Characterising the seasonal nature of meteorological drought onset and termination across Australia
A. J. Gibson
A * , D. C. Verdon-Kidd B and G. R. Hancock B
+ Author Affiliations
- Author Affiliations
A Faculty of Science and Engineering, Southern Cross University, Lismore, NSW, Australia.
B School of Environmental and Life Sciences, The University of Newcastle, Callaghan, NSW, Australia.
* Correspondence to: abe.gibson@scu.edu.au
Journal of Southern Hemisphere Earth Systems Science 72(1) 38-51 https://doi.org/10.1071/ES21009
Submitted: 23 April 2021 Accepted: 28 December 2021 Published: 8 February 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of BoM. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
Drought, and its associated impacts, represents one of the costliest natural hazards worldwide, highlighting the need for prediction and preparedness. While advancements have been made in monitoring current droughts, prediction of onset and termination have proven to be much more challenging. This is because drought is unlike any other natural hazard and cannot be characterised by a single weather event. There is also a high degree of spatial variability in this phenomenon across the vast expanse of the Australian continent. Therefore, by characterising regionally specific expressions of drought, we may improve drought predictability. In this study, we analyse the timing of onset and termination of meteorological droughts across Australia from 1900 to 2015, as well as their local and regional climate controls. We show that meteorological drought onset has a strong seasonal signature across Australia that varies spatially, whereas termination is less seasonally restricted. Using a Random Forest modelling approach with predictor variables representative of large-scale ocean-atmosphere phenomena and local climate, up to 75% of the variance in the Standardised Precipitation Index during both onset and termination could be explained. This study offers support to continued development in long-lead forecasting of local and large-scale ocean/atmosphere conditions to improve drought prediction in Australia and elsewhere.
Keywords: drought management, drought onset, drought prediction, drought termination, machine learning, meteorological drought, random forest, Standardised Precipitation Index.
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