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Potential application of end-use demand modelling in South Africa


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End-use water demand modelling is used to generate water demand projections by modelling various end uses, for example showers, toilets and washing machines. End-use models can be used to estimate water demand changes due to various scenarios, such as price increases, housing densification and conservation programmes. This study reports on the potential application of end-use modelling in South Africa, based on a pilot study that was done for Rand Water. The model includes elasticities of water demand with respect to variations in water price, household income, stand size and pressure. The study highlights many of the difficulties and limitations, as well as the potential applications of end-use modelling as a water deman predictor. A special effort is made to explain the meaning and application of elasticity in end-use modelling. Various data sources were used to determine elasticities for the variables used, and to identify minimum and maximum elasticity values. The implications of the elasticities are illustrated using a sensitivity analysis and case study.
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JE van Zyl, MSAICE, J Haarhoff, FSAICE, and ML Husselmann,
End-use water demand modelling is used to generate water demand projections by modelling
various end uses, for example showers, toilets and washing machines. End-use models can be
used to estimate water demand changes due to various scenarios, such as price increases,
housing densification and conservation programmes. This study reports on the potential
application of end-use modelling in South Africa, based on a pilot study that was done for
Rand Water. The model includes elasticities of water demand with respect to variations in
water price, household income, stand size and pressure. The study highlights many of the
difficulties and limitations, as well as the potential applications of end-use modelling as a
water demand predictor. A special effort is made to explain the meaning and application of
elasticity in end-use modelling. Various data sources were used to determine elasticities for
the variables used, and to identify minimum and maximum elasticity values. The implications
of the elasticities are illustrated using a sensitivity analysis and case study.
Water demand modelling is used by engineers to analyse and predict water consumption in
cities and towns under varying conditions. In end-use modelling, the points of water
consumption, for example showers, toilets and washing machines, are modelled individually
or in groups. The characteristics of various water-using fixtures, the behaviour of users and
the sensitivity of water demand to different parameters can be incorporated in a very detailed
end-use model of water demand. Once an end-use model of a supply area is available, water
demand can be predicted under hypothetical scenarios.
Rand Water commissioned a pilot study to demonstrate the strengths and weaknesses of end-
use modelling as a water demand predictor for their supply area. The study included the
entire residential sectors of Alberton, Boksburg, Centurion and Midrand, consisting of more
than 110 000 stands. For the purpose of this pilot study, end-uses were grouped into outdoor
consumption, indoor consumption and leakage. The variables and elasticities in the model
were limited to water price, household income, stand size and pressure. Data for the end-use
model were obtained from numerous Rand Water consumer surveys, as well as published
international and local research.
The study was not aimed at developing a comprehensive model of water demand in the study
area, but limited to a pilot study to illustrate the difficulties, as well as the potential
application of end-use modelling as a water demand predictor. Various causative factors of
water demand, such as temperature, rainfall, level of service and age of infrastructure were
not considered. Although the study focussed on a restricted number of variables in a specific
geographical area, its results provide general pointers to the potential application of end-use
modelling in South Africa.
The body of the paper starts with some background on end-use modelling, followed by a
discussion on the construction of the end-use model including the choice of modelling
parameters and data collection. The results of a sensitivity analysis in which each of the
elasticity parameters were varied between minimum, normal and maximum expected values
are then discussed. Finally, a case study comprising of the four areas included in the study
(Alberton, Boksburg, Centurion and Midrand) is used to show some of the possible
applications of end-use modelling in the Rand Water supply area.
A commonly used definition of elasticity is the relative change in demand if the given
causative factor doubles. For example, if the water price increases by 100 % and the demand
drops by 20 %, the elasticity would be –0,20. In mathematical terms:
... (1)
With = a change
F = a causative factor
D = water demand
E = a measure of elasticity
To determine the elasticity E, demand is plotted on a linear scale against the causative factor,
with a smoothing or regression line to achieve continuity. By determining the slope of the
line at a specific point, the elasticity E at that point can be determined:
... (2)
By repeating this calculation for all the points on the curve, a second curve can be
constructed, showing elasticity as a function of the causative factor. In a previous Rand Water
study (Business Enterprises at University of Pretoria, 2000), the elasticity of water demand
with respect to water price was determined in this way. The demand versus price (as obtained
by consumer survey) was first obtained and the elasticity versus price was calculated next.
An example from this study is shown in Figure 1.
Ideal position for Figure 1
Although the above definition of elasticity is the one commonly used by economists, it is not
convenient for modelling. The elasticity E as defined above is not constant and is only
meaningful at a specific value of the causative factor. An alternative expression of elasticity β
can be formulated as:
... (3)
With β = a measure of elasticity
Subscript 1 = before a change
Subscript 2 = after a change
To obtain β, the demand is plotted against the causative factor F on a log-log scale. The slope
of the linear regression line will then be β. The same data shown in Figure 1 are re-analysed
in this way in Figure 2, yielding a β-value of -0,295.
Ideal position for Figure 2
The advantage of the constancy of this measure of elasticity β is obvious, and β has therefore
been adopted for use in this study.
The relationship between E and β
Although the elasticity definitions used for E and β (Equations 1 and 3) are different, it is
useful to explore the relationship between them mathematically in the region of the base
First, write Equation 3 in terms of D and D:
... (4)
Then replace Equation 1 into this equation:
... (5)
The variable β can now be written in terms of E by taking the natural logarithm on both sides
of the equation and simplifying.
