Articles | Volume 16, issue 12
https://doi.org/10.5194/gmd-16-3407-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-3407-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States
Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York, NY, USA
A. Park Williams
Department of Geography, University of California, Los Angeles, CA, USA
Caroline S. Juang
Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York, NY, USA
Department of Earth and Environmental Sciences, Columbia University, New York, NY, USA
Winslow D. Hansen
Cary Institute of Ecosystem Studies, Millbrook, NY, USA
Pierre Gentine
Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA
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Cited articles
Abatzoglou, J. T.: Development of gridded surface meteorological data for
ecological applications and modelling, Int. J. Climatol.,
33, 121–131, https://doi.org/10.1002/joc.3413, 2013. a
Abatzoglou, J. T. and Kolden, C. A.: Relationships between climate and
macroscale area burned in the western United States, Int. J. Wildland Fire, 22, 1003–1020, https://doi.org/10.1071/WF13019, 2013. a
Abatzoglou, J. T. and Williams, A. P.: Impact of anthropogenic climate change
on wildfire across western US forests, P. Nl. Acad.
Sci. USA, 113, 11770–11775, https://doi.org/10.1073/pnas.1607171113, 2016. a, b, c
Abatzoglou, J. T., Kolden, C. A., Williams, A. P., Lutz, J. A., and Smith, A.
M. S.: Climatic influences on interannual variability in regional burn
severity across western US forests, Int. J. Wildland Fire,
26, 269–275, https://doi.org/10.1071/WF16165, 2017. a, b
Abatzoglou, J. T., Battisti, D. S., Williams, A. P., Hansen, W. D., Harvey,
B. J., and Kolden, C. A.: Projected increases in western US forest fire
despite growing fuel constraints, Commun. Earth Environ., 2, 227,
https://doi.org/10.1038/s43247-021-00299-0, 2021a. a
Abatzoglou, J. T., Juang, C. S., Williams, A. P., Kolden, C. A., and
Westerling, A. L.: Increasing Synchronous Fire Danger in Forests of the
Western United States, Geophys. Res. Lett., 48, e2020GL091377,
https://doi.org/10.1029/2020GL091377, 2021b. a
Abolafia-Rosenzweig, R., He, C., and Chen, F.: Winter and spring climate
explains a large portion of interannual variability and trend in western
U.S. summer fire burned area, Environ. Res. Lett., 17, 054030,
https://doi.org/10.1088/1748-9326/ac6886, 2022. a
Alvarez-Melis, D. and Jaakkola, T. S.: Towards Robust Interpretability
with Self-Explaining Neural Networks, ArXiv, arXiv e-prints, 2018. a
Andela, N., Morton, D. C., Giglio, L., Chen, Y., van der Werf, G. R.,
Kasibhatla, P. S., DeFries, R. S., Collatz, G. J., Hantson, S., Kloster, S.,
Bachelet, D., Forrest, M., Lasslop, G., Li, F., Mangeon, S., Melton, J. R.,
Yue, C., and Randerson, J. T.: A human-driven decline in global burned area,
Science, 356, 1356–1362, https://doi.org/10.1126/science.aal4108, 2017. a, b
Anderson, D. B.: Relative Humidity or Vapor Pressure Deficit,
Ecology, 17, 277–282, http://www.jstor.org/stable/1931468,
1936. a
Andrews, P. L.: The Rothermel surface fire spread model and associated
developments: A comprehensive explanation, Gen. Tech. Rep. RMRS-GTR-371. Fort
Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain
Research Station. 121 pp., 371, 2018. a
Bailey, R. G.: Ecoregions of the United States, in: Ecosystem Geography, Springer New York, New York, NY,
83–104,
https://doi.org/10.1007/978-1-4612-2358-0_7, 1996. a
Bakhshaii, A. and Johnson, E.: A review of a new generation of
wildfire–atmosphere modeling, Can. J. Forest Res., 49,
565–574, https://doi.org/10.1139/cjfr-2018-0138, 2019. a
