Drivers of future water demand in Sydney,
Australia: examining the contribution from
population and climate change
Adrian Barker 1, Andrew Pitman 1, Jason P. Evans 1, Frank
Spaninks 2 and Luther Uthayakumaran2
1 ARC Centre of Excellence for Climate Extremes and Climate Change Research
Centre, UNSW, Sydney, Australia
2 Sydney Water, Level 14, 1 Smith Street, Parramatta, New South Wales, 2150,
Australia
E-mail:
Abstract.
We examine the relative impact of population increases and climate
change in affecting future water demand for Sydney, Australia. We use the Weather
and Research Forecasting model, a water demand model and a stochastic weather
generator to downscale four different global climate models for the present (1990-
2010), near (2020-2040) and far (2060-2080) future. Each climate model is downscaled
three times with variations in the boundary layer and convection schemes. Projected
climate change would increase median consumption, at 2019/20 population levels, from
around 484 GL in the present to 484-494 GL in the near future, and 495-505 GL in
the far future. Population changes from 2014/15 to 2024/25 have a far larger impact,
increasing median consumption from 457GL to 508 GL under present climate, 463GL to
515GL under near future climate and from 471GL to 524GL under far future climate.
The projected changes in consumption are sensitive to the climate model used, but
differences caused by varying the boundary layer and convection schemes rarely exceeds
1-2%. Overall, while population growth is a far stronger driver of increasing demand
than climate change for Sydney, both act in parallel to reduce the time it would take
for all storage to be exhausted. Failing to account for climate change would lead to
overconfidence in the reliability of Sydney’s water supply.
1. Introduction
Major cities are confronted by how best to manage water consumption under the joint
challenge of growing populations framed by changing climate and climate variability
(Gain & Wada (2014); Hoekstra, Buurman & van Ginkel (2018)). Long term planning
for future water demand needs a mixture of social science, providing an understanding of
how population growth (Polebitski & Palmer 2010), economic development (Tortajada
& Joshi 2013) and social factors (Schleich & Hillenbrand 2009) will change over time,
combined with the physical science challenge of predicting future regional patterns of
Drivers of future water demand in Sydney, Australia
2
weather and climate. These lead to an increasing demand for better information to
plan engineering and policy actions to reduce demand, or increase supply of water, and
thereby help the management of water resources in a changing environment (Padula,
Harou, Papageorgiou, Ji, Ahmad & Hepworth 2013). Given increasing supply commonly
involves billion dollar infrastructure investments (dams for example) and complex
engineering solutions (desalinisation for example), evidence of any trends in water supply
or water demand can be very valuable.
Future changes in average temperature and precipitation (Griffin & Chang 1991),
changes in seasonality, and changes in extremes such as heatwaves or drought severity
and length would have a major impact on water consumption (Meehl & Tebaldi 2004).
To obtain estimates of how climate and climate variability will change in the future
requires modelling, but the spatial resolution of most global climate models remains
coarser than 1◦ x 1◦ making their direct use for city-scale projections of future climate
difficult. Solutions to help link global models with scales relevant to major cities include
dynamical downscaling.
This approach is now widespread (see reviews by Fowler,
Blenkinsop & Tebaldi (2007) and Ekstrom, Grose & Whetton (2015)) and groups have
now downscaled multiple climate models, using combinations of methods that reflect
uncertainties in key processes including the planetary boundary layer and convective
processes (Evans, Ekstrom & Ji (2012); Evans, Ji, Lee, Smith, Argueso & Fita (2014)).
In this paper we bring together a major downscaling effort, the New South
Wales/Australian Capital Territory Regional Climate Modelling (NARCliM) project
with an established water demand model developed for New South Wales, Australia.
The NARCliM project uses the Weather and Research Forecasting (WRF, Skamarock &
Klemp (2008)) model to downscale four different global climate models for the present
(1990-2010), near (2020-2040) and far (2060-2080) future.
Unusually, each climate
model is downscaled three times with variations in the boundary layer and convection
parameterisation to capture the uncertainty in these processes. The water demand
model, a method common in forecasting water demand (Arbues, Garcia-Valinas &
Martinez-Espineira (2003); House-Peters & Chang (2011); Donkor, Mazzuchi, Soyer &
Roberson (2014)), consists of multiple observations of the same population cross section
at different points in time (Wooldridge 2010) and can include past values of the response
variable as explanatory variables. We link the physical modelling of NARCliM with the
water demand modelling via a stochastic weather generator (see Wilks & Wilby (1999);
Ailliot, Allard, Monbet & Naveau (2015)) to enable probabilistic forecasting of Sydney’s
future water consumption.
