Articles | Volume 18, issue 12
https://doi.org/10.5194/gmd-18-3533-2025
© 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/gmd-18-3533-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Jun 2025
Model description paper |
| 17 Jun 2025
| 17 Jun 2025
FLAME 1.0: a novel approach for modelling burned area in the Brazilian biomes using the maximum entropy concept
Maria Lucia Ferreira Barbosa
CORRESPONDING AUTHOR
National Institute for Space Research – INPE, Avenida dos Astronautas, 1758. Jd. Granja – São José dos Campos – São Paulo, 12227-010, Brazil
UK Centre for Ecology and Hydrology, Wallingford, OX10 8BB UK
Federal University of São Carlos, Rodovia Lauri Simões de Barros, km 12 – SP-189 – Aracaçu, Buri – São Paulo, 18290-000, Brazil
Douglas I. Kelley
UK Centre for Ecology and Hydrology, Wallingford, OX10 8BB UK
Chantelle A. Burton
Met Office Hadley Centre, Fitzroy Road, Exeter, EX1 3PB UK
Igor J. M. Ferreira
National Institute for Space Research – INPE, Avenida dos Astronautas, 1758. Jd. Granja – São José dos Campos – São Paulo, 12227-010, Brazil
Faculty of Environment, Science and Economy, University of Exeter, Exeter, UK
Renata Moura da Veiga
National Institute for Space Research – INPE, Avenida dos Astronautas, 1758. Jd. Granja – São José dos Campos – São Paulo, 12227-010, Brazil
Anna Bradley
Met Office Hadley Centre, Fitzroy Road, Exeter, EX1 3PB UK
Paulo Guilherme Molin
Federal University of São Carlos, Rodovia Lauri Simões de Barros, km 12 – SP-189 – Aracaçu, Buri – São Paulo, 18290-000, Brazil
Liana O. Anderson
National Centre for Monitoring and Early Warning of Natural Disasters – Cemaden, Estrada Doutor Altino Bondensan, 500 – Distrito de Eugênio de Melo, São José dos Campos – São Paulo, Brazil
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Douglas I. Kelley, Chantelle Burton, Francesca Di Giuseppe, Matthew W. Jones, Maria L. F. Barbosa, Esther Brambleby, Joe R. McNorton, Zhongwei Liu, Anna S. I. Bradley, Katie Blackford, Eleanor Burke, Andrew Ciavarella, Enza Di Tomaso, Jonathan Eden, Igor José M. Ferreira, Lukas Fiedler, Andrew J. Hartley, Theodore R. Keeping, Seppe Lampe, Anna Lombardi, Guilherme Mataveli, Yuquan Qu, Patrícia S. Silva, Fiona R. Spuler, Carmen B. Steinmann, Miguel Ángel Torres-Vázquez, Renata Veiga, Dave van Wees, Jakob B. Wessel, Emily Wright, Bibiana Bilbao, Mathieu Bourbonnais, Gao Cong, Carlos M. Di Bella, Kebonye Dintwe, Victoria M. Donovan, Sarah Harris, Elena A. Kukavskaya, Brigitte N’Dri, Cristina Santín, Galia Selaya, Johan Sjöström, John Abatzoglou, Niels Andela, Rachel Carmenta, Emilio Chuvieco, Louis Giglio, Douglas S. Hamilton, Stijn Hantson, Sarah Meier, Mark Parrington, Mojtaba Sadegh, Jesus San-Miguel-Ayanz, Fernando Sedano, Marco Turco, Guido R. van der Werf, Sander Veraverbeke, Liana O. Anderson, Hamish Clarke, Paulo M. Fernandes, and Crystal A. Kolden
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The second State of Wildfires report examines extreme wildfire events from 2024 to early 2025. It analyses key regional events in Southern California, Northeast Amazonia, Pantanal-Chiquitano, and the Congo Basin, assessing their drivers, predictability, and attributing them to climate change and land use. Seasonal outlooks and decadal projections are provided. Climate change greatly increased the likelihood of these fires, and without strong mitigation, such events will become more frequent.
Matthew W. Jones, Douglas I. Kelley, Chantelle A. Burton, Francesca Di Giuseppe, Maria Lucia F. Barbosa, Esther Brambleby, Andrew J. Hartley, Anna Lombardi, Guilherme Mataveli, Joe R. McNorton, Fiona R. Spuler, Jakob B. Wessel, John T. Abatzoglou, Liana O. Anderson, Niels Andela, Sally Archibald, Dolors Armenteras, Eleanor Burke, Rachel Carmenta, Emilio Chuvieco, Hamish Clarke, Stefan H. Doerr, Paulo M. Fernandes, Louis Giglio, Douglas S. Hamilton, Stijn Hantson, Sarah Harris, Piyush Jain, Crystal A. Kolden, Tiina Kurvits, Seppe Lampe, Sarah Meier, Stacey New, Mark Parrington, Morgane M. G. Perron, Yuquan Qu, Natasha S. Ribeiro, Bambang H. Saharjo, Jesus San-Miguel-Ayanz, Jacquelyn K. Shuman, Veerachai Tanpipat, Guido R. van der Werf, Sander Veraverbeke, and Gavriil Xanthopoulos
Earth Syst. Sci. Data, 16, 3601–3685, https://doi.org/10.5194/essd-16-3601-2024, https://doi.org/10.5194/essd-16-3601-2024, 2024
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This inaugural State of Wildfires report catalogues extreme fires of the 2023–2024 fire season. For key events, we analyse their predictability and drivers and attribute them to climate change and land use. We provide a seasonal outlook and decadal projections. Key anomalies occurred in Canada, Greece, and western Amazonia, with other high-impact events catalogued worldwide. Climate change significantly increased the likelihood of extreme fires, and mitigation is required to lessen future risk.
