Regression Analysis of Variation of Rainfall-Derived Inflow/Infiltration in Columbus, Ohio

Li Zhang, Fang Cheng, Gregory Barden, Hunter Kelly, Timothy Fallara and Edward Burgess


Rainfall-derived inflow and infiltration (RDII) into sanitary sewers is often a major factor contributing to sanitary sewer overflows (SSOs) and water in basement complaints (WIB’s). SSOs pose serious problems by contaminating the environment, causing property damage and threatening public health. Control of SSOs is therefore a priority of many sewer system agencies throughout the U.S. and Canada. Modeling of sewer collection systems is commonly employed to facilitate the control of SSOs. Understanding the variation of RDII as a function of rainfall conditions is critical to improving the simulation results of modeled sanitary sewer systems.

This paper presents a statistical analysis of variation of RDII and its relationship with rainfall characteristics and other weather conditions. As part of the City of Columbus long term collection system modeling, sewer flow, rainfall and temperature have been collected for periods of up to nearly ten years, providing a large dataset for analysis. RDII analysis was performed in the U.S.EPA’s recently released Sanitary Sewer Overflow Analysis and Planning (SSOAP) Toolbox program to generate the total RDII capture fraction (R), which is commonly used to model RDII in sanitary sewer systems, as well as the rainfall characteristics. Multivariate regression analysis was performed on these data using the Minitab 15 statistical software to evaluate the relationship between the total R and rainfall characteristics such as rainfall volume, rainfall duration, peak intensity and antecedent dry days, and other weather factors such as temperature. A generalized linear regression model was used to reveal the possibility of nonlinear relationships.

The results showed significant relationships between total R and rainfall volume, antecedent dry days and temperature. Seasonal or monthly unit hydrograph (RTK) parameters for RDII are typically used in SWMM 5 modeling for long-term continuous simulation of RDII in sanitary sewers. Integration of a RDII regression model into the SWMM 5 model could enable storm-specific RTK values to be used, which would potentially improve continuous simulation of sanitary sewer systems.

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