... (6)
Our interest is to find the relationship at the base point; in other words, where F approaches
zero. Expanding each natural logarithm as a Maclaurin series,
ln ++=+ 432
432 xxx
xx ,
and then neglecting higher order terms (assuming small absolute elasticity values) further
simplifies the equation to:
... (7)
The result shows that β and E has the same value at the base point and that these values can
be interchanged in calculations. However, care should be taken not to extrapolate calculations
too far beyond the base point, unless the elasticity value is based on a range of data points to
justify the greater range of application.
Working with unit consumptions
In some instances it is convenient to express demand as a consumption per unit of the
causative factor, rather than per stand. For example, water demand is sometimes expressed in
m3 consumption per square metre of stand area instead of m3 per stand. It is thus necessary to
derive an expression for the relationship between the elasticity values based on per unit
consumption and per stand consumption. Consider a unit consumption d, defined by
d= ... (8)
With D = water demand per stand
F = the value of the causative factor per stand
The variation of unit consumption with causative factor F can be expressed as (from Equation
d ... (9)
With α = the elasticity based on unit consumption
Now, to convert elasticity based on unit consumption (α) to elasticity based on per-stand
consumption (β), the fraction (D2/D1) is first written in terms of d and F:
D= ... (10)
Equation 9 is replaced into Equation 10 and simplified to obtain:
D ... (11)
In other words, the relationship between elasticities based on D and d is given by:
... (12)
A water demand model incorporating water price, household income, stand size and pressure
was used in this study. The model is expressed mathematically as:
AADDAADD average
With AADD = annual average daily demand
T = water price
I = household income
A = area or stand size
P = water pressure
The most basic classification of domestic consumption is between indoor and outdoor
consumption. The above model was thus applied separately to indoor and outdoor water
demand. System leakage was included as a third demand type in the model.
To differentiate between different classes of consumers, Rand Water adopted a three-tier
classification in its consumer surveys – informal settlements, townships and suburbs. Since it
is very difficult to obtain reliable data for informal settlements, only suburbs and townships
were included in this study.
Modelling was done using the software package IWR-Main (Planning and Management
Consultants, 1999). This package allows various different user types, elasticities and demand
scenarios to be modelled simultaneously, making it a powerful modelling tool.
In order to model the response of the water demand to various scenarios, the elasticity of
water demand with respect to its causative factors must be known. In this study, four
causative factors were analysed, namely water price, household income, stand size and water
pressure. Elasticity values were estimated based on stand meter readings, thus excluding the
effect of leakage in the municipal pipe networks.
Price Elasticity
The price of water is arguably the most important determinant of water demand. It is also one
of the easiest and cheapest for a water supply authority to implement. Metcalf published the
first price elasticity values for water demand in 1926 (Wong, 1972). How and Linaweaver
(1967) presented the first detailed account of price elasticity for water demand.
Water consumption response to changes in price is reasonably simple to calculate when a
single water tariff is used. However, the problem becomes more complex for more
complicated tariff structures such as block rates. In such cases, the use of an average price
would result in a loss of modelling accuracy (Billings and Agthe, 1980). Block rates are
normally handled using the marginal price and a difference value. The difference value
typically represents the difference between a user’s actual water bill and what would have
been paid if the user’s full consumption were charged at the marginal rate (Billings and
Agthe, 1980).
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There are both upper and lower limits to the applicability of price as a water demand
management tool. Water is necessary for life, and a certain minimum quantity of water will
thus always be consumed, no matter what the price is. On the other hand, there is a physical
limitation to the quantity of water that a user can obtain from the distribution system and thus
will never exceed, even if water is free.
The price elasticity of water is normally higher for outdoor use than for indoor use because
the outdoor consumption is typically less critical to the user. An important factor here is the
fraction of the total water consumption that is used outdoors. Areas with small stands and no
garden irrigation would typically have a smaller fraction of outdoor use, thus increasing the
significance of the indoor price elasticity values.
Price elasticity changes with time and it is possible to differentiate between short-term and
long-term elasticities. Price increases will have an immediate (short-term) effect by changing
consumers’ water use patterns, but will have little immediate effect on house fixtures.
However, in the longer term, the increased cost of water stimulates the installation of water
saving fixtures, resulting in higher elasticity values. Veck and Bill (2000) found from
literature that the average short-term price elasticity is –0,21, while the average long-term
price elasticity is –0,6. This gives some indication of how much more price increases can
affect water consumption in the long-term than in the short-term.
A number of international and local studies on short-term price elasticity have been identified
in the literature. A summary of the results of these studies is given in Table 1. Three South
African studies are included: Veck and Bill (2000) conducted a study on price elasticity by
means of a contingent valuation approach. In this approach, a survey of water users is done in
which they are asked to indicate how they would adjust their water consumption if the price
of water is increased or decreased by certain quantities. The results of this type of study are
not as reliable as actual measured responses, but unfortunately there is very little good data
available. Veck and Bill studied different income groups in Alberton and Thokoza and
determined indoor and outdoor price elasticity values of –0,13 and –0,47 respectively for
Alberton, and –0,14 and –0,19 respectively for Thokoza.
Ideal position for Table 1
In the second South African study, Döckel (1973) also used a contingent valuation approach
to study price elasticity in Gauteng. He found a price elasticity value of –0,69.
A third South African price elasticity study was done by a commercial company for Rand
Water (Business Enterprises at University of Pretoria, 2000) . The study was done in the
Alberton and Thokoza areas, also using a contingent valuation approach. This study provided
much more detailed data values for price elasticity and could be used to fit a β elasticity value
over a range of data points, instead of basing it on a single data point. The study only
determined overall elasticity values. To differentiate between indoor and outdoor
consumption, average outdoor consumptions of 40 % and 15 % were assumed respectively
for suburbs and townships.