Renata Moura da Veiga, Celso von Randow, Chantelle Burton, Douglas I. Kelley, Manoel Cardoso, and Fabiano Morelli
Nat. Hazards Earth Syst. Sci., 25, 3581–3601, https://doi.org/10.5194/nhess-25-3581-2025, https://doi.org/10.5194/nhess-25-3581-2025, 2025
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We systematically reviewed 77 papers to understand the Cerrado’s fire emissions within the global carbon budget by evaluating how fire parameters can inform emission estimates and mitigation strategies. Estimating fire emissions in the Cerrado requires a holistic approach, combining fire carbon emission estimates, fire dynamic parameters, and fire management and policy. We highlight key research gaps that could provide more comprehensive insights into accounting for fire emissions in the Cerrado.
Anastasios Rovithakis, Eleanor Burke, Chantelle Burton, Matthew Kasoar, Manolis G. Grillakis, Konstantinos D. Seiradakis, and Apostolos Voulgarakis
Nat. Hazards Earth Syst. Sci., 25, 3185–3200, https://doi.org/10.5194/nhess-25-3185-2025, https://doi.org/10.5194/nhess-25-3185-2025, 2025
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We used a land surface computer model to forecast how climate change will impact wildfires in Greece. Our results show a significant increase in future burnt area due to hotter, drier climate. Allowing vegetation to change with the climate lessens this increase overall, since fire is no longer igniting in areas already burnt, and it even led to projected decreases in the agricultural areas in the north of the country.
Seppe Lampe, Lukas Gudmundsson, Basil Kraft, Stijn Hantson, Douglas Kelley, Vincent Humphrey, Bertrand Le Saux, Emilio Chuvieco, and Wim Thiery
EGUsphere, https://doi.org/10.5194/egusphere-2025-3550, https://doi.org/10.5194/egusphere-2025-3550, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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We introduce BuRNN, a model which estimates monthly burned area based on satellite observations and climate, vegetation, and socio-economic data using machine learning. BuRNN outperforms existing process-based fire models. However, the model tends to underestimate burned area in parts of Africa and Australia. We identify the extent of bare ground, the presence of grasses, and fire weather conditions (long periods of warm and dry weather) as key regional drivers of fire activity in BuRNN.
Douglas I. Kelley, Chantelle Burton, Francesca Di Giuseppe, Matthew W. Jones, Maria L. F. Barbosa, Esther Brambleby, Joe R. McNorton, Zhongwei Liu, Anna S. I. Bradley, Katie Blackford, Eleanor Burke, Andrew Ciavarella, Enza Di Tomaso, Jonathan Eden, Igor José M. Ferreira, Lukas Fiedler, Andrew J. Hartley, Theodore R. Keeping, Seppe Lampe, Anna Lombardi, Guilherme Mataveli, Yuquan Qu, Patrícia S. Silva, Fiona R. Spuler, Carmen B. Steinmann, Miguel Ángel Torres-Vázquez, Renata Veiga, Dave van Wees, Jakob B. Wessel, Emily Wright, Bibiana Bilbao, Mathieu Bourbonnais, Gao Cong, Carlos M. Di Bella, Kebonye Dintwe, Victoria M. Donovan, Sarah Harris, Elena A. Kukavskaya, Brigitte N’Dri, Cristina Santín, Galia Selaya, Johan Sjöström, John Abatzoglou, Niels Andela, Rachel Carmenta, Emilio Chuvieco, Louis Giglio, Douglas S. Hamilton, Stijn Hantson, Sarah Meier, Mark Parrington, Mojtaba Sadegh, Jesus San-Miguel-Ayanz, Fernando Sedano, Marco Turco, Guido R. van der Werf, Sander Veraverbeke, Liana O. Anderson, Hamish Clarke, Paulo M. Fernandes, and Crystal A. Kolden
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-483, https://doi.org/10.5194/essd-2025-483, 2025
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The second State of Wildfires report examines extreme wildfire events from 2024 to early 2025. It analyses key regional events in Southern California, Northeast Amazonia, Pantanal-Chiquitano, and the Congo Basin, assessing their drivers, predictability, and attributing them to climate change and land use. Seasonal outlooks and decadal projections are provided. Climate change greatly increased the likelihood of these fires, and without strong mitigation, such events will become more frequent.
