000061333 001__ 61333
000061333 005__ 20170520135542.0
000061333 0247_ $$2doi$$a10.1007/s00477-014-0953-9
000061333 0248_ $$2sideral$$a89059
000061333 037__ $$aART-2015-89059
000061333 041__ $$aeng
000061333 100__ $$0(orcid)0000-0002-7974-7435$$aAbaurrea, J.$$uUniversidad de Zaragoza
000061333 245__ $$aModeling and projecting the occurrence of bivariate extreme heat events using a non-homogeneous common Poisson shock process
000061333 260__ $$c2015
000061333 5060_ $$aAccess copy available to the general public$$fUnrestricted
000061333 5203_ $$aA joint model is proposed for analyzing and predicting the occurrence of extreme heat events in two temperature series, these being daily maximum and minimum temperatures. Extreme heat events are defined using a threshold approach and the suggested model, a non-homogeneous common Poisson shock process, accounts for the mutual dependence between the extreme events in the two series. This model is used to study the time evolution of the occurrence of extreme events and its relationship with temperature predictors. A wide range of tools for validating the model is provided, including influence analysis. The main application of this model is to obtain medium-term local projections of the occurrence of extreme heat events in a climate change scenario. Future temperature trajectories from general circulation models, conveniently downscaled, are used as predictors of the model. These trajectories show a generalized increase in temperatures, which may lead to extrapolation errors when the model is used to obtain projections. Various solutions for dealing with this problem are suggested. The results of the fitted model for the temperature series in Barcelona in 1951–2005 and future projections of extreme heat events for the period 2031–2060 are discussed, using three global circulation model trajectories under the SRES A1B scenario.
000061333 536__ $$9info:eu-repo/grantAgreement/ES/MEC/CGL2009-09646$$9info:eu-repo/grantAgreement/ES/MMA/ESTCENA 2009-0017
000061333 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000061333 590__ $$a2.237$$b2015
000061333 591__ $$aSTATISTICS & PROBABILITY$$b8 / 123 = 0.065$$c2015$$dQ1$$eT1
000061333 591__ $$aENGINEERING, CIVIL$$b19 / 126 = 0.151$$c2015$$dQ1$$eT1
000061333 591__ $$aWATER RESOURCES$$b15 / 84 = 0.179$$c2015$$dQ1$$eT1
000061333 591__ $$aENGINEERING, ENVIRONMENTAL$$b21 / 49 = 0.429$$c2015$$dQ2$$eT2
000061333 591__ $$aENVIRONMENTAL SCIENCES$$b88 / 224 = 0.393$$c2015$$dQ2$$eT2
000061333 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000061333 700__ $$0(orcid)0000-0002-0174-789X$$aAsín, J.$$uUniversidad de Zaragoza
000061333 700__ $$0(orcid)0000-0002-9052-9674$$aCebrián, A.C.$$uUniversidad de Zaragoza
000061333 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDepartamento de Métodos Estadísticos$$cEstadística e Investigación Operativa
000061333 773__ $$g29, 1 (2015), 309-322$$pStoch. environ. res. risk assess.$$tSTOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT$$x1436-3240
000061333 8564_ $$s545923$$uhttp://zaguan.unizar.es/record/61333/files/texto_completo.pdf$$yPostprint
000061333 8564_ $$s53968$$uhttp://zaguan.unizar.es/record/61333/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000061333 909CO $$ooai:zaguan.unizar.es:61333$$particulos$$pdriver
000061333 951__ $$a2017-05-19-13:44:10
000061333 980__ $$aARTICLE