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Improving prediction and assessment of global fires using multilayer neural networks

  • 1.

    Bowman, D. M. et al. Fire in the earth system. Science 324, 481–484. https://doi.org/10.1126/science.1163886 (2009).

    ADS  CAS  Article  PubMed  Google Scholar 

  • 2.

    Scott, A. C. The pre-quaternary history of fire. Palaeogeogr. Palaeoclimatol. Palaeoecol. 164, 281–329. https://doi.org/10.1016/s0031-0182(00)00192-9 (2000).

    Article  Google Scholar 

  • 3.

    Roebroeks, W. & Villa, P. On the earliest evidence for habitual use of fire in Europe. Proc. Natl. Acad. Sci. 108, 5209–5214. https://doi.org/10.1073/pnas.1018116108 (2011).

    ADS  Article  PubMed  Google Scholar 

  • 4.

    Flannigan, M. D., Krawchuk, M. A., de Groot, W. J., Wotton, B. M. & Gowman, L. M. Implications of changing climate for global wildland fire. Int. J. Wildl. Fire 18, 483–507. https://doi.org/10.1071/wf08187 (2009).

    Article  Google Scholar 

  • 5.

    Hantson, S., Pueyo, S. & Chuvieco, E. Global fire size distribution is driven by human impact and climate: Spatial trends in global fire size distribution. Glob. Ecol. Biogeogr. 24, 77–86. https://doi.org/10.1111/geb.12246 (2015).

    Article  Google Scholar 

  • 6.

    Bond, W. J., Woodward, F. I. & Midgley, G. F. The global distribution of ecosystems in a world without fire. New Phytol. 165, 525–538 (2005).

    CAS  Article  Google Scholar 

  • 7.

    Lasslop, G., Brovkin, V., Reick, C. H., Bathiany, S. & Kloster, S. Multiple stable states of tree cover in a global land surface model due to a fire-vegetation feedback. Geophys. Res. Lett. 43, 6324–6331. https://doi.org/10.1002/2016gl069365 (2016).

    ADS  Article  Google Scholar 

  • 8.

    Giglio, L., Randerson, J. T. & van der Werf, G. R. Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4): ANALYSIS OF BURNED AREA. J. Geophys. Res. Biogeosci. 118, 317–328. https://doi.org/10.1002/jgrg.20042 (2013).

    Article  Google Scholar 

  • 9.

    van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720. https://doi.org/10.5194/essd-9-697-2017 (2017).

    ADS  Article  Google Scholar 

  • 10.

    Loehman, R. A., Reinhardt, E. & Riley, K. L. Wildland fire emissions, carbon, and climate: Seeing the forest and the trees-a cross-scale assessment of wildfire and carbon dynamics in fire-prone, forested ecosystems. For. Ecol. Manag. 317, 9–19. https://doi.org/10.1016/j.foreco.2013.04.014 (2014).

    Article  Google Scholar 

  • 11.

    Landry, J.-S. & Matthews, H. D. Non-deforestation fire vs. fossil fuel combustion: the source of CO(_{{2}}) emissions affects the global carbon cycle and climate responses. Biogeosciences 13, 2137–2149. https://doi.org/10.5194/bg-13-2137-2016 (2016).

    ADS  Article  Google Scholar 

  • 12.

    Fischer, A. P. et al. Wildfire risk as a socioecological pathology. Front. Ecol. Environ. 14, 276–284. https://doi.org/10.1126/science.11638860 (2016).

    Article  Google Scholar 

  • 13.

    Langmann, B., Duncan, B., Textor, C., Trentmann, J. & van der Werf, G. R. Vegetation fire emissions and their impact on air pollution and climate. Atmos. Environ. 43, 107–116. https://doi.org/10.1126/science.11638861 (2009).

    ADS  CAS  Article  Google Scholar 

  • 14.

    Urbanski, S. Wildland fire emissions, carbon, and climate: Emission factors. For. Ecol. Manag. 317, 51–60. https://doi.org/10.1016/j.foreco.2013.05.045 (2014).

    Article  Google Scholar 

  • 15.

    Veraverbeke, S., Verstraeten, W. W., Lhermitte, S., Van De Kerchove, R. & Goossens, R. Assessment of post-fire changes in land surface temperature and surface albedo, and their relation with fire – burn severity using multitemporal MODIS imagery. Int. J. Wildl. Fire 21, 243. https://doi.org/10.1071/WF10075 (2012).

    Article  Google Scholar 

  • 16.

