- Email: [email protected]
Modelling runoff and diffuse pollution loads in urban areas
~ Pergamon
Wat. Sci. Tech. Vol. 39. No. 12, pp. 241-248.1999 CI999IAWQ Published by ElsevierScience Ltd Printed in Great Britain. All rights reserved 0273-1223199520.00 + 0.00
PIT: S0273-1223(99)00340-6
MODELLING RUNOFF AND DIFFUSE POLLUTION LOADS IN URBAN AREAS F. H. S. Chiew and T. A. McMahon Cooperative Research Centre/or Catchment Hydrology. Department ofCivil and Environmental Engineering. University 0/Melbourne. Parkville. Victoria 3052. Australia
ABSTRACT This paper presents a simple approach for estimating long-term runoff and diffuse pollution loads in urban catchments. and discusses conceptual modelling methods for simulating daily runoff and pollution loads. The modelling results for several catchments in Australian capital cities are presented. The study indicates that long-term and daily runoff can be estimated reasonably accurately using simple approaches. However, the water quality characteristic can vary considerably between catchments, and in the absence of data, the models can only provide a guide to the probable range of diffuse pollution load generated from a catchment. C> 1999 IAWQ Published by Elsevier Science Ltd. AUrights reserved
KEYWORDS Catchment;diffuse pollution; model; runoff; stormwater;urban. INTRODUCTION Runoff volumes and pollution loads increase considerably when a catchment is urbanised. Methods for estimating runoff and pollution loads are required to investigate the impacts of urbanisation and to evaluate management and design options for controlling water quality in urban waterways and receiving waters. Estimates over a range of time scales are used for different management studies and data availability. This paper presents a simple approach for estimating long-term runoff volumes and diffuse pollution loads, and describes two conceptual models for simulating daily runoff and pollution loads. ESTIMATIONOF AVERAGEANNUALRUNOFF AND POLLUTION LOADS
The average annual runoff can be estimated as Runoff = rei A, Rainfall + rep (1 - Ai)(Rainfall + Outdoor Water Use)
(1)
where A, is the fraction of effective impervious area in the catchment, and rei and rep arc runoff coefficients (proportion ofrainfall that becomes runofi) in the imperviousarea and pervious area. respectively.
241
242
F. H. S. ClIIEW and T. A. MCMAHON
Most of the runoff in urban areas come from impervious surfaces. The key variable in estimating runoff is therefore the fraction of effective impervious area. All the rain falling onto effective impervious surfaces becomes runoff after an initial loss (due to water filling the surface depressions and pores) is satisfied. The fraction of effective impervious area can be estimated from a runoff-rainfall plot of small events (see Figure 1), but there can be a large scatter in the data (see Figure 1b). As runoff from small events is generated only from effective impervious surfaces, the slope gives an estimate of the fraction of effective impervious area, and the intercept ofthe rainfall axis is an estimate of the initial loss. The fraction of effective impervious area can also be estimated from aerial photographs and knowledge about the drainage system. The aerial photograph can be used to estimate the fraction of directly connected impervious area (impervious surfaces that are directly connected to the drainage system). This is often similar to the fraction of total impervious area, except in catchments where roofs are not connected directly to the drainage system. The fraction effective impervious area is typically about 80 to 90% of the fraction of directly connected impervious area (see Boyd et al., 1993), because not all impervious surfaces that appear to be directly connected are directly connected (for example, blockages in gutters can result in water flowing onto pervious surfaces). The impervious area runoff coefficient, rei, in equation (I) is typically about 0.8 to 0.95, depending on the amount of initial loss and the number of raindays. The pervious area runoff coefficient, rep, is dependent on the climate and physical catchment characteristics. In Australian capital cities, it ranges from 0.05 in drier areas to 0.4 in the catchments. (b) Blackburn Lake 200 ha catchment in Melbourne
(a) Dee Why Creek 170 ha catchment in Sydney fraction effective impervious = 0.35 initial loss = Imm
40 .-::--:---;:::--:-:---:-__--:---:::-::----, fraction effective impervious = 0.2 initial loss = Imm
•
30
.~....
l::I 20
~
10
20
40
60
80
100
Rainfall (mm)
•
•
10
20
40
60
Rainfall (mm)
Plots show all storm events. Points above the line indicate events where surface runoff is also generated from the pervious surfaces. Figure 1. Event rainfall-runoff plot to estimate fraction of effective impervious area in the catchment.
Pollution load The average annual pollution load can be estimated as Pollution Load
=
EMC x Runoff
(2)
The event mean concentration (EMC) is defined as the pollutant load washed off by a storm event divided by the event runoff volume. It can be estimated by monitoring water quality concentration and discharge over a storm event. As the EMC can vary considerably between storms, monitoring should be carried out over several events, and the EMCs of the different storms averaged to provide the EMC value for the catchment. The EMC, rather than dry weather concentration, is used because most of the loads are transported by the big events.
Modelling runoff and diffuse pollution loads in urban areas
243
The EMC depends on catchment and climate characteristics, and can vary by more than an order of magnitude between catchments. A good event monitoring program is thus essential where accurate estimates of pollution loads are required . However, in the absence of water quality data, EMC values reported in the literature can be used as a guide to estimate the likely range of pollution load. Athayde et al. (1983) and Torno (1984) provide a summary of EMC data, mainly from the US Nationwide Urban Runoff Program, and more recently, Duncan (1999) (see also Chiew et al., 1997; Duncan, 1997; and Mudgway et al., 1997) gives summaries of EMC for 21 water quality parameters from data reported in over 400 separate studies worldwide (mainly North America, Europe, Australia and Japan). Figure 2 shows examples ofEMC data summarised by Duncan (1999). Plots give mean ± one standard deviation of values reported in the literature Total Suspended Solids
Tota l Pho sphorus
Roads Roofs Residential Industrial Commercial Agricultura l Forest 10
100
Concentration (mgfL)
1000
0.01
0.1
I
Concentration (rng/L)
Figure 2. Compressed examples ofEMC data summaries given in Duncan (1998).
