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Large wildfires pose a major environmental concern, and precise maps of firerisk can improve disaster relief planning. Fosberg Fire Weather Index (FFWI) isoften used to measure wildfire risk; FFWI exhibits non-Gaussian marginaldistributions as well as strong spatiotemporal extremal dependence and thus,modeling FFWI using geostatistical models like Gaussian processes isquestionable. Extreme value theory (EVT)-driven models like max-stableprocesses are theoretically appealing but are computationally demanding andapplicable only for threshold exceedances or block maxima. Disaster managementpolicies often consider moderate-to-extreme quantiles of climate parameters andhence, joint modeling of the bulk and the tail of the data is required. In thispaper, we consider a Dirichlet process mixture of spatial skew-t processes thatcan flexibly model the bulk as well as the tail. The proposed model hasnonstationary mean and covariance structure, and also nonzero spatiotemporalextremal dependence. A simulation study demonstrates that the proposed modelhas better spatial prediction performance compared to some competing models. Wedevelop spatial maps of FFWI medians and extremes, and discuss the wildfirerisk throughout the Santa Ana region of California.

A semiparametric spatiotemporal Bayesian model for the bulk and extremes of the Fosberg Fire Weather Index