Income Elasticity
Quality data on income elasticity of water use are scarcer than data on price elasticity.
Fortunately, Rand Water did a detailed survey (MSSA, 2001) on water consumption
behaviour in its supply area, providing a good basis for estimating elasticity values. No
logical differentiation could be made in this case between indoor and outdoor elasticities.
The survey data had to be sieved to obtain relevant elasticity values: the data were first
categorized according to town type (e.g. suburbs and townships). All non-residential users
and estates (townhouses, clusters, etc.) were removed from the data set to ensure that the
elasticities reflect only normal housing units. Data points in which the demand was estimated
by the user (rather than obtained from actual meter readings) were also eliminated from the
data set. The remaining data were then analysed to obtain income elasticity values.
The data used to calculate income elasticities are shown in Figures 3 and 4 for suburbs and
townships respectively. The resulting elasticity values (β) are 0,28 for suburbs and 0,21 for
Ideal position for Figures 3 and 4
Area Elasticity
Data for the investigation of area or stand size elasticity were extracted from treasury
databases using the SWIFT (Sewer & Water Interface to Treasury) software package. Data
for more than 110 000 domestic users in Alberton, Boksburg, Centurion and Midrand were
included in the analysis.
To ensure that the data used in the analysis were of good quality, a number of filters were
used to exclude suspect data points. The first filter excluded users with a stand AADD below
0,1 kl/d and above 30 kl/d. The second filter excluded users with a stand size smaller than
200 m² and larger than 2 000 m². A final filter excluded stands with a value (using municipal
valuations) below 10 R/m² and above 150 R/m².
The data were grouped according to their municipal valuations. Four stand value categories
were used as shown in Table 2. The number of stands and average AADD for each category
are also given in the table.
Ideal position for Table 2
A strong link exists between stand size and outdoor consumption. Larger stands would
typically have larger gardens and thus require more water for outdoor use. Within the same
stand value category, it may reasonably be assumed that the effect of stand size on indoor
consumption is negligible, i.e. have elasticities of zero. To separate indoor and outdoor
consumption figures it was subsequently assumed that the smallest stands in suburbs and
townships have outdoor consumptions of 10 % and 0 % of their total consumption
respectively. Increases in consumption with increasing stand size were then assigned to
outdoor consumption. The 10-30 R/m2 value category was taken as representative of
townships and the 50-70 R/m2 value category as representative of suburbs.
A summary of the outdoor unit consumption elasticities calculated for the different stand
value categories is shown in Figure 5. To obtain the elasticities for total consumption, the unit
consumption elasticities should be increased by 1 (see Equation 12).
Ideal position for Figure 5
Pressure Elasticity
Pressure affects the flow rate through an opening in a pipe, and thus the leakage rate in a
water distribution system. The theoretical relationship between pressure and flow rate dictates
that the flow rate should be proportional to the square root of the pressure (hence a β
elasticity value of 0,5). However, experience in actual systems indicates much higher values
for β, possibly due to the fact that the cross-sectional areas of some types of leaks are not
fixed, but expand with increasing pressure. A default β value of 1 is often used for water loss
estimation where measured data are not available (McKenzie, 2001).
Pressure can be expected to have an effect on non-leakage consumption as well, especially
when the consumption is not measured in terms of volume (for example a bath or toilet
cistern), but in terms of time. Wasteful water consumption (such as taps being left open for
unnecessary long periods) was assumed to have the theoretical β value of 0,5. Since irrigation
consumption can be controlled by time or volume, the elasticity value will typically vary
between 0,5 and 0. Taking all these factors into account, the elasticity of household
consumption was assumed to vary between 0,15 and 0,25.
Typical elasticity values for the causative parameters water price, household income, stand
size and pressure were estimated based on the literature review and data analyses. Probable
minimum and maximum elasticity values were also estimated. These values are given in
Table 3.
Ideal position for Table 3
The numerical values of the elasticities in Table 3 indicate which variables will have the
greatest effect on water demand. Increasing the price of water will, for instance, have the
greatest long-term effect on outside use in townships. However, this does not necessarily
translate into the largest total saving of water, since townships typically only use a small
fraction of their consumption outdoors.
To get the full picture, it is necessary to consider how the consumption is distributed between
indoor and outdoor use and what fraction of the total use falls in the category under
consideration. For the purpose of the study, suburbs were assumed to use 50 % of their
consumption outdoors, and townships 20 %.
A sensitivity analysis was performed by plotting the consumption response to normal,
minimum and maximum values for each parameter as given in Table 3. Only one parameter
was changed at a time.
Price Elasticity
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Both long and short-term price elasticity values were considered in the sensitivity analysis.
Short-term price elasticity reflects the immediate change in consumption behaviour of
consumers due to a change in the water price, while long-term price elasticity also includes
longer-term effects such as the introduction of water-saving fixtures in homes. Veck and Bill
(2000) noted that long-term price elasticities can be three times higher than short-term price
elasticities. In this study, long-term price elasticities were conservatively estimated to be
twice that of the corresponding short-term elasticities.
The projected short-term changes in consumption due to changes in the water price are shown
in Figures 6 and 7 for suburbs and townships respectively. The graphs show that a 50 %
increase in water price will result in consumption reducing by between 7 % and 15 % for
suburbs, and between 2 % and 25 % for townships in the short term. The larger variation in
the response of townships to price increases can be explained by considering two factors.
Firstly, people in townships are generally poorer and will thus be influenced more by water
price increases. On the other hand, many people in townships already use little water and will
not be able to reduce their consumption by much, even if the water price increases.