Average annual estimates Table 1 compares the annual runoff estimated using equation (I) with the recorded runoff in II catchments in capital cities in eastern Australia. The comparison indicates that long-term runoff volumes can be estimated reasonably accurately (estimates in nine of the II catchments are within 20% of the recorded volumes), if the fraction of effective impervious area is known. However, the pollution load can vary considerably between catchments, and in the absence of local event water quality data, the EMCs in Figure 2 can only be used as a guide to estimate the probable range of diffuse pollution load generated from a catchment (see also Table 1). MODELLING DAILY RUNOFF Daily estimates of runoff and pollution loads are often required to investigate shorter term impacts, to determine seasonal characteristics of urban runoff, to study alternative water quality management options , and as inputs to water quality models . Runoff from effective imperv ious surfaces can easily be modelled because all the rainfall becomes runoff after an initial loss has been satisfied. Runoff from the pervious area ('pervious' refers to all surfaces that are not effective impervious area) can be simulated using conceptual models that mimic the catchment processes. These models cons ist of one or more storages and equations that describe the movement of water between the storages. Chiew et al. (1995) and Chiew and McMahon (1996) showed that there is little difference between the better conceptual approaches for modelling daily runoff.
F. H. S. CHIEW and T. A. MCMAHON
244
Table 1. Estimates of average annual runoff and pollution loads in 11 catchments Catchment
Area (ha)
Data period
Frac. eff. imp. (Ai)
Recorded values
Values Estimated using model in Figure 3 Total Ann. phosphorus runoff (kglha) (mm)
Values estimated using equation ( I)
Ann. Out. Ann. rain water runoff (mm) use (mm)
Ann. runoff
Total suspended solids
(mm)
(mm)
(kg/ha)
Sydney Powells Creek Salt Pan Creek Cup & Saucer Creek Greendale Creek Bumtbridge Creek Dee Why Creek
287 668 478 178 372 171
0.40 0.27 0.35 0.47 0.12 0.35
8/92-6/94 8/92-6/94 8/92-6/94 6/92-7/94 6/92-7/94 6/92-7/94
740 650 780 970 970 970
75 75 65 45 70 55
470 220 430 630 200 520
380 280 370 510 290 430
200 - 2000 100 - 1000 200 - 2000 300 - 2000 200 - 1000 200 - 2000
0.6 - 3 0.4 - 2 0.6 - 3 0.8 - 4 0.4 - 3 0.6 - 4
400 270 390 560 380 490
Canberra Yarralumla Creek Long Gullv Creek
445 490
0.25 1172-12/95 0.18 1/92-12/95
640 740
75 70
290 220
250 230
100 - 1000 100 - 1000
0.4 - 2 0.4 - 2
300 300
Brisbane Sandy Creek Cressev Street
227 207
0.20 0.17
8/94-6/97 8/94-6/97
1130 1310
70 70
600 670
540 590
300 - 2000 300 - 2000
0.8 - 5 0.9 - 5
750 870
Melbourne Blackburn Lake
202
0.28 1/96-12/97
730
40
340
280
100 - 1000
0.4·2
370
• • •
• •
The fraction of effective impervious area is estimated from plots of storm runoff versus storm rainfall. The annual values are averaged over the period of data (i.e., they are not long-term averages). Outdoor water use is estimated to be 75 mm/year for fully residential areas and 65 mm/year for parks and playgrounds (see Mitchell, 1998). Values in the table are averaged over the entire catchment, but all outdoor water use is attributed only to the pervious areas. In using equation (1), rc;=0.85 is used, for Sydney, Canberra and Melbourne, r,,=0.2, and Brisbane, r,p··0.3. In calculating pollution load, TSS EMCs of 50 - 400 mg/L and TP EMCs of 0.15 - 0.85 mg/L are used (see Figure 2).
In the absence of data, the simple model in Figure 3 can be used to characterise daily runoff. Surface runoff is generated from the pervious area when saturation occurs, and baseflow is simulated using a linear recession. Evapotranspiration is dependent on the amount of water in the soil store, but cannot exceed the potential rate (see Figure 3). The annual runoff volumes estimated by this model for 11 catchments are given in Table 1. The reliability of the estimates are similar to those estimated using equation (I), because no model calibration was performed (the storage capacity and baseflow factor were set at 80 mm and 0.03 respectively). However, the model can provide an indication of the daily flow characteristics, as illustrated in Figure 5 for two catchments. ou tdoor
water usc
R! !
lOT · min
( io '
10, PET)
(\CC ChiC'" and McMahun. Illt)..)
am
surface runoff
1 111111
Ai
. -,
80 mm
.j~j;
51 4
buse flo w =
003, S
•
Figure 3, Structure of a simple conceptual model of daily runoff..
