Application of USEPA SSOAP Software to Sewer System Modeling

Abstract

Rainfall derived infiltration and inflow (RDII) is one of the leading causes of combined sewer overflows (CSO) and sanitary sewer overflows (SSO) in collection sewer systems. To help evaluate and quantify the impacts of RDII in communities, the U.S. Environmental Protection Agency (USEPA) public-domain tool called the Sanitary Sewer Overflow Analysis and Planning (SSOAP) tool can be used. SSOAP uses the synthetic unit hydrograph (SUH) approach to develop RTK parameters and to predict RDII in sewer systems. The RTK parameters developed can be incorporated into sewer system modeling software, such as USEPA's Storm Water Management Model (SWMM), to simulate peak flows under different design storm events. Well developed models can help communities efficiently plan capital improvement projects that would reduce potential sewer overflows. Three main data inputs for SSOAP are sewershed data, flow monitoring data and rainfall data. The quality of the flow and rainfall monitoring data has a significant impact on the quality of the RDII analysis and model simulation results. Therefore, preprocessing of rainfall and flow response data is critical before using the SSOAP Tool. The team developed a preprocessing Microsoft Excel tool to aid in the analysis and selection of data to be used for the RTK analysis. The SSOAP Tool and SWMM software were utilized to develop collection system models for the cities of Topeka, Kansas and Council Bluffs, Iowa. The city of Topeka collected data from 38 flow meters and 5 rain gauges from approximately 148 square miles and 110 miles of sewer system. CDM Smith chose four events for calibration. The city of Council Bluffs collected data from five flow meters and one rain gauge from an area with approximately 4 square miles of drainage area and 73 miles of sanitary sewers. Two events were analyzed for calibration. This paper looks in depth at the analysis of the flow and rainfall monitoring data using the SSOAP application and the impact that this analysis had on the model development and calibration for these two case studies.