Balch, J. K., Bradley, B. A., D'Antonio, C. M., and Gómez-Dans, J.: Introduced
annual grass increases regional fire activity across the arid western USA
(1980–2009), Global Change Biol., 19, 173–183, https://doi.org/10.1111/gcb.12046,
2013. a
Balch, J. K., Bradley, B. A., Abatzoglou, J. T., Nagy, R. C., Fusco, E. J., and
Mahood, A. L.: Human-started wildfires expand the fire niche across the
United States, P. Natl. Acad. Sci. USA, 114,
2946–2951, https://doi.org/10.1073/pnas.1617394114, 2017. a, b
Bastos, A., Ciais, P., Friedlingstein, P., Sitch, S., Pongratz, J., Fan, L.,
Wigneron, J. P., Weber, U., Reichstein, M., Fu, Z., Anthoni, P., Arneth, A.,
Haverd, V., Jain, A. K., Joetzjer, E., Knauer, J., Lienert, S., Loughran, T.,
McGuire, P. C., Tian, H., Viovy, N., and Zaehle, S.: Direct and seasonal
legacy effects of the 2018 heat wave and drought on European ecosystem
productivity, Sci. Adv., 6, eaba2724, https://doi.org/10.1126/sciadv.aba2724, 2020. a, b
Bishop, C.: Mixture density networks, Working paper, Aston University, https://publications.aston.ac.uk/id/eprint/373/ (last access: 16 June 2023), 1994. a
Bowman, D. M. J. S., Balch, J. K., Artaxo, P., Bond, W. J., Carlson, J. M.,
Cochrane, M. A., D'Antonio, C. M., DeFries, R. S., Doyle, J. C., Harrison,
S. P., Johnston, F. H., Keeley, J. E., Krawchuk, M. A., Kull, C. A., Marston,
J. B., Moritz, M. A., Prentice, I. C., Roos, C. I., Scott, A. C., Swetnam,
T. W., van der Werf, G. R., and Pyne, S. J.: Fire in the Earth System,
Science, 324, 481–484, https://doi.org/10.1126/science.1163886, 2009. a
Bradstock, R. A.: A biogeographic model of fire regimes in Australia: current
and future implications, Global Ecol. Biogeogr., 19, 145–158,
https://doi.org/10.1111/j.1466-8238.2009.00512.x, 2010. a
Brey, S. J., Barnes, E. A., Pierce, J. R., Wiedinmyer, C., and Fischer, E. V.:
Environmental Conditions, Ignition Type, and Air Quality Impacts
of Wildfires in the Southeastern and Western United States, Earth's
Future, 6, 1442–1456, https://doi.org/10.1029/2018EF000972, 2018. a
Brey, S. J., Barnes, E. A., Pierce, J. R., Swann, A. L. S., and Fischer, E. V.:
Past Variance and Future Projections of the Environmental Conditions Driving
Western U.S. Summertime Wildfire Burn Area, Earth's Future, 9, e2020EF001645,
https://doi.org/10.1029/2020EF001645, 2021. a
Buch, J., Williams, A. P., Juang, C., Hansen, W. D., and Gentine, P.: SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States (1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7277980, 2022. a
Burke, M., Heft-Neal, S., Li, J., Driscoll, A., Baylis, P., Stigler, M., Weill,
J. A., Burney, J. A., Wen, J., Childs, M. L., and Gould, C. F.: Exposures and
behavioural responses to wildfire smoke, Nature Human Behaviour, 1351–1361,
https://doi.org/10.1038/s41562-022-01396-6, 2022. a
Carreau, J. and Bengio, Y.: A Hybrid Pareto Model for Conditional Density
Estimation of Asymmetric Fat-Tail Data, in: Proceedings of the Eleventh
International Conference on Artificial Intelligence and Statistics, edited by:
Meila, M. and Shen, X., vol. 2 of Proceedings of Machine Learning
Research, 51–58, PMLR, San Juan, Puerto Rico,
https://proceedings.mlr.press/v2/carreau07a.html (last access: 23 October 2022), 2007. a
Chatterji, N. S., Haque, S., and Hashimoto, T.: Undersampling is a
Minimax Optimal Robustness Intervention in Nonparametric Classification, ArXiv,
arXiv e-prints, 2022. a
Chen, B., Jin, Y., Scaduto, E., Moritz, M. A., Goulden, M. L., and Randerson,
J. T.: Climate, Fuel, and Land Use Shaped the Spatial Pattern of
Wildfire in California's Sierra Nevada, J. Geophys.
