Articles | Volume 22, issue 22
https://doi.org/10.5194/bg-22-7001-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-22-7001-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Nov 2025
Research article |
| 21 Nov 2025
| 21 Nov 2025
Development of a statistical model for global burned area simulation within a DGVM-compatible framework
Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, Germany
Environmental Studies, University of California, Santa Cruz, Santa Cruz, California, USA
Finnish Geospatial Research Institute, National Land Survey of Finland, Espoo, Finland
Matthew Forrest
Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, Germany
Thomas Hickler
Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, Germany
Institute of Physical Geography, Goethe University Frankfurt am Main, Frankfurt am Main, Germany
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Daniel J. Lunt, Nicky M. Wright, Bram Vaes, Ulrich Salzmann, James W. B. Rae, Thomas Hickler, David K. Hutchinson, Julia Brugger, Jiang Zhu, Sebastian Steinig, A. Nele Meckler, Gordon N. Inglis, David Evans, Agatha M. de Boer, Bette L. Otto-Bliesner, Natalie Burls, Yurui Zhang, Appy Sluijs, Tammo Reichgelt, Igor Niezgodzki, Katrin Meissner, Jean-Baptiste Ladant, Fanni D. Kelemen, Matthew Huber, David Greenwood, Mattias Green, Flavia Boscolo-Galazzo, Mauel Tobias Blau, and Michiel Baatsen
EGUsphere, https://doi.org/10.5194/egusphere-2025-6135, https://doi.org/10.5194/egusphere-2025-6135, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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The early Eocene, about 50 million years ago, was a super-warm period of Earth's history, with high concentrations of carbon dioxide in the atmosphere. Here, we provide a framework and experimental design for climate modellers to carry out a coordinated project, simulating this period. This is the second phase of this project, and here we provide updated maps of the Earth's mountains and ocean floor, and vegetation, to enable more accurate modelling.
Simon Scheiter, Jinfeng Chang, Philippe Ciais, Marie Dury, Louis Francois, Matthew Forrest, Alexandra Henrot, Christopher P. O. Reyer, Sonia Seneviratne, Jörg Steinkamp, Wim Thiery, Wenfang Xu, and Thomas Hickler
EGUsphere, https://doi.org/10.5194/egusphere-2026-221, https://doi.org/10.5194/egusphere-2026-221, 2026
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Our study shows how climate change may reshape the world's major biomes, such as forests, grasslands, and tundra. Using dynamic vegetation models, machine learning and real-world observations, we found that many biomes are likely to shift toward the poles as temperatures rise. Even under low-emission scenarios, large areas could change. These results help identify regions most susceptible to climate change and support global efforts to protect and manage natural ecosystems in the future.
Mateus Dantas de Paula, Tatiana Reichert, Laynara F. Lugli, Erica McGale, Kerstin Pierick, João Paulo Darela-Filho, Liam Langan, Jürgen Homeier, Anja Rammig, and Thomas Hickler
Biogeosciences, 22, 2707–2732, https://doi.org/10.5194/bg-22-2707-2025, https://doi.org/10.5194/bg-22-2707-2025, 2025
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This study explores how plant roots with different forms and functions rely on fungal partnerships for nutrient uptake. This relationship was integrated into a vegetation model and was tested in a tropical forest in Ecuador. The model accurately predicted root traits and showed that without fungi, biomass decreased by up to 80 %. The findings highlight the critical role of fungi in ecosystem processes and suggest that root–fungal interactions should be considered in vegetation models.
Konstantin Gregor, Benjamin F. Meyer, Tillmann Gaida, Victor Justo Vasquez, Karina Bett-Williams, Matthew Forrest, João P. Darela-Filho, Sam Rabin, Marcos Longo, Joe R. Melton, Johan Nord, Peter Anthoni, Vladislav Bastrikov, Thomas Colligan, Christine Delire, Michael C. Dietze, George Hurtt, Akihiko Ito, Lasse T. Keetz, Jürgen Knauer, Johannes Köster, Tzu-Shun Lin, Lei Ma, Marie Minvielle, Stefan Olin, Sebastian Ostberg, Hao Shi, Reiner Schnur, Urs Schönenberger, Qing Sun, Peter E. Thornton, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2025-1733, https://doi.org/10.5194/egusphere-2025-1733, 2025
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Geoscientific models are crucial for understanding Earth’s processes. However, they sometimes do not adhere to highest software quality standards, and scientific results are often hard to reproduce due to the complexity of the workflows. Here we gather the expertise of 20 modeling groups and software engineers to define best practices for making geoscientific models maintainable, usable, and reproducible. We conclude with an open-source example serving as a reference for modeling communities.