Modelling runoff and diffuse pollution loads in urban areas
245
Cooperative Research Centre for Catchment Hydrology (CRCCH>daily urban runotTmodel Where there is runotT data for model calibration, better estimates of daily runoff can be obtained by simulating the catchment processes in more detail. Figure 4 shows the CRCCH daily urban runoff model which retains the simplicity of the earlier model, but provides a better conceptual representation of the processes. Like the earlier model, all the daily rainfall in the effective impervious area becomes runoff once the daily initial loss is satisfied. The remaining area is modelled as two separate parts with ditTerent storage capacities (related to effective soil depth). The first has a smaller storage capacity and represents parts of the catchment that saturates easily. The second represents the remainder of the catchment with a greater soil storage capacity. Surface runotToccurs when the storage capacities are exceeded (when saturation occurs). This pervious area runoff model is based on the partial area saturation excess runotTgeneration, a concept favoured by the newer hydrologic models (see Zhao, 1992; Robinson, 1993). The model also simulates infiltration excess runotT, but this is not shown in Figure 4. Water from the soil stores recharges a groundwater store when the storage exceeds a certain amount ('field capacity'). Recharge is calculated as a parameter (which mimics the hydraulic conductivity) times the amount the storage exceeds the 'field capacity'. Baseflow from the groundwater store is simulated using a linear recession.
rain!
ETl
! Outdoor
water USe
surface runoff
•
effective impervious
.....
~ "... ...eo ~r!
~-..
... ~ "
~
I
0
I::'!"i"'""
r
'--
no effective in pervious
~
.~:
.
groundwater store (GW)
I
baseflow
- k GW
•
Need to spec ify effective fraction imperviousness and the initial loss in the impervious area. the two fractions of the remai ning area (A l and Al) and their storage capacities (S / cap and Slcap ). Model can also rout flows and simulate infi ltration excess runoff(two parame ters for each process) Figure 4. Structure of the CRCCH daily urban runoff model (modet parameters are highlighted in bold and italics) ,
Evapotranspiration is calculated using the algorithm in Figure 3. The evapotranspiration demand is satisfied first from the larger store, therefore allowing for some redistribution of water between the two stores. The routing of flows to the catchment outlet can also be simulated by the model. The model has six parameters (if the parameters for the impervious area are determined from event rainfallrunotTplot, and routing is not required). Application of the model to the 11 catchments in Table I, and other catchments in Australia, indicated that the model consistently provides a satisfactory simulation of daily runoff. Testing of the model using independent data sets (data that are not used for the model calibration) showed that the total estimated runoff volumes were generally within 10% of the recorded runotT volumes,
246
F. H. S. CHIEW and T. A. MCMAHON
and the correlations (coefficient of efficiency) between the daily simulated and recorded runoff were generally greater than 0.8. The characteristics of the daily flows in two catchments, and the daily flows simulated by the CRCCR model and the simple conceptual model in Figure 3, are compared in Figure 5. As expected, the calibrated model performed better, particularly in the simulation of high flows, because the model parameters were calibrated to minimise an objective function that reflects the simulation of high flows. The simple model, without any calibration, can often provide a satisfactory simulation of daily flows (see Figure 5b), although in some cases, the runoff estimates can be very poor (see Figure Sa). Thus, where adequate rainfall-runoff data are available, the CRCCR model should be used to estimate daily runoff, but where there is little data, the use of a simpler model is sufficient to provide an indication ofthe daily flow characteristics.
-
Recorded runoff
- - Estimated using model in Figure 3 (no calibration) - - - - Estimated using CRCCR model (model was calibrated)
100 . . . - - - - - - - - - - - - - - , (a) Sandy Creek (Brisbane)
100 . . . - - - - - - - - - - - - - - . (b) Yarralumla Creek (Canberra)
10
0.1
0.1 12510
30
50
70
90959899
12 510
30
SO
70
90959899
Percentage of time daily flow is exceeded Figure 5 Flow duration plots comparing daily runoff simulated by the models with the recorded runoff MODELLING DAILY POLLUTANT LOAD There are various water quality models that attempt to simulate dry weather pollutant accumulation and washoff over storm events in urban catchments. These models may be useful in studying pollutant buildup and transport processes, and estimating pollutant loads generated over storm events (see Chiew et al., 1997). However, where daily loads are required, the available data rarely justify the use of these models. In most studies, there is only sufficient data to estimate daily diffuse pollutant load as
Load
+
surface runoff(from impervious and pervious surfaces) x EMC baseflow x dry weather concentration
(3)
The daily conceptual models described in the previous section allow for the modelling of the different water quality characteristics associated with the different runoff components. The literature can provide a guide to the typical range of EMC values (see Figure 2), but where accurate estimates of diffuse pollution loads are needed, event monitoring should be undertaken to determine the EMC for the catchment. The dry weather water quality concentration is usually lower than the EMC, and it can be determined from several dry weather baseflow samplings. There is usually little reason to use a more complex model than the linear one described by equation (3) because it is difficult to define a clear relationship between runoff and EMC (see Figure 6).
Modelling runoff and diffuse pollution loads in urban areas
247
Nevertheless, in some catchments, a power relationship between pollutant load and runoff may provide a better description of the data LOAD
=
a RUNOFF
(4)
b
Here, the parameters, a and b, should be obtained by optimising an objective function that reflects the needs of the study, rather than by simply minimising the surn of square of errors (SSE) between the predicted and 'recorded' loads in the log scale, as is conventionally done because of mathematical simplicity (see Figure 7). This is because the log scale gives similar weight when minimising the errors in the big and small predicted loads, while in practice, it is usually more important to estimate the bigger loads accurately. Blackburn Lake (Melbourne)
0.8 r------....:----~--.,
.
i
•
U 0.4
• 400
'-'
-.....
