Drivers of future water demand in Sydney, Australia: examining the contribution from population and climate change

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. Projected climate change would increase median metered consumption, at 2019/2020 population levels, from around 484 GL under present climate to 484–494 GL under near future climate and 495–505 GL under far future climate. Population changes from 2014/2015 to 2024/2025 have a far larger impact, increasing median metered consumption from 457 to 508 GL under the present climate, 463 to 515 GL under near future climate and from 471 to 524 GL under far future climate. The projected changes in consumption are sensitive to the climate model used. Overall, while population growth is a far stronger driver of increasing water 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 therefore lead to overconfidence in the reliability of Sydney’s water supply.


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 ; Hoekstra et al. ). Long-term planning for future water demand needs a mixture of social science, providing an understanding of how population growth (Polebitski & Palmer ), economic development (Tortajada & Joshi ) and social factors (Schleich & Hillenbrand ) will change over time, combined with the physical science challenge of predicting future regional patterns of weather and climate. These lead to an increasing demand for better information to plan engineering and policy actions to reduce demand or increase the supply of water and thereby help the management of water resources in a changing environment (Padula et al. ). 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. In this paper, we examine future water demand in the area serviced by Sydney Water, (Figure 1). A model for the water supply to Sydney, WATHNET, has been developed by Water NSW, the operator of water supply systems throughout NSW including the Sydney basin (WaterNSW ).
Future changes in average temperature and precipitation (Griffin & Chang ), 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 ). 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 (GCMs) remains coarser than 1 × 1 making their direct use for city-scale projections of future climate difficult. This is a significant problem for Sydney, which has a varying topography and a strong temperature and rainfall gradient from the coast, across the Sydney Basin, through to the location of the main water storages west of Warragamba ( Figure 1).
Solutions to help link coarse global models with scales relevant to major cities include dynamical downscaling.
This approach is now widespread (see reviews by Fowler et al. () and Ekström et al. ()) 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 et al. , ).
In this paper, we bring together a major dynamical 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 for Australia's largest city.   The SWCM model predicts the water consumption at a residential property based on the dwelling type, compliance with the Building Sustainability Index 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 is 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.

METHODOLOGY
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 2 mm (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 shown in The GCMs used were the CCCMA CGCM3.1(T74),    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 exceed 1-2%.
We next examine how future changes in water consumption due to population growth compared to changes due to climate change. Figure   We next explain these results in terms of changes in weather variables. Figure 6 shows precipitation, number of days with more than 2 mm 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 6)  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 7 shows that using the median estimates is very likely a poor basis for managing risk.
The NARCliM product provides estimates of near future and far future climate from four climate models, each strongly. This is reassuring given Figure 6(a) showed changes in rainfall to be uncertain. However, we remind the reader that the SWCM does not currently include some higher-order rainfall statistics, such as dry spells. It is conceivable that given climate change, the length of dry spells will change in ways that increase water demand and this is not reflected in our results. In contrast to the insensitivity to the change in rainfall, 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/ 2020 population levels. Sydney's water supply is considerable and at maximum capacity is of the order of 2,582 GL   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 note that throughout this discussion, we have highlighted ranges in future demand and a water manager might ask 'but which one should be used.' There is no answer to this question because uncertainty is inherent in the climate projections, the population changes, the technological innovation and so on. At this time, each of the water demand estimates is equally probable. Whether a water manager takes a precautionary approach and uses the worst scenario or hopes for the best and uses the least confronting scenario is not something we can recommend. We note that the trajectory for climate science projections is toward much larger ensembles and our recommendation is that the software engineering linked with water demand modelling should enable water planners to use all forthcoming climate simulations and explore how changes in individual variables drive water demand sensitivity. This knowledge can then inform decision making using an evidence-based approach and utilising all available information.
We conclude by noting that, based on our results, 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 ). 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 unfeasible given the likely impact of technological innovation on water demand and supply management.