Ideal position for Figures 6 and 7
The results for townships are complicated by a further two factors, namely the problem of
non-payment and the new free basic water policy of government. It can be expected that users
not paying for water will also not adjust their consumption to changes in the price of water.
Price increases may, in some cases, have the opposite effect by increasing the rate of non-
payment as a form of protest against the price increase.
The projected long-term changes in consumption due to changes in the water price follow the
same pattern as that for short-term changes, but with larger effects on the final water
consumption. The long-term price elasticity curves are shown in Figures 8 and 9 for suburbs
and townships respectively. The graphs show that a 50 % increase in water price will result in
consumption reducing by between 13 % and 27 % for suburbs, and between 3 % and 44 %
for townships in the long-term.
Ideal position for Figures 8 and 9
An interesting result is that local authorities will not only reduce consumption by increasing
the price of water, but will also increase their income from water sales. In the suburbs
example above, for instance, the local authority will increase their income from water sales
by between 28 % and 40 % in the short term, and between 10 % and 30 % in the long term.
Income Elasticity
The estimated changes in consumption due to changes in household income are shown in
Figures 10 and 11 for suburbs and townships respectively. The graphs show that a 20 %
increase in real income will result in consumption increasing by between 4 % and 7 % for
suburbs, and between 2 % and 8 % for townships. The effect of income on water
consumption is clearly much smaller than that of price. A factor that should be taken into
consideration when interpreting the income elasticity graphs is that large changes in income
would probably result in people moving out of a given area to poorer or more affluent areas.
Changes in income to specific areas should thus be limited to what can realistically be
expected to occur within a given area.
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Ideal position for Figures 10 and 11
Area Elasticity
The estimated changes in consumption due to changes in the stand size are shown in Figures
12 and 13 for suburbs and townships respectively. The graphs show that a 50 % reduction in
stand size (for example when stands are sub-divided) will result in per-stand consumption
decreasing by between 28 % and 40 % for suburbs, and about 12 % for townships. The sub-
division will have the effect of doubling the number of stands, thus resulting in a nett increase
in consumption by between 20 % and 44 % for suburbs, and by 76 % for townships.
Since it has been assumed that indoor consumption is not affected by stand size, the nett
effect of a change in stand size is a function of the outdoor elasticities (which are high for
both suburbs and townships) and the fraction of consumption that is used outdoors. In
townships this figure is generally low (20 % was assumed in the sensitivity analysis)
resulting in a relatively small reduction as a result of subdivision of stands. Densification in
townships, even if it is not by formal subdivision, will thus have a much greater effect on the
total water consumption than the same factor of densification in suburbs.
Ideal position for Figures 12 and 13
Pressure Elasticity
Pressure affects certain aspects of water demand in which time is generally used as a measure
instead of volume (for example irrigation). The pressure elasticities in this study were based
on the estimated effect on actual consumption and specifically exclude losses in the system.
As a result, the estimated pressure elasticity values are much lower than those normally used
in pressure management studies.
The estimated changes in consumption due to changes in pressure are shown in Figures 14
and 15 for suburbs and townships respectively. The graphs show that a 50 % reduction in
pressure will result in consumption decreasing by between 10 % and 16 % for suburbs, and
between 7 % and 13 % for townships. The effect of pressure reduction on demand is thus
expected to be small, although the main benefit of pressure control will be in the area of
leakage reduction.
Ideal position for Figures 14 and 15
The sensitivity analysis gives an indication of how much water demand would be affected by
changing a single parameter at a time. However, it does not provide information on the
cumulative effect of different parameters changing simultaneously. Modelling real life
scenarios requires the use of a software package, such as IWR-Main (Planning and
Management Consultants, 1999), to handle the complexities of the model.
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End-use software packages allow the user to model very detailed end-use behaviour. Typical
behaviour of individual components, such as toilets, baths and dishwashers, can be included
in the model, each with their own elasticity values. In this pilot study, demand was grouped
according to user category (suburbs and townships), type of demand (indoor and outdoor),
and losses. Losses were modelled as a separate user with a pressure elasticity value of 1,0.
This grouping is adequate to illustrate the mechanisms and capabilities of end-use modelling
without getting caught up in unnecessary detail.
The IWR-Main end-use model covers the residential areas of Alberton, Boksburg, Centurion
and Midrand. A general layout map of the areas is shown in Figure 16. The area considered
includes two township areas, namely Thokoza in Alberton and Vosloorus in Boksburg. The
GIS maps for the areas showed that the 10 – 30 R/m2 stand value category in Alberton falls
mainly in Thokoza. Similarly the 10 – 30 and 30 – 50 R/m2 categories in Boksburg falls
mainly in Vosloorus. These categories were subsequently modelled as township areas in the
IWR-Main model. The basic data used for suburbs and townships in the IWR-Main model is
given in Table 4.
Ideal position for Figure 16
Ideal position for Table 4
It was assumed that suburbs have 50 % of their consumption outdoors, and townships 20 %.
Losses were assumed to be 20 % of consumption for suburbs and 40 % for townships.
To show how end-use modelling can be employed to model future water demand, a
hypothetical scenario was compiled. The scenario consisted of the following:
A projected real increase in household income of 1,5 % p.a. for both suburbs and
A rate of densification of 0,5 % p.a. for suburbs and 2,0 % p.a. for townships.
An immediate program to reduce the pressure in both suburbs and townships by 10 %
p.a. for three years.