Our goal therefore is to estimate the future of water consumption in Sydney and
examine the extent to which future trends reflect population change, or climate change.
We seek to determine the value of using multiple climate models relative to downscaling
a single climate model with different physical options in the higher resolution model.
Finally, where changes are identified, we seek to identify the climate variables that
explain the changes in consumption. Ultimately, we seek to determine the scale of the
threat climate change represents to managing water demand in the near and far future
Drivers of future water demand in Sydney, Australia
3
for Australia’s largest city.
2. Methodology
2.1. Sydney Water Consumption Model
The Sydney Water Consumption Model (SWCM) is a dynamic panel data model
(Wooldridge (2010); Bun & Sarafidis (2015)) used for the prediction of water
consumption by Sydney Water customers based on the work of Abrams, Kumaradevan,
Spaninks & Sarafidis (2012). The component of SWCM considered here models metered
consumption only, which is about 90% of the total. The remaining 10%, approximately
57 GL per year, including leakage and meter under-read, is generally insensitive to
weather and population.
Water consumption is divided into residential and non-
residential consumption. Residential properties are categorised into five dwelling types:
single dwellings, townhouse units, strata units, flats and dual occupancies. Estimates
for dwelling type numbers are made for the financial years 2014/15 to 2024/25 and
are largely based on New South Wales Department of Planning and the Environment
projections, adjusted to Sydney Water’s area of operations. Three dwelling types are
projected to increase between 2014/15 and 2024/25 (number of single dwellings, 1.05
million to 1.15 million; townhouse units, 103,000 to 131,000 and strata units 431,000
to 561,000) and two dwelling types are expected to remain constant (flats, 114,000 and
dual occupancies, 26,000). The increase in some dwelling types relative to others leads
to a small change in the mix of dwelling types in the population estimates over the
period 2014/15 to 2024/25. We note that these estimates are regularly updated and
while current when we undertook this analysis will inevitably be updated in the future.
The SWCM model predicts the water consumption at a residential property based
on the dwelling type, compliance with the Building Sustainability Index (BASIX)
regulation, participation in water efficiency programs and lot size. External drivers
of water consumption include the weather, water price and season. Forecast water
consumption for the individual properties are averaged to obtain the average demand
for each segment, and then multiplied by the forecast number of dwellings for each
segment to obtain total residential consumption.
The non-residential sector includes all property types not included in the residential
models. These properties were hierarchically segmented on the basis of consumption
levels, participation in water conservation programs and property types.
The SWCM uses five weather variables: average daily precipitation (PRE, mm);
number of days when precipitation exceeds 2mm (GT2MM); average daily maximum
temperature (TMAX, ◦C); number of days when maximum temperature exceeds 30◦C
(GT30C) and average daily pan evaporation (EVAP, mm). The weather stations used
to provide weather variable data are listed in Table 1 and Figure 1. Weather variables
are aggregated to quarterly variables when calculating residential consumption and to
monthly variables when calculating non-residential consumption.
Drivers of future water demand in Sydney, Australia
4
2.2. New South Wales / Australian Capital Territory Regional Climate Modelling
Project
The New South Wales/Australian Capital Territory Regional Climate Modelling
(NARCliM) project provides precipitation and temperature data from four different
global climate models for the present (1990-2010), near (2020-2040) and far (2060-
2080) futures. All future simulations used the SRES A2 emission scenario (Nakicenovic
& Swart (2000)).
The climate models were CCCMA3.1, CSIRO-MK3.0, ECHAM5
and MIROC3.0.
Three simulations were conducted for each period/climate model
combination. Data is available on a 10km x 10km grid, which covers south eastern
Australia, including the greater Sydney metropolitan area.
The choice of which climate models were downscaled,
and the physical
parameterisations used with WRF is provided in Evans et al. (2014). Briefly, the climate
models were chosen based on performance over eastern Australia (Evans et al. 2012)
combined with a test of model independence proposed by Bishop & Abramowitz
(2013).
