Attribution and Prediction of Precipitation and Temperature Trends within the Sydney Catchment Using Machine Learning

Hartigan, J; MacNamara, S; Leslie, LM; Speer, M

Attribution and Prediction of Precipitation and Temperature Trends within the Sydney Catchment Using Machine Learning

Hartigan, J

MacNamara, S Leslie, LM Speer, M

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Climate, 2020, 8, (10), pp. 120-120
Issue Date:: 2020-10-20

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Field	Value	Language
dc.contributor.author	Hartigan, J https://orcid.org/0000-0002-7783-9440
dc.contributor.author	MacNamara, S
dc.contributor.author	Leslie, LM
dc.contributor.author	Speer, M https://orcid.org/0000-0003-0421-7744
dc.date.accessioned	2020-11-19T05:20:19Z
dc.date.available	2020-11-19T05:20:19Z
dc.date.issued	2020-10-20
dc.identifier.citation	Climate, 2020, 8, (10), pp. 120-120
dc.identifier.issn	2225-1154
dc.identifier.issn	2225-1154
dc.identifier.uri	http://hdl.handle.net/10453/144165
dc.description.abstract	<jats:p>Droughts in southeastern Australia can profoundly affect the water supply to Sydney, Australia’s largest city. Increasing population, a warming climate, land surface changes and expanded agricultural use increase water demand and reduce catchment runoff. Studying Sydney’s water supply is necessary to manage water resources and lower the risk of severe water shortages. This study aims at understanding Sydney’s water supply by analysing precipitation and temperature trends across the catchment. A decreasing trend in annual precipitation was found across the Sydney catchment area. Annual precipitation also is significantly less variable, due to fewer years above the 80th percentile. These trends result from significant reductions in precipitation during spring and autumn, especially over the last 20 years. Wavelet analysis was applied to assess how the influence of climate drivers has changed over time. Attribute selection was carried out using linear regression and machine learning techniques, including random forests and support vector regression. Drivers of annual precipitation included Niño3.4, Southern Annular Mode (SAM) and DMI, and measures of global warming such as the Tasman Sea sea surface temperature anomalies. The support vector regression model with a polynomial kernel achieved correlations of 0.921 and a skill score compared to climatology of 0.721. The linear regression model also performed well with a correlation of 0.815 and skill score of 0.567, highlighting the importance of considering both linear and non-linear methods when developing statistical models. Models were also developed on autumn and winter precipitation but performed worse than annual precipitation on prediction. For example, the best performing model on autumn precipitation, which accounts for approximately one quarter of annual precipitation, achieved an RMSE of 418.036 mm2 on the testing data, while annual precipitation achieved an RMSE of 613.704 mm2. However, the seasonal models provided valuable insights into whether the season would be wet or dry compared to the climatology.</jats:p>
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Climate
dc.relation.isbasedon	10.3390/cli8100120
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Attribution and Prediction of Precipitation and Temperature Trends within the Sydney Catchment Using Machine Learning
dc.type	Journal Article
utslib.citation.volume	8
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access	*
dc.date.updated	2020-11-19T05:20:12Z
pubs.issue	10
pubs.publication-status	Published online
pubs.volume	8
utslib.citation.issue	10

Abstract:

Droughts in southeastern Australia can profoundly affect the water supply to Sydney, Australia’s largest city. Increasing population, a warming climate, land surface changes and expanded agricultural use increase water demand and reduce catchment runoff. Studying Sydney’s water supply is necessary to manage water resources and lower the risk of severe water shortages. This study aims at understanding Sydney’s water supply by analysing precipitation and temperature trends across the catchment. A decreasing trend in annual precipitation was found across the Sydney catchment area. Annual precipitation also is significantly less variable, due to fewer years above the 80th percentile. These trends result from significant reductions in precipitation during spring and autumn, especially over the last 20 years. Wavelet analysis was applied to assess how the influence of climate drivers has changed over time. Attribute selection was carried out using linear regression and machine learning techniques, including random forests and support vector regression. Drivers of annual precipitation included Niño3.4, Southern Annular Mode (SAM) and DMI, and measures of global warming such as the Tasman Sea sea surface temperature anomalies. The support vector regression model with a polynomial kernel achieved correlations of 0.921 and a skill score compared to climatology of 0.721. The linear regression model also performed well with a correlation of 0.815 and skill score of 0.567, highlighting the importance of considering both linear and non-linear methods when developing statistical models. Models were also developed on autumn and winter precipitation but performed worse than annual precipitation on prediction. For example, the best performing model on autumn precipitation, which accounts for approximately one quarter of annual precipitation, achieved an RMSE of 418.036 mm2 on the testing data, while annual precipitation achieved an RMSE of 613.704 mm2. However, the seasonal models provided valuable insights into whether the season would be wet or dry compared to the climatology.

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http://hdl.handle.net/10453/144165