Machine Learning Identification of Attributes and Predictors for a Flash Drought in Eastern Australia

Speer, M; Hartigan, J; Leslie, LM

Machine Learning Identification of Attributes and Predictors for a Flash Drought in Eastern Australia

Speer, M

Hartigan, J Leslie, LM

Permalink

Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Climate, 12, (4), pp. 49-49

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (3.26 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Speer, M https://orcid.org/0000-0003-0421-7744
dc.contributor.author	Hartigan, J
dc.contributor.author	Leslie, LM
dc.date.accessioned	2024-04-10T05:22:26Z
dc.date.available	2024-04-10T05:22:26Z
dc.identifier.citation	Climate, 12, (4), pp. 49-49
dc.identifier.issn	2225-1154
dc.identifier.uri	http://hdl.handle.net/10453/177644
dc.description.abstract	<jats:p>Flash droughts (FDs) are natural disasters that strike suddenly and intensify quickly. They occur almost anywhere, anytime of the year, and can have severe socio-economic, health and environmental impacts. This study focuses on a recent FD that began in the cool season of the Upper Hunter region of Eastern Australia, an important energy and agricultural local and global exporter that is both flood- and drought-prone. Here, the authors investigate the FD that started abruptly in May 2023 and extended to October 2023. The FD followed floods in November 2021 and much above-average May–October 2022 rainfall. Eight machine learning (ML) regression techniques were applied to the 60 May–October periods from 1963–2022, using a rolling windows attribution search from 45 possible climate drivers, both individually and in combination. The six most prominent climate drivers, and likely predictors, provide an understanding of the major contributors to the FD. Next, the 1963–2022 data were divided into two shorter timespans, 1963–1992 and 1993–2022, generally accepted as representing the early and accelerated global warming periods, respectively. The key attributes were markedly different for the two timespans. These differences are readily explained by the impacts of global warming on hemispheric and synoptic-scale atmospheric circulations.</jats:p>
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Climate
dc.relation.isbasedon	10.3390/cli12040049
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	3701 Atmospheric sciences
dc.subject.classification	3702 Climate change science
dc.subject.classification	4101 Climate change impacts and adaptation
dc.title	Machine Learning Identification of Attributes and Predictors for a Flash Drought in Eastern Australia
dc.type	Journal Article
utslib.citation.volume	12
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access	*
dc.date.updated	2024-04-10T05:22:24Z
pubs.issue	4
pubs.publication-status	Published online
pubs.volume	12
utslib.citation.issue	4

Abstract:

Flash droughts (FDs) are natural disasters that strike suddenly and intensify quickly. They occur almost anywhere, anytime of the year, and can have severe socio-economic, health and environmental impacts. This study focuses on a recent FD that began in the cool season of the Upper Hunter region of Eastern Australia, an important energy and agricultural local and global exporter that is both flood- and drought-prone. Here, the authors investigate the FD that started abruptly in May 2023 and extended to October 2023. The FD followed floods in November 2021 and much above-average May–October 2022 rainfall. Eight machine learning (ML) regression techniques were applied to the 60 May–October periods from 1963–2022, using a rolling windows attribution search from 45 possible climate drivers, both individually and in combination. The six most prominent climate drivers, and likely predictors, provide an understanding of the major contributors to the FD. Next, the 1963–2022 data were divided into two shorter timespans, 1963–1992 and 1993–2022, generally accepted as representing the early and accelerated global warming periods, respectively. The key attributes were markedly different for the two timespans. These differences are readily explained by the impacts of global warming on hemispheric and synoptic-scale atmospheric circulations.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/177644