A Bayesian hierarchical spatio-temporal rainfall model

John Mashford, Yong Song, Q. J. Wang and David Robertson

Journal of Applied Statistics, 2019, vol. 46, issue 2, 217-229

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.

Date: 2019
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DOI: 10.1080/02664763.2018.1473347

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