Katja Frieler, Stefan Lange, Jacob Schewe, Matthias Mengel, Simon Treu, Christian Otto, Jan Volkholz, Christopher P. O. Reyer, Stefanie Heinicke, Colin Jones, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Ryan Heneghan, Derek P. Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Dánnell Quesada Chacón, Kerry Emanuel, Chia-Ying Lee, Suzana J. Camargo, Jonas Jägermeyr, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Lisa Novak, Inga J. Sauer, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, Michel Bechtold, Robert Reinecke, Inge de Graaf, Jed O. Kaplan, Alexander Koch, and Matthieu Lengaigne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2103, https://doi.org/10.5194/egusphere-2025-2103, 2025
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This paper describes the experiments and data sets necessary to run historic and future impact projections, and the underlying assumptions of future climate change as defined by the 3rd round of the ISIMIP Project (Inter-sectoral Impactmodel Intercomparison Project, isimip.org). ISIMIP provides a framework for cross-sectorally consistent climate impact simulations to contribute to a comprehensive and consistent picture of the world under different climate-change scenarios.
Friedrich J. Bohn, Ana Bastos, Romina Martin, Anja Rammig, Niak Sian Koh, Giles B. Sioen, Bram Buscher, Louise Carver, Fabrice DeClerck, Moritz Drupp, Robert Fletcher, Matthew Forrest, Alexandros Gasparatos, Alex Godoy-Faúndez, Gregor Hagedorn, Martin C. Hänsel, Jessica Hetzer, Thomas Hickler, Cornelia B. Krug, Stasja Koot, Xiuzhen Li, Amy Luers, Shelby Matevich, H. Damon Matthews, Ina C. Meier, Mirco Migliavacca, Awaz Mohamed, Sungmin O, David Obura, Ben Orlove, Rene Orth, Laura Pereira, Markus Reichstein, Lerato Thakholi, Peter H. Verburg, and Yuki Yoshida
Biogeosciences, 22, 2425–2460, https://doi.org/10.5194/bg-22-2425-2025, https://doi.org/10.5194/bg-22-2425-2025, 2025
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An interdisciplinary collaboration of 36 international researchers from 35 institutions highlights recent findings in biosphere research. Within eight themes, they discuss issues arising from climate change and other anthropogenic stressors and highlight the co-benefits of nature-based solutions and ecosystem services. Based on an analysis of these eight topics, we have synthesized four overarching insights.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025, https://doi.org/10.5194/gmd-18-2249-2025, 2025
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Our study maps global nitrogen (N) and phosphorus (P) availability and how they changed from 1901 to 2018. We find that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation increased, especially in the tropics, while N limitation decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
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Under climate change, the conditions necessary for wildfires to form are occurring more frequently in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a basis for future improvements.
Martin Thurner, Kailiang Yu, Stefano Manzoni, Anatoly Prokushkin, Melanie A. Thurner, Zhiqiang Wang, and Thomas Hickler
Biogeosciences, 22, 1475–1493, https://doi.org/10.5194/bg-22-1475-2025, https://doi.org/10.5194/bg-22-1475-2025, 2025
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Nitrogen concentrations in tree tissues (leaves, branches, stems, and roots) are related to photosynthesis, growth, and respiration and thus to vegetation carbon uptake. Our novel database allows us to identify the controls of tree tissue nitrogen concentrations in boreal and temperate forests, such as tree age/size, species, and climate. Changes therein will affect tissue nitrogen concentrations and thus also vegetation carbon uptake.
Ryan Vella, Matthew Forrest, Andrea Pozzer, Alexandra P. Tsimpidi, Thomas Hickler, Jos Lelieveld, and Holger Tost
Atmos. Chem. Phys., 25, 243–262, https://doi.org/10.5194/acp-25-243-2025, https://doi.org/10.5194/acp-25-243-2025, 2025
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This study examines how land cover changes influence biogenic volatile organic compound (BVOC) emissions and atmospheric states. Using a coupled chemistry–climate–vegetation model, we compare present-day land cover (deforested for crops and grazing) with natural vegetation and an extreme reforestation scenario. We find that vegetation changes significantly impact global BVOC emissions and organic aerosols but have a relatively small effect on total aerosols, clouds, and radiative effects.
Matthew Forrest, Jessica Hetzer, Maik Billing, Simon P. K. Bowring, Eric Kosczor, Luke Oberhagemann, Oliver Perkins, Dan Warren, Fátima Arrogante-Funes, Kirsten Thonicke, and Thomas Hickler
Biogeosciences, 21, 5539–5560, https://doi.org/10.5194/bg-21-5539-2024, https://doi.org/10.5194/bg-21-5539-2024, 2024
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Climate change is causing an increase in extreme wildfires in Europe, but drivers of fire are not well understood, especially across different land cover types. We used statistical models with satellite data, climate data, and socioeconomic data to determine what affects burning in cropland and non-cropland areas of Europe. We found different drivers of burning in cropland burning vs. non-cropland to the point that some variables, e.g. population density, had the complete opposite effects.