•••
~ • !: 0.2 1-.0.0
Salt Pan Creek (Sydney)
r------~~...::..:;-----,
.-..
~0.6 ~
g
600
••
o ::E
•
Ul
• • •
tI) tI)
200
•
E-<
••
L..-_ _.a...-_ _.a...-_ _- ' - - _ - - - '
o
2
4
6
0
Storm runoff (mm)
•
• •• •
•
0
8
•
•• 8
246
10
Storm runoff (mm)
Figure 6. Event mean concentrationversus storm runoff in two catchments.
~
100000 r-----------~___,
]
:9
10000
~
40000 , - - - - - - - - - - - _ - - - , Dotted line shows power relationship with a and b optimised to minimise 30000 the SSE between the predicted and actual loads in the linear scale.
Full line is the same regression in the log plot on the left.
20000
J
,
1000
•
Intercept and gradient of the linear regression in this log plot gives a and b directly 10
100
10000
.' "
,
, ,,
,"
o o
5
10
15
Storm runoff (mm) Figure 7. Total suspended solids versus runoff in BurntbridgeCreek (Sydney) plotted on linear and log scales (showing that minimising SSE of Load, rather than 10g(Load) puts more weight on estimatingthe large loads accurately).
SUMMARY AND CONCLUSIONS This paper presents a simple approach for estimating long-term runoff and diffuse pollution loads in urban catchments, and describes two conceptual models for simulating daily runoff.and pollution loads. Testing of the models on several catchments in capital cities in eastern Australia indicates that long-term runoff can be estimated reasonably accurately, and daily runoff can be characterised adequately using simple conceptual models.
248
F. H. S. CHIEW and T. A. MCMAHON
In the absence oflocal data, the runoff models and water quality data reported in the literature can be used to estimate the likely range of diffuse pollution load generated from a catchment. However, because water quality characteristics can vary considerably between catchments, water quality monitoring over several storm events should be undertaken where accurate estimates of pollution loads are required.
ACKNOWLEDGEMENTS The data have been provided by Sydney Water, AWT-Ensight, ACT Electricity & Water, Brisbane City Council and Melbourne Water. The authors would like to thank Sharyn Ross and Jai Vaze for compiling and analysing some ofthe data. REFERENCES Athayde, D. N., Shelley, P. E., Driscoll, E. D., Gaboury, D. and Boyd, G. (1983). Results of the Nationwide Urban Runoff Program. U.S. Environmental Protection Agency, Washington D.C., PB84-185537. Boyd, M. J., Bufill, M. C. and Knee, R. M. (1993). Pervious and impervious runoff in urban catchments. Hydrological Sciences, 38(6), 463-478. Chiew, F. H. S., Duncan, H. P. and Smith, W. (1997). Modelling pollutant buildup and washoff: keep It simple. Proceedings of the 24,h International Hydrology and Water Resources Symposium, November 1997, Auckland, New Zealand Hydrological Society, pp. 131-136. Chiew, F. H. S. and McMahon, T. A. (1994). Application of the daily rainfall-runoff model MODHYDROLOG to 28 Australia catchments. Journal ofHydrology, 153,383-416. Chiew, F. H. S. and McMahon, T. A. (1996). Conceptual modelling of daily runoff in urban catchments. Proceedings of the International Conference on Urban Storm Drainage, September 1996, Hannover, Germany, IAHRJIAWQ Joint Committee on Urban Storm Drainage, Seeliger Sofort-Druck, Hannover, 1,323-328. Chiew, F. H. S., Mudgway, L. B., Duncan, H. P. and McMahon, T. A. (1997). Urban Stormwater Pollution. Cooperative Research Centre for Catchment Hydrology, Melbourne, Australia, Industry Report 97/5, 18 pp. Chiew, F. H. S., Osman, E. H. and McMahon, T. A. (1995). Modelling daily and monthly runoff in urban catchments. Proceedings of the Intemauonal Symposium on Urban Stormwater Management, July 1995, Melbourne, Australia, Institution of Engineers, National Conference Publication, 95/3( I), 255-260. Duncan, H. P. (1997). An overview of urban stormwater quality. Proceedings of the 24'· International Hydrology and Water Resources Symposium, November 1997, Auckland, New Zealand Hydrological Society, pp. 143-148. Duncan, H. P. (1999). Urban Stormwater Quality: A Statistical Overview. Cooperative Research Centre for Catchment Hydrology, In Press. Mitchell, V. G. (1998). Development of an Urban Water Balance Model to Assess the Re-Use Potential of Stormwater and Wastewater. Ph.D. Thesis, Department of Civil and Environmental Engineering, University of Melbourne, Australia, 273 pp. Mudgway. L. B., Duncan, H. P., McMahon, T. A. and Chiew, F. H. S. (1997). Best Practice Environmental Management Guidelinesfor Urban Stormwater. Cooperative Research Centre for Catchment Hydrology, Report 97n, 125 pp. Robmson, M. (1993) Changing ideas regarding storm runoff processes in small basins. In: Flow Regimes from International and Experimental Network Data (FRIEND) (Ed: Mark Robinson), Institute of Hydrology, United Kingdom, Volume 3, pp.3-16. Torno, H. C. (1984). The nationwide urban runoff program. Proceedings of the 3'" International Conference on Urban Storm Drainage (Editors: P.Balmer, P. Malmquist and A.Sjoberg), Goteborg, Sweden, pp. 1465-1474. Zhao, R. J. (1992). The Xinanjiang model applied in China. Journal ofHydrology, 135, 371-381.
