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A Bayesian hierarchical spatio-temporal rainfall model

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  • John Mashford
  • Yong Song
  • Q. J. Wang
  • David Robertson

Abstract

A Bayesian hierarchical spatio-temporal rainfall model is presented and analysed. The model has the ability to deal with extensive missing or null values, uses a sophisticated variance stabilising rainfall pre-transformation, incorporates a new elevation model and can provide sub-catchment rainfall estimation and interpolation using a sequential kriging scheme. The model uses a vector autoregressive stochastic process to represent the time dependence of the rainfall field and an exponential covariogram to model the spatial correlation of the rainfall field. The model can be readily generalised to other types of stochastic processes. In this paper, some results of applying the model to a particular rainfall catchment are presented.

Suggested Citation

  • John Mashford & Yong Song & Q. J. Wang & David Robertson, 2019. "A Bayesian hierarchical spatio-temporal rainfall model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(2), pages 217-229, January.
    • Handle: RePEc:taf:japsta:v:46:y:2019:i:2:p:217-229
    DOI: 10.1080/02664763.2018.1473347
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