Dana A. Lapides, W. Jesse Hahm, Matthew Forrest, Daniella M. Rempe, Thomas Hickler, and David N. Dralle
Biogeosciences, 21, 1801–1826, https://doi.org/10.5194/bg-21-1801-2024, https://doi.org/10.5194/bg-21-1801-2024, 2024
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Water stored in weathered bedrock is rarely incorporated into vegetation and Earth system models despite increasing recognition of its importance. Here, we add a weathered bedrock component to a widely used vegetation model. Using a case study of two sites in California and model runs across the United States, we show that more accurately representing subsurface water storage and hydrology increases summer plant water use so that it better matches patterns in distributed data products.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
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Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Ryan Vella, Andrea Pozzer, Matthew Forrest, Jos Lelieveld, Thomas Hickler, and Holger Tost
Biogeosciences, 20, 4391–4412, https://doi.org/10.5194/bg-20-4391-2023, https://doi.org/10.5194/bg-20-4391-2023, 2023
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We investigated the effect of the El Niño–Southern Oscillation (ENSO) on biogenic volatile organic compound (BVOC) emissions from plants. ENSO events can cause a significant increase in these emissions, which have a long-term impact on the Earth's atmosphere. Persistent ENSO conditions can cause long-term changes in vegetation, resulting in even higher BVOC emissions. We link ENSO-induced emission anomalies with driving atmospheric and vegetational variables.
Ryan Vella, Matthew Forrest, Jos Lelieveld, and Holger Tost
Geosci. Model Dev., 16, 885–906, https://doi.org/10.5194/gmd-16-885-2023, https://doi.org/10.5194/gmd-16-885-2023, 2023
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Biogenic volatile organic compounds (BVOCs) are released by vegetation and have a major impact on atmospheric chemistry and aerosol formation. Non-interacting vegetation constrains the majority of numerical models used to estimate global BVOC emissions, and thus, the effects of changing vegetation on emissions are not addressed. In this work, we replace the offline vegetation with dynamic vegetation states by linking a chemistry–climate model with a global dynamic vegetation model.
Cited articles
Aldersley, A., Murray, S. J., and Cornell, S. E.: Global and regional analysis of climate and human drivers of wildfire, Sci. Total Environ., 409, 3472–3481, 2011.
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., and Kloster, S.: A human-driven decline in global burned area, Science, 356, 1356–1362, 2017.
Archibald, S.: Managing the human component of fire regimes: lessons from Africa, Philos. T. R. Soc. B, 371, 20150346, https://doi.org/10.1098/rstb.2015.0346, 2016.
Australian Government: Estimating greenhouse gas emissions from bushfires in Australia's temperate forests: focus on 2019–20, Australian Government, Department of Industry, Science, Energy and Resources, 2020.
Bergado, J. R., Persello, C., Reinke, K., and Stein, A.: Predicting wildfire burns from big geodata using deep learning, Saf. Sci., 140, 105276, https://doi.org/10.1016/j.ssci.2021.105276, 2021.
Bistinas, I., Harrison, S. P., Prentice, I. C., and Pereira, J. M. C.: Causal relationships versus emergent patterns in the global controls of fire frequency, Biogeosciences, 11, 5087–5101, https://doi.org/10.5194/bg-11-5087-2014, 2014.
Blouin, K. D., Flannigan, M. D., Wang, X., and Kochtubajda, B.: Ensemble lightning prediction models for the province of Alberta, Canada, Int. J. Wildland Fire, 25, 421–432, 2016.
Bowman, D. M., O'Brien, J. A., and Goldammer, J. G.: Pyrogeography and the global quest for sustainable fire management, Annu. Rev. Environ. Res., 38, 57–80, 2013.
Bowman, D. M., Williamson, G. J., Abatzoglou, J. T., Kolden, C. A., Cochrane, M. A., and Smith, A. M.: Human exposure and sensitivity to globally extreme wildfire events, Nat. Ecol. Evol., 1, 0058, https://doi.org/10.1038/s41559-016-0058, 2017.
Bowman, D. M., Kolden, C. A., Abatzoglou, J. T., Johnston, F. H., van der Werf, G. R., and Flannigan, M.: Vegetation fires in the Anthropocene, Nat. Rev. Earth Environ., 1, 500–515, 2020.
Brown, P. T., Hanley, H., Mahesh, A., Reed, C., Strenfel, S. J., Davis, S. J., Kochanski, A. K., and Clements, C. B.: Climate warming increases extreme daily wildfire growth risk in California, Nature, 621, 760–766, 2023.
Callen, T.: What is gross domestic product, Finance Dev., 45, 48–49, 2008.
Canadell, J. G., Meyer, C. P., Cook, G. D., Dowdy, A., Briggs, P. R., Knauer, J., Pepler, A., and Haverd, V.: Multi-decadal increase of forest burned area in Australia is linked to climate change, Nat. Commun., 12, 6921, https://doi.org/10.1038/s41467-021-27225-4, 2021.