The climate models spanned the range of future change simulated using
the A2 emission scenario in terms of precipitation and mean temperature. A large
ensemble of WRF simulations were conducted and three configurations were selected
that involved varying the convection, boundary layer, radiation and cloud microphysics
schemes. Full details are provided by Evans et al. (2012) and are not repeated here;
it will be shown later that the impact of these variations were very small.
The
NARCliM product has been used extensively to evaluate future climate change over
south eastern Australia (e.g.
Olson, Fan & Evans (2016); Evans, Argueso, Olson
& Di Luca (2017)), to assess changes in future wind energy (Evans, Kay, Prasad &
Pitman 2018) and the impact of urban expansion on temperatures (Argueso, Evans,
Pitman & Di Luca 2015). Further details on NARCliM can be found at the AdaptNSW
website (climatechange.environment.nsw.gov.au).
A bias correction is imposed on the NARCliM data so that the temperature
and precipitation of each present day simulation has the same yearly averages as the
Australian Water Availability Project (AWAP) data (Jones, Wang & Fawcett 2009)
over the same period. A modification to the original NARCliM bias-corrected data was
necessary in order to obtain realistic values for the GT2MM weather variable.
2.3. Stochastic Weather Generator
A stochastic weather generator developed by Barker, Pitman, Evans, Spaninks &
Uthayakumaran (2018) was used for the generation of weather scenarios as inputs for
the SWCM. A weather generator was used to overcome the problem that each NARCliM
member only produces a single realisation of a stochastic process (i.e. weather). The
weather generator enables multiple (in this case 100) realisations to be generated, each
consistent with a NARCliM ensemble member, to examine the statistical distribution
of weather and water consumption forecasts.
For each period/climate model/run combination, the stochastic weather generator
Drivers of future water demand in Sydney, Australia
5
Figure 1. Area serviced with water by Sydney Water (orange) and location of the
weather stations (red) used by the SWCM (see also Table 1). Inset of south eastern
Australia.
Drivers of future water demand in Sydney, Australia
6
Table 1. Weather data provided by weather stations for the SWCM. Figure 1 shows
the geographical location of these stations.
The variables are: daily precipitation
(PRE, mm); number of days when precipitation exceeds 2mm (GT2MM); average daily
maximum temperature (TMAX, ◦C); number of days when maximum temperature
exceeds 30◦C (GT30C) and average daily pan evaporation (EVAP, mm).
Station Name
PRE
GT2MM
TMAX
GT30C
EVAP
Albion Park
Y
Y
Y
Y
N
Bellambi
Y
Y
Y
Y
N
Camden
Y
Y
Y
Y
N
Holsworthy
Y
Y
Y
Y
N
Katoomba
Y
Y
Y
Y
N
Penrith
Y
Y
Y
Y
N
Prospect
Y
Y
Y
Y
Y
Richmond
Y
Y
Y
Y
Y
Riverview
Y
N
Y
N
Y
Springwood
Y
Y
Y
Y
N
Sydney Airport
Y
Y
Y
Y
Y
Terrey Hills
Y
Y
Y
Y
N
was calibrated to produce weather scenarios with statistical properties similar to those
of the NARCliM data. NARCliM weather data from the closest grid point to each of the
weather stations in Table 1 was used to calibrate the stochastic weather generator. Each
weather scenario contains data for the 11 financial years from 2014/15 to 2024/25 and
100 weather scenarios were generated for each period/climate model/run combination.
In total 13,200 years of data are generated for each time period (present, near future,
far future) allowing quantification of the variance due to changing weather.
All weather variables were assumed not to have a yearly trend within the 20 year
NARCliM period. Estimates of water demand by SWCM requires pan evaporation, a
variable not generated by most weather and climate models including the NARCliM
project. Instead, the evaporation model described by Barker et al. (2018) was used to
generate evaporation data as a function of precipitation and maximum temperature.
2.4. Experiments Performed
In summary, our consumption forecasts reflect changes in population and weather with
weather responding to climate change in the future. The population data associated
with a given forecast is estimated for each of the financial years between 2014/15 and
2024/25, allowing population to vary over this ten year period.