A planned increase in the water price of 10 % p.a. for suburbs and 5 % p.a. for
townships. The price increases will start in 2007 and will be implemented over a
period of five years.
The projected consumption of the study area was calculated for a design horizon of 10 years.
To obtain an envelope of minimum and maximum values, combinations of elasticities were
selected from Table 3 for the various parameters to either maximize or minimize the total
demand. For maximum demand, the minimum price, stand size and pressures elasticities, and
the maximum income elasticity value were used. Conversely, for minimum demand, the
maximum price, stand size and pressure elasticities, and the minimum income elasticity value
were used. The results of the simulation are shown in Figure 17.
Ideal position for Figure 17
The figure shows the cumulative effect of the various factors included in the scenario. A
number of these factors will increase demand, namely the increases in income and housing
density. Factors that will decrease the demand are decreases in system pressure, stand size
and increases in the water price. The reduction in system pressure takes place in the first three
years and is responsible for the initial reduction in demand. Most of this reduction is due to a
reduction in leakage.
The second reduction in demand is caused by the increases in water price from 2007 to 2011.
In years where none of the reducing factors were active (2006 and 2012), the demand shows
a steady increase. Both factors decreasing demand can only be implemented up to a certain
level, after which they will not be viable. Under the assumed conditions, demand would thus
continue to increase in the long term unless more permanent water demand measures can be
The minimum and maximum water demand curves in Figure 17 are the theoretical envelope
based on the elasticities used in the sensitivity analysis. It is highly unlikely that all the
elasticities would vary from their normal values in such a way that one of these extreme
curves will occur in practice. The actual demand curve can realistically be expected to be
much closer to the expected value curve.
A potential source of modelling errors in the case study is possible interdependence between
modelling variables. A relationship may, for instance, exist between stand size and household
income. To apply the water demand model to a real life system with increased accuracy, it is
thus necessary to calibrate the model using measured data. Consequently, developing a water
demand model should not be seen as a once-off exercise, but as a continuous project
requiring frequent updating and refinement.
The case study shows how powerful end-use modelling can be in making predictions of water
demand. Various possible scenarios can be identified and modelled to identify the most
critical ones. Other factors can also be included in the model, such as:
The implementation over time of plumbing codes to install water saving fittings in
Including seasonal variations in demand to estimate minimum and maximum
demands during each modelling year.
Various active and passive conservation scenarios.
Emergency conservation.
Cost-Benefit analyses of various programmes.
Finally, it is important to stress that modelling results are dependent on the quality of the
input data. It is thus imperative to understand the area being modelled and collect accurate
and representative data for modelling purposes.
The aim of this study was to demonstrate the strengths and weaknesses of end-use modelling
as a water demand predictor for the Rand Water supply area. The study focussed on four
areas, namely Alberton, Boksburg, Centurion and Midrand. Data were collected from various
Rand Water consumer surveys and studies, local and international literature and from
treasury data basis via the SWIFT interface to treasury databases of the study area. A special
effort was also made to understand and explain the meaning and application of elasticity in
end-use modelling.
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The data were used to identify ranges of elasticity values for the modelling parameters
selected, which were water price, household income, stand size and pressure. The effect of
these elasticity values on water demand was illustrated in a sensitivity analysis, which
highlighted the following factors:
Elasticities for indoor and outdoor use differ for various parameters. The nett effect of
changes in these parameters do not only depend on the elasticity values, but also on
the quantities of indoor and outdoor consumption.
When designing water demand management measures, the total demand of a given
user group should be taken into consideration, and not only the effect that changes in
parameters will have on the demand. A large reduction in the use of a group with a
relatively small consumption will not reduce the total consumption by much.
On a purely technical level, an increase in the price of water can be a good method for
reducing water consumption. Not only does it have a large effect on demand, with the
effect increasing in the long term, but the nett income of the local authority also
increases. However, price may not be a good water demand measure in townships
where price increases may increase the number of non-paying customers and where
the effect of non-payment and the new free basic water will impact on the actual
water savings made in a way that is difficult to estimate. There may also be significant
political pressures against increasing the price of water.
Income has a significant effect on consumption, but may not affect the overall
consumption of a given area by much due to movement of people with increasing or
decreasing income out of the area.
Increase in consumption due to densification of suburban areas is tempered by the
reduced consumption due to smaller stand sizes. However, since township areas
generally have only a small fraction of their consumption outdoors, densification in
townships can substantially increase the water demand of these areas.
Pressure management has a small, but significant effect on consumption. However,
the main benefit of pressure management will remain as a measure to decrease water
losses due to leakage in the system.
It is necessary to view a water demand model as a continuous project, with increased
accuracy gained over time through frequent updating and refining the model, and
using measured data to calibrate the model variables.
Although the sensitivity analysis was useful in highlighting certain important aspects of
individual model parameters, it cannot be used to estimate the combined effects of different
areas, user types and parameters. Software packages are typically employed for this purpose.
A case study with arbitrarily selected parameters was used to illustrate the way end-use
modelling can be used to predict future water demand. This technique can also be applied to
estimate the effect of different water conservation measures on demand and prepare plans for
use in very dry periods or other emergencies affecting the availability of water.
End-use modelling can be expanded to include various factors such as plumbing codes,
conservation measures, emergency conservation and cost-benefit analyses. As with all types
of modelling, the quality of the input data are of the highest importance to obtain accurate
results from the analyses.
The study showed that end-use modelling is a powerful tool for estimating future water
demand that can be of great benefit to a bulk water supplier like Rand Water for planning and
emergency preparedness purposes.