Res.-Biogeo., 126, e2020JG005786, https://doi.org/10.1029/2020JG005786, 2021. a
Coffield, S. R., Graff, C. A., Chen, Y., Smyth, P., Foufoula-Georgiou, E.,
Randerson, J. T., Coffield, S. R., Graff, C. A., Chen, Y., Smyth, P.,
Foufoula-Georgiou, E., and Randerson, J. T.: Machine learning to predict
final fire size at the time of ignition, Int. J. Wildland Fire, 28, 861–873, https://doi.org/10.1071/WF19023, 2019. a
Cohen, J. E. and Xu, M.: Random sampling of skewed distributions implies
Taylor's power law of fluctuation scaling, P. Natl. Acad. Sci. USA, 112, 7749–7754, https://doi.org/10.1073/pnas.1503824112, 2015. a
Coop, J. D., Parks, S. A., Stevens-Rumann, C. S., Crausbay, S. D., Higuera,
P. E., Hurteau, M. D., Tepley, A., Whitman, E., Assal, T., Collins, B. M.,
Davis, K. T., Dobrowski, S., Falk, D. A., Fornwalt, P. J., Fulé, P. Z.,
Harvey, B. J., Kane, V. R., Littlefield, C. E., Margolis, E. Q., North, M.,
Parisien, M.-A., Prichard, S., and Rodman, K. C.: Wildfire-Driven Forest
Conversion in Western North American Landscapes, BioScience, 70,
659–673, https://doi.org/10.1093/biosci/biaa061, 2020. a
Crimmins, M. A., Comrie, A. C., Crimmins, M. A., and Comrie, A. C.:
Interactions between antecedent climate and wildfire variability across
south-eastern Arizona, Int. J. Wildland Fire, 13,
455–466, https://doi.org/10.1071/WF03064, 2004. a
Daly, C., Gibson, W., Doggett, M., Smith, J., and Taylor, G.: Up-to-date
monthly climate maps for the conterminous United States, Proc., 14th AMS
Conf. on Applied Climatology, 13–16 January 2004, Seattle, WA, USA, 84th AMS Annual Meeting Combined Preprints, Paper P5.1,
2004. a
Dennison, P. E., Brewer, S. C., Arnold, J. D., and Moritz, M. A.: Large
wildfire trends in the western United States, 1984–2011, Geophys. Res. Lett., 41, 2928–2933, https://doi.org/10.1002/2014GL059576, 2014. a, b
Didan, K.: MOD13Q1 MODIS/Terra vegetation indices 16-day L3 global 250m SIN
grid V006, NASA EOSDIS Land Processes DAAC, 10, 415, https://doi.org/10.5067/MODIS/MOD13Q1.006, 2015. a
Dillon, G. K., Holden, Z. A., Morgan, P., Crimmins, M. A., Heyerdahl, E. K.,
and Luce, C. H.: Both topography and climate affected forest and woodland
burn severity in two regions of the western US, 1984 to 2006, Ecosphere, 2, 130,
https://doi.org/10.1890/ES11-00271.1, 2011. a
Ebert-Uphoff, I., Lagerquist, R., Hilburn, K., Lee, Y., Haynes, K., Stock, J.,
Kumler, C., and Stewart, J. Q.: CIRA Guide to Custom Loss Functions for
Neural Networks in Environmental Sciences – Version 1,
https://arxiv.org/abs/2106.09757 (last access: 14 June 2023), 2021. a
Eidenshink, J. C., Schwind, B., Brewer, K., Zhu, Z.-L., Quayle, B., and Howard,
S. M.: A project for monitoring trends in burn severity, Fire Ecology, 3,
3–21, https://doi.org/10.4996/fireecology.0301003, 2007. a
Fosberg, M. A.: Weather in wildland fire management: The fire-weather index,
Paper presented at the Conference on Sierra Nevada Meteorology, 19–21 June 1978, South Lake Tahoe, California, Am.
Meteorol. Soc., 1978. a
Giglio, L., Randerson, J. T., and van der Werf, G. R.: Analysis of daily,
monthly, and annual burned area using the fourth-generation global fire
emissions database (GFED4), J. Geophys. Res.-Biogeo.,
118, 317–328, https://doi.org/10.1002/jgrg.20042, 2013. a
Gutierrez, A. A., Hantson, S., Langenbrunner, B., Chen, B., Jin, Y., Goulden,
M. L., and Randerson, J. T.: Wildfire response to changing daily temperature
extremes in California's Sierra Nevada, Sci. Adv., 7, eabe6417,
https://doi.org/10.1126/sciadv.abe6417, 2021. a
Hansen, W. D., Braziunas, K. H., Rammer, W., Seidl, R., and Turner, M. G.: It
takes a few to tango: changing climate and fire regimes can cause
regeneration failure of two subalpine conifers, Ecology, 99, 966–977,
https://doi.org/10.1002/ecy.2181, 2018. a
Hansen, W. D., Krawchuk, M. A., Trugman, A. T., and Williams, A. P.: The
Dynamic Temperate and Boreal Fire and Forest-Ecosystem Simulator
(DYNAFFOREST): Development and evaluation, Environ. Model.
Softw., 156, 105473, https://doi.org/10.1016/j.envsoft.2022.105473,
2022. a, b
Harris, L. and Taylor, A. H.: Previous burns and topography limit and reinforce
fire severity in a large wildfire, Ecosphere, 8, e02019,
https://doi.org/10.1002/ecs2.2019, 2017. a
Higuera, P. E., Brubaker, L. B., Anderson, P. M., Hu, F. S., and Brown, T. A.:
Vegetation mediated the impacts of postglacial climate change on fire regimes
in the south-central Brooks Range, Alaska, Ecol. Monogr., 79,
201–219, https://doi.org/10.1890/07-2019.1, 2009. a
Holsinger, L., Parks, S. A., and Miller, C.: Weather, fuels, and topography
impede wildland fire spread in western US landscapes, Forest Ecol.