The weather data
associated with a forecast is taken from a stochastic weather generator simulation based
on data from a NARCliM ensemble member in one of the present, near or far future
periods. We can vary the NARCliM ensemble member and time period represented, such
Drivers of future water demand in Sydney, Australia
7
that the weather reflects the present, near or far future. We can therefore examine the
consumption forecasts for combinations of populations between 2014/15 and 2024/25
with weather for the present, near future or far future. We therefore undertake three
analyses, each for the present, near and far future:
(a) isolate the effect of climate change on water consumption. Here, population is held
at 2019/2020 levels and the dwelling type mix uses the population estimates;
(b) isolate the effect of population change on water consumption. Here, population
varies from 2014/15-2024/25 and the dwelling type mix uses population estimates;
(c) isolate the effect of dwelling type mix. Here, population varies from 2014/15-
2024/25 and the dwelling type mix varies between the dwelling type mix estimate,
simulations with no single dwellings, and simulations assuming all single dwellings.
The average number of people in each dwelling type was 3.11 per single dwelling;
2.17 per unit and 2.39 per townhouse. The ratio of units to townhouses was 4.2:1
and the number of flats and dual occupancies remained constant.
3. Results
Figure 2 shows the annual consumption for the present, near future and far future
climates across all dwelling types projected using four global climate models each
downscaled three times using different configurations of WRF. These simulations reflect
population and dwelling configuration representative of 2019/20 and therefore isolates
the effect of climate change. The range for an individual projection stems from the
use of 100 stochastic weather time series. Figure 2 shows a trend upward with median
consumption increasing from around 484 GL in the present to 484-494 GL in the near
future, and 495-505 GL in the far future due to climate change. There are differences
between the projected consumption with MIROC3.2 tending toward lower estimates
than the other models. Given the small differences between the WRF configurations,
we average them to calculate the change in demand. Median annual demand increases
from the present to the near future by between 1.1 GL (0.2%, MIROC3.2) and 9.2 GL
(1.9%, ECHAM5) and increases further by between 11.1 GL (2.3%, MIROC3.2) to 19.4
GL (4%, ECHAM5) between the present and the far future. CCCMA3.1 displays higher
variability in the near future (the range from the minimum to the maximum estimate is
10% in comparison to 6% for CSIRO and ECHAM5 and 8% for MIROC3.2). However,
CCCMA3.1 predicts lower variability in the far future (range 6-7% compared with 8-
10% for the other models). However, if an individual model, for an individual time
period is examined, the differences caused by varying the boundary layer and convection
parameterisations rarely exceeds 1-2%.
We next examine how future changes in water consumption due to population
growth compare to changes due to climate change. Figure 3 shows the total annual
consumption forecasts for each level of population between 2014/15 and 2024/25 for the
present day, near and far future weather. Figure 3 shows the total annual consumption
Drivers of future water demand in Sydney, Australia
8
All Segments - Annual Consumption (2019/20)
560
540
520
500
480
Consumption Forecast (GL) 460
440
Present
Near Future
Far Future
Figure 2. Consumption forecasts by model (CCCMA3.1 - red, CSIRO-MK3.0 - green,
ECHAM5 blue and MIROC3.2 - orange) showing three ensemble members for each
model. Total consumption for all dwellings types (includes single dwellings, units,
town houses and non-residential). Each bar shows the median (open circle), the range
derived using the stochastic weather generator. Three time periods are shown: the
present, near future and far future and assuming 2019/2020 populations.
increases with population (the overall trend from 2014/15 to 2024/25) and that changes
due to weather between the present (red bars), near future (green bars) and far future
(blue bars) have a relatively small impact relative to the changes due to population. The
increase in median consumption from 2014/15 to 2024/25 due to population increase
over the same period is from 457.1 GL to 507.6 GL (50.5 GL) in the present, 462.6 GL
to 514.5 GL (51.9 GL) in the near future and from 471.2 GL to 524.2 GL (53 GL) in
the far future. The increase in median consumption from the present to the far future
due to climate is between 14.1 GL (2014/15) to 16.6 GL (2024/25). In comparison to
the small increases in consumption shown in Figure 2, the increases due to population
growth are very large. To compare, the climate driven increase between the present and
far future is matched by about 3 years of population growth.
Population growth clearly increases water demand, and dominates the climate
contribution, but how much water demand increases depends on the nature of the
dwellings people occupy. We therefore explored how water demand would vary into
the future if all population growth was accommodated via single dwellings, or via a
mixture of dwellings, or without any single dwellings. In the present, all three planning
options lead to similar median water consumption (Figure 4a) for a given year with the
overall trend upwards between 2014/15 and 2024/25 caused by the population growth.
