Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

How contemporary bioclimatic and human controls change global fire regimes

Nature Climate Change volume 9, pages 690–696 (2019)Cite this article

Subjects

Abstract

Anthropogenically driven declines in tropical savannah burnt area1,2 have recently received attention due to their effect on trends in global burnt area3,4. Large-scale trends in ecosystems where vegetation has adapted to infrequent fire, especially in cooler and wetter forested areas, are less well understood. Here, small changes in fire regimes can have a substantial impact on local biogeochemistry5. To investigate trends in fire across a wide range of ecosystems, we used Bayesian inference6 to quantify four primary controls on burnt area: fuel continuity, fuel moisture, ignitions and anthropogenic suppression. We found that fuel continuity and moisture are the dominant limiting factors of burnt area globally. Suppression is most important in cropland areas, whereas savannahs and boreal forests are most sensitive to ignitions. We quantify fire regime shifts in areas with more than one, and often counteracting, trends in these controls. Forests are of particular concern, where we show average shifts in controls of 2.3–2.6% of their potential maximum per year, mainly driven by trends in fuel continuity and moisture. This study gives added importance to understanding long-term future changes in the controls on fire and the effect of fire trends on ecosystem function.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Limitation on burnt area by each control.
Fig. 2: The relative limits and sensitivity imposed on burnt area by each control.
Fig. 3: Drivers of trends in burnt area.
Fig. 4: Normalized trends in controls on burnt area for the period 2000–2014.
Fig. 5: Annual average effects of trends in controls on burnt area.

Similar content being viewed by others

Data availability

The data that support the findings in this study are available from the corresponding author on request.

Code availability

We were able to find control relationships using a Bayesian Inference framework that could be extended to other areas of high uncertainty in land surface modelling and that we have made available for use. See https://github.com/rhyswhitley/fire_limitation/ for more information.

References

  1. Knorr, W., Arneth, A. & Jiang, L. Demographic controls of future global fire risk. Nat. Clim. Change 6, 781–785 (2016).

    Article  Google Scholar 

  2. Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362 (2017).

    Article  CAS  Google Scholar 

  3. 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 

  4. Arora, V. K. & Melton, J. R. Reduction in global area burned and wildfire emissions since 1930s enhances carbon uptake by land. Nat. Commun. 9, 1326 (2018).

    Article  Google Scholar 

  5. 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 (2016).

    Article  Google Scholar 

  6. Gelman, A. et al. Bayesian Data Analysis 3rd edn (CRC, 2013).

  7. Kelley, D. I. Modelling Australian Fire Regimes. PhD thesis, Macquarie Univ. (2014).

  8. Forkel, M. et al. A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1). Geosci. Model Dev. 10, 4443–4476 (2017).

    Article  Google Scholar 

  9. 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 (2018).

    Article  Google Scholar 

  10. Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 7537 (2015).

    Article  CAS  Google Scholar 

  11. Burton, C., Betts, R. A. & Jones, C. D. Will fire danger be reduced by using Solar Radiation Management to limit global warming to 1.5 °C compared to 2.0 °C? Geophys. Res. Lett. 45, 3644–3652 (2018).

    Article  Google Scholar 

  12. De Groot, W. J., Goldammer, J. G., Justice, C. O. & Lynham, T. J. Implementing a global early warning system for wildland fire. In Proc. VI International Conference on Forest Fire Research (ed. Viegas, D. X.) 15–18 (ADAI/CEIF, 2010).

  13. Kelley, D. I. & Harrison, S. P. Enhanced Australian carbon sink despite increased wildfire during the 21st century. Environ. Res. Lett. 9, 104015 (2014).

    Article  Google Scholar 

  14. Prentice, I. C. et al. Modeling fire and the terrestrial carbon balance. Global Biogeochem. Cycles 25, GB3005 (2011).

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. 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. Glob. Biogeochem. Cycles 22, GB3028 (2008).

    Article  Google Scholar 

  17. Williams, A. P. & Abatzoglou, J. T. Recent advances and remaining uncertainties in resolving past and future climate effects on global fire activity. Curr. Clim. Change Rep. 2, 1–14 (2016).

    Article  Google Scholar 

  18. Krawchuk, M. A. & Moritz, M. A. Burning issues: statistical analyses of global fire data to inform assessments of environmental change. Environmetrics 25, 472–481 (2014).

    Article  Google Scholar 

  19. Krawchuk, M. A. & Moritz, M. A. Constraints on global fire activity vary across a resource gradient. Ecology 92, 121–132 (2011).

    Article  Google Scholar 

  20. Mann, M. L. et al. Incorporating anthropogenic influences into fire probability models: effects of human activity and climate change on fire activity in california. PLoS ONE 11, e0153589 (2016).

    Article  Google Scholar 

  21. Parisien, M.-A. et al. The spatially varying influence of humans on fire probability in North America. Environ. Res. Lett. 11, 075005 (2016).