.,4
r
Wat. Sci. Tech. Vol. 39. No. 12, pp. 241-248.1999 CI999IAWQ Published by ElsevierScience Ltd Printed in Great Britain. All rights reserved 0273-1223199520.00 + 0.00
PIT: S0273-1223(99)00340-6
MODELLING RUNOFF AND DIFFUSE POLLUTION LOADS IN URBAN AREAS F. H. S. Chiew and T. A. McMahon Cooperative Research Centre/or Catchment Hydrology. Department ofCivil and Environmental Engineering. University 0/Melbourne. Parkville. Victoria 3052. Australia
ABSTRACT This paper presents a simple approach for estimating long-term runoff and diffuse pollution loads in urban catchments. and discusses conceptual modelling methods for simulating daily runoff and pollution loads. The modelling results for several catchments in Australian capital cities are presented. The study indicates that long-term and daily runoff can be estimated reasonably accurately using simple approaches. However, the water quality characteristic can vary considerably between catchments, and in the absence of data, the models can only provide a guide to the probable range of diffuse pollution load generated from a catchment. C> 1999 IAWQ Published by Elsevier Science Ltd. AUrights reserved
KEYWORDS Catchment;diffuse pollution; model; runoff; stormwater;urban. INTRODUCTION Runoff volumes and pollution loads increase considerably when a catchment is urbanised. Methods for estimating runoff and pollution loads are required to investigate the impacts of urbanisation and to evaluate management and design options for controlling water quality in urban waterways and receiving waters. Estimates over a range of time scales are used for different management studies and data availability. This paper presents a simple approach for estimating long-term runoff volumes and diffuse pollution loads, and describes two conceptual models for simulating daily runoff and pollution loads. ESTIMATIONOF AVERAGEANNUALRUNOFF AND POLLUTION LOADS
The average annual runoff can be estimated as Runoff = rei A, Rainfall + rep (1 - Ai)(Rainfall + Outdoor Water Use)
(1)
where A, is the fraction of effective impervious area in the catchment, and rei and rep arc runoff coefficients (proportion ofrainfall that becomes runofi) in the imperviousarea and pervious area. respectively.
241
242
F. H. S. ClIIEW and T. A. MCMAHON
Most of the runoff in urban areas come from impervious surfaces. The key variable in estimating runoff is therefore the fraction of effective impervious area. All the rain falling onto effective impervious surfaces becomes runoff after an initial loss (due to water filling the surface depressions and pores) is satisfied. The fraction of effective impervious area can be estimated from a runoff-rainfall plot of small events (see Figure 1), but there can be a large scatter in the data (see Figure 1b). As runoff from small events is generated only from effective impervious surfaces, the slope gives an estimate of the fraction of effective impervious area, and the intercept ofthe rainfall axis is an estimate of the initial loss. The fraction of effective impervious area can also be estimated from aerial photographs and knowledge about the drainage system. The aerial photograph can be used to estimate the fraction of directly connected impervious area (impervious surfaces that are directly connected to the drainage system). This is often similar to the fraction of total impervious area, except in catchments where roofs are not connected directly to the drainage system. The fraction effective impervious area is typically about 80 to 90% of the fraction of directly connected impervious area (see Boyd et al., 1993), because not all impervious surfaces that appear to be directly connected are directly connected (for example, blockages in gutters can result in water flowing onto pervious surfaces). The impervious area runoff coefficient, rei, in equation (I) is typically about 0.8 to 0.95, depending on the amount of initial loss and the number of raindays. The pervious area runoff coefficient, rep, is dependent on the climate and physical catchment characteristics. In Australian capital cities, it ranges from 0.05 in drier areas to 0.4 in the catchments. (b) Blackburn Lake 200 ha catchment in Melbourne
(a) Dee Why Creek 170 ha catchment in Sydney fraction effective impervious = 0.35 initial loss = Imm
40 .-::--:---;:::--:-:---:-__--:---:::-::----, fraction effective impervious = 0.2 initial loss = Imm
•
30
.~....
l::I 20
~
10
20
40
60
80
100
Rainfall (mm)
•
•
10
20
40
60
Rainfall (mm)
Plots show all storm events. Points above the line indicate events where surface runoff is also generated from the pervious surfaces. Figure 1. Event rainfall-runoff plot to estimate fraction of effective impervious area in the catchment.
Pollution load The average annual pollution load can be estimated as Pollution Load
=
EMC x Runoff
(2)
The event mean concentration (EMC) is defined as the pollutant load washed off by a storm event divided by the event runoff volume. It can be estimated by monitoring water quality concentration and discharge over a storm event. As the EMC can vary considerably between storms, monitoring should be carried out over several events, and the EMCs of the different storms averaged to provide the EMC value for the catchment. The EMC, rather than dry weather concentration, is used because most of the loads are transported by the big events.
Modelling runoff and diffuse pollution loads in urban areas
243
The EMC depends on catchment and climate characteristics, and can vary by more than an order of magnitude between catchments. A good event monitoring program is thus essential where accurate estimates of pollution loads are required . However, in the absence of water quality data, EMC values reported in the literature can be used as a guide to estimate the likely range of pollution load. Athayde et al. (1983) and Torno (1984) provide a summary of EMC data, mainly from the US Nationwide Urban Runoff Program, and more recently, Duncan (1999) (see also Chiew et al., 1997; Duncan, 1997; and Mudgway et al., 1997) gives summaries of EMC for 21 water quality parameters from data reported in over 400 separate studies worldwide (mainly North America, Europe, Australia and Japan). Figure 2 shows examples ofEMC data summarised by Duncan (1999). Plots give mean ± one standard deviation of values reported in the literature Total Suspended Solids
Tota l Pho sphorus
Roads Roofs Residential Industrial Commercial Agricultura l Forest 10
100
Concentration (mgfL)
1000
0.01
0.1
I
Concentration (rng/L)
Figure 2. Compressed examples ofEMC data summaries given in Duncan (1998).
