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A Bayesian hierarchical spatio-temporal rainfall model

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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.
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Keywords: Time series; formal properties; rainfall forecasting; rainfall versus elevation model; sequential kriging

Document Type: Research Article

Affiliations: 1: School of Mathematics and Statistics, University of Melbourne, Parkville, VIC, Australia 2: Commonwealth Scientific and Industrial Research Organisation (CSIRO), Division of Land and Water, Clayton South, VIC, Australia

Publication date: January 25, 2019

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