Climatic Change

, Volume 148, Issue 1–2, pp 139–153 | Cite as

Spatio-temporal quantitative links between climatic extremes and population flows: a case study in the Murray-Darling Basin, Australia

  • K. Shuvo Bakar
  • Huidong Jin


A growing body of research shows that extreme climatic events, e.g. heatwave, rainstorms and droughts, are becoming more frequent and intensified across various regions of the world. Australia is not isolated from these changes with marked increase in both rainfall and temperature extremes. Inherently, we understand that exposure to these extreme events could encourage decisions about population flow, and quantifying this linkage is challenging, especially for communities in small areas with an average of 10,000 people. Using spatio-temporal statistical techniques, this paper examines the possible environmental and socio-economic drivers associated with population flows of small communities as well as the possible predictive scenarios due to the effects introduced by climatic extremes. The analysis has been undertaken for a case-study region in the Murray-Darling Basin, Australia, where the economy is underpinned by agriculture and is sensitive to climate variability and extremes. The analysis reveals that in addition to the socio-economic factors, the environmental variables have a statistically significant association on shaping the distribution of the population flows in the study area. This statistical analysis can direct further data collection and causality analysis and be beneficial for policy makers, stakeholders and local communities to work together to adapt the Basin to climate extremes and changes.


Funding information

We thank the three reviewers for their important comments. We thank Professor James Raymer, Department of Demography, ANU; Dr Nicholas Biddle, CSR&M, ANU and Mr Steven Crimp, Climate Change Institute, ANU for their valuable comments on an earlier version of this paper. We also thank Mr Joe Meehan from the Department of Employment, Australia, for providing the unemployment rate data. The work was partially funded by the CSR&M, ANU, CSIRO DigiScape future science platform, and the Digital Agriculture initiative.

Supplementary material

10584_2018_2182_MOESM1_ESM.docx (310 kb)
ESM 1 (DOCX 310 kb)


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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.CSR&MAustralian National UniversityCanberraAustralia
  2. 2.Data 61, CSIROCanberraAustralia

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