Summary and Discussion

In this study, the physical process of three major rainfall derived infiltration and inflow (RDII) sources: roof downspout, sump pump, and leaky lateral, were investigated using physics-based models. These three sources represent three different flow patterns of RDII entering into a sanitary sewer system through: a direct connection of runoff catchments, course porous media, and compacted soil, respectively. The typical flow response of each RDII source was expressed as impulse response functions (IRFs) that indicate the flow responses to a representative rainfall. Three IRFs were weighted and superposed to produce the total RDII flows by calibrating the weighting factors so that the modeled hydrograph closely match with the actual sewer flow data in a test sewershed in Illinois using a genetic algorithm (GA) technique.
The three IRFs displayed distinctly different patterns. Roof connection IRF was directly reflects the input rainfall in terms of flow duration and the runoff pattern. The leaky lateral IRF showed a delayed and dampened flow pattern as percolation through porous media being the major flow pattern. The sump pump IRF fell between the roof IRF and the leaky lateral IRF. The sump pump flow path also involves a flow through porous media but it is “faster” than the leaky lateral flow path as the travel distance of surface water in the sump pump model was shorter than that of the leaky lateral model and the medium has a larger hydraulic conductivity. The shapes of the three IRFs were easily distinguishable from one another which in turn made them suitable as RDII hydrograph building blocks.
The IRF method was further compared to one of the most widely used I&I estimation methods, SWMM RTK method. The RTK method uses a simple curve fitting approach of three triangular hydrographs that represent fast, medium, and slow I&I sources. Because of its flexibility and ability to manipulate any hydrographs, the model tends to provide a decent calibration result. However, the RTK method has many local optimal solutions as nine calibratable coefficients are not independent from each other. While RTK method displayed better model fitness than the IRF method, the model performance became reduced in the validation period which might be an evidence of its lack of robustness as the physical processes are not reflected in the modeling. The IRF result showed improved model efficiency in the validation period than the calibration period which might imply the robustness of the modeling approach of using physics-based models.
The results of this study need to be interpreted with caution as it only presents one realization of the method in a specific sewershed. However, application of physics-based modeling in RDII estimation can provide an opportunity to understand the hydrological processes behind the problematic urban drainage issue. By using preconstructed IRFs and scaling them based on rainfall rather than running entire physics-based models every time would save time and energy especially when an evaluation and decision need to be made in timely manner for urban drainage management. In addition, as a result of the IRF approach, relative contributions of three types of RDII sources can be identified. In turn, this study can shed light on defining RDII based on its sources which helps decision makers to better understand the local RDII issues and facilitate a more effective management of a sewer system.