Spatiotemporal prediction of wildfire 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 year wildfire record with meteorological and housing data in spatiotemporal Bayesian statis...

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Veröffentlicht in:bioRxiv
Hauptverfasser: Joseph, Maxwell B, Rossi, Matthew W, Mietkiewicz, Nathan P, Mahood, Adam L, Cattau, Megan E, St Denis, Lise Ann, Nagy, R Chelsea, Iglesias, Virginia, Abatzoglou, John T, Balch, Jennifer K
Format: Paper
Sprache:Englisch
Veröffentlicht: Cold Spring Harbor Cold Spring Harbor Laboratory Press 16.02.2019
Cold Spring Harbor Laboratory
Ausgabe:1.3
Schlagworte:
Air temperature
Bayesian analysis
Ecology
Fires
Forest & brush fires
Mathematical models
Statistical analysis
Wildfires
United States > US
fire
climate
spatiotemporal
extremes
wildfire
Bayesian
ISSN:2692-8205, 2692-8205
Online-Zugang:Volltext
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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 year 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. Footnotes * Adding new MTBS data, which includes 2016 data to the validation data set. Expanding the Wallow Fire case study to explore the temporal mismatch between the features used to drive the model, and the conditions that drove the wildfire. Cleaning up language around fire dynamics and causality.
Bibliographie:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2692-8205
2692-8205
DOI:10.1101/384115