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.