Manage., 380, 59–69, https://doi.org/10.1016/j.foreco.2016.08.035,
2016. a
Hooker, G., Mentch, L., and Zhou, S.: Unrestricted permutation forces
extrapolation: variable importance requires at least one more model, or there
is no free variable importance, Stat. Comput., 31, 82,
https://doi.org/10.1007/s11222-021-10057-z, 2021. a
Hurteau, M. D., Liang, S., Westerling, A. L., and Wiedinmyer, C.:
Vegetation-fire feedback reduces projected area burned under climate change,
Sci. Rep., 9, 2838, https://doi.org/10.1038/s41598-019-39284-1, 2019. a
Iglesias, V., Balch, J. K., and Travis, W. R.: U.S. fires became larger, more
frequent, and more widespread in the 2000s, Sci. Adv., 8, eabc0020,
https://doi.org/10.1126/sciadv.abc0020, 2022. a
Jain, P., Coogan, S. C., Subramanian, S. G., Crowley, M., Taylor, S., and
Flannigan, M. D.: A review of machine learning applications in wildfire
science and management, Environ. Rev., 28, 478–505,
https://doi.org/10.1139/er-2020-0019, 2020. a
Jia, S., Kim, S. H., Nghiem, S. V., Doherty, P., and Kafatos, M. C.: Patterns
of population displacement during mega-fires in California detected using
Facebook Disaster Maps, Environ. Res. Lett., 15, 074029,
https://doi.org/10.1088/1748-9326/ab8847, 2020. a
Jong-Levinger, A., Banerjee, T., Houston, D., and Sanders, B. F.: Compound
Post-Fire Flood Hazards Considering Infrastructure Sedimentation, Earth's
Future, 10, e2022EF002670, https://doi.org/10.1029/2022EF002670, 2022. a
Joseph, M. B., Rossi, M. W., Mietkiewicz, N. P., Mahood, A. L., Cattau, M. E.,
Denis, L. A. S., Nagy, R. C., Iglesias, V., Abatzoglou, J. T., and Balch,
J. K.: Spatiotemporal prediction of wildfire size extremes with Bayesian
finite sample maxima, Ecol. Appl., 29, e01898, https://doi.org/10.1002/eap.1898,
2019. a, b, c
Joshi, J. and Sukumar, R.: Improving prediction and assessment of global fires
using multilayer neural networks, Sci. Rep., 11, 3295,
https://doi.org/10.1038/s41598-021-81233-4, 2021. a
Juang, C. and Williams, P.: Western US MTBS-Interagency (WUMI) wildfire dataset, Dryad [data set], https://doi.org/10.5061/dryad.sf7m0cg72, 2022. a
Juang, C. S., Williams, A. P., Abatzoglou, J. T., Balch, J. K., Hurteau, M. D.,
and Moritz, M. A.: Rapid Growth of Large Forest Fires Drives the
Exponential Response of Annual Forest-Fire Area to Aridity in
the Western United States, Geophys. Res. Lett., 49, e2021GL097131,
https://doi.org/10.1029/2021GL097131, 2022. a, b, c, d, e
Kalashnikov, D. A., Abatzoglou, J. T., Nauslar, N. J., Swain, D. L., Touma, D.,
and Singh, D.: Meteorological and geographical factors associated with dry
lightning in central and northern California, Environ. Res.-Climate, 1, 025001, https://doi.org/10.1088/2752-5295/ac84a0, 2022. a
Keeley, J. E. and Syphard, A. D.: Historical patterns of wildfire ignition
sources in California ecosystems, Int. J. Wildland Fire,
27, 781–799, https://doi.org/10.1071/WF18026, 2018. a, b
Keeley, J. E., Guzman-Morales, J., Gershunov, A., Syphard, A. D., Cayan, D.,
Pierce, D. W., Flannigan, M., and Brown, T. J.: Ignitions explain more than
temperature or precipitation in driving Santa Ana wind fires, Sci. Adv., 7, eabh2262, https://doi.org/10.1126/sciadv.abh2262, 2021. a
Kitzberger, T., Falk, D. A., Westerling, A. L., and Swetnam, T. W.: Direct and
indirect climate controls predict heterogeneous early-mid 21st century
wildfire burned area across western and boreal North America, PLOS ONE,
12, e0188486, https://doi.org/10.1371/journal.pone.0188486, 2017. a
Klein Goldewijk, K. and Ramankutty, N.: Land cover change over the last three
centuries due to human activities: The availability of new global data
sets, GeoJournal, 61, 335–344, https://doi.org/10.1007/s10708-004-5050-z, 2004. a
Knapp, P. A.: Spatio-Temporal Patterns of Large Grassland Fires in
the Intermountain West, U.S.A., Global Ecol. Biogeogr.