However, in the present, the variability in the consumption forecast (the length of the
bars for each period) increases as the fraction of single dwellings increases. In the near
future (Figure 4b) there are hints that the median increases as a function of the fraction
Drivers of future water demand in Sydney, Australia
9
All Segments - Annual Consumption
560
540
520
500
480
Consumption Forecast (GL) 460
440
14/15
15/16
16/17
17/18
18/19
19/20
20/21
21/22
22/23
23/24
24/25
Population year
Figure 3. Consumption forecasts across all dwelling types by year for each NARCliM
period (Present - red, Near Future - green, Far Future - blue) for population increases
from 2014/15 to 2024/25. Each bar shows the range across the 12 NARCliM ensemble
members (4 climate models, 3 perturbations).
of single dwellings, and this becomes clearer in the far future (Figure 4c). In addition,
the variability increases markedly as the fraction of single dwellings increase.
We next explain these results in terms of changes in weather variables. Figure 5
shows precipitation, number of days with more than 2mm of precipitation, maximum
temperature and number of days where the temperature exceeds 30◦C. Bias correction
of NARCliM results constrains total precipitation and mean temperature for the present
to be similar to observations (red symbols in Figure 5), but the standard deviations of
each variable are less constrained. The CSIRO-MK3.0 model simulates a reduction in
rainfall in the near and far future, ECHAM5 shows little change for the near future but
increases in the far future, CCCMA3.1 and MIROC3.2 increase in both the near and
far future. The resulting range in NARCliM results shown in Figure 5a is considerable,
with some models predicting decreases of 100 mm y−1 and others predicting increases of
200 mm y−1. This reflects the well-known challenge in climate modeling of constraining
the regional projections of future rainfall and is an uncertainty that is very difficult
to reduce. To add to this uncertainty, Figure 5b shows projections of rainfall events
exceeding 2 mm d−1 range from 70 to 85 days a year with almost no clustering amongst
the models, or by time period. There are projections for both the near and far future
in the range of 70-75 days, and in the range exceeding 80 days.
The projections of maximum temperature (Figure 5c) and days over 30◦C (Figure
5d) clearly depend on the time period associated with the emission scenario. The climate
models provide distinct projections for both temperature metrics, increasing by 0.5◦C
in the near future, through to 1.5-2.0◦C in the far future with reasonable agreement
amongst the models in terms of the maximum temperature change (Figure 5c). The
Drivers of future water demand in Sydney, Australia
10
Present
580
560
540
520
500
480
460
Consumption Forecast (GL)
440
420
14/15
15/16
16/17
17/18
18/19
19/20
20/21
21/22
22/23
23/24
24/25
Population year
(a)
Near Future
580
560
540
520
500
480
460
Consumption Forecast (GL)
440
420
14/15
15/16
16/17
17/18
18/19
19/20
20/21
21/22
22/23
23/24
24/25
Population year
(b)
Far Future
580
560
540
520
500
480
460
Consumption Forecast (GL)
440
420
14/15
15/16
16/17
17/18
18/19
19/20
20/21
21/22
22/23
23/24
24/25
Population year
(c)
Figure 4. Consumption forecasts for the present, near and far future climate as a
function of population growth and the nature of the dwelling type. Red bars indicate
no single dwellings, green indicates the dwelling mixture and blue indicates where all
properties are single dwellings.
Drivers of future water demand in Sydney, Australia
11
(a)
450
viation
350
250
standard de
150
800
900
1000
1100
1200
20
(b)
18
16
viation
14
12
standard de
10
8
70
75
80
0.8
(c)
0.7
viation
0.6
0.5
standard de
0.4
22.5
23.0
23.5
24.0
24.5
16
(d)
14
viation
12
10
standard de
8
35
40
45
50
55
60
65
70
Figure 5. Plots of annual standard deviation versus annual mean of weather variables
for each of the NARCliM ensemble members. (CCCMA3.1 - square, CSIRO-MK3.0
- circle, ECHAM5 - triangle and MIROC3.2 - diamond), (Present - red, Near Future
- green, Far Future - blue). (a) precipitation (mm), (b) number of days > 2mm, (c)
maximum temperature (◦C) and (d) number of days > 30◦C.
Drivers of future water demand in Sydney, Australia
12
number of days over 30◦C increase from 35-40 in the present, to 40-50 days in the
near future to 52-65 days in the far future, highlighting increasing uncertainty based on
climate model choice further into the future.
