UNSW Assignment: Catchment Modelling of Powell Creek Stormwater System

This report presents an analysis of the Powell Creek catchment modelling, focusing on the simulation of flows within the stormwater system using the Stormwater Management Model (SWMM). The catchment, located in the western suburbs of Sydney, is characterized by mixed land uses. The primary objective is to compare the SWMM output with recorded data from a gauging station operated by UNSW to evaluate the model's calibration and improve its usefulness. The methodology involves analyzing SWMM output data (date, time, and predicted flow) and recorded data (gage height and time), converting gage height to discharge using a rating table. The results, presented in graphs such as a water level vs. discharge curve and hydrographs, reveal discrepancies between the predicted and recorded data. The report explores the impact of infiltration rates and surface runoff on the model's accuracy. Furthermore, the report discusses the application of a delayed system to reduce errors. The Root Mean Square Error (RMSE) is calculated for both delayed and non-delayed systems to quantify the errors. The conclusion highlights the need for model amendments to improve accuracy, suggesting file input as a more reliable method than hand drawing. The report underscores the importance of model calibration in catchment modelling and provides insights into potential improvements for the SWMM.

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CATCHMENT MODELLING
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INTRODUCTION
Powell Creek is a tributary for the River Parramatta and it is located in the western part of
the city of Sydney. This tributary joins River Parramatta at a bay known as Homebush as
illustrated by the map below;
Figure 1: Showing the location of Powell Creek catchment
The area of this catchment is approximated to be 2.4 km2 having a mixed land use. The study of
the management of the floodplain of the Powell Creek single out some perils of this catchment
area (Kragt, 2011). And most of the choices for the mitigation of the flood risks takes more
environmental actions. For the analysis of this catchment modelling of the Powell Creek a
software known as the Storm Water Management Model (SWMM) is employed for the
simulation (McDonnell, 2010). The results which will be obtained from the simulation will be

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employed in drawing some graphs for analysis. The results obtained after simulation are used in
calibration against the recorded data. The diagram below illustrates part of this catchment area.
Figure 2: Showing part of the Powell Creek catchment (Callow, 2013).
Actually, the Powel Creek has been transformed into a natural waterway and this was done
through replacing the old with the gently sloping banks which are made of sandstone (Todini,
2011). And the overall transformation of this catchment is illustrated by the diagram below;
Figure 3: Showing the transformation which has taken place in Powell Creek after some
time(Callow, 2013).

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The main aim of this report is to do a comparison of the recorded data against the results of the
SWMM then to increase the effectiveness of the model in case the model does not work perfectly
as required (Birch, 2010).
METHODOLOGY
The Powell Creek assessment basically includes two types of data which are the outputs
of the Storm Water Management Model (given as SWMM) and the obtained data (Kraft, 2011).
The analysis of the outputs obtained from Storm Water Management Model includes the
prediction of the catchment flow, the time when the analysis was undertaken and also the data
when the analysis was undertaken (D'Agostino, 2010). The other type of data (obtained data)
were recorded for the height and time which were then transformed into flow discharge Q. These
data were then employed to scrutinize the correlation between the stream of water and the stream
discharge (Pechlivanidis, 2011). And for this particular assessment, the maximum gauge height
is 0.8770 while the water level is approximately 0.02 which is treated as zero (Starkey, 2017).
The results of the experiment obtained after using the Storm Water Management Model to record
the flow is given in the following section.
RESULT

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We will only consider the results from 0 to 0.010 because the maximum gage height obtained is
just 0.877. In so many scopes are considered then the errors will be more in the results obtained
for the analysis and this will result in the wrong deduction of the analysis (Carroll, 2012). Due to
increased water level, the rate of infiltration will be small because the moisture in the soil is high
(saturated) this makes it difficult for the soil to accommodate any further infiltration into the soil
(Lam, 2010). With the higher concentration of the water in the soil most water will form surface
runoff in case there is a downpour (Powell, 2011). The graph/curve was drawn from the data
obtained from the Storm Water Management Model.

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Figure 4: Showing Water Level (m) above CFT against Discharge ( cumes) ( Power 2/5 scale).
The relationship between the discharge and the level of water can be expressed using the
equation below;
y = 2.5836x6 - 8.0094x5 + 7.6556x4 - 6.55x3 + 13.168x2 + 0.0172x - 0.004 . . . . . . . . . . 1
The coefficient of the curve for this analysis is approximated to be 1 since its exact value is
0.9999 and with this, it is highly possible to show the trendline in the data ( Salvadore, 2015).
The diagram below is a hydrograph graph which gives the recorded data and the output of the

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system. After obtaining some minor calculations from the equation above the Storm Water
management Modeö was gotten by the use of Ms excel. And it is given as below;
Figure 5: Showing the hydrograph Model obtained from the excel of the experiment.
In the above hydrograph graph, the discrepancy between the prediction and the data recorded is
clearly seen in the graph as the graphs have different peaks (Safari, 2013). The peaks of the
hydrograph occur at different points (BALL, 2013). From our graph above we assume that the
error was due to the simulation of the catchment modelling using the SWMM (Piotrowski,
2013). The error is observed due to the variation in the peak of these two types of data. By
delaying the system by about fifty minutes we are able to reduce the variation of these
hydrograph peaks and the data recorded. The delay is introduced into the system and another
graph is plotted to confirm if the delayed system actually resulted in any improvement of the

