Discussion
The suggested Impulse Response Function (IRF) method was directly
compared with the conventional RTK method. The RTK method displayed a
better model efficiency than the IRF method. However, the RTK method
being a simple curve fitting method caused the solutions to be variable
each time. This might give a modeler a decent representation of the
overall RDII, but each RTK parameter might not provide any physical
meanings as they are not unique. The combinations of the nine RTK
parameters can be vastly different depending on the level of experience
that a model has for the model basin. In contrast, the IRF method
presented consistent results.
The IRF method is a physics-based RDII estimation method that is
combined with a synthetic hydrograph approach. 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 of each other. While the RTK method displayed better model
fitness than the IRF method, 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.
Moreover, predefining IRFs for each modeling unit can speed up the
modeling process, which could help to develop a real-time RDII forecast
model in the future. Running the entire physics-based model from scratch
for every storm event might not be a feasible option. Thus the IRF
approach might be desirable for decision making in urban drainage
management.
Another benefit of using the three IRF approach is being able to
identify relative contributions of different RDII sources when the model
is calibrated. Weighting factors of each modeling unit may provide
insights on which RDII source is most problematic in the test sewershed.
In turn, this study can shed light on defining RDII based on its
sources, which helps decision-makers to better understand their unique
local RDII issues and facilitate more effective management of the sewer
system.
The results of this study need to be interpreted with caution as it
presents only one realization of the method in a selected sewershed.
Each sewershed has unique characteristics, e.g., age and material of the
sewer system, typical house configuration, drainage practices. Every
system deals with different RDII challenges, and some do not even have
RDII issues, especially in a newly constructed area. The value of this
study is to demonstrate the possibility of modeling RDII using
physics-based models that take into account hydrological processes.