109478 20240319080947.0 doi 10.1007/s00477-021-02157-z sideral 125903 ART-2022-125903 eng Cebrián, Ana C. Universidad de Zaragoza (orcid)0000-0002-9052-9674 Spatio-temporal analysis of the extent of an extreme heat event 2022 Access copy available to the general public Unrestricted Evidence of global warming induced from the increasing concentration of greenhouse gases in the atmosphere suggests more frequent warm days and heat waves. The concept of an extreme heat event (EHE), defined locally based on exceedance of a suitable local threshold, enables us to capture the notion of a period of persistent extremely high temperatures. Modeling for extreme heat events is customarily implemented using time series of temperatures collected at a set of locations. Since spatial dependence is anticipated in the occurrence of EHE’s, a joint model for the time series, incorporating spatial dependence is needed. Recent work by Schliep et al. (J R Stat Soc Ser A Stat Soc 184(3):1070–1092, 2021) develops a space-time model based on a point-referenced collection of temperature time series that enables the prediction of both the incidence and characteristics of EHE’s occurring at any location in a study region. The contribution here is to introduce a formal definition of the notion of the spatial extent of an extreme heat event and then to employ output from the Schliep et al. (J R Stat Soc Ser A Stat Soc 184(3):1070–1092, 2021) modeling work to illustrate the notion. For a specified region and a given day, the definition takes the form of a block average of indicator functions over the region. Our risk assessment examines extents for the Comunidad Autónoma de Aragón in northeastern Spain. We calculate daily, seasonal and decadal averages of the extents for two subregions in this comunidad. We generalize our definition to capture extents of persistence of extreme heat and make comparisons across decades to reveal evidence of increasing extent over time. info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00 info:eu-repo/grantAgreement/ES/MINECO-AEI-FEDER/PID2019-106099RB-C44 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 4.2 2022 STATISTICS & PROBABILITY 10 / 125 = 0.08 2022 Q1 T1 WATER RESOURCES 27 / 103 = 0.262 2022 Q2 T1 ENGINEERING, CIVIL 37 / 139 = 0.266 2022 Q2 T1 ENVIRONMENTAL SCIENCES 104 / 275 = 0.378 2022 Q2 T2 ENGINEERING, ENVIRONMENTAL 28 / 55 = 0.509 2022 Q3 T2 0.814 2022 Environmental Science (miscellaneous) 2022 Q1 Environmental Engineering 2022 Q1 Water Science and Technology 2022 Q1 Safety, Risk, Reliability and Quality 2022 Q1 Environmental Chemistry 2022 Q2 6.5 2022 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Asín, Jesús Universidad de Zaragoza (orcid)0000-0002-0174-789X Gelfand, Alan E. Schliep, Erin M. Castillo-Mateo, Jorge Universidad de Zaragoza (orcid)0000-0003-3859-0248 Beamonte, María A. Universidad de Zaragoza (orcid)0000-0003-2617-4167 Abaurrea, Jesús Universidad de Zaragoza (orcid)0000-0002-7974-7435 2007 265 Universidad de Zaragoza Dpto. Métodos Estadísticos Área Estadís. Investig. Opera. 4014 623 Universidad de Zaragoza Dpto. Economía Aplicada Área Métodos Cuant.Econ.Empres 36 (2022), 2737–2751 Stoch. environ. res. risk assess. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 1436-3240 933163 http://zaguan.unizar.es/record/109478/files/texto_completo.pdf Versión publicada 2351178 http://zaguan.unizar.es/record/109478/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:109478 articulos driver 2024-03-18-12:44:33 ARTICLE