Journal of Water and Climate Change (Jun 2021)

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

  • Adrian Barker,
  • Andrew Pitman,
  • Jason P. Evans,
  • Frank Spaninks,
  • Luther Uthayakumaran

DOI
https://doi.org/10.2166/wcc.2020.230
Journal volume & issue
Vol. 12, no. 4
pp. 1168 – 1183

Abstract

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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. HIGHLIGHTS This paper combines an urban water consumption model, regional climate models and a stochastic weather generator to generate probabilistic consumption forecasts.; This paper analyses the effect of climate change on urban water consumption.; This paper compares the relative effect of climate change and population on urban water consumption.; This paper analyses the effect of dwelling type on urban water consumption.; This paper compares the statistical properties of weather variables from different regional climate models.;

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