Cardil, A., Vega-García, C., Ascoli, D., Molina-Terrén, D. M., Silva, C. A., and Rodrigues, M.: How does drought impact burned area in Mediterranean vegetation communities?, Sci. Total Environ., 693, 133603, https://doi.org/10.1016/j.scitotenv.2019.133603, 2019.
Carmona-Moreno, C., Belward, A., Malingreau, J.-P., Hartley, A., Garcia-Alegre, M., Antonovskiy, M., Buchshtaber, V., and Pivovarov, V.: Characterizing interannual variations in global fire calendar using data from Earth observing satellites, Glob. Change Biol., 11, 1537–1555, 2005.
Cary, G. J., Keane, R. E., Gardner, R. H., Lavorel, S., Flannigan, M. D., Davies, I. D., Li, C., Lenihan, J. M., Rupp, T. S., and Mouillot, F.: Comparison of the Sensitivity of Landscape-fire-succession Models to Variation in Terrain, Fuel Pattern, Climate and Weather, Landsc. Ecol., 21, 121–137, https://doi.org/10.1007/s10980-005-7302-9, 2006.
Chen, Y., Hall, J., van Wees, D., Andela, N., Hantson, S., Giglio, L., van der Werf, G. R., Morton, D. C., and Randerson, J. T.: Multi-decadal trends and variability in burned area from the fifth version of the Global Fire Emissions Database (GFED5), Earth Syst. Sci. Data, 15, 5227–5259, https://doi.org/10.5194/essd-15-5227-2023, 2023.
Chuvieco, E., Pettinari, M. L., Koutsias, N., Forkel, M., Hantson, S., and Turco, M.: Human and climate drivers of global biomass burning variability, Sci. Total Environ., 779, 146361, https://doi.org/10.1016/j.scitotenv.2021.146361, 2021.
Clarke, H., Tran, B., Boer, M. M., Price, O., Kenny, B., and Bradstock, R.: Climate change effects on the frequency, seasonality and interannual variability of suitable prescribed burning weather conditions in south-eastern Australia, Agr. Forest Meteorol., 271, 148–157, 2019.
Copernicus Climate Change Service: Downscaled bioclimatic indicators for selected regions from 1979 to 2018 derived from reanalysis, climate data store, https://doi.org/10.24381/CDS.FE90A594, 2021.
Cunningham, C. X., Williamson, G. J., and Bowman, D. M. J. S.: Increasing frequency and intensity of the most extreme wildfires on Earth, Nat. Ecol. Evol., 8, 1420–1425, https://doi.org/10.1038/s41559-024-02452-2, 2024.
Curasi, S. R., Melton, J. R., Arora, V. K., Humphreys, E. R., and Whaley, C. H.: Global climate change below 2 °C avoids large end century increases in burned area in Canada, Npj Clim. Atmospheric Sci., 7, 228, https://doi.org/10.1038/s41612-024-00781-4, 2024.
Dantas De Paula, M., Gómez Giménez, M., Niamir, A., Thurner, M., and Hickler, T.: Combining European Earth Observation products with Dynamic Global Vegetation Models for estimating Essential Biodiversity Variables, Int. J. Digit. Earth, 13, 262–277, https://doi.org/10.1080/17538947.2019.1597187, 2020.
Davies, K. W., Bates, J. D., Svejcar, T. J., and Boyd, C. S.: Effects of long-term livestock grazing on fuel characteristics in rangelands: an example from the sagebrush steppe, Rangel. Ecol. Manag., 63, 662–669, 2010.
de Jong, M. C., Wooster, M. J., Kitchen, K., Manley, C., Gazzard, R., and McCall, F. F.: Calibration and evaluation of the Canadian Forest Fire Weather Index (FWI) System for improved wildland fire danger rating in the United Kingdom, Nat. Hazards Earth Syst. Sci., 16, 1217–1237, https://doi.org/10.5194/nhess-16-1217-2016, 2016.
DeWilde, L. and Chapin, F. S.: Human Impacts on the Fire Regime of Interior Alaska: Interactions among Fuels, Ignition Sources, and Fire Suppression, Ecosystems, 9, 1342–1353, https://doi.org/10.1007/s10021-006-0095-0, 2006.
DiMiceli, C., Carroll, M., Sohlberg, R., Kim, D.-H., Kelly, M., and Townshend, J.: MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250 m SIN Grid V006, NASA Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MOD44B.006, 2015.
Doerr, S. H. and Santín, C.: Global trends in wildfire and its impacts: perceptions versus realities in a changing world, Philos. T. R. Soc. B, 371, 20150345, https://doi.org/10.1098/rstb.2015.0345, 2016.
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., and Leitão, P. J.: Collinearity: a review of methods to deal with it and a simulation study evaluating their performance, Ecography, 36, 27–46, 2013.
Dwyer, E., Pinnock, S., Grégoire, J.-M., and Pereira, J. M. C.: Global spatial and temporal distribution of vegetation fire as determined from satellite observations, Int. J. Remote Sens., 21, 1289–1302, 2000.
