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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Cold Spring Harbor
Cold Spring Harbor Laboratory Press
16.02.2019
Cold Spring Harbor Laboratory |
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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 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. |
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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 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. 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. |
| Author | Nagy, R Chelsea Abatzoglou, John T Mietkiewicz, Nathan P Balch, Jennifer K Rossi, Matthew W Iglesias, Virginia Mahood, Adam L Joseph, Maxwell B Cattau, Megan E St Denis, Lise Ann |
| Author_xml | – sequence: 1 givenname: Maxwell surname: Joseph middlename: B fullname: Joseph, Maxwell B – sequence: 2 givenname: Matthew surname: Rossi middlename: W fullname: Rossi, Matthew W – sequence: 3 givenname: Nathan surname: Mietkiewicz middlename: P fullname: Mietkiewicz, Nathan P – sequence: 4 givenname: Adam surname: Mahood middlename: L fullname: Mahood, Adam L – sequence: 5 givenname: Megan surname: Cattau middlename: E fullname: Cattau, Megan E – sequence: 6 givenname: Lise surname: St Denis middlename: Ann fullname: St Denis, Lise Ann – sequence: 7 givenname: R surname: Nagy middlename: Chelsea fullname: Nagy, R Chelsea – sequence: 8 givenname: Virginia surname: Iglesias fullname: Iglesias, Virginia – sequence: 9 givenname: John surname: Abatzoglou middlename: T fullname: Abatzoglou, John T – sequence: 10 givenname: Jennifer surname: Balch middlename: K fullname: Balch, Jennifer K |
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| Cites_doi | 10.1007/s10021-002-0115-7 10.18637/jss.v014.i06 10.1890/09-1843.1 10.1371/journal.pone.0081188 10.1073/pnas.1713885114 10.1071/WF07136 10.1029/2011JG001695 10.1038/ngeo313 10.5281/zenodo.1326858 10.5194/bgd-10-15735-2013 10.1073/pnas.1607171113 |
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| Copyright | 2019. This article is published under http://creativecommons.org/licenses/by/4.0/ (“the License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2019, Posted by Cold Spring Harbor Laboratory |
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| DOI | 10.1101/384115 |
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| Keywords | fire climate spatiotemporal extremes wildfire Bayesian |
| Language | English |
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Classes and Methods – year: 2017a ident: 384115v3.105 publication-title: Assertthat: Easy Pre and Post Assertions – volume: 65 start-page: 626 issue: 2 year: 2009 end-page: 34 ident: 384115v3.48 publication-title: Biometrics – volume: 126 start-page: 455 issue: 3-4 year: 2014 end-page: 68 ident: 384115v3.97 publication-title: Climatic Change – volume: 115 start-page: 3314 issue: 13 year: 2018 end-page: 9 ident: 384115v3.82 publication-title: Proceedings of the National Academy of Sciences – volume: 28 start-page: 2031 issue: 15 year: 2008 end-page: 64 ident: 384115v3.27 publication-title: International Journal of Climatology – volume: 15 start-page: 1593 issue: 1 year: 2014 end-page: 1623 ident: 384115v3.44 article-title: The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo publication-title: Journal of Machine Learning Research – year: 2014 ident: 384115v3.66 publication-title: RColorBrewer: ColorBrewer Palettes – year: 2017 ident: 384115v3.99 article-title: Human presence diminishes the importance of climate in driving fire activity across the United States publication-title: Proceedings of the National Academy of Sciences doi: 10.1073/pnas.1713885114 – volume: 41 start-page: 2928 issue: 8 year: 2014 end-page: 33 ident: 384115v3.29 publication-title: Geophysical Research Letters – volume: 18 start-page: 640 issue: 6 year: 2009 ident: 384115v3.56 publication-title: International Journal of Wildland Fire doi: 10.1071/WF07136 – volume: 116 issue: G3 year: 2011 ident: 384115v3.87 article-title: Impacts of Climate Change on Fire Regimes and Carbon Stocks of the U.S. Pacific Northwest publication-title: Journal of Geophysical Research: Biogeosciences doi: 10.1029/2011JG001695 – volume: 7 start-page: 434 issue: 4 year: 1998 end-page: 55 ident: 384115v3.21 publication-title: Journal of Computational and Graphical Statistics – volume: 9 start-page: 124009 issue: 12 year: 2014 ident: 384115v3.8 publication-title: Environmental Research Letters – year: 2018 ident: 384115v3.94 publication-title: Pbapply: Adding Progress Bar to ‘*Apply’ Functions – year: 2013 ident: 384115v3.36 publication-title: Bayesian Data Analysis, Third Edition – volume: 96 start-page: 229 issue: 2 year: 2015 end-page: 47 ident: 384115v3.75 article-title: The 2013 Rim Fire: Implications for Predicting Extreme Fire Spread, Pyroconvection, and Smoke Emissions publication-title: Bulletin of the American Meteorological Society – start-page: 51 year: 1999 ident: 384115v3.107 publication-title: Fire Economics, Planning, and Policy: Bottom Lines – volume: 17 start-page: 1 issue: 1 year: 2010 end-page: 28 ident: 384115v3.62 publication-title: Environmental and Ecological Statistics – volume: 33 start-page: 121 issue: 1 year: 2013 end-page: 31 ident: 384115v3.1 publication-title: International Journal of Climatology – volume: 26 start-page: 90 issue: 1 year: 2008 end-page: 104 ident: 384115v3.19 publication-title: Journal of Business & Economic Statistics – year: 2008 ident: 384115v3.59 article-title: Climate and human influences on global biomass burning over the past two millennia publication-title: Nature Geoscience doi: 10.1038/ngeo313 – volume: 1 start-page: 18 issue: 1 year: 2018 ident: 384115v3.65 publication-title: Fire – volume: 273 start-page: 35 issue: 1-4 year: 2003 end-page: 50 ident: 384115v3.25 publication-title: Journal of Hydrology – volume: 124 start-page: 1334 issue: 9 year: 2016 ident: 384115v3.86 publication-title: Environmental Health Perspectives – volume: 208 year: 2001 ident: 384115v3.24 publication-title: An Introduction to Statistical Modeling of Extreme Values – volume: 322 start-page: 267 year: 2003 end-page: 75 ident: 384115v3.10 publication-title: Physica A: Statistical Mechanics and Its Applications – volume: 49 start-page: 71 issue: 1 year: 2015 end-page: 79 ident: 384115v3.16 publication-title: ACM SIGOPS Operating Systems Review – year: 1978 ident: 384115v3.35 article-title: Weather in 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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 Bayesian analysis Ecology Fires Forest & brush fires Mathematical models Statistical analysis Wildfires |
| Title | Spatiotemporal prediction of wildfire extremes with Bayesian finite sample maxima |
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