Time-varying nonstationary multivariate risk analysis using a dynamic Bayesian copula Articles uri icon

publication date

  • March 2016

start page

  • 2327

end page

  • 2349

issue

  • 3

volume

  • 52

international standard serial number (ISSN)

  • 0043-1397

electronic international standard serial number (EISSN)

  • 1944-7973

abstract

  • A time-varying risk analysis is proposed for an adaptive design framework in nonstationary conditions arising from climate change. A Bayesian, dynamic conditional copula is developed for modeling the time-varying dependence structure between mixed continuous and discrete multiattributes of multidimensional hydrometeorological phenomena. Joint Bayesian inference is carried out to fit the marginals and copula in an illustrative example using an adaptive, Gibbs Markov Chain Monte Carlo (MCMC) sampler. Posterior mean estimates and credible intervals are provided for the model parameters and the Deviance Information Criterion (DIC) is used to select the model that best captures different forms of nonstationarity over time. This study also introduces a fully Bayesian, time-varying joint return period for multivariate time-dependent risk analysis in nonstationary environments.

keywords

  • time-varying multivariate risk analysis; dynamic bayesian copula; markov chain monte carlo sampling; time-varying joint return period; bivariate frequency-analysis; low-flow series; water management; return periods; climate-change; drought; design; stationarity; duration; dead