Average annual estimates Table 1 compares the annual runoff estimated using equation (I) with the recorded runoff in II catchments in capital cities in eastern Australia. The comparison indicates that long-term runoff volumes can be estimated reasonably accurately (estimates in nine of the II catchments are within 20% of the recorded volumes), if the fraction of effective impervious area is known. However, the pollution load can vary considerably between catchments, and in the absence of local event water quality data, the EMCs in Figure 2 can only be used as a guide to estimate the probable range of diffuse pollution load generated from a catchment (see also Table 1). MODELLING DAILY RUNOFF Daily estimates of runoff and pollution loads are often required to investigate shorter term impacts, to determine seasonal characteristics of urban runoff, to study alternative water quality management options , and as inputs to water quality models . Runoff from effective imperv ious surfaces can easily be modelled because all the rainfall becomes runoff after an initial loss has been satisfied. Runoff from the pervious area ('pervious' refers to all surfaces that are not effective impervious area) can be simulated using conceptual models that mimic the catchment processes. These models cons ist of one or more storages and equations that describe the movement of water between the storages. Chiew et al. (1995) and Chiew and McMahon (1996) showed that there is little difference between the better conceptual approaches for modelling daily runoff.
F. H. S. CHIEW and T. A. MCMAHON
244
Table 1. Estimates of average annual runoff and pollution loads in 11 catchments Catchment
Area (ha)
Data period
Frac. eff. imp. (Ai)
Recorded values
Values Estimated using model in Figure 3 Total Ann. phosphorus runoff (kglha) (mm)
Values estimated using equation ( I)
Ann. Out. Ann. rain water runoff (mm) use (mm)
Ann. runoff
Total suspended solids
(mm)
(mm)
(kg/ha)
Sydney Powells Creek Salt Pan Creek Cup & Saucer Creek Greendale Creek Bumtbridge Creek Dee Why Creek
287 668 478 178 372 171
0.40 0.27 0.35 0.47 0.12 0.35
8/92-6/94 8/92-6/94 8/92-6/94 6/92-7/94 6/92-7/94 6/92-7/94
740 650 780 970 970 970
75 75 65 45 70 55
470 220 430 630 200 520
380 280 370 510 290 430
200 - 2000 100 - 1000 200 - 2000 300 - 2000 200 - 1000 200 - 2000
0.6 - 3 0.4 - 2 0.6 - 3 0.8 - 4 0.4 - 3 0.6 - 4
400 270 390 560 380 490
Canberra Yarralumla Creek Long Gullv Creek
445 490
0.25 1172-12/95 0.18 1/92-12/95
640 740
75 70
290 220
250 230
100 - 1000 100 - 1000
0.4 - 2 0.4 - 2
300 300
Brisbane Sandy Creek Cressev Street
227 207
0.20 0.17
8/94-6/97 8/94-6/97
1130 1310
70 70
600 670
540 590
300 - 2000 300 - 2000
0.8 - 5 0.9 - 5
750 870
Melbourne Blackburn Lake
202
0.28 1/96-12/97
730
40
340
280
100 - 1000
0.4·2
370
• • •
• •
The fraction of effective impervious area is estimated from plots of storm runoff versus storm rainfall. The annual values are averaged over the period of data (i.e., they are not long-term averages). Outdoor water use is estimated to be 75 mm/year for fully residential areas and 65 mm/year for parks and playgrounds (see Mitchell, 1998). Values in the table are averaged over the entire catchment, but all outdoor water use is attributed only to the pervious areas. In using equation (1), rc;=0.85 is used, for Sydney, Canberra and Melbourne, r,,=0.2, and Brisbane, r,p··0.3. In calculating pollution load, TSS EMCs of 50 - 400 mg/L and TP EMCs of 0.15 - 0.85 mg/L are used (see Figure 2).
In the absence of data, the simple model in Figure 3 can be used to characterise daily runoff. Surface runoff is generated from the pervious area when saturation occurs, and baseflow is simulated using a linear recession. Evapotranspiration is dependent on the amount of water in the soil store, but cannot exceed the potential rate (see Figure 3). The annual runoff volumes estimated by this model for 11 catchments are given in Table 1. The reliability of the estimates are similar to those estimated using equation (I), because no model calibration was performed (the storage capacity and baseflow factor were set at 80 mm and 0.03 respectively). However, the model can provide an indication of the daily flow characteristics, as illustrated in Figure 5 for two catchments. ou tdoor
water usc
R! !
lOT · min
( io '
10, PET)
(\CC ChiC'" and McMahun. Illt)..)
am
surface runoff
1 111111
Ai
. -,
80 mm
.j~j;
51 4
buse flo w =
003, S
•
Figure 3, Structure of a simple conceptual model of daily runoff..
