The City of Seattle owns and operates a combined sewer system that overflows during heavy rain events to surrounding water bodies, potentially impacting their quality and uses. Hydraulic and hydrologic models of the City’s uncontrolled CSO basins were developed to identify projects and programs that will limit untreated overflows at each CSO outfall to an average of no more than one per year, a performance standard established in the City’s CSO National Pollutant Discharge Elimination System (NPDES) permit. This paper discusses the process established to identify the overflow control levels using a continuous long term simulation, taking into account the effect of the several uncertainties due to precipitation time series, collected flow data, climate changes and model hydrology and hydraulics uncertainties.
The models were constructed using the EPA SWMM platform to enable the City to investigate retrofit opportunities, green stormwater infrastructure projects, and traditional infrastructure to reduce the City’s CSO volume. Approximately 350 flow meters were installed for 18 months for use in the calibration process. Each model was autocalibrated using the Automated Calibration and Uncertainty Analysis for Stormwater Management Model (ACU-SWMM), a program developed by MGS Consultants Incorporated.
ACU-SWMM is a software package designed for use with the EPA Storm Water Management Model (SWMM)for combined sewer systems where uncertainties from multiple sources can make model calibration difficult and severely impact the reliability of sewer flow predictions. Modelers set sampling ranges for parameter values (impervious percentage, aquifers, and groundwater parameters) and then ACU-SWMM conducts numerous simulations by executing the SWMM basin model in batch mode. ACU-SWMM then produces numerous hydrographs for the modeler to compare simulated results with measured data for pre-defined storm events. From the user selected storms and weights, ACU-SWMM computes numerical goodness-of-fit (GOF) measures for use in computing global GOF measures.
Once the models were calibrated, models were simulated for 31 years with precipitation scaling factors that were used to scale the long-term 31-year precipitation time-series to mimic the variability (e.g. rainfall intensity changes from climate change) in sewer flow predictions arising from uncertainty in representativeness of the precipitation time series, possible effects of climate change, model uncertainty, and residual uncertainties. Control volumes were determined as the 32nd largest overflow during the 31-year simulation. Each control volume estimate has an amplification factor that incorporates the uncertainties.
The automated calibration process and the overflow control level from the long term simulation is detailed for the Interbay basin. The Interbay Area covers 287 acres (0.45 square miles) in northwest Seattle. Eighty-two percent of the Interbay Area is partially separated. Stormwater from partially separated areas of the Interbay Area is discharged south into Elliot Bay. The area consists of NPDES068A and NPDES068B Basins, each containing permitted combined sewer overflow (CSO) structures that discharge overflows to Elliot Bay during large precipitation events when the capacity of the combined sewer system is exceeded. A computer model of the combined sewer system in the Interbay Area was used to assess the performance of the existing system, determine the CSO control volume, and support the analysis of system modifications and new CSO control facilities that will meet the City of Seattle’s Long-Term Control Plan (LTCP).