4. Discussion and Conclusions
In this paper we estimate Sydney’s future water consumption by combining the
physical modelling of NARCliM with water demand modelling using the Sydney Water
Consumption Model (SWCM) via a stochastic weather generator. We can separate the
impact of changes in climate from changes in population through to 2025. We find
that population changes are the dominant driver of increases in future water demand,
increasing demand by 51.9 GL per decade. This contrasts with a far smaller impact
from climate change from the present to the near future of between 1.1 GL and 9.2 GL
based on 2019/20 population. However, there are two caveats to this outcome: first both
drivers act in parallel and thus are additive and second there is no reason why planning
for climate change should pick any single estimate of the increase in consumption and
any one climate scenario can produce a wide range of future consumption forecasts.
The increase in median consumption due to population, approximately 5 GL per
year, is much greater than the increase due to climate change, which ranges between 14.1
GL and 16.6 GL in 70 years. However, for any single year and any one NARCliM climate
period, changes in the weather can produce a large range of consumption forecasts
(Figure 3). Density functions of the difference in consumption forecasts from 2019/20
to 2020/21 (Figure 6) shows that while the median of these differences is a measure of
the increase in consumption due to the increase in population from 2019/20 to 2020/21,
there are examples where the difference between the consumption forecasts is as low as
-31 GL and as high as 43 GL. Indeed, with this pair of financial years for 30-34% of
the time the increase in consumption forecast due to the weather is greater than the
increase due to population. In terms of water demand, Figure 6 shows that using the
median estimates is a very poor basis for managing risk.
The NARCliM product provides estimates of near future and far future climate
from four climate models, each downscaled three times. A bias correction procedure
ensures that the average annual maximum temperatures and total annual number of
wet days are almost identical for all climate models in the present, but due to divergent
future projections there is no such constraint in the near and far future for averages
or variability. We have shown that the three regional simulations driven by the same
climate model provide future climate information with very similar statistical properties.
However, when considering projections driven by the same climate model, the difference
between the near future and present is a poor predictor of the difference between far
future and near future for all weather variables except mean temperature (Figure 5).
For example, in the CSIRO MK3.0 model, precipitation decreases from the present
to the near future by 50-100 mm but increases between the near future and the far
future. In contrast, ECHAM5.0 precipitation changes little from the present to the near
Drivers of future water demand in Sydney, Australia
13
Present
0.31
-40
-20
0
20
40
60
Difference in Consumption Forecasts from 2019/20 to 2020/21 (GL)
Near Future
0.30
-40
-20
0
20
40
60
Difference in Consumption Forecasts from 2019/20 to 2020/21 (GL)
Far Future
0.34
-40
-20
0
20
40
60
Difference in Consumption Forecasts from 2019/20 to 2020/21 (GL)
Figure 6. Density function of difference in consumption forecasts from 2019/20 to
2020/21 for the present, near and far futures.
Solid red line is at the median of
the consumption forecast differences and the dashed red line is at twice that median.
The filled green region represents the consumption forecasts where the increase in
consumption due to the weather is greater than the increase in consumption due to
population. The area of the filled green region is written as a probability in the figure.
Each probability density function was estimated from 1200 consumption forecasts, 100
from each of the 12 NARCliM ensemble members.
Drivers of future water demand in Sydney, Australia
14
future, increases by about 100mm between the near and far future. This is also true
for the standard deviation of weather variables. The standard deviation of maximum
temperature for CCCMA3.1 increases from the present to the near future by about 0.1
and decreases from the near to the far future by about 0.2. In contrast, the standard
deviation of maximum temperature from the CSIRO Mk3.0 model is almost unchanged
from the present to the near future, but increases by 0.2 from the near to the far
future. These results suggest that future climate change will occur non-linearly with
time. Better characterisation of uncertainty in projecting climate-related water demand
requires more global climate models to be downscaled as a priority over downscaling
individual climate models multiple times.
