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A spatiodynamic model for assessing frost risk in south-eastern Australia

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  • K. Shuvo Bakar
  • Philip Kokic
  • Huidong Jin

Abstract

type="main" xml:id="rssc12103-abs-0001"> Previous climate research concluded that causal influences which have contributed to changes in frost risk in south-eastern Australia include greenhouse gas concentration, El-Niño southern oscillation and other effects. Some of the climatic indices representing these effects have spatiotemporal misalignment and may have a spatially and temporally varying effect on observed data. Other indices are constructed from grid-referenced physical models, which creates a point-to-area problem. To address these issues we use a spatiodynamic model, which comprises a blending of spatially varying and temporally dynamic parameters. For the data that we examine the model proposed performs well in out-of-sample validation compared with a spatiotemporal model.

Suggested Citation

  • K. Shuvo Bakar & Philip Kokic & Huidong Jin, 2015. "A spatiodynamic model for assessing frost risk in south-eastern Australia," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(5), pages 755-778, November.
    • Handle: RePEc:bla:jorssc:v:64:y:2015:i:5:p:755-778
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    File URL: http://hdl.handle.net/10.1111/rssc.2015.64.issue-5
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    Cited by:

    1. K. Shuvo Bakar & Huidong Jin, 2018. "Spatio-temporal quantitative links between climatic extremes and population flows: a case study in the Murray-Darling Basin, Australia," Climatic Change, Springer, vol. 148(1), pages 139-153, May.
    2. K. Shuvo Bakar & Nicholas Biddle & Philip Kokic & Huidong Jin, 2020. "A Bayesian spatial categorical model for prediction to overlapping geographical areas in sample surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 535-563, February.
    3. Myungjin Kim & Li Wang & Yuyu Zhou, 2021. "Spatially Varying Coefficient Models with Sign Preservation of the Coefficient Functions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 367-386, September.
    4. K. Shuvo Bakar, 2020. "Interpolation of daily rainfall data using censored Bayesian spatially varying model," Computational Statistics, Springer, vol. 35(1), pages 135-152, March.
    5. Heng Liu & Caizhu Huang & Heng Lian & Xia Cui, 2023. "Hierarchical Spatially Varying Coefficient Process Regression for Modeling Net Anthropogenic Nitrogen Inputs (NANI) from the Watershed of the Yangtze River, China," Sustainability, MDPI, vol. 15(16), pages 1-15, August.

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