Demographic controls of future global fire risk

Abstract

Wildfires are an important component of terrestrial ecosystem ecology but also a major natural hazard to societies, and their frequency and spatial distribution must be better understood1. At a given location, risk from wildfire is associated with the annual fraction of burned area, which is expected to increase in response to climate warming1,2,3. Until recently, however, only a few global studies of future fire have considered the effects of other important global environmental change factors such as atmospheric CO2 levels and human activities, and how these influence fires in different regions4,5. Here, we contrast the impact of climate change and increasing atmospheric CO2 content on burned area with that of demographic dynamics, using ensembles of climate simulations combined with historical and projected population changes under different socio-economic development pathways for 1901–2100. Historically, humans notably suppressed wildfires. For future scenarios, global burned area will continue to decline under a moderate emissions scenario, except for low population growth and fast urbanization, but start to increase again from around mid-century under high greenhouse gas emissions. Contrary to common perception, we find that human exposure to wildfires increases in the future mainly owing to projected population growth in areas with frequent wildfires, rather than by a general increase in burned area.

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Figure 1: Eight-ESM ensemble means of simulated global burned area based on varying climate alone, varying climate and CO2 alone, and for all factors including population density.
Figure 2: The probability of low-fire regions becoming fire prone (positive values), or of fire-prone areas changing to a low-fire state (negative values) between 1971–2000 and 2071–2100 based on eight-ESM ensembles.
Figure 3: Two-dimensional histogram plots showing mean fire frequency (fractional burned area, colour scale, in yr−1) and fraction of global burned area (%) by ranges of grass fraction of total (grass and woody) vegetation and population density.

Change history

  • 09 May 2016

    In the version of this Letter originally published, in Figure 1 the labels 'Climate only' and 'Climate and CO2' were assigned the incorrect colours; this has been changed so that the former is now represented by a blue line and the latter by a green line. This has been corrected in all versions of this Letter.

References

  1. 1

    Bowman, D. M. J. S. et al. Fire in the Earth system. Science 324, 481–484 (2009).

    CAS  Article  Google Scholar 

  2. 2

    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. Wildland Fire 483–507 (2009).

  3. 3

    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, e502 (2009).

    Article  Google Scholar 

  4. 4

    Pechony, O. & Shindell, D. T. Driving forces of global wildfires over the past millennium and the forthcoming century. Proc. Natl Acad. Sci. USA 107, 19167–19170 (2010).

    CAS  Article  Google Scholar 

  5. 5

    Kloster, S. et al. Fire dynamics during the 20th century simulated by the Community Land Model. Biogeosciences 7, 1877–1902 (2010).

    Article  Google Scholar 

  6. 6

    Running, S. W. Ecosystem disturbance, carbon, and climate. Science 321, 652–653 (2008).

    CAS  Article  Google Scholar 

  7. 7

    Kasischke, E. S. & Penner, J. E. Improving global estimates of atmospheric emissions from biomass burning. J. Geophys. Res. 109, D14S01 (2004).

    Article  Google Scholar 

  8. 8

    Moritz, M. A. et al. Learning to coexist with wildfire. Nature 515, 58–66 (2014).

    CAS  Article  Google Scholar 

  9. 9

    Böttcher, H., Kurz, W. A. & Freibauer, A. Accounting of forest carbon sinks and sources under a future climate protocol-factoring out past disturbance and management effects on age-class structure. Environ. Sci. Policy 11, 669–686 (2008).

    Article  Google Scholar 

  10. 10

    Guyette, R. P., Muzika, R. M. & Dey, D. C. Dynamics of an anthropogenic fire regime. Ecosystems 5, 472–486 (2002).

    Google Scholar 

  11. 11

    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 (2009).

    Article  Google Scholar 

  12. 12

    Archibald, S., Scholes, R. J., Roy, D. P., Roberts, G. & Boschetti, L. Southern African fire regimes as revealed by remote sensing. Int. J. Wildland Fire 19, 861–878 (2010).

    Article  Google Scholar 

  13. 13

    Lehsten, V., Harmand, P., Palumbo, I. & Arneth, A. Modelling burned area in Africa. Biogeosciences 7, 3199–3214 (2010).