Earl, N. and Simmonds, I.: Spatial and temporal variability and trends in 2001–2016 global fire activity, J. Geophys. Res.-Atmos., 123, 2524–2536, 2018.
Fang, L., Yang, J., Zu, J., Li, G., and Zhang, J.: Quantifying influences and relative importance of fire weather, topography, and vegetation on fire size and fire severity in a Chinese boreal forest landscape, Forest Ecol. Manag., 356, 2–12, 2015.
Flannigan, M. D., Krawchuk, M. A., de Groot, W. J., Wotton, B. M., and Gowman, L. M.: Implications of changing climate for global wildland fire, Int. J. Wildland Fire, 18, 483–507, 2009.
Forkel, M., Andela, N., Harrison, S. P., Lasslop, G., van Marle, M., Chuvieco, E., Dorigo, W., Forrest, M., Hantson, S., Heil, A., Li, F., Melton, J., Sitch, S., Yue, C., and Arneth, A.: Emergent relationships with respect to burned area in global satellite observations and fire-enabled vegetation models, Biogeosciences, 16, 57–76, https://doi.org/10.5194/bg-16-57-2019, 2019.
Forrest, M., Hetzer, J., Billing, M., Bowring, S. P. K., Kosczor, E., Oberhagemann, L., Perkins, O., Warren, D., Arrogante-Funes, F., Thonicke, K., and Hickler, T.: Understanding and simulating cropland and non-cropland burning in Europe using the BASE (Burnt Area Simulator for Europe) model, Biogeosciences, 21, 5539–5560, https://doi.org/10.5194/bg-21-5539-2024, 2024.
Fosberg, M. A., Cramer, W., Brovkin, V., Fleming, R., Gardner, R., Gill, A. M., Goldammer, J. G., Keane, R., Koehler, P., and Lenihan, J.: Strategy for a fire module in dynamic global vegetation models, Int. J. Wildland Fire, 9, 79–84, 1999.
Gallardo, M., Gómez, I., Vilar, L., Martínez-Vega, J., and Martín, M. P.: Impacts of future land use/land cover on wildfire occurrence in the Madrid region (Spain), Reg. Environ. Change, 16, 1047–1061, 2016.
Haas, O., Prentice, I. C., and Harrison, S. P.: Global environmental controls on wildfire burnt area, size, and intensity, Environ. Res. Lett., 17, 065004, https://doi.org/10.1088/1748-9326/ac6a69, 2022.
Hantson, S., Lasslop, G., Kloster, S., and Chuvieco, E.: Anthropogenic effects on global mean fire size, Int. J. Wildland Fire, 24, 589–596, 2015.
Hantson, S., Arneth, A., Harrison, S. P., Kelley, D. I., Prentice, I. C., Rabin, S. S., Archibald, S., Mouillot, F., Arnold, S. R., Artaxo, P., Bachelet, D., Ciais, P., Forrest, M., Friedlingstein, P., Hickler, T., Kaplan, J. O., Kloster, S., Knorr, W., Lasslop, G., Li, F., Mangeon, S., Melton, J. R., Meyn, A., Sitch, S., Spessa, A., van der Werf, G. R., Voulgarakis, A., and Yue, C.: The status and challenge of global fire modelling, Biogeosciences, 13, 3359–3375, https://doi.org/10.5194/bg-13-3359-2016, 2016.
Hantson, S., Kelley, D. I., Arneth, A., Harrison, S. P., Archibald, S., Bachelet, D., Forrest, M., Hickler, T., Lasslop, G., Li, F., Mangeon, S., Melton, J. R., Nieradzik, L., Rabin, S. S., Prentice, I. C., Sheehan, T., Sitch, S., Teckentrup, L., Voulgarakis, A., and Yue, C.: 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.
International Union of Forest Research Organizations: Global Fire Challenges in a Warming World, edited by: Robinne F.-N., Burns J., Kant P., de Groot B., Flannigan M. D., Kleine M., and Wotton D. M., Occasional Paper No. 32, IUFRO, Vienna, 2018.
Jain, P., Barber, Q. E., Taylor, S., Whitman, E., Acuna D. C., Boulanger., Chavardès, R. D., Chen, J., Englefield, P., Flannigan, M., Girardin, M. P., Hanes, C. C., Little, J., Morrison, K., Skakun, R. S., Thompson, D. K., Wang, X., and Parisien, M.-A.: Drivers and Impacts of the Record-Breaking 2023 Wildfire Season in Canada, Nat Commun, 15, 6764, https://doi.org/10.1038/s41467-024-51154-7, 2024.
Jarvis, A., Reuter, H. I., Nelson, A., and Guevara, E.: Hole-filled SRTM for the globe Version 4, Available CGIAR-CSI SRTM 90m Database Httpsrtm Csi Cgiar Org, 15, 5, 2008.
