Spatiotemporal prediction of wildfire size extremes with Bayesian finite sample maxima
Maxwell B. Joseph,Matthew W. Rossi,Nathan Mietkiewicz,Adam L. Mahood,Megan E. Cattau,Lise Ann St. Denis,R. Chelsea Nagy,Virginia Iglesias,John T. Abatzoglou,Jennifer K. Balch +9 more
Reads0
Chats0
TLDR
It is concluded that recent extremes should not be surprising, and that the contiguous United States may be on the verge of even larger wildfire extremes.Abstract:
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.read more
Citations
More filters
Journal ArticleDOI
U.S. fires became larger, more frequent, and more widespread in the 2000s
TL;DR: The authors found that average fire events in regions of the United States are up to four times the size, triple the frequency, and more widespread in the 2000s than in the previous two decades.
Journal ArticleDOI
Warming weakens the night-time barrier to global fire
Jennifer K. Balch,John T. Abatzoglou,Maxwell B. Joseph,Michael J. Koontz,Adam L. Mahood,Joseph Mcglinchy,Megan E. Cattau,A. Park Williams +7 more
Journal ArticleDOI
Identifying large fire weather typologies in the Iberian Peninsula
Marcos Rodrigues,Marcos Rodrigues,Ricardo M. Trigo,Ricardo M. Trigo,Cristina Vega-García,Cristina Vega-García,Adrián Cardil +6 more
TL;DR: In this article, the influence of two fire-weather components and their input weather variables in large fire incidence across the entire Iberian Peninsula was analyzed, where the authors explored several spatial (four regions) and temporal (three levels of aggregation) aggregations to account for potential dissimilarities on fire weather associations in space and time.
Journal ArticleDOI
Social-Environmental Extremes: Rethinking Extraordinary Events as Outcomes of Interacting Biophysical and Social Systems
Jennifer K. Balch,Virginia Iglesias,Anna E. Braswell,Matthew W. Rossi,Maxwell B. Joseph,Adam L. Mahood,Trisha R. Shrum,Caitlin T. White,Victoria M. Scholl,Bryce McGuire,Claire Karban,Mollie Buckland,William R. Travis +12 more
Journal ArticleDOI
Predicting Paradise: Modeling future wildfire disasters in the western US
Alan A. Ager,Michelle A. Day,Fermín J. Alcasena,Cody R. Evers,Karen C. Short,Isaac C. Grenfell +5 more
TL;DR: This study is the first to explore large scale extreme wildfire exposure in terms of both annual variability and magnitude, providing a broad foundation of methods to advance wildfire disaster prediction.
References
More filters
Journal ArticleDOI
General methods for monitoring convergence of iterative simulations
Stephen P. Brooks,Andrew Gelman +1 more
TL;DR: This work generalizes the method proposed by Gelman and Rubin (1992a) for monitoring the convergence of iterative simulations by comparing between and within variances of multiple chains, in order to obtain a family of tests for convergence.
Journal ArticleDOI
An Introduction to Statistical Modeling of Extreme Values
TL;DR: In this article, an Introduction to Statistical Modeling of Extreme Values is presented, along with a discussion of statistical models of extreme values and their application in statistical modeling of extreme value.
Journal ArticleDOI
Zero-inflated Poisson regression, with an application to defects in manufacturing
TL;DR: Zero-inflated Poisson (ZIP) regression as discussed by the authors is a model for counting data with excess zeros, which assumes that with probability p the only possible observation is 0, and with probability 1 − p, a Poisson(λ) random variable is observed.
Stan: A Probabilistic Programming Language.
Bob Carpenter,Andrew Gelman,Matthew D. Hoffman,Daniel D. Lee,Ben Goodrich,Michael Betancourt,Marcus A. Brubaker,Jiqiang Guo,Peter Li,Allen Riddell +9 more
TL;DR: Stan is a probabilistic programming language for specifying statistical models that provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler and an adaptive form of Hamiltonian Monte Carlo sampling.
Journal ArticleDOI
Ecoregions of the Conterminous United States
TL;DR: A map of ecoregions of the conterminous United States has been compiled to assist managers of aquatic and terrestrial resources in understanding the regional patterns of the realistically attainable quality of these resources.