A Bayesian hierarchical spatio-temporal model for extreme rainfall in Extremadura (Spain). Issue 6 (26th April 2018)

• Record Type: Journal Article
• Authors: García, J. A.; Martín, J.; Naranjo, L.; Acero, F. J.
• Is Part Of: Hydrological sciences journal. Volume 63:Issue 6(2018)
• Page Start: 878
• Page End: 894
• Publication Date: 2018-04-26
• Subjects: Bayesian hierarchical model -- extreme rainfall -- generalized extreme value distribution; Hydrology -- Periodicals; 551.4805
• DOI: 10.1080/02626667.2018.1457219
• Languages: English
• ISSNs: 0262-6667

Abstract

ABSTRACT: A statistical study was made of the temporal trend in extreme rainfall in the region of Extremadura (Spain) during the period 1961–2009. A hierarchical spatio-temporal Bayesian model with a GEV parameterization of the extreme data was employed. The Bayesian model was implemented in a Markov chain Monte Carlo framework that allows the posterior distribution of the parameters that intervene in the model to be estimated. The results show a decrease of extreme rainfall in winter and spring and a slight increase in autumn. The uncertainty in the trend parameters obtained with the hierarchical approach is much smaller than the uncertainties obtained from the GEV model applied locally. Also found was a negative relationship between the NAO index and the extreme rainfall in Extremadura during winter. An increase was observed in the intensity of the NAO index in winter and spring, and a slight decrease in autumn.