    Bowman, D. M. J. S., Murphy, B. P., Williamson, G. J. & Cochrane, M. A. Pyrogeographic models, feedbacks and the future of global fire regimes: Correspondence. Glob. Ecol. Biogeogr. 23, 821–824. https://doi.org/10.1126/science.11638864 (2014).

    Article  Google Scholar 

  • 17.

    Harris, R. M. B., Remenyi, T. A., Williamson, G. J., Bindoff, N. L. & Bowman, D. M. J. S. Climate-vegetation-fire interactions and feedbacks: Trivial detail or major barrier to projecting the future of the Earth system?: Climate-vegetation-fire interactions and feedbacks. Wiley Interdiscip. Rev. Clim. Change 7, 910–931. https://doi.org/10.1002/wcc.428 (2016).

    Article  Google Scholar 

  • 18.

    Bradstock, R. A. A biogeographic model of fire regimes in Australia: Current and future implications: A biogeographic model of fire in Australia. Glob. Ecol. Biogeogr. 19, 145–158. https://doi.org/10.1111/j.1466-8238.2009.00512.x (2010).

    Article  Google Scholar 

  • 19.

    Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362. https://doi.org/10.1126/science.aal4108 (2017).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 20.

    Pechony, O. & Shindell, D. T. Driving forces of global wildfires over the past millennium and the forthcoming century. Proc. Natl. Acad. Sci. 107, 19167–19170. https://doi.org/10.1073/pnas.1003669107 (2010).

    ADS  Article  PubMed  Google Scholar 

  • 21.

    Krawchuk, M. A., Moritz, M. A., Parisien, M.-A., Van Dorn, J. & Hayhoe, K. Global pyrogeography: The current and future distribution of wildfire. PLoS One 4, e5102 (2009).

    ADS  Article  Google Scholar 

  • 22.

    Pausas, J. G. & Keeley, J. E. Abrupt climate-independent fire regime changes. Ecosystems 17, 1109–1120. https://doi.org/10.1007/s10021-014-9773-5 (2014).

    CAS  Article  Google Scholar 

  • 23.

    Pausas, J. G. & Ribeiro, E. The global fire-productivity relationship: Fire and productivity. Glob. Ecol. Biogeogr. 22, 728–736. https://doi.org/10.1016/s0031-0182(00)00192-90 (2013).

    Article  Google Scholar 

  • 24.

    Mondal, N. & Sukumar, R. Fires in seasonally dry tropical forest: Testing the varying constraints hypothesis across a regional rainfall gradient. PLoS One 11, e0159691. https://doi.org/10.1371/journal.pone.0159691 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 25.

    Foley, J. A. et al. An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Glob. Biogeochem. Cycles 10, 603–628. https://doi.org/10.1029/96gb02692 (1996).

    ADS  CAS  Article  Google Scholar 

  • 26.

    Thonicke, K., Venevsky, S., Sitch, S. & Cramer, W. The role of fire disturbance for global vegetation dynamics: Coupling fire into a dynamic global vegetation model. Glob. Ecol. Biogeogr. 10, 661–677. https://doi.org/10.1046/j.1466-822x.2001.00175.x (2001).

    Article  Google Scholar 

  • 27.

    Thonicke, K. et al. The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model. Biogeosciences 7, 1991. https://doi.org/10.1016/s0031-0182(00)00192-94 (2010).

    ADS  CAS  Article  Google Scholar 

  • 28.

    Rabin, S. S. et al. 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).

    ADS  CAS  Article  Google Scholar 

  • 29.

    Li, F., Zeng, X. & Levis, S. A process-based fire parameterization of intermediate complexity in a dynamic global vegetation model. Biogeosciences 9, 2761–2780. https://doi.org/10.1016/s0031-0182(00)00192-96 (2012).

    ADS  Article  Google Scholar 

  • 30.

    Li, F., Levis, S. & Ward, D. Quantifying the role of fire in the earth system-part 1: Improved global fire modeling in the community earth system model (cesm1). Biogeosciences 10, 2293 (2013).

    ADS  CAS  Article  Google Scholar 

  • 31.

    Hantson, S. et al. Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project. Geosci. Model Dev. 13, 3299–3318. https://doi.org/10.5194/gmd-13-3299-2020 (2020).

    ADS  Article  Google Scholar 

  • 32.

    Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 7537. https://doi.org/10.1038/ncomms8537 (2015).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 33.