Jones, M. W., Abatzoglou, J. T., Veraverbeke, S., Andela, N., Lasslop, G., Forkel, M., Smith, A. J., Burton, C., Betts, R. A., and van der Werf, G. R.: Global and regional trends and drivers of fire under climate change, Rev. Geophys., 60, e2020RG000726, https://doi.org/10.1029/2020RG000726, 2022.
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.
Juli, G., Jon, E., and Dylan, W.: Flammability as an ecological and evolutionary driver, J. Ecol., 105, https://doi.org/10.1111/1365-2745.12691, 2017.
Kavhu, B. and Ndaimani, H.: Analysing factors influencing fire frequency in Hwange National Park, South Afr. Geogr. J., 104, 177–192, https://doi.org/10.1080/03736245.2021.1941219, 2022.
Kavhu, B.: A statistical global burned area model for seamless integration into Dynamic Global Vegetation Models(Submission release v1), Zenodo [code], https://doi.org/10.5281/zenodo.14177016, 2024a.
Kavhu, B.: Data for fitting a statistical global burned area model for seamless integration into Dynamic Global Vegetation Models (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.14110150, 2024b.
Kelley, D. I., Prentice, I. C., Harrison, S. P., Wang, H., Simard, M., Fisher, J. B., and Willis, K. O.: A comprehensive benchmarking system for evaluating global vegetation models, Biogeosciences, 10, 3313–3340, https://doi.org/10.5194/bg-10-3313-2013, 2013.
Kelly, L. T., Fletcher, M.-S., Menor, I. O., Pellegrini, A. F., Plumanns-Pouton, E. S., Pons, P., Williamson, G. J., and Bowman, D. M.: Understanding Fire Regimes for a Better Anthropocene, Annu. Rev. Environ. Resour., 48, https://doi.org/10.1146/annurev-environ-120220-055357, 2023.
Kendall, M.: Multivariate analysis, Charles Griffin, ISBN 9780852642344, 1975.
Klein Goldewijk, K., Beusen, A., Doelman, J., and Stehfest, E.: Anthropogenic land use estimates for the Holocene – HYDE 3.2, Earth Syst. Sci. Data, 9, 927–953, https://doi.org/10.5194/essd-9-927-2017, 2017.
Kloster, S., Mahowald, N. M., Randerson, J. T., Thornton, P. E., Hoffman, F. M., Levis, S., Lawrence, P. J., Feddema, J. J., Oleson, K. W., and Lawrence, D. M.: Fire dynamics during the 20th century simulated by the Community Land Model, Biogeosciences, 7, 1877–1902, https://doi.org/10.5194/bg-7-1877-2010, 2010.
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.
Knorr, W., Arneth, A., and Jiang, L.: Demographic controls of future global fire risk, Nat. Clim. Change, 6, 781–785, 2016.
Koubi, V.: Sustainable development impacts of climate change and natural disaster, Backgr. Pap. Prep. Sustain. Dev. Outlook, 2019.
Kraaij, T., Baard, J. A., Arndt, J., Vhengani, L., and Van Wilgen, B. W.: An assessment of climate, weather, and fuel factors influencing a large, destructive wildfire in the Knysna region, South Africa, Fire Ecol., 14, 1–12, 2018.
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.
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.
Larsen, W. A. and McCleary, S. J.: The Use of Partial Residual Plots in Regression Analysis, Technometrics, 14, 781–790, https://doi.org/10.1080/00401706.1972.10488966, 1972.
Le Page, Y., Morton, D., Bond-Lamberty, B., Pereira, J. M. C., and Hurtt, G.: HESFIRE: a global fire model to explore the role of anthropogenic and weather drivers, Biogeosciences, 12, 887–903, https://doi.org/10.5194/bg-12-887-2015, 2015.
Lehsten, V., Arneth, A., Spessa, A., Thonicke, K., and Moustakas, A.: The effect of fire on tree–grass coexistence in savannas: a simulation study, Int. J. Wildland Fire, 25, 137–146, 2016.
MacCarthy, J., Tyukavina, A., Weisse, M. J., Harris, N., and Glen, E.: Extreme wildfires in Canada and their contribution to global loss in tree cover and carbon emissions in 2023, Glob. Change Biol., 30, e17392, https://doi.org/10.1111/gcb.17392, 2024.
Mann, H. B.: Nonparametric tests against trend, Econom. J. Econom. Soc., 13, 245–259, ISSN 1468-0262, 1945.
Meijer, J. R., Huijbregts, M. A., Schotten, K. C., and Schipper, A. M.: Global patterns of current and future road infrastructure, Environ. Res. Lett., 13, 064006, 2018.
Morvan, D.: Physical phenomena and length scales governing the behaviour of wildfires: a case for physical modelling, Fire Technol., 47, 437–460, 2011.