This pilot study was aimed at demonstrating the principles and potential application of end-
use modelling. A number of assumptions and simplifications were therefore made. To obtain
meaningful results from an end-use demand model, the following points should be
Additional water use categories should be defined beyond “townships” and “suburbs”,
such as business districts, industries, parks, schools, flats, townhouses, etc. The water
use for each category needs to conceptualised to obtain an appropriate modelling
The simple “indoor/outdoor” split may have to be extended to include pools, washing
machines, dishwashers, etc.
With the extension of the model, there also comes the need for calibration of the
increasing number of model parameters. However daunting this may seem, this study
showed how a systematic analysis of the scant data available can render a fairly robust
set of model parameters, even for such notoriously whimsical parameters such as
income elasticity.
The water demand of informal settlements is particularly troublesome from a
modelling perspective. A special effort will be required to reach consensus of how
non-payment and free water can be incorporated in a realistic model.
It seems inevitable that even the best parameter estimates will be bounded by a
defined band of uncertainty. It was already pointed out that the upper and lower
estimates in the hypothetical case study presented are unrealistic – one would not
expect all four model parameters to be simultaneously low or high. As more
parameters are introduced, it will become necessary to apply a probabilistic technique
such as Monte Carlo Simulation to make more realistic estimates of area-wide water
The authors wish to record its appreciation towards Mr Hannes Buckle of Rand Water for his
management and encouragement of this project, and for Mr Alheit du Toit of Rand Water for
unconditionally sharing the unpublished results of previous consumer surveys. The
municipalities of Alberton, Boksburg, Centurion and Midrand are also thanked for allowing
their data to be used in this study.
Formatted: Bullets and
Billings RB and Agthe DE (1980) Price Elasticities for Water: a Case of Increasing Block
Rates, Land Economics 56(1).
Business Enterprises at University of Pretoria (2000) Rand Water: Elasticity Survey, Excel
Spreadsheets (unpublished).
Döckel JA (1973) The Influence of the Price of Water on Certain Water Demand Categories,
Agrikom 12(3)
How CW and Linaweaver FP (1967) The Impact of Price on Residential Water Demand and
its Relation to System Design and Price Structure, Water Resources Research 3(1)
McKenzie (2001) Presmac Pressure Management Program, WRC Report No TT 152/01,
Water Research Commission, Pretoria
MSSA (2001) Rand Water Household Survey: July 2001, Marketing Surveys & Statistical
Analysis (unpublished)
Planning and Management Consultants (1999) IWRMain Water Demand Suite – User’s
Manual and System Description, Planning and Management Consultants, Carbondale,
Illinois, USA
Veck GA and Bill MR (2000) Estimation of the Residential Price Elasticity of Demand for
Water by Means of a Contingent Valuation Approach, WRC Report No. 790/1/00, Water
Research Commission, Pretoria
Wong ST (1972) A Model on Municipal Water Demand: a Case Study from Northeastern
Illinois, Land Economics 48(1)
Table 1 Summary of published of short-term price elasticities (adapted from Veck and Bill,
Price Elasticity Authors Year Location
Indoor Outdo or Total
Carver and Boland 1969 Washington, DC - - -0,1
Hanke and de Mare 1971 Malmo, Sweden - - -0,15
Döckel 1973 Gauteng, South Africa - - -0,69
Billings and Agthe 1974 Tucson, Arizona - - -0,18
Martin et al 1976 Tucson, Arizona - - -0,26
Gallagher et al 1972/3 &
Toowoonba, Queensland - - -0,26
Thomas and Syme 1979 Perth, Australia -0,04 -0,31 -0,18
Boistard 1985 France - - -0,17
Alberton, South Africa -0,13 -0,47 -0,18
Thokoza, South Africa -0,14 -0,19 -0,14
Veck and Bill
Alberton and Thokoza, South
-0,13 -0,38 -0,17
Alberton, South Africa -0,24 -0,39 -0,29 Rand Water study 2000
Thokoza, South Africa -0,67 -0,79 -0,69
Table 2 Distribution of stands used in the stand size elasticity analysis
Alberton Boksburg Centurion Midrand
Stand Value
R/m² No of
No of
No of
No of
10-30 16168 1,76 8956 1,81 1706 0,95 2890 1,55
30-50 13219 1,33 20351 2,10 13090 1,23 7537 1,48
50-70 1245 1,23 10040 1,82 5964 1,67 2646 1,14
70-150 538 1,40 4584 1,36 1258 1,67 2967 1,69
Total 31170 43931 22018 16040
Table 3 Final elasticity parameters used in the sensitivity analysis
Note: Minimum and maximum values relate to the absolute of the elasticity values
Suburbs Townships Description
Inside Outside Inside Outside
Fraction of consumption 50 % 50 % 80 % 20 %
Min abs. -0,05 -0,30 -0,00 -0,20
Norm -0,20 -0,40 -0,30 -0,50
β price (short-term)
Max abs. -0,30 -0,50 -0,70 -0,80
Min abs. -0,10 -0,60 -0,00 -0,40
Norm -0,40 -0,80 -0,60 -1,00
β price (long-term)
Max abs. -0,60 -1,0 -1,40 -1,60
Min abs. 0,20 0,10
Norm 0,28 0,21
β income
Max abs. 0,35 0,40
Min abs. 0 1,2 0 1,2
Norm 0 1,6 0 1,28
β stand size
Max abs. 0 2,3 0 1,4
Min abs. 0,15 0,10
Norm 0,20 0,15
β pressure
Max abs. 0,25 0,20
Table 4 Basic data used in the IWR-Main model
Item Suburbs Townships Total
Number of stands 67 684 45 475 113 159
Total daily consumption (kl) 74 882 36 047 110 929
Daily consumption per stand (l) 1 106 793 -
Fraction of outdoor consumption assumed 50 % 20 % -
Fraction losses assumed 20 % 40 % -
Total daily losses (kl) 14 976 14 419 29 395
Figure 1 Elasticity values using the traditional approach (Equation 2). The elasticity value
(E) varies with water price. Data from Business Enterprises at University of Pretoria (2000)
50 100 150 200
Price (-)
Consumption (-)
50 100 150 200
Price (-)
Elast ici t
Figure 2 Elasticity value using the β approach (Equation 3). The slope of the curve (-0.295)
gives the value of β.