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output (Tritz, 2011). Hence the second graph which will be checked if there is any improvement
is shown in the following diagram
Figure 6: Showing new hydrograph graph with reduced error
After getting the graph in figure six above (new graph for the delayed system) a formula of Root
Mean Square Error (RMSE) was employed to get both systematic error and random errors
(Darrah, 2015). This is illustrated in equation 2 below. Other equations give the modelling
efficiency, mean deviation and also the value record which are very significant during the
analysis of the Model evaluation.
RMSE= √ 1
n ∑
i=1
n
❑ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

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Modeling Efficiency: E= 1- MSE
Se
2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
Mean Square Error: MSE= 1
n ∑
i=1
n
❑. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Error Variance: Se
2= 1
n−1 ∑
i=1
n
❑. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
We use the above equations to obtain the following parameters for both delayed and non-delayed
system.
Calculation for the 50 Minutes delayed system
RMSE=0.358135
Se2=0.12871
MSE=0.128261
E=0.00349
The calculation for the non-delayed system
RMSE=1.286234
Se2=1.66014
MSE=1.654399
E=0.003458

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The value of errors E obtained from the calculation for both delayed system and the non-delayed
system is less than 1, this illustrates that the model was not good enough since the value of E
should at least be 1 to conclude that the model is perfect (Peel, 2011). Delaying the system did
not help as the value of E was still far much less than 1 in both.
CONCLUSION
The software employed for the Storm Water Management Model (SWMM) did not work
as expected as it can be seen from the hydrograph graphs where for both delayed and non-
delayed the results were not precise. We can thus say that the Model requires some amendments
to make it give accurate results. There are some two ways in which the model can be modified.
The first one is to directly draw the data by hand and the second way is through inputting the
files. For a better result inputting the files is more recommended as the hand drawing technique
is not much accurate and it will have some prevalence errors.

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REFERENCES
Kragt, M.E., Newham, L.T., Bennett, J. and Jakeman, A.J., 2011. An integrated approach to
linking economic valuation and catchment modelling. Environmental Modelling &
Software, 26(1), pp.92-102.
McDonnell, J.J., McGuire, K., Aggarwal, P., Beven, K.J., Biondi, D., Destouni, G., Dunn, S.,
James, A., Kirchner, J., Kraft, P.J.H.P. and Lyon, S., 2010. How old is streamwater? Open
questions in catchment transit time conceptualization, modelling and analysis. Hydrological
Processes, 24(12), pp.1745-1754.
Todini, E., 2011. History and perspectives of hydrological catchment modelling. Hydrology
Research, 42(2-3), pp.73-85.
Kraft, P., Vaché, K.B., Frede, H.G. and Breuer, L., 2011. CMF: a hydrological programming
language extension for integrated catchment models. Environmental Modelling &
Software, 26(6), pp.828-830.
D'Agostino, D.R., Trisorio, L.G., Lamaddalena, N. and Ragab, R., 2010. Assessing the results of
scenarios of climate and land use changes on the hydrology of an Italian catchment: modelling
study. Hydrological processes, 24(19), pp.2693-2704.
Pechlivanidis, I.G., Jackson, B.M., McIntyre, N.R. and Wheater, H.S., 2011. Catchment scale
hydrological modelling: a review of model types, calibration approaches and uncertainty analysis

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methods in the context of recent developments in technology and applications. Global NEST
journal, 13(3), pp.193-214.
Starkey, E., Parkin, G., Birkinshaw, S., Large, A., Quinn, P. and Gibson, C., 2017.
Demonstrating the value of community-based (‘citizen science’) observations for catchment
modelling and characterisation. Journal of hydrology, 548, pp.801-817.
Carroll, C., Waters, D., Vardy, S., Silburn, D.M., Attard, S., Thorburn, P.J., Davis, A.M., Halpin,
N., Schmidt, M., Wilson, B. and Clark, A., 2012. A paddock to reef monitoring and modelling
framework for the Great Barrier Reef: paddock and catchment component. Marine pollution
bulletin, 65(4-9), pp.136-149.
Lam, Q.D., Schmalz, B. and Fohrer, N., 2010. Modelling point and diffuse source pollution of
nitrate in a rural lowland catchment using the SWAT model. Agricultural Water
Management, 97(2), pp.317-325.
Piotrowski, A.P. and Napiorkowski, J.J., 2013. A comparison of methods to avoid overfitting in
neural networks training in the case of catchment runoff modelling. Journal of Hydrology, 476,
pp.97-111.
Tritz, S., Guinot, V. and Jourde, H., 2011. Modelling the behaviour of a karst system catchment
using non-linear hysteretic conceptual model. Journal of hydrology, 397(3-4), pp.250-262.
Peel, M.C. and Blöschl, G., 2011. Hydrological modelling in a changing world. Progress in
Physical Geography, 35(2), pp.249-261.
Darrah, W.C., 2015. Powell of the Colorado (Vol. 2320). Princeton University Press.