Abstract
Rainfall-derived infiltration and inflow (RDII) is extraneous water in a sanitary sewer system that originates from surface runoff. Most RDII enters sanitary sewer systems through illegal connections or mechanical faults, especially in aged sewer systems. In this study, the physical process of three primary RDII sources: roof downspout, sump pump, and leaky lateral, are investigated using physics-based models. These three sources represent three different flow paths: direct connection of impervious catchments, mixed flow through coarse porous media followed by a direct connection, and percolated flow through compacted soil. Due to the differences in medium and the lengths of flow paths, flow responses of these three RDII sources differ in time and magnitude. In turn, they can be distinctly identified from each other. The typical flow response of each RDII source is represented as an impulse response function (IRF), a flow response to a pre-specified representative rainfall computed using physics-based models. The total RDII flow hydrograph is presented as a combination of these three IRFs. The weighting factors of each IRF are calculated using a genetic algorithm technique in a test sewer basin in a suburb of Chicago, IL. The model results suggest leaky lateral might be the biggest RDII contributor to the system. The model performance was compared with one of the more widely used RDII estimation methods, the Storm Water Management Model RTK method. While the RTK method shows better performance overall, the IRF method provides a unique solution with robust performance. The suggested physics-based approach may shed light on identifying local RDII issues with more detail, facilitating more effective management of a sewer system.
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Acknowledgements
This research was supported by the Tunnel and Reservoir Plan (TARP) project from the Metropolitan Water Reclamation District of Greater Chicago (MWRDGC). The authors thank the Illinois State Water Survey (ISWS) for providing the rainfall data, and the U.S. Geological Survey (USGS) for sharing the sewage flow monitoring data. We also thank Andrea Zimmer and Sarah Berastegui-Vidalle for their technical support and guidance related to the genetic algorithm technique. This paper is based on the model methodology and result portion of the first author’s Ph.D. dissertation (Choi 2016).
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Choi, N., Schmidt, A.R. Rainfall-Derived Infiltration and Inflow Estimate in a Sanitary Sewer System Using Three Impulse Response Functions Derived from Physics-Based Models. Water Resour Manage 37, 305–319 (2023). https://doi.org/10.1007/s11269-022-03370-3
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DOI: https://doi.org/10.1007/s11269-022-03370-3