Long-Term Spatial Modelling for Characteristics of Extreme Heat Events

Schliep, Erin M.; Gelfand, Alan E.; Abaurrea, Jesús; Asín, Jesús; Beamonte, María Asunción; Cebrián, Ana C.

doi:10.1111/rssa.12710

Cited by 5 publications

(1 citation statement)

References 33 publications

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“…The tools suggested in this work will be used to analyse the temperature evolution in Aragón using as input the posterior realizations of daily temperature obtained from the spatiotemporal model in Castillo‐Mateo et al (2022). This is a complex model fitted in a Bayesian framework but, as noted above, any alternative model able to generate adequate replicates of time series at a fine grid of geo‐coded locations could be used equally well to generate the temperature realizations required; for example, the space–time model by Schliep et al (2021) or the SWG by Verdin et al (2019).…”

Section: Regional Setting and Model To Generate Datamentioning

confidence: 99%

Assessing space and time changes in daily maximum temperature in the Ebro basin (Spain) using model‐based statistical tools

Cebrián,

Asín,

Castillo‐Mateo

et al. 2023

Intl Journal of Climatology

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There is continuing interest in the investigation of change in temperature over space and time. For this analysis, we offer statistical tools to illuminate changes temporally, at desired temporal resolution, and spatially, using data generated from suitable space–time models. The proposed tools can be used with the output from any suitable model fitted to any set of spatially referenced time series data. The tools to assess space and time changes include spatial surfaces of probabilities and spatial extents for events defined by exceeding a threshold. The spatial surfaces capture the spatial variation in the probability or risk of an exceedance event, while the spatial extents capture the expected proportion of incidence of an event for a region of interest. This approach is used analyse the changes in daily maximum temperature in an inland Mediterranean region (NE of Spain) in the period 1956–2015. The area is very heterogeneous in orography and climate, including the central Ebro valley and part of the Pyrenees. We use a collection of daily temperature series obtained from simulation under a Bayesian daily temperature model fitted to 18 stations in that area. The results for the summer period show that, although there is an increasing risk in all the events used to quantify the effects of climate change, it is not spatially homogeneous, with the largest increase arising in the centre of the Ebro valley and the Eastern Pyrenees area. The risk of an increase in the average daily maximum temperature from 1966–1975 to 2006–2015 higher than 1°C is higher than 0.5 over all of the region, and close to 1 in the previous areas. The extent of daily maximum temperature higher than the reference mean has increased 3.5% per decade. The mean of the extent indicates that 95% of the area under study has suffered a positive increment of the average temperature, and almost 70% an increment higher than 1°C.

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Section: Regional Setting and Model To Generate Datamentioning

confidence: 99%

Assessing space and time changes in daily maximum temperature in the Ebro basin (Spain) using model‐based statistical tools

Cebrián,

Asín,

Castillo‐Mateo

et al. 2023

Intl Journal of Climatology

Self Cite

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

Cebrián

Asín

Gelfand

et al. 2021

Stoch Environ Res Risk Assess

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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.

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