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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| Vydané v: | Ecological applications Ročník 29; číslo 6; s. 1266 - 1281 |
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| Hlavní autori: | , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
John Wiley and Sons, Inc
01.09.2019
Ecological Society of America John Wiley and Sons Inc |
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| ISSN: | 1051-0761, 1939-5582 |
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| 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. |
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| AbstractList | 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. 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.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. |
| Author | St. Denis, Lise Ann Rossi, Matthew W. Iglesias, Virginia Nagy, Chelsea R. Balch, Jennifer K. Mietkiewicz, Nathan P. Abatzoglou, John T. Joseph, Maxwell B. L. Mahood, Adam Cattau, Megan E. |
| AuthorAffiliation | 2 Department of Geography University of Idaho 875 Perimeter Drive, MS 3021 Moscow Idaho 83844‐3021 USA 1 Earth Lab University of Colorado Boulder 4001 Discovery Drive, Suite S348 611 UCB Boulder Colorado 80303 USA |
| AuthorAffiliation_xml | – name: 2 Department of Geography University of Idaho 875 Perimeter Drive, MS 3021 Moscow Idaho 83844‐3021 USA – name: 1 Earth Lab University of Colorado Boulder 4001 Discovery Drive, Suite S348 611 UCB Boulder Colorado 80303 USA |
| Author_xml | – sequence: 1 givenname: Maxwell B. surname: Joseph fullname: Joseph, Maxwell B. – sequence: 2 givenname: Matthew W. surname: Rossi fullname: Rossi, Matthew W. – sequence: 3 givenname: Nathan P. surname: Mietkiewicz fullname: Mietkiewicz, Nathan P. – sequence: 4 givenname: Adam surname: L. Mahood fullname: L. Mahood, Adam – sequence: 5 givenname: Megan E. surname: Cattau fullname: Cattau, Megan E. – sequence: 6 givenname: Lise Ann surname: St. Denis fullname: St. Denis, Lise Ann – sequence: 7 givenname: Chelsea R. surname: Nagy fullname: Nagy, Chelsea R. – sequence: 8 givenname: Virginia surname: Iglesias fullname: Iglesias, Virginia – sequence: 9 givenname: John T. surname: Abatzoglou fullname: Abatzoglou, John T. – sequence: 10 givenname: Jennifer K. surname: Balch fullname: Balch, Jennifer K. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30980779$$D View this record in MEDLINE/PubMed |
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| Keywords | fire climate spatiotemporal extremes wildfire Bayesian |
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| Snippet | Wildfires are becoming more frequent in parts of the globe, but predicting where and when wildfires occur remains difficult. To predict wildfire extremes... |
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| SubjectTerms | 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 |
| Title | Spatiotemporal prediction of wildfire size extremes with Bayesian finite sample maxima |
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