Lett., 7, 259, https://doi.org/10.2307/2997600, 1998. a
Knorr, W., Kaminski, T., Arneth, A., and Weber, U.: Impact of human population density on fire frequency at the global scale, Biogeosciences, 11, 1085–1102, https://doi.org/10.5194/bg-11-1085-2014, 2014. a
Kondylatos, S., Prapas, I., Ronco, M., Papoutsis, I., Camps-Valls, G., Piles,
M., Fernandez-Torres, M.-A., and Carvalhais, N.: Wildfire Danger Prediction
and Understanding With Deep Learning, Geophys. Res. Lett., 49, e2022GL099368,
https://doi.org/10.1029/2022GL099368, 2022. a
Krawchuk, M. A., Moritz, M. A., Parisien, M.-A., Van Dorn, J., and Hayhoe, K.:
Global Pyrogeography: the Current and Future Distribution of
Wildfire, PLoS ONE, 4, e5102, https://doi.org/10.1371/journal.pone.0005102, 2009. a
Kuhn-Régnier, A., Voulgarakis, A., Nowack, P., Forkel, M., Prentice, I. C., and Harrison, S. P.: The importance of antecedent vegetation and drought conditions as global drivers of burnt area, Biogeosciences, 18, 3861–3879, https://doi.org/10.5194/bg-18-3861-2021, 2021. a, b
Levin, R., Cherepanova, V., Schwarzschild, A., Bansal, A., Bruss, C. B.,
Goldstein, T., Wilson, A. G., and Goldblum, M.: Transfer Learning with Deep
Tabular Models, ArXiv, arXiv preprint arXiv:2206.15306, 2022. a
Li, F., Zeng, X. D., and Levis, S.: A process-based fire parameterization of intermediate complexity in a Dynamic Global Vegetation Model, Biogeosciences, 9, 2761–2780, https://doi.org/10.5194/bg-9-2761-2012, 2012. a
Li, S. and Banerjee, T.: Spatial and temporal pattern of wildfires in
California from 2000 to 2019, Sci. Rep., 11, 8779,
https://doi.org/10.1038/s41598-021-88131-9, 2021. a
Littell, J. S., McKenzie, D., Peterson, D. L., and Westerling, A. L.: Climate
and wildfire area burned in western U.S. ecoprovinces, 1916–2003,
Ecol. Appl., 19, 1003–1021,
https://doi.org/10.1890/07-1183.1, 2009. a, b
Lundberg, S. M. and Lee, S.-I.: A Unified Approach to Interpreting Model
Predictions, in: Advances in Neural Information Processing Systems 30, edited
by: Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R.,
Vishwanathan, S., and Garnett, R., 4765–4774,
http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf (last access: 23 October 2022),
2017. a
Marlon, J. R., Bartlein, P. J., Carcaillet, C., Gavin, D. G., Harrison, S. P.,
Higuera, P. E., Joos, F., Power, M. J., and Prentice, I. C.: Climate and
human influences on global biomass burning over the past two millennia,
Nat. Geosci., 1, 697–702, https://doi.org/10.1038/ngeo313, 2008. a
Marlon, J. R., Bartlein, P. J., Gavin, D. G., Long, C. J., Anderson, R. S.,
Briles, C. E., Brown, K. J., Colombaroli, D., Hallett, D. J., Power, M. J.,
Scharf, E. A., and Walsh, M. K.: Long-term perspective on wildfires in the
western USA, P. Natl. Acad. Sci. USA, 109, E535–E543,
https://doi.org/10.1073/pnas.1112839109, 2012. a
Monteith, J. L.: Evaporation and environment, in: Symposia of the society for
experimental biology, 19, 205–234, Cambridge University Press
(CUP), https://scholar.google.com/scholar_lookup?title=Evaporation+and+environment+in+the+State+and+Movement+of+Water+in+Living+Organisms&author=Monteith,+J.L.&publication_year=1965&pages=205-234 (last access: 16 June 2023), 1965. a
Moritz, M. A., Moody, T. J., Krawchuk, M. A., Hughes, M., and Hall, A.: Spatial
variation in extreme winds predicts large wildfire locations in chaparral
ecosystems, Geophys. Res. Lett., 37, L04801,
https://doi.org/10.1029/2009GL041735, 2010. a
Nadarajah, S., Zhang, Y., and Pogány, T. K.: On sums of independent
Generalized Pareto random variables with applications to Insurance and CAT
bonds, Probab. Eng. Inform. Sc., 32,
296–305, https://doi.org/10.1017/S0269964817000055, 2018. a
O'Dell, K., Ford, B., Fischer, E. V., and Pierce, J. R.: Contribution of
Wildland-Fire Smoke to US PM2.5 and Its Influence on Recent
Trends, Environ. Sci. Technol., 53, 1797–1804,
https://doi.org/10.1021/acs.est.8b05430, 2019. a
Orville, R. E. and Huffines, G. R.: Cloud-to-Ground Lightning in the United
States: NLDN Results in the First Decade, 1989–98, Mon. Weather
Rev., 129, 1179–1193,
https://doi.org/10.1175/1520-0493(2001)129<1179:CTGLIT>2.0.CO;2, 2001. a
Parisien, M.-A. and Moritz, M. A.: Environmental controls on the distribution
of wildfire at multiple spatial scales, Ecol. Monogr., 79, 127–154,
https://doi.org/10.1890/07-1289.1, 2009. a, b, c, d
Parisien, M.-A., Snetsinger, S., Greenberg, J. A., Nelson, C. R., Schoennagel,