Modelling runoff and diffuse pollution loads in urban areas
245
Cooperative Research Centre for Catchment Hydrology (CRCCH>daily urban runotTmodel Where there is runotT data for model calibration, better estimates of daily runoff can be obtained by simulating the catchment processes in more detail. Figure 4 shows the CRCCH daily urban runoff model which retains the simplicity of the earlier model, but provides a better conceptual representation of the processes. Like the earlier model, all the daily rainfall in the effective impervious area becomes runoff once the daily initial loss is satisfied. The remaining area is modelled as two separate parts with ditTerent storage capacities (related to effective soil depth). The first has a smaller storage capacity and represents parts of the catchment that saturates easily. The second represents the remainder of the catchment with a greater soil storage capacity. Surface runotToccurs when the storage capacities are exceeded (when saturation occurs). This pervious area runoff model is based on the partial area saturation excess runotTgeneration, a concept favoured by the newer hydrologic models (see Zhao, 1992; Robinson, 1993). The model also simulates infiltration excess runotT, but this is not shown in Figure 4. Water from the soil stores recharges a groundwater store when the storage exceeds a certain amount ('field capacity'). Recharge is calculated as a parameter (which mimics the hydraulic conductivity) times the amount the storage exceeds the 'field capacity'. Baseflow from the groundwater store is simulated using a linear recession.
rain!
ETl
! Outdoor
water USe
surface runoff
•
effective impervious
.....
~ "... ...eo ~r!
~-..
... ~ "
~
I
0
I::'!"i"'""
r
'--
no effective in pervious
~
.~:
.
groundwater store (GW)
I
baseflow
- k GW
•
Need to spec ify effective fraction imperviousness and the initial loss in the impervious area. the two fractions of the remai ning area (A l and Al) and their storage capacities (S / cap and Slcap ). Model can also rout flows and simulate infi ltration excess runoff(two parame ters for each process) Figure 4. Structure of the CRCCH daily urban runoff model (modet parameters are highlighted in bold and italics) ,
Evapotranspiration is calculated using the algorithm in Figure 3. The evapotranspiration demand is satisfied first from the larger store, therefore allowing for some redistribution of water between the two stores. The routing of flows to the catchment outlet can also be simulated by the model. The model has six parameters (if the parameters for the impervious area are determined from event rainfallrunotTplot, and routing is not required). Application of the model to the 11 catchments in Table I, and other catchments in Australia, indicated that the model consistently provides a satisfactory simulation of daily runoff. Testing of the model using independent data sets (data that are not used for the model calibration) showed that the total estimated runoff volumes were generally within 10% of the recorded runotT volumes,
246
F. H. S. CHIEW and T. A. MCMAHON
and the correlations (coefficient of efficiency) between the daily simulated and recorded runoff were generally greater than 0.8. The characteristics of the daily flows in two catchments, and the daily flows simulated by the CRCCR model and the simple conceptual model in Figure 3, are compared in Figure 5. As expected, the calibrated model performed better, particularly in the simulation of high flows, because the model parameters were calibrated to minimise an objective function that reflects the simulation of high flows. The simple model, without any calibration, can often provide a satisfactory simulation of daily flows (see Figure 5b), although in some cases, the runoff estimates can be very poor (see Figure Sa). Thus, where adequate rainfall-runoff data are available, the CRCCR model should be used to estimate daily runoff, but where there is little data, the use of a simpler model is sufficient to provide an indication ofthe daily flow characteristics.
-
Recorded runoff
- - Estimated using model in Figure 3 (no calibration) - - - - Estimated using CRCCR model (model was calibrated)
100 . . . - - - - - - - - - - - - - - , (a) Sandy Creek (Brisbane)
100 . . . - - - - - - - - - - - - - - . (b) Yarralumla Creek (Canberra)
10
0.1
0.1 12510
30
50
70
90959899
12 510
30
SO
70
90959899
Percentage of time daily flow is exceeded Figure 5 Flow duration plots comparing daily runoff simulated by the models with the recorded runoff MODELLING DAILY POLLUTANT LOAD There are various water quality models that attempt to simulate dry weather pollutant accumulation and washoff over storm events in urban catchments. These models may be useful in studying pollutant buildup and transport processes, and estimating pollutant loads generated over storm events (see Chiew et al., 1997). However, where daily loads are required, the available data rarely justify the use of these models. In most studies, there is only sufficient data to estimate daily diffuse pollutant load as
Load
+
surface runoff(from impervious and pervious surfaces) x EMC baseflow x dry weather concentration
(3)
The daily conceptual models described in the previous section allow for the modelling of the different water quality characteristics associated with the different runoff components. The literature can provide a guide to the typical range of EMC values (see Figure 2), but where accurate estimates of diffuse pollution loads are needed, event monitoring should be undertaken to determine the EMC for the catchment. The dry weather water quality concentration is usually lower than the EMC, and it can be determined from several dry weather baseflow samplings. There is usually little reason to use a more complex model than the linear one described by equation (3) because it is difficult to define a clear relationship between runoff and EMC (see Figure 6).
Modelling runoff and diffuse pollution loads in urban areas
247
Nevertheless, in some catchments, a power relationship between pollutant load and runoff may provide a better description of the data LOAD
=
a RUNOFF
(4)
b
Here, the parameters, a and b, should be obtained by optimising an objective function that reflects the needs of the study, rather than by simply minimising the surn of square of errors (SSE) between the predicted and 'recorded' loads in the log scale, as is conventionally done because of mathematical simplicity (see Figure 7). This is because the log scale gives similar weight when minimising the errors in the big and small predicted loads, while in practice, it is usually more important to estimate the bigger loads accurately. Blackburn Lake (Melbourne)
0.8 r------....:----~--.,
.
i
•
U 0.4
• 400
'-'
-.....