Mukunga, T., Forkel, M., Forrest, M., Zotta, R.-M., Pande, N., Schlaffer, S., and Dorigo, W.: Effect of Socioeconomic Variables in Predicting Global Fire Ignition Occurrence, Fire, 6, 197, https://doi.org/10.3390/fire6050197, 2023.
Nolan, R. H., Anderson, L. O., Poulter, B., and Varner, J. M.: Increasing threat of wildfires: the year 2020 in perspective: A Global Ecology and Biogeography special issue, Glob. Ecol. Biogeogr., 31, 1898–1905, https://doi.org/10.1111/geb.13588, 2022.
Nurrohman, R. K., Kato, T., Ninomiya, H., Végh, L., Delbart, N., Miyauchi, T., Sato, H., Shiraishi, T., and Hirata, R.: Future prediction of Siberian wildfire and aerosol emissions via the improved fire module of the spatially explicit individual-based dynamic global vegetation model, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-105, 2024.
O'Brien, R. M.: A Caution Regarding Rules of Thumb for Variance Inflation Factors, Qual. Quant., 41, 673–690, https://doi.org/10.1007/s11135-006-9018-6, 2007.
Oliveira, S., Pereira, J. M., San-Miguel-Ayanz, J., and Lourenço, L.: Exploring the spatial patterns of fire density in Southern Europe using Geographically Weighted Regression, Appl. Geogr., 51, 143–157, 2014.
O'Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, D. S., van Ruijven, B. J., van Vuuren, D. P., Birkmann, J., Kok, K., Levy, M., and Solecki, W.: The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century, Glob. Environ. Change, 42, 169–180, https://doi.org/10.1016/j.gloenvcha.2015.01.004, 2017.
Parisien, M.-A., Parks, S. A., Krawchuk, M. A., Flannigan, M. D., Bowman, L. M., and Moritz, M. A.: Scale-dependent controls on the area burned in the boreal forest of Canada, 1980–2005, Ecol. Appl., 21, 789–805, https://doi.org/10.1890/10-0326.1, 2011.
Pausas, J. G. and Keeley, J. E.: Wildfires and global change, Front. Ecol. Environ., 19, 387–395, 2021.
Pausas, J. G. and Ribeiro, E.: The global fire–productivity relationship, Glob. Ecol. Biogeogr., 22, 728–736, 2013.
Pechony, O. and Shindell, D. T.: Driving forces of global wildfires over the past millennium and the forthcoming century, P. Natl. Acad. Sci. USA, 107, 19167–19170, 2010.
Perkins, O., Matej, S., Erb, K., and Millington, J.: Towards a global behavioural model of anthropogenic fire: The spatiotemporal distribution of land-fire systems, Socio-Environ. Syst. Model., 4, 18130–18130, 2022.
Perry, G. L. W.: Current approaches to modelling the spread of wildland fire: a review, Prog. Phys. Geogr., 22, 222–245, 1998.
Pfeiffer, M., Spessa, A., and Kaplan, J. O.: A model for global biomass burning in preindustrial time: LPJ-LMfire (v1.0), Geosci. Model Dev., 6, 643–685, https://doi.org/10.5194/gmd-6-643-2013, 2013.
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.
R Core Team: A Language and Environment for Statistical Computing R Foundation for Statistical Computing (ISBN 3-900051-07-0), 2012.
Running, S. and Zhao, M.: MODIS/Terra gross primary productivity gap-filled 8-day L4 global 500m SIN grid V061, NASA EOSDIS Land Process. Distrib. Act. Arch. Cent. DAAC Data Set, MOD17A2HGF-061, https://doi.org/10.5067/MODIS/MOD17A2HGF.06, 2021.
Saha, M. V., Scanlon, T. M., and D'Odorico, P.: Climate seasonality as an essential predictor of global fire activity, Glob. Ecol. Biogeogr., 28, 198–210, 2019.
Santoro, M. and Cartus, O.: ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020, v4, https://doi.org/10.5285/5f331c418e9f4935b8eb1b836f8a91b8, 2023.
Saunders, D. A., Hobbs, R. J., and Margules, C. R.: Biological Consequences of Ecosystem Fragmentation: A Review, Conserv. Biol., 5, 18–32, https://doi.org/10.1111/j.1523-1739.1991.tb00384.x, 1991.
Shekede, M. D., Kusangaya, S., Chavava, C. B., Gwitira, I., and Chemura, A.: A two-decade analysis of the spatial and temporal variations in burned areas across Zimbabwe, PLOS Clim., 3, e0000201, https://doi.org/10.1371/journal.pclm.0000201, 2024.
Shikwambana, L., Kganyago, M., and Xulu, S.: Analysis of wildfires and associated emissions during the recent strong ENSO phases in Southern Africa using multi-source remotely-derived products, Geocarto Int., 37, 16654–16670, 2022.
Smits, J. and Permanyer, I.: The subnational human development database, Sci. Data, 6, 1–15, 2019.