T., Dobrowski, S. Z., and Moritz, M. A.: Spatial variability in wildfire
probability across the western United States, Int. J. Wildland Fire, 21, 313, https://doi.org/10.1071/WF11044, 2012. a
Parks, S. A., Miller, C., Parisien, M.-A., Holsinger, L. M., Dobrowski, S. Z.,
and Abatzoglou, J.: Wildland fire deficit and surplus in the western United
States, 1984–2012, Ecosphere, 6, 1–13,
https://doi.org/10.1890/ES15-00294.1, 2015. a
Parks, S. A., Parisien, M.-A., Miller, C., Holsinger, L. M., and Baggett,
L. S.: Fine-scale spatial climate variation and drought mediate the
likelihood of reburning, Ecol. Appl., 28, 573–586,
https://doi.org/10.1002/eap.1671, 2018. a
Perez-Cruz, F.: Kullback-Leibler divergence estimation of continuous
distributions, in: 2008 IEEE International Symposium on Information Theory, 6–11 July 2008, Toronto, ON, Canada,
1666–1670, https://doi.org/10.1109/ISIT.2008.4595271, 2008. a
Potter, B. E. and McEvoy, D.: Weather Factors Associated with Extremely Large
Fires and Fire Growth Days, Earth Interactions, 25, 160–176,
https://doi.org/10.1175/EI-D-21-0008.1, 2021. a
Pureswaran, D. S., Roques, A., and Battisti, A.: Forest Insects and Climate
Change, Current Forestry Reports, 4, 35–50,
https://doi.org/10.1007/s40725-018-0075-6, 2018. a
Rabin, S. S., Melton, J. R., Lasslop, G., Bachelet, D., Forrest, M., Hantson, S., Kaplan, J. O., Li, F., Mangeon, S., Ward, D. S., Yue, C., Arora, V. K., Hickler, T., Kloster, S., Knorr, W., Nieradzik, L., Spessa, A., Folberth, G. A., Sheehan, T., Voulgarakis, A., Kelley, D. I., Prentice, I. C., Sitch, S., Harrison, S., and Arneth, A.: The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols with detailed model descriptions, Geosci. Model Dev., 10, 1175–1197, https://doi.org/10.5194/gmd-10-1175-2017, 2017. a
Radeloff, V. C., Hammer, R. B., Stewart, S. I., Fried, J. S., Holcomb, S. S.,
and McKeefry, J. F.: The Wildland-Urban Interface in the United
States, Ecol. Appl., 15, 799–805,
https://doi.org/10.1890/04-1413, 2005. a
Rahimi, S., Krantz, W., Lin, Y., Bass, B., Goldenson, N., Hall, A., Jebo, Z.,
and Norris, J.: Evaluation of a Reanalysis-Driven Configuration of WRF4 Over
the Western United States From 1980–2020, J. Geophys. Res.-Atmos., 127, e2021JD035699, https://doi.org/10.1029/2021JD035699, 2022. a
Rao, K., Williams, A. P., Diffenbaugh, N. S., Yebra, M., and Konings, A. G.:
Plant-water sensitivity regulates wildfire vulnerability, Nat. Ecol.
Evol., 6, 332–339, https://doi.org/10.1038/s41559-021-01654-2, 2022. a
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep learning to represent subgrid
processes in climate models, P. Natl. Acad. Sci. USA,
115, 9684–9689, https://doi.org/10.1073/pnas.1810286115, 2018. a
Richards, J., Huser, R., Bevacqua, E., and Zscheischler, J.: Insights into the
drivers and spatio-temporal trends of extreme Mediterranean wildfires with
statistical deep-learning, ArXiv, arXiv preprint arXiv:2212.01796, 2022. a
Rigden, A. J., Powell, R. S., Trevino, A., McColl, K. A., and Huybers, P.:
Microwave Retrievals of Soil Moisture Improve Grassland Wildfire
Predictions, Geophys. Res. Lett., 47, e2020GL091410,
https://doi.org/10.1029/2020GL091410, 2020. a
Riley, K. and Thompson, M.: An Uncertainty Analysis of Wildfire Modeling,
chap. 13, 191–213, American Geophysical Union (AGU),
https://doi.org/10.1002/9781119028116.ch13, 2016. a
Rollins, M. G.: LANDFIRE: a nationally consistent vegetation, wildland fire,
and fuel assessment, Int. J. Wildland Fire, 18, 235–249,
https://doi.org/10.1071/WF08088, 2009. a
Rollins, M. G., Morgan, P., and Swetnam, T.: Landscape-scale controls over 20th
century fire occurrence in two large Rocky Mountain (USA) wilderness
areas, Landscape Ecol., 17, 539–557, https://doi.org/10.1023/A:1021584519109, 2002. a, b
Romps, D. M., Seeley, J. T., Vollaro, D., and Molinari, J.: Projected increase
in lightning strikes in the United States due to global warming, Science,
346, 851–854, https://doi.org/10.1126/science.1259100, 2014. a
Schoenberg, F. P., Peng, R., and Woods, J.: On the distribution of wildfire
sizes, Environmetrics, 14, 583–592, https://doi.org/10.1002/env.605, 2003. a
Scollnik, D. P. M.: On composite lognormal-Pareto models, Scandinavian
Actuarial Journal, 2007, 20–33, https://doi.org/10.1080/03461230601110447, 2007. a
Seager, R., Hooks, A., Williams, A. P., Cook, B., Nakamura, J., and Henderson,
N.: Climatology, Variability, and Trends in the U.S. Vapor Pressure
Deficit, an Important Fire-Related Meteorological Quantity, J.
