A low-rank semiparametric Bayesian spatial model for estimating extreme Red Sea surface temperature hotspots
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In this work, we focus on estimating sea surface temperature (SST) hotspots, i.e., high threshold exceedance regions, for the Red Sea, a vital region of endangered coral reefs. We analyze satellite-derived high-dimensional SST data comprising daily measurements at 16703 grid cells across the Red Sea over the period 1985-2015. We propose a semiparametric Bayesian spatial mixed-effects linear model with a flexible mean structure to capture the spatially-varying trend and seasonality, while the residual spatial variability is modeled through a Dirichlet process mixture (DPM) of low-rank spatial Student-t processes (LTPs). By specifying cluster-specific parameters for each LTP mixture component, the bulk of the SST residuals influence tail inference and hotspot estimation only moderately. Our proposed model has a nonstationary mean, covariance and tail dependence, and posterior inference can be drawn efficiently through Gibbs sampling. In our application, we show that the proposed method outperforms some parametric and semiparametric alternatives. Moreover, we show how hotspots can be identified and we estimate the extreme SST hotspots for the whole Red Sea, projected for the year 2100. The estimated 95 covering major coral reefs in the southern Red Sea.
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