    Abatzoglou, J. T., Williams, A. P., Boschetti, L., Zubkova, M. & Kolden, C. A. Global patterns of interannual climate-fire relationships. Glob. Change Biol. 24, 5164–5175. https://doi.org/10.1016/s0031-0182(00)00192-98 (2018).

    ADS  Article  Google Scholar 

  • 34.

    Archibald, S., Roy, D. P., van Wilgen, B. W. & Scholes, R. J. What limits fire? An examination of drivers of burnt area in Southern Africa. Glob. Change Biol. 15, 613–630. https://doi.org/10.1111/j.1365-2486.2008.01754.x (2009).

    ADS  Article  Google Scholar 

  • 35.

    Aldersley, A., Murray, S. J. & Cornell, S. E. Global and regional analysis of climate and human drivers of wildfire. Sci. Total Environ. 409, 3472–3481. https://doi.org/10.1016/s0031-0182(00)00192-99 (2011).

    ADS  CAS  Article  PubMed  Google Scholar 

  • 36.

    Yang, L., Dawson, C. W., Brown, M. R. & Gell, M. Neural network and GA approaches for dwelling fire occurrence prediction. Knowl. Based Syst. 19, 213–219. https://doi.org/10.1016/j.knosys.2005.11.021 (2006).

    Article  Google Scholar 

  • 37.

    Dutta, R., Aryal, J., Das, A. & Kirkpatrick, J. B. Deep cognitive imaging systems enable estimation of continental-scale fire incidence from climate data. Sci. Rep. 3, 3188. https://doi.org/10.1038/srep03188 (2013).

    ADS  Article  PubMed  PubMed Central  Google Scholar 

  • 38.

    Satir, O., Berberoglu, S. & Donmez, C. Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem. Geomat. Nat. Hazards Risk 7, 1645–1658. https://doi.org/10.1080/19475705.2015.1084541 (2016).

    Article  Google Scholar 

  • 39.

    Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the cru ts3. 10 dataset. Int. J. Climatol. 34, 623–642 (2014).

    Article  Google Scholar 

  • 40.

    Adler, R. F. et al. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present). J. Hydrometeorol. 4, 1147–1167 (2003).

    ADS  Article  Google Scholar 

  • 41.

    Zhao, M., Heinsch, F. A., Nemani, R. R. & Running, S. W. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens. Environ. 95, 164–176. https://doi.org/10.1073/pnas.10181161083 (2005).

    ADS  Article  Google Scholar 

  • 42.

    Freire, S. & Pesaresi, M. Ghs population grid, derived from gpw4, multitemporal (1975, 1990, 2000, 2015).European Commission Joint Research Centre (JRC) (2015).

  • 43.

    Meijer, J. R., Huijbregts, M. A., Schotten, K. C. & Schipper, A. M. Global patterns of current and future road infrastructure. Environ. Res. Lett. 13, 064006 (2018).

    ADS  Article  Google Scholar 

  • 44.

    Friedl, M. A. et al. Modis collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182. https://doi.org/10.1073/pnas.10181161084 (2010).

    ADS  Article  Google Scholar 

  • 45.

    Channan, S., Collins, K. & Emanuel, W. Global Mosaics of the Standard Modis Land Cover Type Data Vol. 30 (University of Maryland and the Pacific Northwest National Laboratory, College Park, 2014).

    Google Scholar 

  • 46.

    Lay, E. H. et al. Wwll global lightning detection system: Regional validation study in brazil. Geophys. Res. Lett. 31, 20 (2004).

    Google Scholar 

  • 47.

    Veraverbeke, S. et al. Lightning as a major driver of recent large fire years in north American boreal forests. Nat. Clim. Change 7, 529–534 (2017).

    ADS  Article  Google Scholar 

  • 48.

    Chen, Y. et al. A pan-tropical cascade of fire driven by el niño/southern oscillation. Nat. Clim. Change 7, 906. https://doi.org/10.1038/s41558-017-0014-8 (2017).

    ADS  CAS  Article  Google Scholar 

  • 49.

    Aragão, L. E. O. C. et al. 21st century drought-related fires counteract the decline of amazon deforestation carbon emissions. Nat. Commun. 9, 536. https://doi.org/10.1038/s41467-017-02771-y (2018).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 50.

    Yin, Y. et al. Variability of fire carbon emissions in equatorial Asia and its nonlinear sensitivity to El Niño: FIRE CARBON EMISSIONS IN EQUATORIAL ASIA. Geophys. Res. Lett. 43, 10472–10479. https://doi.org/10.1073/pnas.10181161087 (2016).