We now combine the changes in climate variables (Figure 5) with the changes in
water demand (Figures 2 and 3). Figure 7 shows the changes in maximum temperature,
precipitation and demand for the present, near future and far future. Consumption tends
to increase with temperature and decrease weakly as precipitation increases and there
is a major increase in demand from the relatively cool present, to the relatively warm
far future. In the present, the median forecasts of water consumption are all around
484 GL due to the bias correction process used. While the change in rainfall between
the present and the near future (Figure 5a) affects water consumption, forecasts remain
between 484-494 GL that is the forecasts are relatively insensitive to the precipitation
change (Figure 7). In the far future, forecasts remain similar (495-505 GL) but some
models are always on the dry-end of the range (CSIRO-MK3.0), some commonly in
the centre (ECHAM5) and some at the upper end (MIROC3.0) but demand does not
respond to changes in precipitation strongly. This is reassuring given Figure 5a showed
changes in rainfall to be uncertain. In contrast, the increasing maximum temperature
drives demand such that consumption is clearly higher in the far future than in the near
future or the present.
A key implication of our results is that if we take median climate projections from
the NARCliM product and use them to project water consumption, the impact of
climate change in the near future and far future are small compared to population
growth.
We can quantify this in terms of the ratio of dam capacity to metered
consumption at 2019/20 population levels. Sydney’s water supply is considerable and at
maximum capacity is of order 2,582 GL (https://www.waternsw.com.au/supply/dam-
levels/greater-sydneys-dam-levels).
Using the median estimate of demand for the
present day (484.4 GL), this represents about 5.3 years of storage. This decreases under
the single climate model, maximum consumption scenario (509.8 GL) to 5.1 years of
storage. Taking changes in climate into account and considering the near future, the
median estimate of demand (489.9 GL) represents 5.3 years of storage and under the
most extreme weather scenario consumption reaches 517.0 GL but there is still 5.0
years of storage. Note that the years of storage ratios calculated here are not intended
as precise estimates of the length of available water supply because they do not take
into account the ∼57GL per year of un-metered consumption, or any water loss due to
evaporation. They are also not adjusted to account for the desalination plant, opened
Drivers of future water demand in Sydney, Australia
15
510
505
500
495
Consumption
490
485
480
25
24.5
24
23.5
23
1400
1300
Temperature
22.5
1200
1100
1000
22
900
800
Precipitation
(a)
26.5
520
510
26
25.5
510
520
500
25
510
E
24.5
510
500
S
E
E
500 C
S
M
24
S
C
C
M
M
490
500
23.5
490
Maximum Temperature
S
E
S
E E
S
23
C
C
490
M
C
M
M
480
22.5
C S
M C
CS
E
E
E M
M
480
22
490
21.5
470
480
400
600
800
1000
1200
1400
1600
1800
Precipitation
(b)
Figure 7. (a) 3D bar chart map of consumption forecasts from all NARCliM ensemble
members for the financial year 2019/20 as a function of precipitation and maximum
temperature. The NARCliM periods are indicated by red for the present, green the
near future and blue for the far future; (b) Contour map of consumption forecasts
from all NARCliM ensemble members for the financial year 2019/20 as a function of
precipitation and maximum temperature. Letters represent the average precipitation
and maximum temperature for each ensemble member over 100 weather scenarios. The
NARCliM models are indicated by the letters C for CCCMA3.1, S for CSIRO-MK3.0,
E for ECHAM5 and M for MIROC3.0. The NARCliM periods are indicated by the
colours red for the present, green for the near future and blue for the far future.
Drivers of future water demand in Sydney, Australia
16
in 2010, which has a current capacity of about 90 GL per year and the ability to be
extended to 180 GL per year. Despite these caveats, in a climate influenced by the El
Nino-Southern Oscillation which is associated with above and below normal rainfall over
south eastern Australia, the reduction in the effective storage implied by the combination
of population growth and climate change increases the vulnerability of Sydney’s water
supply.
We conclude by noting that the dominant driver of Sydney’s water demand is
population not climate change. However, we have not examined the impact of climate
change on supply; water storage for Sydney is very sensitive to the frequency of east
coast lows that provide the key synoptic scale mechanism to fill water storages (Pepler
& Rakich 2010). If these systems changed in frequency or magnitude they would have
a profound impact on water storage and could significantly change the vulnerability of
Sydney to climate change. In the absence of changes in water supply, our results point
to two drivers of changes in water demand for Sydney, population and climate change,
acting in parallel to reduce the storage in the near future significantly. We do not
attempt to estimate the impact of population change in the far future and interpolating
the population changes relevant to the near future into the far future is infeasible given
the likely impact of technological innovation on water demand and supply management.
Acknowledgments
We acknowledge the NSW Office of Environment and Heritage backed NSW/ACT
Regional Climate Modelling Project (NARCliM) project for providing the climate
projection data.
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