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A Bayesian hierarchical framework with a Gaussian copula and a generalized extreme value (GEV) marginal distribution is proposed for the description of spatial dependencies in data. This spatial copula model was applied to extreme summer temperatures over the Extremadura Region, in the southwest of Spain, during the period 1980–2015, and compared with the spatial noncopula model. The Bayesian hierarchical model was implemented with a Monte Carlo Markov Chain (MCMC) method that allows the distribution of the model's parameters to be estimated. The results show the GEV distribution's shape parameter to take constant negative values, the location parameter to be altitude dependent, and the scale parameter values to be concentrated around the same value throughout the region. Further, the spatial copula model chosen presents lower deviance information criterion (DIC) values when spatial distributions are assumed for the GEV distribution's location and scale parameters than when the scale parameter is taken to be constant over the region. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to statistically characterize and simulate the wave climate under storm conditions. The methodology includes the joint selection of the parameters that identify storm events (significant wave height threshold, minimum storm duration and minimum interarrival time between consecutive storms) by means of hypothesis testing on the distribution functions of the number of storm events and the elapsing time between storms, providing an improved characterization of the parameters that define storm events. The main wave variables and their temporal dependence are characterized by non-stationary mixture distribution functions and a vector autoregressive model. This allows to adequately reproduce the random temporal evolution of storm events, crucial for the study of damage progression in maritime structures without the use of predefined geometries. The long-term time series of storm events and calm periods is obtained using copula functions which analyze the joint dependence of storm duration and interarrival time for separate climate intervals. The model is applied to hindcast data at a location of the Mediterranean sea close to the Granada coast in Spain to show its ability to reproduce wave storm conditions accounting for the time variability of the storminess. An example of application, using a large number of simulations and a damage progression model in a maritime structure, is presented. • A statistical model for long-term simulation of extreme events is developed. • The methodology allows for the rigorous selection of the parameters that define a storm event. • The model accurately reproduces the multivariate storm events and their temporal evolution. • A case study in the Mediterranean coast of Spain is presented as a validation of the methodology. [ABSTRACT FROM AUTHOR]