generate a flow recurrence curve. The predicted peak flow recurrence was corrected to account for the difference between using the 1-h. time step rain data from Port Columbus International Airport versus the 15-min. time step rain data from the downtown rain gauges that were used during the GAC process. The proposed procedure allowed for a reasonable prediction of the 1-, 2-, 5-, and 10-y recurrence peak combined flow while overcoming the lack of adequate long-term flow data.

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