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.
Subscribe to Journal
Get full journal access for 1 year
only $17.42 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
The data that support the findings in this study are available from the corresponding author on request.
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.
Knorr, W., Arneth, A. & Jiang, L. Demographic controls of future global fire risk. Nat. Clim. Change 6, 781–785 (2016).
Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362 (2017).
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).
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).
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).
Gelman, A. et al. Bayesian Data Analysis 3rd edn (CRC, 2013).
Kelley, D. I. Modelling Australian Fire Regimes. PhD thesis, Macquarie Univ. (2014).
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).
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).
Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 7537 (2015).
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).
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).
Kelley, D. I. & Harrison, S. P. Enhanced Australian carbon sink despite increased wildfire during the 21st century. Environ. Res. Lett. 9, 104015 (2014).
Prentice, I. C. et al. Modeling fire and the terrestrial carbon balance. Global Biogeochem. Cycles 25, GB3005 (2011).
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).
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).
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).
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).
Krawchuk, M. A. & Moritz, M. A. Constraints on global fire activity vary across a resource gradient. Ecology 92, 121–132 (2011).
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).
Parisien, M.-A. et al. The spatially varying influence of humans on fire probability in North America. Environ. Res. Lett. 11, 075005 (2016).
Hantson, S. et al. The status and challenge of global fire modelling. Biogeosciences 13, 3359–3375 (2016).
van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017).
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).
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).
Balch, J. K. et al. Negative fire feedback in a transitional forest of southeastern Amazonia. Glob. Change Biol. 14, 2276–2287 (2008).
Lapola, D. M. et al. Pervasive transition of the Brazilian land-use system. Nat. Clim. Change 4, 27–35 (2014).
Kauppi, P. E., Sandström, V. & Lipponen, A. Forest resources of nations in relation to human well-being. PLoS ONE 13, e0196248 (2018).
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).
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).
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).
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).
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).
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).
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).
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).
Moritz, M. A. et al. Climate change and disruptions to global fire activity. Ecosphere 3, 1–22 (2012).
Bradstock, R. A. A biogeographic model of fire regimes in Australia: current and future implications. Glob. Ecol. Biogeogr. 19, 145–158 (2010).
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
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).
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).
Leigh, E. G. Jr Tropical Forest Ecology: A View from Barro Colorado Island (Oxford Univ. Press, 1999).
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).
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).
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).
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).
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).
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).
Hijmans, R. J. & van Etten, J. raster: geographic data analysis and modeling. R package version 2 (2014).
GDAL/OGR Geospatial Data Abstraction Software Library (Open Source Geospatial Foundation, 2018).
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).
Salvatier, J., Wiecki, T. V. & Fonnesbeck, C. Probabilistic programming in Python using PyMC3. PeerJ Comput. Sci. 2, e55 (2016).
Al-Rfou, R. et al. Theano: A Python framework for fast computation of mathematical expressions. Preprint at https://arxiv.org/abs/1605.02688 (2016).
Kelley, D. I. et al. A comprehensive benchmarking system for evaluating global vegetation models. Biogeosciences 10, 3313–3340 (2013).
Rabin, S. S. et al. The fire modeling intercomparison project (FireMIP), phase 1: experimental and analytical protocols. Geosci. Model Dev. 10, 1175–1197 (2017).
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).
Brown, J. K. Field Test of a Rate-of-Fire-Spread Model in Slash Fuels (Intermountain Forest and Range Experiment Station, USDA, 1972).
Blackmarr, W. H. Moisture Content Influences Ignitability of Slash Pine Litter Research Note (USDA, 1972).
Danson, F. M. & Bowyer, P. Estimating live fuel moisture content from remotely sensed reflectance. Remote Sens. Environ. 92, 309–321 (2004).
Staal, A. et al. Resilience of tropical tree cover: the roles of climate, fire, and herbivory. Glob. Change Biol. 24, 5096–5109 (2018).
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).
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.
The authors declare no competing interests.
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.
About this article
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
Nature Reviews Earth & Environment (2020)
Frontiers in Earth Science (2020)
Trends in Ecology & Evolution (2020)
Environmental Research Letters (2020)
Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project
Geoscientific Model Development (2020)