    Article  Google Scholar 

  22. Hantson, S. et al. The status and challenge of global fire modelling. Biogeosciences 13, 3359–3375 (2016).

    Article  Google Scholar 

  23. van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017).

    Article  Google Scholar 

  24. van der Werf, G. R. et al. Interannual variability in global biomass burning emissions from 1997 to 2004. Atmos. Chem. Phys. 6, 3423–3441 (2006).

    Article  Google Scholar 

  25. Marthews, T. R., Burslem, D. F. R. P., Phillips, R. T. & Mullins, C. E. Modelling direct radiation and canopy gap regimes in tropical forests. Biotropica 40, 676–685 (2008).

    Article  Google Scholar 

  26. Balch, J. K. et al. Negative fire feedback in a transitional forest of southeastern Amazonia. Glob. Change Biol. 14, 2276–2287 (2008).

    Article  Google Scholar 

  27. Lapola, D. M. et al. Pervasive transition of the Brazilian land-use system. Nat. Clim. Change 4, 27–35 (2014).

    Article  Google Scholar 

  28. Kauppi, P. E., Sandström, V. & Lipponen, A. Forest resources of nations in relation to human well-being. PLoS ONE 13, e0196248 (2018).

    Article  Google Scholar 

  29. Ciais, P. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 465–570 (IPCC, Cambridge Univ. Press, 2014).

  30. Rabin, S. S. et al. A fire model with distinct crop, pasture, and non-agricultural burning: use of new data and a model-fitting algorithm for FINAL.1. Geosci. Model Dev. 11, 815–842 (2018).

    Article  Google Scholar 

  31. 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 

  32. Kelley, D. I., Harrison, S. P. & Prentice, I. C. Improved simulation of fire–vegetation interactions in the Land surface Processes and eXchanges dynamic global vegetation model (LPX-Mv1). Geosci. Model Dev. 7, 2411–2433 (2014).

    Article  Google Scholar 

  33. Romps, D. M., Seeley, J. T., Vollaro, D. & Molinari, J. Projected increase in lightning strikes in the United States due to global warming. Science 346, 851–854 (2014).

    Article  CAS  Google Scholar 

  34. Lehmann, C. E. R., Archibald, S. A., Hoffmann, W. A. & Bond, W. J. Deciphering the distribution of the savanna biome. New Phytol. 191, 197–209 (2011).

    Article  Google Scholar 

  35. Knutti, R., Rogelj, J., Sedláček, J. & Fischer, E. M. A scientific critique of the two-degree climate change target. Nat. Geosci. 9, 13–18 (2015).

    Article  Google Scholar 

  36. Kloster, S. & Lasslop, G. Historical and future fire occurrence (1850 to 2100) simulated in CMIP5 Earth System Models. Glob. Planet. Change 150, 58–69 (2017).

    Article  Google Scholar 

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

    Article  Google Scholar 

  38. Bradstock, R. A. A biogeographic model of fire regimes in Australia: current and future implications. Glob. Ecol. Biogeogr. 19, 145–158 (2010).

    Article  Google Scholar 

  39. Dimiceli, C. et al. MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V006 (USGS, 2015); https://doi.org/10.5067/MODIS/MOD44B.006

  40. Barone, J. A. Effects of light availability and rainfall on leaf production in a moist tropical forest in central Panama. J. Trop. Ecol. 14, 309–321 (1998).

    Article  Google Scholar 

  41. Valim, E. A. R., Nalini, H. A. Jr & Kozovits, A. R. Litterfall dynamics in an iron-rich rock outcrop complex in the southeastern portion of the Iron Quadrangle of Brazil. Acta Bot. Bras. 27, 286–293 (2013).

    Article  Google Scholar 

  42. Leigh, E. G. Jr Tropical Forest Ecology: A View from Barro Colorado Island (Oxford Univ. Press, 1999).

  43. 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).

    Article  Google Scholar 

  44. 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 (2013).

    Article  Google Scholar 

  45. Davis, T. W. et al. Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture. Geosci. Model Dev. 10, 689–708 (2017).

    Article  Google Scholar 

  46. Cecil, D. J., Buechler, D. E. & Blakeslee, R. J. Gridded lightning climatology from TRMM-LIS and OTD: dataset description. Atmos. Res. 135-136, 404–414 (2014).

    Article  Google Scholar 

  47. Klein Goldewijk, K., Goldewijk, K. K., Beusen, A., Van Drecht, G. & De Vos, M. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. Glob. Ecol. Biogeogr. 20, 73–86 (2010).

    Article  Google Scholar 

  48. Burton, C. et al. Representation of fire, land-use change and vegetation dynamics in the Joint UK Land Environment Simulatorvn4. 9 (JULES). Geosci. Model Dev. 12, 179–193 (2019).

