English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this work, we estimate extreme sea surface temperature (SST) hotspots,i.e., high threshold exceedance regions, for the Red Sea, a vital region ofhigh biodiversity. We analyze high-resolution satellite-derived SST datacomprising daily measurements at 16703 grid cells across the Red Sea over theperiod 1985-2015. We propose a semiparametric Bayesian spatial mixed-effectslinear model with a flexible mean structure to capture spatially-varying trendand seasonality, while the residual spatial variability is modeled through aDirichlet process mixture (DPM) of low-rank spatial Student-$t$ processes(LTPs). By specifying cluster-specific parameters for each LTP mixturecomponent, the bulk of the SST residuals influence tail inference and hotspotestimation only moderately. Our proposed model has a nonstationary mean,covariance and tail dependence, and posterior inference can be drawnefficiently through Gibbs sampling. In our application, we show that theproposed method outperforms some natural parametric and semiparametricalternatives. Moreover, we show how hotspots can be identified and we estimateextreme SST hotspots for the whole Red Sea, projected until the year 2100,based on the Representative Concentration Pathways 4.5 and 8.5. The estimated95\% credible region for joint high threshold exceedances include large areascovering major endangered coral reefs in the southern Red Sea.

Estimating high-resolution Red Sea surface temperature hotspots, using a low-rank semiparametric spatial model