Spatio-temporal analysis of the extent of an extreme heat event

Cebrián, Ana C. (Universidad de Zaragoza) ; Asín, Jesús (Universidad de Zaragoza) ; Gelfand, Alan E. ; Schliep, Erin M. ; Castillo-Mateo, Jorge (Universidad de Zaragoza) ; Beamonte, María A. (Universidad de Zaragoza) ; Abaurrea, Jesús (Universidad de Zaragoza)

Resumen: 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.

Idioma: Inglés

DOI: 10.1007/s00477-021-02157-z

Año: 2022

Publicado en: STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 36 (2022), 2737–2751
ISSN: 1436-3240

Factor impacto JCR: 4.2 (2022)

Categ. JCR: STATISTICS & PROBABILITY rank: 10 / 125 = 0.08 (2022) - Q1 - T1
Categ. JCR: WATER RESOURCES rank: 27 / 103 = 0.262 (2022) - Q2 - T1
Categ. JCR: ENGINEERING, CIVIL rank: 37 / 139 = 0.266 (2022) - Q2 - T1
Categ. JCR: ENVIRONMENTAL SCIENCES rank: 104 / 275 = 0.378 (2022) - Q2 - T2
Categ. JCR: ENGINEERING, ENVIRONMENTAL rank: 28 / 55 = 0.509 (2022) - Q3 - T2

Factor impacto CITESCORE: 6.5 - Environmental Science (Q1) - Engineering (Q1)

Factor impacto SCIMAGO: 0.814 - Environmental Science (miscellaneous) (Q1) - Environmental Engineering (Q1) - Water Science and Technology (Q1) - Safety, Risk, Reliability and Quality (Q1) - Environmental Chemistry (Q2)

Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00
Financiación: info:eu-repo/grantAgreement/ES/MINECO-AEI-FEDER/PID2019-106099RB-C44

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Estadís. Investig. Opera. (Dpto. Métodos Estadísticos)
Área (Departamento): Área Métodos Cuant.Econ.Empres (Dpto. Economía Aplicada)