Spatiotemporal prediction of wildfire size extremes with Bayesian finite sample maxima

Wildfires are becoming more frequent in parts of the globe, but predicting where and when wildfires occur remains difficult. To predict wildfire extremes across the contiguous United States, we integrate a 30-yr wildfire record with meteorological and housing data in spatiotemporal Bayesian statisti...

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Veröffentlicht in:Ecological applications Jg. 29; H. 6; S. 1266 - 1281
Hauptverfasser: Joseph, Maxwell B., Rossi, Matthew W., Mietkiewicz, Nathan P., L. Mahood, Adam, Cattau, Megan E., St. Denis, Lise Ann, Nagy, Chelsea R., Iglesias, Virginia, Abatzoglou, John T., Balch, Jennifer K.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States John Wiley and Sons, Inc 01.09.2019
Ecological Society of America
John Wiley and Sons Inc
Schlagworte:
Air temperature
Atmospheric models
Bayes Theorem
Bayesian
Bayesian analysis
Bayesian theory
climate
data collection
Domains
extremes
fire
fire frequency
Fires
Forest & brush fires
Housing
Mathematical models
Models, Statistical
prediction
Predictions
spatiotemporal
Statistical analysis
Statistical models
United States
wildfire
Wildfires
United States
United States > US
fire
climate
spatiotemporal
extremes
wildfire
Bayesian
ISSN:1051-0761, 1939-5582
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Beschreibung
Zusammenfassung:Wildfires are becoming more frequent in parts of the globe, but predicting where and when wildfires occur remains difficult. To predict wildfire extremes across the contiguous United States, we integrate a 30-yr wildfire record with meteorological and housing data in spatiotemporal Bayesian statistical models with spatially varying nonlinear effects. We compared different distributions for the number and sizes of large fires to generate a posterior predictive distribution based on finite sample maxima for extreme events (the largest fires over bounded spatiotemporal domains). A zero-inflated negative binomial model for fire counts and a lognormal model for burned areas provided the best performance. This model attains 99% interval coverage for the number of fires and 93% coverage for fire sizes over a six year withheld data set. Dryness and air temperature strongly predict extreme wildfire probabilities. Housing density has a hump-shaped relationship with fire occurrence, with more fires occurring at intermediate housing densities. Statistically, these drivers affect the chance of an extreme wildfire in two ways: by altering fire size distributions, and by altering fire frequency, which influences sampling from the tails of fire size distributions. We conclude that recent extremes should not be surprising, and that the contiguous United States may be on the verge of even larger wildfire extremes.
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Corresponding Editor: David S. Schimel.
ISSN:1051-0761
1939-5582
DOI:10.1002/eap.1898