    Article  Google Scholar 

  49. Hijmans, R. J. & van Etten, J. raster: geographic data analysis and modeling. R package version 2 (2014).

  50. GDAL/OGR Geospatial Data Abstraction Software Library (Open Source Geospatial Foundation, 2018).

  51. Randerson, J. T., Chen, Y., van der Werf, G. R., Rogers, B. M. & Morton, D. C. Global burned area and biomass burning emissions from small fires. J. Geophys. Res. 117, G04012 (2012).

    Article  Google Scholar 

  52. Salvatier, J., Wiecki, T. V. & Fonnesbeck, C. Probabilistic programming in Python using PyMC3. PeerJ Comput. Sci. 2, e55 (2016).

    Article  Google Scholar 

  53. Al-Rfou, R. et al. Theano: A Python framework for fast computation of mathematical expressions. Preprint at https://arxiv.org/abs/1605.02688 (2016).

  54. Kelley, D. I. et al. A comprehensive benchmarking system for evaluating global vegetation models. Biogeosciences 10, 3313–3340 (2013).

    Article  Google Scholar 

  55. Rabin, S. S. et al. The fire modeling intercomparison project (FireMIP), phase 1: experimental and analytical protocols. Geosci. Model Dev. 10, 1175–1197 (2017).

    Article  CAS  Google Scholar 

  56. Lasslop, G., Thonicke, K. & Kloster, S. SPITFIRE within the MPI Earth system model: model development and evaluation. J. Adv. Model. Earth Syst. 6, 740–755 (2014).

    Article  Google Scholar 

  57. Brown, J. K. Field Test of a Rate-of-Fire-Spread Model in Slash Fuels (Intermountain Forest and Range Experiment Station, USDA, 1972).

  58. Blackmarr, W. H. Moisture Content Influences Ignitability of Slash Pine Litter Research Note (USDA, 1972).

  59. Danson, F. M. & Bowyer, P. Estimating live fuel moisture content from remotely sensed reflectance. Remote Sens. Environ. 92, 309–321 (2004).

    Article  Google Scholar 

  60. Staal, A. et al. Resilience of tropical tree cover: the roles of climate, fire, and herbivory. Glob. Change Biol. 24, 5096–5109 (2018).

    Article  Google Scholar 

  61. Dennison, P. E., Brewer, S. C., Arnold, J. D. & Moritz, M. A. Large wildfire trends in the western United States, 1984–2011. Geophys. Res. Lett. 41, 2928–2933 (2014).

    Article  Google Scholar 

Download references

Acknowledgements

The contribution by D.K. was supported by the UK Natural Environment Research Council through The UK Earth System Modelling Project (UKESM, grant no. NE/N017951/1). N.D. was funded by the European Research Council through Reading University (GC2.0 grant no. 694481). C.B. was supported by the Newton Fund through the Met Office Climate Science for Service Partnership Brazil.

Author information

Authors and Affiliations

  1. Centre for Ecology and Hydrology, Wallingford, UK

    Douglas I. Kelley & Toby R. Marthews

  2. ATOS Nederland B.V., Amstelveen, the Netherlands

    Ioannis Bistinas

  3. Department of Earth Sciences, Faculty of Sciences, Vrije Universiteit, Amsterdam, the Netherlands

    Ioannis Bistinas

  4. Natural Perils Pricing, Commercial and Consumer Portfolio and Product, Suncorp Group, Sydney, New South Wales, Australia

    Rhys Whitley

  5. Met Office Hadley Centre for Climate Science and Services, Exeter, UK

    Chantelle Burton

  6. School of Archaeology, Geography and Environmental Sciences, University of Reading, Reading, UK

    Ning Dong

  7. Department of Biological Sciences, Macquarie University, North Ryde, New South Wales, Australia

    Ning Dong

Authors
  1. Douglas I. Kelley
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ioannis Bistinas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rhys Whitley
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chantelle Burton
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Toby R. Marthews
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ning Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.K. and I.B. devised the modelling framework. R.W. designed the Bayesian inference framework. D.K., I.B. and C.B. identified drivers for use in the framework. D.K., I.B., C.B. and T.M. designed the limitation and sensitivity assessments and fire regime shift index. D.K., C.B. and N.D. collated and regridded input data. D.K. performed trend analysis. D.K. wrote the first draft of the paper with input from I.B., C.B. and R.W. All authors contributed to the final manuscript.

Corresponding author

Correspondence to Douglas I. Kelley.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information: Nature Climate Change thanks Niels Andela, Sam Rabin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–11, Tables 1–3 and references.

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kelley, D.I., Bistinas, I., Whitley, R. et al. How contemporary bioclimatic and human controls change global fire regimes. Nat. Clim. Chang. 9, 690–696 (2019). https://doi.org/10.1038/s41558-019-0540-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-019-0540-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Microbiology