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

Abstract

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.

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Notes

  1. 1.

    http://www.mdba.gov.au/discover-basin/people/economy-basin

  2. 2.

    http://stat.data.abs.gov.au/Index.aspx?DataSetCode=RIME_REGION. Our population flow data, ideally should, but cannot, include overseas migration first settlement data because of two reasons. One is that the majority of oversea migrants settled in urban areas with a population cluster of 100,000 or more people (http://www.abs.gov.au/ausstats/abs@.nsf/lookup/4102.0main+features102014). Secondly, there lacks of yearly data about overseas migration settlement at the SA2 level.

  3. 3.

    http://www.abs.gov.au/ausstats/abs@.nsf/Latestproducts/88F6A0EDEB8879C0CA257801000C64D9

  4. 4.

    http://www.abs.gov.au/ausstats/abs@.nsf/Latestproducts/5C874AA51936C94FCA257801000C6467?opendocument

  5. 5.

    http://www.bom.gov.au/climate/how/newproducts/map-periods.shtml

References

  1. Abel GJ, Sander N (2014) Quantifying global international migration flows. Science 343(6178):1520–1522

    Article  Google Scholar 

  2. ABS (2008) Water and the Murray-Darling basin: a statistical profile 2000–01 to 2005–06. Retrieved from

  3. Adger WN, Dessai S, Goulden M, Hulme M, Lorenzoni I, Nelson DR, … Wreford A (2009). Are there social limits to adaptation to climate change? Clim Chang, 93(3–4): 335–354

  4. Adger WN, Arnell NW, Black R, Dercon S, Geddes A, Thomas DS (2015) Focus on environmental risks and migration: causes and consequences. Environ Res Lett 10(6):060201

    Article  Google Scholar 

  5. Bakar KS (2017) Bayesian Gaussian models for interpolating large-dimensional data at misaligned areal units. In: Syme G, MacDonald D, Fulton B, Piantadosi J (eds) 22nd International congress on modelling and simulation. Thomson-Reuters, Toronto, pp 95–91

    Google Scholar 

  6. Bakar KS, Kokic P (2017) Bayesian Gaussian models for point referenced spatial and spatio-temporal data. J Stat Res 51(1):17–40

    Google Scholar 

  7. Bakar KS, Sahu SK (2015) spTimer: Spatio-temporal Bayesian modelling using R. J Stat Softw 63(15):1–32

    Article  Google Scholar 

  8. Bakar KS, Kokic P, Jin H (2015) A spatiodynamic model for assessing frost risk in south-eastern Australia. J R Stat Soc Ser C Appl Stat 64:755–778

    Article  Google Scholar 

  9. Bakar KS, Kokic P, Jin H (2016) Hierarchical spatially varying coefficient and temporal dynamic process models using spTDyn. J Stat Comput Simul 86(4):820–840

    Article  Google Scholar 

  10. Banerjee S, Carlin BP, Gelfand AE (2014) Hierarchical modeling and analysis for spatial data. CRC Press, Boca Raton

    Google Scholar 

  11. Black R, Adger WN, Arnell NW, Dercon S, Geddes A, Thomas D (2011) The effect of environmental change on human migration. Glob Environ Chang 21:S3–S11

    Article  Google Scholar 

  12. Call MA, Gray C, Yunus M, Emch M (2017) Disruption, not displacement: environmental variability and temporary migration in Bangladesh. Glob Environ Chang 46:157–165

    Article  Google Scholar 

  13. Clark TS, Linzer DA (2015) Should I use fixed or random effects? Pol Sci Res Methods 3(2):399–408

    Article  Google Scholar 

  14. Cressie N, Wikle CK (2011) Statistics for spatio-temporal data. Wiley, Amsterdam

    Google Scholar 

  15. Crimp S, Bakar KS, Kokic P, Jin H, Nicholls N, Howden M (2015) Bayesian space–time model to analyse frost risk for agriculture in Southeast Australia. Int J Climatol 35(8):2092–2108

    Article  Google Scholar 

  16. Cross JA (2014) Disaster devastation of US communities: long-term demographic consequences. Environ Hazards 13(1):73–91

    Article  Google Scholar 

  17. Curran SR, Meijer-Irons J (2014) Climate variability, land ownership and migration: evidence from Thailand about gender impacts. Wash J Environ Law Policy 4(1):37

    Google Scholar 

  18. Cutter SL, Boruff BJ, Shirley WL (2003) Social vulnerability to environmental hazards. Soc Sci Q 84(2):242–261

    Article  Google Scholar 

  19. Davis J, Sellers S, Gray C, Bilsborrow R (2017) Indigenous migration dynamics in the Ecuadorian Amazon: a longitudinal and hierarchical analysis. J Dev Stud 53(11):1849–1864

    Article  Google Scholar 

  20. Drees L, Liehr S (2015) Using Bayesian belief networks to analyse social-ecological conditions for migration in the Sahel. Glob Environ Chang 35:323–339

    Article  Google Scholar 

  21. Duncan L, Alastair R, Gary N (2016) CARBayesST: Spatio-temporal generalised linear mixed models for areal unit data. R package version 2.4

  22. Elliott JR (2014) Natural hazards and residential mobility: general patterns and racially unequal outcomes in the United States. Soc Forces 93(4):1723–1747

