Modelling Drought Risk Using Bivariate Spatial Extremes: Application to the Limpopo Lowveld Region of South Africa

Weather and climate extremes such as heat waves, droughts floods are projected to become more frequent intense in several regions. There is compelling evidence indicating that changes its over time influence the living conditions of society surrounding environment across globe. This study applies max-stable models capture spatio–temporal with dependence. The objective was analyse risk drought caused by extremely high temperatures deficient rainfall. Hopkin’s statistic used assess clustering tendency before using agglomerative method hierarchical cluster area into n=3 temperature clusters precipitation clusters. For data, values were 0.7317 0.8446, respectively, which shows both significantly clusterable. Various process then fitted each variable, Schlather model covariance functions found be a good fit on datasets compared Smith Gaussian function. modelling approach presented this paper could useful hydrologists, meteorologists climatologists, including decision-makers agricultural sector, enhancing their understanding behaviour low these compound also assist assessing impact change. It can seen from size, topography location (cluster/region), provides important information about strength extremal