Spatio-temporal dependence modelling of extreme rainfall in South Africa: A Bayesian integrated nested Laplace approximation technique

The spatial and spatio-temporal dependence modeling to extreme value distributions have been used to analyze the extremes of daily maximum rainfall data across selected weather stations in South Africa combining generalized Pareto distribution (GPD) with the flexible Bayesian Latent Gaussian Model (LGM). The paper demonstrated the spatio-temporal GPD model for applications in extreme rainfall data that capture systematic variation through spatial and spatio-temporal modeling framework, in which the temporal constitutes the week and month as random separately. The paper uses the Bayesian integrated Nested Laplace approximation (INLA) algorithm to estimate marginal posterior means of the parameters and hyper-parameters for Bayesian spatio-temporal models. The Bayesian inferences using INLA technique were applied to obtain prediction of the return levels at each station, which incorporate uncertainty due to model estimation, as well as the randomness that is inherent in the processes.KEYWORD: Spatio-temporal modelingextreme valuesgeneralized Pareto distributionintegrated nested Laplace approximationextreme rainfall AcknowledgmentsThe first author is very indebted to the University of South Africa for the financial support. The authors also would like to thank the South African Weather Service for providing the data.Code AvailabilityR Code is available and can be obtained on reasonable request from the authors.Data availability statementThe data that support the findings of this study are available from South African Weather Service (SAWS) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of SAWS.Disclosure statementThe authors declare that they have no conflict of interest.