Water Conservation Technologies

Kumar, Arun

doi:10.1016/b978-0-12-812210-5.00002-x

Cited by 3 publications

(1 citation statement)

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“…It was the driest three-year period since comprehensive instrumental records began in 1911 (11)(12)(13) and demonstrated the potential for drought events to contribute to cascading and compounding impacts across socio-economic and natural sectors (14). The 2017-2019 drought brought rural townships to the brink of running out of water (15), caused severe agricultural losses (16), and threatened the water supply of Australia's largest city, Sydney (17). The drought culminated in catastrophic forest fires in the spring and summer of 2019/2020 that burnt more than 5.8 million hectares of forest (13,18).…”

Section: Introductionmentioning

confidence: 99%

Australia’s Tinderbox Drought: an extreme natural event likely worsened by human-caused climate change

Devanand,

Falster,

Gillett

et al. 2023

Preprint

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We examine the characteristics and causes of southeast Australia’s Tinderbox Drought (2017–2019) that preceded the Black Summer fire disaster. The Tinderbox Drought was characterised by cool season rainfall deficits of around –50% in three consecutive years, which was exceptionally unlikely in the context of natural variability alone. The precipitation deficits were initiated and sustained by an anomalous atmospheric circulation that diverted oceanic moisture away from the region, despite traditional indicators of increased drought risk in southeast Australia generally being in neutral states. Moisture deficits were later intensified by unusually high temperatures, high vapour pressure deficits and sustained reductions in terrestrial water availability. Anthropogenic forcing intensified the rainfall deficits of the Tinderbox Drought by around 18% with an interquartile range of 34.9% to –13.3% highlighting the considerable uncertainty in attributing droughts of this kind to human activity. Skillful predictability of this drought was possible by incorporating multiple remote and local predictors through machine learning, providing prospects for improving forecasting of multi-year droughts.

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