    Article  Google Scholar 

  23. Fussell E, Curran SR, Dunbar MD, Babb MA, Thompson L, Meijer-Irons J (2017) Weather-related hazards and population change: a study of hurricanes and tropical storms in the United States, 1980–2012. ANNALS Am Acad Pol Soc Sci 669(1):146–167

    Article  Google Scholar 

  24. Gray C, Mueller V (2012) Natural disasters and population mobility in Bangladesh. Proc Natl Acad Sci 109(16):6000–6005

    Article  Google Scholar 

  25. Gutmann MP, Field V (2010) Katrina in historical context: environment and migration in the US. Popul Environ 31(1–3):3–19

    Article  Google Scholar 

  26. Hugo G (2011) Future demographic change and its interactions with migration and climate change. Glob Environ Chang 21:S21–S33

    Article  Google Scholar 

  27. Jones B, O’Neill BC, McDaniel L, McGinnis S, Mearns LO, Tebaldi C (2015) Future population exposure to US heat extremes. Nat Clim Chang 5(7):652–655

    Article  Google Scholar 

  28. Koubi V, Stoll S, Spilker G (2016) Perceptions of environmental change and migration decisions. Clim Chang 138(3–4):439–451

    Article  Google Scholar 

  29. Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18–22

    Google Scholar 

  30. Mallick B, Vogt J (2014) Population displacement after cyclone and its consequences: empirical evidence from coastal Bangladesh. Nat Hazards 73(2):191–212

    Article  Google Scholar 

  31. Massey DS, Arango J, Hugo G, Kouaouci A, Pellegrino A, Taylor JE (1993) Theories of international migration: a review and appraisal. Popul Dev Rev 19:431–466

    Article  Google Scholar 

  32. McLeman R, Smit B (2006) Migration as an adaptation to climate change. Clim Chang 76(1–2):31–53

    Article  Google Scholar 

  33. McLeman R, Herold S, Reljic Z, Sawada M, McKenney D (2010) GIS-based modeling of drought and historical population change on the Canadian prairies. J Hist Geogr 36(1):43–56

    Article  Google Scholar 

  34. Moran PA (1950) Notes on continuous stochastic phenomena. Biometrika 37(1/2):17–23

    Article  Google Scholar 

  35. Morss RE, Wilhelmi OV, Meehl GA, Dilling L (2011) Improving societal outcomes of extreme weather in a changing climate: an integrated perspective. Annu Rev Environ Resour 36:1–25

    Article  Google Scholar 

  36. Pachauri RK, Allen MR, Barros V, Broome J, Cramer W, Christ R, … Dasgupta P (2014) Climate change 2014: synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change: IPCC

  37. Pais JF, Elliott JR (2008) Places as recovery machines: vulnerability and neighborhood change after major hurricanes. Soc Forces 86(4):1415–1453

    Article  Google Scholar 

  38. Potter N, Chiew F, Frost A (2010) An assessment of the severity of recent reductions in rainfall and runoff in the Murray–Darling Basin. J Hydrol 381(1):52–64

    Article  Google Scholar 

  39. Quiggin J, Adamson D, Chambers S, Schrobback P (2010) Climate change, uncertainty, and adaptation: the case of irrigated agriculture in the Murray–Darling Basin in Australia. Can J Agric Econ/Revue canadienne d'agroeconomie 58(4):531–554

    Article  Google Scholar 

  40. Sahu SK, Bakar KS (2012) A comparison of Bayesian models for daily ozone concentration levels. Stat Methodol 9(1):144–157

    Article  Google Scholar 

  41. Sahu SK, Bakar KS, Awang N (2015) Bayesian forecasting using spatiotemporal models with applications to ozone concentration levels in the eastern United States. Geom Driven Stat 121:260

    Article  Google Scholar 

  42. Schultz J, Elliott JR (2013) Natural disasters and local demographic change in the United States. Popul Environ 34(3):293–312. https://doi.org/10.1007/s11111-012-0171-7

    Article  Google Scholar 

  43. Shao Q, Chan C, Jin H, Barry S (2012) Statistical justification of hillside farm dam distribution in eastern Australia. Water Resour Manag 26(11):3139–3151

    Article  Google Scholar 

  44. Shumway JM, Otterstrom S, Glavac S (2014) Environmental hazards as disamenities: selective migration and income change in the United States from 2000–2010. Ann Assoc Am Geogr 104(2):280–291

    Article  Google Scholar 

  45. Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A (2002) Bayesian measures of model complexity and fit. J R Stat Soc Ser B Stat Methodol 64(4):583–639

    Article  Google Scholar 

  46. Thiede BC, Gray CL (2017) Heterogeneous climate effects on human migration in Indonesia. Popul Environ 39(2):147–172

    Article  Google Scholar 

  47. Wei Y, Langford J, Willett IR, Barlow S, Lyle C (2011) Is irrigated agriculture in the Murray Darling Basin well prepared to deal with reductions in water availability? Glob Environ Chang 21(3):906–916

    Article  Google Scholar 

  48. Zander KK, Surjan A, Garnett ST (2016) Exploring the effect of heat on stated intentions to move. Clim Chang 138(1–2):297–308

    Article  Google Scholar 

Download references

Funding

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.

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Correspondence to K. Shuvo Bakar.

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Bakar, K.S., Jin, H. Spatio-temporal quantitative links between climatic extremes and population flows: a case study in the Murray-Darling Basin, Australia. Climatic Change 148, 139–153 (2018). https://doi.org/10.1007/s10584-018-2182-6

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