Appl. Meteorol. Climatol., 54, 1121–1141,
https://doi.org/10.1175/JAMC-D-14-0321.1, 2015. a
Spawn, S. A., Sullivan, C. C., Lark, T. J., and Gibbs, H. K.: Harmonized global
maps of above and belowground biomass carbon density in the year 2010,
Sci. Data, 7, 112, https://doi.org/10.1038/s41597-020-0444-4, 2020. a
Sullivan, A. L.: Wildland surface fire spread modelling, 1990–2007. 3:
Simulation and mathematical analogue models, Int. J. Wildland Fire, 18, 387–403, 2009. a
Swetnam, T. W. and Betancourt, J. L.: Mesoscale Disturbance and Ecological
Response to Decadal Climatic Variability in the American Southwest, J. Climate, 11, 3128–3147,
https://doi.org/10.1175/1520-0442(1998)011<3128:MDAERT>2.0.CO;2, 1998. a
Tschumi, E., Lienert, S., van der Wiel, K., Joos, F., and Zscheischler, J.: The effects of varying drought-heat signatures on terrestrial carbon dynamics and vegetation composition, Biogeosciences, 19, 1979–1993, https://doi.org/10.5194/bg-19-1979-2022, 2022. a
Vose, R., Applequist, S., Squires, M., Durre, I., Menne, M., Williams, C.,
Fenimore, C., Gleason, K., and Arndt, D.: Improved Historical Temperature
and Precipitation Time Series for U.S. Climate Divisions, J.
Appl. Meteorol. Climatol., 53, 1232–1251,
https://doi.org/10.1175/JAMC-D-13-0248.1, 2014. a
Wacker, R. S. and Orville, R. E.: Changes in measured lightning flash count and
return stroke peak current after the 1994 U.S. National Lightning
Detection Network upgrade: 1. Observations, J. Geophys.
Res.-Atmos., 104, 2151–2157,
https://doi.org/10.1029/1998JD200060, 1999. a
Wang, S. S.-C. and Wang, Y.: Quantifying the effects of environmental factors on wildfire burned area in the south central US using integrated machine learning techniques, Atmos. Chem. Phys., 20, 11065–11087, https://doi.org/10.5194/acp-20-11065-2020, 2020. a, b
Wang, S. S.-C., Qian, Y., Leung, L. R., and Zhang, Y.: Identifying Key
Drivers of Wildfires in the Contiguous US Using Machine
Learning and Game Theory Interpretation, Earth's Future, 9, e2020EF001910,
https://doi.org/10.1029/2020EF001910, 2021. a, b, c
Westerling, A. L.: Increasing western US forest wildfire activity:
sensitivity to changes in the timing of spring, Philos. T.