Son, R., Stacke, T., Gayler, V., Nabel, J. E. M. S., Schnur, R., Alonso, L., Requena-Mesa, C., Winkler, A. J., Hantson, S., Zaehle, S., Weber, U., and Carvalhais, N.: Integration of a Deep-Learning-Based Fire Model Into a Global Land Surface Model, J. Adv. Model. Earth Syst., 16, https://doi.org/10.1029/2023ms003710, 2024.
Stott, P.: Combustion in tropical biomass fires: a critical review, Prog. Phys. Geogr. Earth Environ., 24, 355–377, https://doi.org/10.1177/030913330002400303, 2000.
Strand, E. K., Launchbaugh, K. L., Limb, R. F., and Torell, L. A.: Livestock grazing effects on fuel loads for wildland fire in sagebrush dominated ecosystems, J. Rangel. Appl., 1, 35–57, 2014.
Teckentrup, L., Harrison, S. P., Hantson, S., Heil, A., Melton, J. R., Forrest, M., Li, F., Yue, C., Arneth, A., Hickler, T., Sitch, S., and Lasslop, G.: Response of simulated burned area to historical changes in environmental and anthropogenic factors: a comparison of seven fire models, Biogeosciences, 16, 3883–3910, https://doi.org/10.5194/bg-16-3883-2019, 2019.
Teixeira, J. C. M., Burton, C., Kelly, D. I., Folberth, G. A., O'Connor, F. M., Betts, R. A., and Voulgarakis, A.: Representing socio-economic factors in the INFERNO global fire model using the Human Development Index, Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2023-136, 2023.
Thonicke, K., Spessa, A., Prentice, I. C., Harrison, S. P., Dong, L., and Carmona-Moreno, C.: The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model, Biogeosciences, 7, 1991–2011, https://doi.org/10.5194/bg-7-1991-2010, 2010.
UCLouvain: ESA Climate Change Initiative-Land Cover, ESA CCI Land Cover Time-Ser.-Clim. Res. Data Package CRDP, https://doi.org/10.5285/26a0f46c95ee4c29b5c650b129aab788, 2017.
Uddin, G. E.: Human Development Index: A regional perspective, Int. J. Dev. Manag. Rev., 18, 125–140, 2023.
van der Werf, G. R., Randerson, J. T., Giglio, L., van Leeuwen, T. T., Chen, Y., Rogers, B. M., Mu, M., van Marle, M. J. E., Morton, D. C., Collatz, G. J., Yokelson, R. J., and Kasibhatla, P. S.: Global fire emissions estimates during 1997–2016, Earth Syst. Sci. Data, 9, 697–720, https://doi.org/10.5194/essd-9-697-2017, 2017.
Villarreal, S. and Vargas, R.: Representativeness of FLUXNET Sites Across Latin America, J. Geophys. Res.-Biogeo., 126, e2020JG006090, https://doi.org/10.1029/2020JG006090, 2021.
Wragg, P. D., Mielke, T., and Tilman, D.: Forbs, grasses, and grassland fire behaviour, J. Ecol., 106, 1983–2001, 2018.
Wu, C., Venevsky, S., Sitch, S., Mercado, L. M., Huntingford, C., and Staver, A. C.: Historical and future global burned area with changing climate and human demography, One Earth, 4, 517–530, 2021.
Xi, D. D., Taylor, S. W., Woolford, D. G., and Dean, C. B.: Statistical models of key components of wildfire risk, Annu. Rev. Stat. Its Appl., 6, 197–222, 2019.
Yang, H., Ciais, P., Santoro, M., Huang, Y., Li, W., Wang, Y., Bastos, A., Goll, D., Arneth, A., Anthoni, P., Arora, V. K., Friedlingstein, P., Harverd, V., Joetzjer, E., Kautz, M., Lienert, S., Nabel, J. E. M. S., O'Sullivan, M., Sitch, S., Vuichard, N., Wiltshire, A., and Zhu, D.: Comparison of forest above-ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation-based estimates, Glob. Change Biol., 26, 3997–4012, https://doi.org/10.1111/gcb.15117, 2020.
Zhang, Y., Mao, J., Ricciuto, D. M., Jin, M., Yu, Y., Shi, X., Wullschleger, S., Tang, R., and Liu, J.: Global fire modelling and control attributions based on the ensemble machine learning and satellite observations, Sci. Remote Sens., 7, 100088, https://doi.org/10.1016/j.srs.2023.100088, 2023.
Short summary
We developed a statistical model to predict global wildfire patterns based on a parsimonious set of weather, vegetation, and anthropogenic variables. This model is designed within a DGVM (Dynamic Global Vegetation Model)-compatible framework and helps to forecast seasonal fire patterns across diverse regions. It's simplicity makes it valuable for climate and fire management planning, helping communities better prepare for and adapt to rising wildfire threats.
We developed a statistical model to predict global wildfire patterns based on a parsimonious set...
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