10 100 1000
Price (-)
Consumption (-)
Figure 3 Income elasticities for suburbs
y = 1 .7 89 9x
= 0.974
1000 10000 100000
Household I ncom e (R/mo nth)
Cons umption ( kl/m onth)
Figure 4 Income elasticities for townships
y = 3.7789x0.2099
R2 = 0.7412
100 1000 10000
Household I ncome (R/month)
Cons umption ( kl/month)
Figure 5 Summary of average outdoor elasticities based on unit consumption for different
stand value categories
y = 0.0265x
= 0.7865
10 100 1000
Stand va lue (R/m
Figure 6 The short-term effect of changes in water price on consumption in suburbs
50% 75% 100% 125% 150% 175% 200%
Normal Min Max
Figure 7 The short-term effect of changes in water price on consumption in townships
50% 75% 100% 125% 150% 175% 200%
Normal Min Max
Figure 8 The long-term effect of changes in water price on consumption in suburbs
50% 75% 100% 125% 150% 175% 200%
Normal Min Max
Figure 9 The long-term effect of changes in water price on consumption in townships
50% 75% 100% 125% 150% 175% 200%
Normal Min Max
Figure 10 The effect of changes in income on consumption in suburbs
50% 75% 100% 125% 150% 175% 200%
Norma l Min Max
Figure 11 The effect of changes in income on consumption in townships
50% 75% 100% 125% 150% 175% 200%
Norma l Min Max
Figure 12 The effect of changes in stand size on per-stand consumption in suburbs
50% 75% 100% 125% 150% 175% 200%
Stand size
Norma l Min Max
Figure 13 The effect of changes in stand size on per-stand consumption in townships
50% 75% 100% 125% 150% 175% 200%
Stand size
Normal Mi n Max
Figure 14 The effect of changes in pressure on consumption in suburbs (excludes the effect
of pressure on losses)
50% 75% 100% 125% 150% 175% 200%
P re ssu re
Normal Mi n Max
Figure 15 The effect of changes in pressure on consumption in townships (excludes the effect
of pressure on losses)
50% 75% 100% 125% 150% 175% 200%
Normal Min Max
Figure 16 Layout of the study area
Figure 17 The projected future water demand for the study area
2002 2004 2006 2008 2010 2012
Demand (Ml/day)
Expected Minimum demand Maximum demand
Published as: Van Zyl, J.E., Haarhoff, J., Husselmann, M.L. (2003) Potential Application of
End-use Demand Modelling in South Africa, Journal of the South African Institute of Civil
Engineering, 45 (2).
...  price of water (Howe, 1967;Young, 1973;Gibbs, 1977;Agrhe, 1986;Espey et al., 1997;Veck and Bill, 2000;Van Zyl et al., 2003;Arbués et al., 2004); ...
... It has been shown by various studies (Veck and Bill, 2000;Van Zyl et al., 2003) that water demand has a negative price elasticity -in other words water demand decreases with an increase in price, although indoor water demand is generally accepted to be price inelastic (Jacobs et al., 2006). ...
... Van Zyl et al. (2003) investigated the elasticity of water price, system pressure, household income and stand area for residential water consumption in some Gauteng suburbs. The study grouped different end-uses into indoor consumption, outdoor use and leakage, and provides ranges of elasticity values identified for each modelling parameter. ...
Full-text available
This study focuses on the water demand of selected residential properties with access to groundwater in serviced areas of the Cape Peninsula. This winter rainfall region is typified by hot and dry summer months, corresponding to peak garden water demand. Water restrictions in the area are relatively common and primarily target outdoor use. Groundwater serves as an alternative source of water to some consumers in the area, but little is known about the extent of such use and the impact thereof on potable water demand. A major part of the area is underlain by a primary, unconfined aquifer that has been reported to have high exploitation potential. Its unconsolidated sand and shallow water table provides ideal conditions for small scale groundwater abstraction. Several owners of properties situated above the aquifer unit have capitalised on this and utilise groundwater as an alternative to potable water, mostly for garden irrigation purposes. The main objective of this research was to investigate the average extent of the expected reduction in average annual municipal water demand due to private groundwater use at the selected properties in the study area. The methodology involved abstracting data from the City of Cape Town’s registration process for the private use of non-potable water. The data was recorded between 2000 and 2006 and was available only in hard copy format. The registration data was used to identify residential properties with access to private groundwater sources, based on the physical addresses recorded on the registration forms. The rate of groundwater abstraction was not recorded during the registration process, nor was any of the properties spatially referenced. The data set contained information for 4 487 properties, of which 3 764 could ultimately be used in the analysis. Stellenbosch University iv Data from a recent hydro-census in Hermanus (which was done by others prior to this study) was used to test the intended research method first. This trial investigation involved only 114 properties and was used to streamline the proposed methodology for application on the full-scale analysis of the City of Cape Town data. Each address was captured electronically, verified manually and filtered to extract only those representing residential properties for which groundwater use was registered. In order to identify the properties spatially, the addresses had to be converted to coordinates through a procedure called geocoding, so as to plot each spatially and obtain the attributes such as stand size, position and the unique Surveyor General’s code. This was necessary in order to link the addresses to the municipal treasury system and obtain their latest available water consumption records using a commercial software package that incorporates consumer information. Next the actual annual water consumption figures were compared with recently published water demand guidelines based on stand size as single explanatory variable. The selected residential stands were divided into pre-defined stand size categories. The average water consumption of all the stands in each size category was calculated and compared with the suggested water demand as per the guidelines used, based on the centre value of the size range of each category. The results of the comparative analysis confirm findings from two earlier studies where lower municipal water use was reported for residential properties with access to groundwater in a summer rainfall region. The results further showed that the mean average annual potable water demand of consumers in the study area with access to groundwater was on average 31.4% lower than those considered without such access in the same region. This represents an average reduction of 333 l/stand/day (about 10 kl/stand/month) in the potable water demand of the selected residential stands. This study therefore confirms that serviced residential stands with access to private groundwater sources in the Cape Peninsula have lower average metered water consumption from the municipal supply system.