Roy. Soc. B, 371, 20150178, https://doi.org/10.1098/rstb.2015.0178,
2016. a
Westerling, A. L., Hidalgo, H. G., Cayan, D. R., and Swetnam, T. W.: Warming
and Earlier Spring Increase Western U.S. Forest Wildfire Activity, Science,
313, 940–943, https://doi.org/10.1126/science.1128834, 2006. a
Westerling, A. L., Turner, M. G., Smithwick, E. A. H., Romme, W. H., and Ryan,
M. G.: Continued warming could transform Greater Yellowstone fire regimes
by mid-21st century, P. Natl. Acad. Sci. USA, 108,
13165–13170, https://doi.org/10.1073/pnas.1110199108, 2011. a, b
Williams, A. P. and Abatzoglou, J. T.: Recent Advances and Remaining
Uncertainties in Resolving Past and Future Climate Effects on
Global Fire Activity, Current Climate Change Reports, 2, 1–14,
https://doi.org/10.1007/s40641-016-0031-0, 2016. a
Williams, A. P., Allen, C. D., Macalady, A. K., Griffin, D., Woodhouse, C. A.,
Meko, D. M., Swetnam, T. W., Rauscher, S. A., Seager, R., Grissino-Mayer,
H. D., Dean, J. S., Cook, E. R., Gangodagamage, C., Cai, M., and McDowell,
N. G.: Temperature as a potent driver of regional forest drought stress and
tree mortality, Nat. Clim. Change, 3, 292–297,
https://doi.org/10.1038/nclimate1693, 2013. a
Williams, A. P., Abatzoglou, J. T., Gershunov, A., Guzman‐Morales, J.,
Bishop, D. A., Balch, J. K., and Lettenmaier, D. P.: Observed Impacts of
Anthropogenic Climate Change on Wildfire in California, Earth's
Future, 7, 892–910, https://doi.org/10.1029/2019EF001210, 2019. a, b
Williams, A. P., Livneh, B., McKinnon, K. A., Hansen, W. D., Mankin, J. S.,
Cook, B. I., Smerdon, J. E., Varuolo-Clarke, A. M., Bjarke, N. R., Juang,
C. S., and Lettenmaier, D. P.: Growing impact of wildfire on western US water
supply, P. Natl. Acad. Sci. USA, 119, e2114069119,
https://doi.org/10.1073/pnas.2114069119, 2022. a
Wu, X., Liu, H., Hartmann, H., Ciais, P., Kimball, J. S., Schwalm, C. R.,
Camarero, J. J., Chen, A., Gentine, P., Yang, Y., Zhang, S., Li, X., Xu, C.,
Zhang, W., Li, Z., and Chen, D.: Timing and Order of Extreme Drought and
Wetness Determine Bioclimatic Sensitivity of Tree Growth, Earth's Future, 10,
e2021EF002530, https://doi.org/10.1029/2021EF002530, 2022. a
Xie, Y., Lin, M., Decharme, B., Delire, C., Horowitz, L. W., Lawrence, D. M.,
Li, F., and Séférian, R.: Tripling of western US particulate pollution from
wildfires in a warming climate, P. Natl. Acad. Sci. USA, 119, e2111372119, https://doi.org/10.1073/pnas.2111372119, 2022. a
Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Bender, S. M., Case, A.,
Costello, C., Dewitz, J., Fry, J., Funk, M., Granneman, B., Liknes, G. C.,
Rigge, M., and Xian, G.: A new generation of the United States National
Land Cover Database: Requirements, research priorities, design, and
implementation strategies, ISPRS J. Photogramm. Remote, 146, 108–123,
https://doi.org/10.1016/j.isprsjprs.2018.09.006, 2018. a
Yuval, J. and O'Gorman, P. A.: Stable machine-learning parameterization of
subgrid processes for climate modeling at a range of resolutions, Nat.
Commun., 11, 3295, https://doi.org/10.1038/s41467-020-17142-3, 2020. a
Zeng, X., Broxton, P., and Dawson, N.: Snowpack Change From 1982 to 2016 Over
Conterminous United States, Geophys. Res. Lett., 45,
12940–12947, https://doi.org/10.1029/2018GL079621, 2018.
a
Zeng, X., Broxton, P., and Dawson, N.: Daily 4 km Gridded SWE and Snow
Depth from Assimilated In-Situ and Modeled Data over the Conterminous US,
Version 1, NASA National Snow and Ice Data Center Distributed Active Archive
Center [data set], https://doi.org/10.5067/0GGPB220EX6A, 2019. a
Zheng, B., Ciais, P., Chevallier, F., Chuvieco, E., Chen, Y., and Yang, H.:
Increasing forest fire emissions despite the decline in global burned area,
Sci. Adv., 7, eabh2646, https://doi.org/10.1126/sciadv.abh2646, 2021. a
Zhou, S., Williams, A. P., Berg, A. M., Cook, B. I., Zhang, Y., Hagemann, S.,
Lorenz, R., Seneviratne, S. I., and Gentine, P.: Land-atmosphere feedbacks
exacerbate concurrent soil drought and atmospheric aridity, P. Natl. Acad. Sci. USA, 116, 18848–18853,
https://doi.org/10.1073/pnas.1904955116, 2019. a
Zhuang, Y., Fu, R., Santer, B. D., Dickinson, R. E., and Hall, A.: Quantifying
contributions of natural variability and anthropogenic forcings on increased
fire weather risk over the western United States, P. Natl. Acad. Sci. USA, 118, e2111875118, https://doi.org/10.1073/pnas.2111875118, 2021. a
Zou, Y., Wang, Y., Qian, Y., Tian, H., Yang, J., and Alvarado, E.: Using CESM-RESFire to understand climate–fire–ecosystem interactions and the implications for decadal climate variability, Atmos. Chem. Phys., 20, 995–1020, https://doi.org/10.5194/acp-20-995-2020, 2020. a
Short summary
We leverage machine learning techniques to construct a statistical model of grid-scale fire frequencies and sizes using climate, vegetation, and human predictors. Our model reproduces the observed trends in fire activity across multiple regions and timescales. We provide uncertainty estimates to inform resource allocation plans for fuel treatment and fire management. Altogether the accuracy and efficiency of our model make it ideal for coupled use with large-scale dynamical vegetation models.
We leverage machine learning techniques to construct a statistical model of grid-scale fire...