Uniqueness and Causes of the California Drought

Richman, MB; Leslie, LM

Uniqueness and Causes of the California Drought

Richman, MB Leslie, LM

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Publication Type:: Journal Article
Citation:: Procedia Computer Science, 2015, 61 pp. 428 - 435
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Richman, MB	en_US
dc.contributor.author	Leslie, LM https://orcid.org/0000-0001-5616-2330	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Procedia Computer Science, 2015, 61 pp. 428 - 435	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120653
dc.description.abstract	© 2015 The Authors. Published by Elsevier B.V. The current California drought, which is part of the abnormal to extreme drought conditions affecting much of southwest USA, has lasted for 4 years (2011/12 - 2014/15). It has intensified steadily to what at present is likely the worst Californian drought since reliable instrumental records began in 1895. The uniqueness of this drought is demonstrated by assessing the Oct. - Mar. wet seasons for instances <25th percentile of precipitation and >75th percentile in average temperature. Of the 8 seasons since 1895 that met these percentile conditions, only the present drought satisfied these criteria for more than one season. Predictions of California precipitation and temperature anomalies were made using linear regression (LR), and support vector regression (SVR) with several linear and non-linear kernels, applied to a range of climate drivers and local sea surface temperatures (SSTs). Cross-validated correlations were low (LR) to moderate (SVR) for precipitation, but were high (>0.7) for both temperature LR and SVR, with SVR marginally exceeding LR. The leading predictors were global warming and local SSTs near the California coast. Finally, the cool seasons were classified as dry/not-dry and hot/not-hot using logistic regressions and k-means classification clustering. Again, it was found that predictability was low for dry/not-dry classes but was high (>70% correct) for hot/not-hot classes. This research suggests that the climate system has warmed sufficiently so that drought can no longer be assessed solely by the lack of precipitation, but must consider the combination of low precipitation and abnormal warmth.	en_US
dc.relation.ispartof	Procedia Computer Science	en_US
dc.relation.isbasedon	10.1016/j.procs.2015.09.181	en_US
dc.title	Uniqueness and Causes of the California Drought	en_US
dc.type	Journal Article
utslib.citation.volume	61	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	10 Technology	en_US
utslib.for	0899 Other Information and Computing Sciences	en_US
utslib.for	1001 Agricultural Biotechnology	en_US
pubs.embargo.period	Not known	en_US
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
pubs.publication-status	Published	en_US
pubs.volume	61	en_US

Abstract:

© 2015 The Authors. Published by Elsevier B.V. The current California drought, which is part of the abnormal to extreme drought conditions affecting much of southwest USA, has lasted for 4 years (2011/12 - 2014/15). It has intensified steadily to what at present is likely the worst Californian drought since reliable instrumental records began in 1895. The uniqueness of this drought is demonstrated by assessing the Oct. - Mar. wet seasons for instances <25th percentile of precipitation and >75th percentile in average temperature. Of the 8 seasons since 1895 that met these percentile conditions, only the present drought satisfied these criteria for more than one season. Predictions of California precipitation and temperature anomalies were made using linear regression (LR), and support vector regression (SVR) with several linear and non-linear kernels, applied to a range of climate drivers and local sea surface temperatures (SSTs). Cross-validated correlations were low (LR) to moderate (SVR) for precipitation, but were high (>0.7) for both temperature LR and SVR, with SVR marginally exceeding LR. The leading predictors were global warming and local SSTs near the California coast. Finally, the cool seasons were classified as dry/not-dry and hot/not-hot using logistic regressions and k-means classification clustering. Again, it was found that predictability was low for dry/not-dry classes but was high (>70% correct) for hot/not-hot classes. This research suggests that the climate system has warmed sufficiently so that drought can no longer be assessed solely by the lack of precipitation, but must consider the combination of low precipitation and abnormal warmth.

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http://hdl.handle.net/10453/120653