    ADS  CAS  Article  Google Scholar 

  • 51.

    Verdon, D. C., Kiem, A. S. & Franks, S. W. Multi-decadal variability of forest fire risk-eastern Australia. Int. J. Wildl. Fire 13, 165–171. https://doi.org/10.1071/WF03034 (2004).

    Article  Google Scholar 

  • 52.

    Mariani, M., Fletcher, M.-S., Holz, A. & Nyman, P. Enso controls interannual fire activity in southeast Australia: Enso and fire activity in SE Australia. Geophys. Res. Lett. 43, 10891–10900. https://doi.org/10.1002/2016GL070572 (2016).

    ADS  Article  Google Scholar 

  • 53.

    Li, L.-M., Song, W.-G., Ma, J. & Satoh, K. Artificial neural network approach for modeling the impact of population density and weather parameters on forest fire risk. Int. J. Wildl. Fire 18, 640–647. https://doi.org/10.1071/WF07136 (2009).

    Article  Google Scholar 

  • 54.

    Vasilakos, C., Kalabokidis, K., Hatzopoulos, J. & Matsinos, I. Identifying wildland fire ignition factors through sensitivity analysis of a neural network. Nat. Hazards 50, 125–143. https://doi.org/10.1071/wf081871 (2009).

    Article  Google Scholar 

  • 55.

    Whitman, E., Parisien, M.-A., Thompson, D. K. & Flannigan, M. D. Short-interval wildfire and drought overwhelm boreal forest resilience. Sci. Rep. 9, 18796. https://doi.org/10.1038/s41598-019-55036-7 (2019).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 56.

    Hawbaker, T. J. et al. Human and biophysical influences on fire occurrence in the united states. Ecol. Appl. 23, 565–582 (2013).

    Article  Google Scholar 

  • 57.

    Bowman, D. M. J. S. et al. Human exposure and sensitivity to globally extreme wildfire events. Nat. Ecol. Evol. 1, 0058. https://doi.org/10.1038/s41559-016-0058 (2017).

    Article  Google Scholar 

  • 58.

    Andela, N. & van der Werf, G. R. Recent trends in African fires driven by cropland expansion and El Niño to La Niña transition. Nat. Clim. Change 4, 791–795. https://doi.org/10.1038/nclimate2313 (2014).

    ADS  Article  Google Scholar 

  • 59.

    Walker, X. J. et al. Fuel availability not fire weather controls boreal wildfire severity and carbon emissions. Nat. Clim. Changehttps://doi.org/10.1038/s41558-020-00920-8 (2020).

    Article  Google Scholar 

  • 60.

    Zubkova, M., Boschetti, L., Abatzoglou, J. T. & Giglio, L. Changes in fire activity in Africa from 2002 to 2016 and their potential drivers. Geophys. Res. Lett. 46, 7643–7653. https://doi.org/10.1029/2019GL083469 (2019).

    ADS  Article  PubMed  PubMed Central  Google Scholar 

  • 61.

    Moritz, M. A. et al. Climate change and disruptions to global fire activity. Ecosphere 3, 1–22 (2012).

    Article  Google Scholar 

  • 62.

    Kloster, S. & Lasslop, G. Historical and future fire occurrence (1850 to 2100) simulated in CMIP5 Earth System Models. Glob. Planet. Change 150, 58–69. https://doi.org/10.1016/j.gloplacha.2016.12.017 (2017).

    ADS  Article  Google Scholar 

  • 63.

    Van der Werf, G. R. et al. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos. Chem. Phys. 10, 11707–11735. https://doi.org/10.1071/wf081877 (2010).

    ADS  CAS  Article  Google Scholar 

  • 64.

    Archibald, S. et al. Biological and geophysical feedbacks with fire in the earth system. Environ. Res. Lett. 13, 033003. https://doi.org/10.1071/wf081878 (2018).

    ADS  Article  Google Scholar 

  • 65.

    Ponomarev, E., Kharuk, V. & Ranson, K. Wildfires dynamics in Siberian larch forests. Forests 7, 125. https://doi.org/10.3390/f7060125 (2016).

    Article  Google Scholar 

  • 66.

    van der Werf, G. R., Randerson, J. T., Giglio, L., Gobron, N. & Dolman, A. J. Climate controls on the variability of fires in the tropics and subtropics: Climate controls on fires. Glob. Biogeochem. Cycleshttps://doi.org/10.1029/2007GB003122 (2008).

    Article  Google Scholar 


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