    Article  Google Scholar 

  14. 14

    Knorr, W., Kaminski, T., Arneth, A. & Weber, U. Impact of human population density on fire frequency at the global scale. Biogeosciences 11, 1085–1102 (2014).

    Article  Google Scholar 

  15. 15

    Bistinas, I., Harrison, D. E., Prentice, I. C. & Pereira, J. M. C. Causal relationships vs. emergent patterns in the global controls of fire frequency. Biogeosciences 11, 5087–5101 (2014).

    Article  Google Scholar 

  16. 16

    Ahlström, A., Schurgers, G., Arneth, A. & Smith, B. Robustness and uncertainty in terrestrial ecosystem carbon response to CMIP5 climate change projections. Environ. Res. Lett. 7, 044008 (2012).

    Article  Google Scholar 

  17. 17

    KC, S. & Lutz, W. The human core of the shared socioeconomic pathways: population scenarios by age, sex and level of education for all countries to 2100. Glob. Environ. Change http://dx.doi.org/10.1016/j.gloenvcha.2014.06.004 (2014).

  18. 18

    Jiang, L. & O’Neill, B. C. Global urbanization projections for the Shared Socioeconomic Pathways. Glob. Environ. Change http://dx.doi.org/10.1016/j.gloenvcha.2015.03.008 (2015).

  19. 19

    Jiang, L. Internal consistency of demographic assumptions in the shared socioeconomic pathways. Popul. Environ. 35, 261–285 (2014).

    CAS  Article  Google Scholar 

  20. 20

    Marlon, J. R. et al. Climate and human influences on global biomass burning over the past two millennia. Nature Geosci. 1, 697–702 (2008).

    CAS  Article  Google Scholar 

  21. 21

    Wang, Z., Chappellaz, J., Park, K. & Mak, J. E. Large variations in Southern Hemisphere biomass burning during the last 650 years. Science 330, 1663–1666 (2010).

    CAS  Article  Google Scholar 

  22. 22

    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). J. Geophys. Res. 118, 317–328 (2013).

    Article  Google Scholar 

  23. 23

    van der Werf, G. R., Peters, W., van Leeuwen, T. T. & Giglio, L. What could have caused pre-industrial biomass burning emissions to exceed current rates? Clim. Past 9, 289–306 (2013).

    Article  Google Scholar 

  24. 24

    Andela, N. & van der Werf, G. R. Recent trends in African fires driven by cropland expansion and El Niño to La Niña transition. Nature Clim. Change 4, 791–795 (2014).

    Article  Google Scholar 

  25. 25

    Moss, H. R. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010).

    CAS  Article  Google Scholar 

  26. 26

    Donohue, R. J., Roderick, M. L., McVicar, T. R. & Farquhar, G. D. Impact of CO2 fertilization on maximum foliage cover across the globe’s warm, arid environments. Geophys. Res. Lett. 40, 3031–3035 (2013).

    CAS  Article  Google Scholar 

  27. 27

    Long, S. P., Osborne, C. P. & Humphries, S. W. in Global Change: Effects on Coniferous Forests and Grasslands (eds Breymeyer, A. I., Hall, D. O., Melillo, J. M. & Âgren, G. I.) 121–181 (Wiley, 1996).

    Google Scholar 

  28. 28

    Morgan, J. A., Milchunas, D. G., LeCain, D. R., West, M. & Mosier, A. R. Carbon dioxide enrichment alters plant community structure and accelerates shrub growth in the shortgrass steppe. Proc. Natl Acad. Sci. USA 104, 14724–14729 (2007).

    CAS  Article  Google Scholar 

  29. 29

    Wigley, B. J., Bond, W. J. & Hoffman, M. T. Thicket expansion in a South African savanna under divergent land use: local vs. global drivers? Glob. Change Biol. 16, 964–976 (2010).

    Article  Google Scholar 

  30. 30

    Buitenwerf, R., Bond, W. J., Stevens, N. & Trollope, W. S. W. Increased tree densities in South African savannas: >50 years of data suggests CO2 as a driver. Glob. Change Biol. 18, 675–684 (2012).

    Article  Google Scholar 

  31. 31

    Syphard, A. D. et al. Human influence on California fire regimes. Ecol. Appl. 17, 1388–1402 (2007).