... It has been shown by various authors that water demand has a negative price elasticity, in other words water demand decreases with increases in price (Veck & Bill 2000;Van Zyl et al 2003). Over the past two decades consumers have increasingly appreciated water as a scarce and sought after commodity and for this reason it has become more expensive. ...
... It should be kept in mind that elasticity varies along the demand curve for linear demand functions. However, in the definition used by Van Zyl et al (2003) the elasticity remains constant and a good fit can be achieved. Stephenson (1999) provides a more detailed explanation on elasticity and demand functions. ...
It is understandable that an easy method to obtain estimates of residential water demand is often used. These estimates are also extended to calculate peak demand and sewer flow, and impact an authority's water and sewer infrastructure budget and finally its expenditure. Guideline curves are presented in this paper that can be used to estimate annual average residential water demand based on stand size. The measured water consumption and stand size of more than 600 000 single residential stands were obtained. Treasury databases for Cape Town, Ekurhuleni, George, Midrand, Randfontein and Tshwane were analysed in detail and the results compared to similar work in Windhoek. The large number of records made it possible to conduct statistical analyses and to investigate the distribution of data for stand size intervals of 100 m 2. The water demand of similar sized stands in townships and suburbs could be compared. A strong relationship exists between the average annual water demand and stand size. The authors note that a model based on stand size has limited application only when better methods are not available.
... commented on the significance of evaporative coolers use on household consumption. (Zyl et al., 2003). investigated the elasticity of water price, water pressure, and household income for residential water consumption. ...
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The growth of urban areas and constant need for potable water services, have highlighted the importance of accurate water consumption estimates for effective municipal water services infrastructure planning and design. A clear understanding of the drivers of residential water demand is essential in managing water resources appropriate as potable water. In this paper, a total of (1682) daily household water demand computations were carried out in order to calculate the actual indoor water consumption factor in the city of Mosul. The impacts of specific days of the week family size, water supply continuity and, seasonal variations (winter and summer) on water consumption were also considered. Results revealed that the daily average water consumption factors were (126 ± 65 Lcpd) in a winter season, (235 ± 64 Lcpd) in a summer season and the overall water consumption factor for the area was (180 ± 84 Lcpd). Water consumption was also found to be significantly correlated with explanatory variables. Seasonal variation plays the biggest role in controlling the water consumption factor followed by water supply continuity, then family size (negative effect) and in the last order specific days (weekends). General models to explain the effect of all of these parameters on the per capita water consumption were also derived in this paper.
... The study also found that the South African design guidelines (CSIR 2003) over-estimated domestic water consumption for Pretoria. A study by Van Zyl et al (2003) found that the price of water, water pressure, household income and stand area influenced residential water consumption. The data consisted of residential areas in Gauteng only. ...
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This study investigated factors affecting the average domestic water demand of a large number of suburbs in South Africa. Suburbs form an ideal demand grouping since they tend to have similar stand areas, climatic conditions and user characteristics. In addition, since properties within a suburb are close to one another, it may be reasonably assumed that differences in user demands will cancel one another out so that a designer only has to cater for the average demand of the suburb. A database on measured domestic water demands was used to determine the average of the Annual Average Daily Demand (AADD) for a large number of suburbs in South Africa (i.e. the average AADD per suburb), and this data was linked to census and climate data. The combined data set was then subjected to various regression analyses to identify the most important influencing factors. Stand area was found to be the most important influencing factor, validating the approach followed by the current South African design guidelines. However, the current guidelines were found to exclude a large number of measured data points, and thus a new, more comprehensive design envelope is proposed.
... Until late, much of literature approaches demand for water in communities from a consumer's perspective [1][2][3][4][5][6][7]. Under this approach, the major influencing factors to demand for water are household income as well as price elasticity of the water. ...
Conference Paper
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... Hierdie proses staan bekend as eindverbruikmodellering en behels 'n model wat die erf en huis in fyn detail beskryf deurdat eindverbruike afsonderlik wiskundig beskryf word. 19 21,22 Die wiskundige struktuur van 'n eindverbruikmodel wat plaaslik ontwikkel is, kan byvoorbeeld deur vyf vergelykings opgesom word. 20 Elke vergelyking beskryf een komponent van waterverbruik in en om die huis, naamlik binnenshuise verbruik, buitemuurse verbruik, warmwaterverbruik, en rioolvloei asook kwaliteit van die terugvloeistroom. ...
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