•••
~ • !: 0.2 1-.0.0
Salt Pan Creek (Sydney)
r------~~...::..:;-----,
.-..
~0.6 ~
g
600
••
o ::E
•
Ul
• • •
tI) tI)
200
•
E-<
••
L..-_ _.a...-_ _.a...-_ _- ' - - _ - - - '
o
2
4
6
0
Storm runoff (mm)
•
• •• •
•
0
8
•
•• 8
246
10
Storm runoff (mm)
Figure 6. Event mean concentrationversus storm runoff in two catchments.
~
100000 r-----------~___,
]
:9
10000
~
40000 , - - - - - - - - - - - _ - - - , Dotted line shows power relationship with a and b optimised to minimise 30000 the SSE between the predicted and actual loads in the linear scale.
Full line is the same regression in the log plot on the left.
20000
J
,
1000
•
Intercept and gradient of the linear regression in this log plot gives a and b directly 10
100
10000
.' "
,
, ,,
,"
o o
5
10
15
Storm runoff (mm) Figure 7. Total suspended solids versus runoff in BurntbridgeCreek (Sydney) plotted on linear and log scales (showing that minimising SSE of Load, rather than 10g(Load) puts more weight on estimatingthe large loads accurately).
SUMMARY AND CONCLUSIONS This paper presents a simple approach for estimating long-term runoff and diffuse pollution loads in urban catchments, and describes two conceptual models for simulating daily runoff.and pollution loads. Testing of the models on several catchments in capital cities in eastern Australia indicates that long-term runoff can be estimated reasonably accurately, and daily runoff can be characterised adequately using simple conceptual models.
248
F. H. S. CHIEW and T. A. MCMAHON
In the absence oflocal data, the runoff models and water quality data reported in the literature can be used to estimate the likely range of diffuse pollution load generated from a catchment. However, because water quality characteristics can vary considerably between catchments, water quality monitoring over several storm events should be undertaken where accurate estimates of pollution loads are required.
ACKNOWLEDGEMENTS The data have been provided by Sydney Water, AWT-Ensight, ACT Electricity & Water, Brisbane City Council and Melbourne Water. The authors would like to thank Sharyn Ross and Jai Vaze for compiling and analysing some ofthe data. REFERENCES Athayde, D. N., Shelley, P. E., Driscoll, E. D., Gaboury, D. and Boyd, G. (1983). Results of the Nationwide Urban Runoff Program. U.S. Environmental Protection Agency, Washington D.C., PB84-185537. Boyd, M. J., Bufill, M. C. and Knee, R. M. (1993). Pervious and impervious runoff in urban catchments. Hydrological Sciences, 38(6), 463-478. Chiew, F. H. S., Duncan, H. P. and Smith, W. (1997). Modelling pollutant buildup and washoff: keep It simple. Proceedings of the 24,h International Hydrology and Water Resources Symposium, November 1997, Auckland, New Zealand Hydrological Society, pp. 131-136. Chiew, F. H. S. and McMahon, T. A. (1994). Application of the daily rainfall-runoff model MODHYDROLOG to 28 Australia catchments. Journal ofHydrology, 153,383-416. Chiew, F. H. S. and McMahon, T. A. (1996). Conceptual modelling of daily runoff in urban catchments. Proceedings of the International Conference on Urban Storm Drainage, September 1996, Hannover, Germany, IAHRJIAWQ Joint Committee on Urban Storm Drainage, Seeliger Sofort-Druck, Hannover, 1,323-328. Chiew, F. H. S., Mudgway, L. B., Duncan, H. P. and McMahon, T. A. (1997). Urban Stormwater Pollution. Cooperative Research Centre for Catchment Hydrology, Melbourne, Australia, Industry Report 97/5, 18 pp. Chiew, F. H. S., Osman, E. H. and McMahon, T. A. (1995). Modelling daily and monthly runoff in urban catchments. Proceedings of the Intemauonal Symposium on Urban Stormwater Management, July 1995, Melbourne, Australia, Institution of Engineers, National Conference Publication, 95/3( I), 255-260. Duncan, H. P. (1997). An overview of urban stormwater quality. Proceedings of the 24'· International Hydrology and Water Resources Symposium, November 1997, Auckland, New Zealand Hydrological Society, pp. 143-148. Duncan, H. P. (1999). Urban Stormwater Quality: A Statistical Overview. Cooperative Research Centre for Catchment Hydrology, In Press. Mitchell, V. G. (1998). Development of an Urban Water Balance Model to Assess the Re-Use Potential of Stormwater and Wastewater. Ph.D. Thesis, Department of Civil and Environmental Engineering, University of Melbourne, Australia, 273 pp. Mudgway. L. B., Duncan, H. P., McMahon, T. A. and Chiew, F. H. S. (1997). Best Practice Environmental Management Guidelinesfor Urban Stormwater. Cooperative Research Centre for Catchment Hydrology, Report 97n, 125 pp. Robmson, M. (1993) Changing ideas regarding storm runoff processes in small basins. In: Flow Regimes from International and Experimental Network Data (FRIEND) (Ed: Mark Robinson), Institute of Hydrology, United Kingdom, Volume 3, pp.3-16. Torno, H. C. (1984). The nationwide urban runoff program. Proceedings of the 3'" International Conference on Urban Storm Drainage (Editors: P.Balmer, P. Malmquist and A.Sjoberg), Goteborg, Sweden, pp. 1465-1474. Zhao, R. J. (1992). The Xinanjiang model applied in China. Journal ofHydrology, 135, 371-381.
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