    Article  Google Scholar 

  32. 32

    Knorr, W., Lehsten, V. & Arneth, A. Determinants and predictability of global wildfire emissions. Atmos. Chem. Phys. 12, 6845–6861 (2012).

    CAS  Article  Google Scholar 

  33. 33

    Manea, A., Grootemaat, S. & Leishman, M. R. Leaf flammability and fuel load increase under elevated CO2 levels in a model grassland. Int. J. Wildland Fire 24, 819–827 (2015).

    Article  Google Scholar 

  34. 34

    Syphard, A. D., Radeloff, V. C., Hawbaker, T. J. & Stewart, S. I. Conservation threats due to human-caused increases in fire frequency in mediterranean-climate ecosystems. Conserv. Biol. 23, 758–769 (2009).

    Article  Google Scholar 

  35. 35

    Saarnak, C. F. A shift from natural to human-driven fire regime: implications for trace-gas emissions. Holocene 11, 373–375 (2001).

    Article  Google Scholar 

  36. 36

    Giglio, L. et al. Assessing variability and long-term trends in burned area by merging multiple satellite fire products. Biogeosciences 7, 1171–1186 (2010).

    Article  Google Scholar 

  37. 37

    Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Climatic Change 109, 117–161 (2011).

    Article  Google Scholar 

  38. 38

    Moritz, M. A., Moody, T. J., Krawchuk, M. A., Hughes, M. & Hall, A. Spatial variation in extreme winds predicts large wildfire locations in chaparral ecosystems. Geophys. Res. Lett. 37, L04801 (2010).

    Article  Google Scholar 

  39. 39

    Lasslop, G., Hantson, S. & Kloster, S. Influence of wind speed on the global variability of burned fraction: a global fire model’s perspective. Int. J. Wildland Fire 24, 989–1000 (2015).

    Article  Google Scholar 

  40. 40

    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Article  Google Scholar 

  41. 41

    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 

  42. 42

    Weedon, G. P. et al. Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. J. Hydrometeorol. 12, 823–848 (2011).

    Article  Google Scholar 

  43. 43

    Gobron, N., Belward, A., Pinty, B. & Knorr, W. Monitoring biosphere vegetation 1998–2009. Geophys. Res. Lett. 37, L15402 (2010).

    Article  Google Scholar 

  44. 44

    Ramankutty, N. & Foley, J. A. Estimating historical changes in global land cover: croplands from 1700 to 1992. Glob. Biogeochem. Cycles 13, 997–1027 (1999).

    CAS  Article  Google Scholar 

  45. 45

    Friedl, M. A. et al. Global land cover mapping from MODIS: algorithms and early results. Remote Sensing Environ. 83, 287–302 (2002).

    Article  Google Scholar 

  46. 46

    Wu, M. et al. Sensitivity of burned area in Europe to climate change, atmospheric CO2 levels and demography: a comparison of two fire-vegetation models. J. Geophys. Res. 120, 2256–2272 (2015).

    Article  Google Scholar 

  47. 47

    Goldewijk, K. K., Beusen, A. & Janssen, P. Long-term dynamic modeling of global population and built-up area in a spatially explicit way: HYDE 3.1. Holocene 20, 565–573 (2010).

    Article  Google Scholar 

  48. 48

    Roy, D. P., Boschetti, L., Justice, C. O. & Ju, J. The collection 5 MODIS burned area product - Global evaluation by comparison with the MODIS active fire product. Remote Sensing Environ. 112, 3690–3707 (2008).

    Article  Google Scholar 

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Acknowledgements

A.A. and W.K. acknowledge support from the EU FP7 projects PEGASOS (265148) and LUC4C (603542). A.A. also acknowledges support from the Helmholtz Association in the ATMO programme and through its Innovation and Networking fund. W.K. thanks A. Ahlström for providing gridded driving data from the CMIP5 simulations.

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W.K. and A.A. conceived the study, L.J. provided the population scenarios, and W.K. performed simulations and numerical analyses. W.K. and A.A. wrote the first draft of the manuscript. All authors contributed to discussion of results and writing.

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Correspondence to W. Knorr.

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The authors declare no competing financial interests.

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Knorr, W., Arneth, A. & Jiang, L. Demographic controls of future global fire risk. Nature Clim Change 6, 781–785 (2016). https://doi.org/10.1038/nclimate2999

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