Forrest M. Hoffman, Birgit Hassler, Ranjini Swaminathan, Jared Lewis, Bouwe Andela, Nathaniel Collier, Dóra Hegedűs, Jiwoo Lee, Charlotte Pascoe, Mika Pflüger, Martina Stockhause, Paul Ullrich, Min Xu, Lisa Bock, Felicity Chun, Bettina K. Gier, Douglas I. Kelley, Axel Lauer, Julien Lenhardt, Manuel Schlund, Mohanan G. Sreeush, Katja Weigel, Ed Blockley, Rebecca Beadling, Romain Beucher, Demiso D. Dugassa, Valerio Lembo, Jianhua Lu, Swen Brands, Jerry Tjiputra, Elizaveta Malinina, Brian Mederios, Enrico Scoccimarro, Jeremy Walton, Philip Kershaw, André L. Marquez, Malcolm J. Roberts, Eleanor O’Rourke, Elisabeth Dingley, Briony Turner, Helene Hewitt, and John P. Dunne
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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Joao C. M. Teixeira, Chantelle Burton, Douglas I. Kelley, Gerd A. Folberth, Fiona M. O'Connor, Richard A. Betts, and Apostolos Voulgarakis
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Burnt areas produced by wildfires around the world are decreasing, especially in tropical regions, but many climate models fail to show this trend. Our study looks at whether adding a measure of human development to a fire model could improve its representation of these processes. We found that including these factors helped the model better match observations in many regions. This shows that human activity plays a key role in shaping fire trends.
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.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
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Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Inika Taylor, Douglas I. Kelley, Camilla Mathison, Karina E. Williams, Andrew J. Hartley, Richard A. Betts, and Chantelle Burton
EGUsphere, https://doi.org/10.5194/egusphere-2025-720, https://doi.org/10.5194/egusphere-2025-720, 2025
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Climate change is reshaping fire seasons worldwide and, in many places, increasing fire weather risk. We use climate model simulations to project future changes in fire danger at different levels of global warming, focusing on Australia, Brazil, and the USA. Keeping warming below 2 °C significantly limits the increase in fire risk, but even at 1.5 °C, fire seasons lengthen, with more extreme conditions. However, low-fire weather periods remain, offering critical windows for fire management.
Matthew W. Jones, Douglas I. Kelley, Chantelle A. Burton, Francesca Di Giuseppe, Maria Lucia F. Barbosa, Esther Brambleby, Andrew J. Hartley, Anna Lombardi, Guilherme Mataveli, Joe R. McNorton, Fiona R. Spuler, Jakob B. Wessel, John T. Abatzoglou, Liana O. Anderson, Niels Andela, Sally Archibald, Dolors Armenteras, Eleanor Burke, Rachel Carmenta, Emilio Chuvieco, Hamish Clarke, Stefan H. Doerr, Paulo M. Fernandes, Louis Giglio, Douglas S. Hamilton, Stijn Hantson, Sarah Harris, Piyush Jain, Crystal A. Kolden, Tiina Kurvits, Seppe Lampe, Sarah Meier, Stacey New, Mark Parrington, Morgane M. G. Perron, Yuquan Qu, Natasha S. Ribeiro, Bambang H. Saharjo, Jesus San-Miguel-Ayanz, Jacquelyn K. Shuman, Veerachai Tanpipat, Guido R. van der Werf, Sander Veraverbeke, and Gavriil Xanthopoulos
Earth Syst. Sci. Data, 16, 3601–3685, https://doi.org/10.5194/essd-16-3601-2024, https://doi.org/10.5194/essd-16-3601-2024, 2024
Short summary
Short summary
This inaugural State of Wildfires report catalogues extreme fires of the 2023–2024 fire season. For key events, we analyse their predictability and drivers and attribute them to climate change and land use. We provide a seasonal outlook and decadal projections. Key anomalies occurred in Canada, Greece, and western Amazonia, with other high-impact events catalogued worldwide. Climate change significantly increased the likelihood of extreme fires, and mitigation is required to lessen future risk.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
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Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
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.
Lee de Mora, Ranjini Swaminathan, Richard P. Allan, Jerry C. Blackford, Douglas I. Kelley, Phil Harris, Chris D. Jones, Colin G. Jones, Spencer Liddicoat, Robert J. Parker, Tristan Quaife, Jeremy Walton, and Andrew Yool
Earth Syst. Dynam., 14, 1295–1315, https://doi.org/10.5194/esd-14-1295-2023, https://doi.org/10.5194/esd-14-1295-2023, 2023
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We investigate the flux of carbon from the atmosphere into the land surface and ocean for multiple models and over a range of future scenarios. We did this by comparing simulations after the same change in the global-mean near-surface temperature. Using this method, we show that the choice of scenario can impact the carbon allocation to the land, ocean, and atmosphere. Scenarios with higher emissions reach the same warming levels sooner, but also with relatively more carbon in the atmosphere.
Joao Carlos Martins Teixeira, Chantelle Burton, Douglas I. Kelly, Gerd A. Folberth, Fiona M. O'Connor, Richard A. Betts, and Apostolos Voulgarakis
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-136, https://doi.org/10.5194/bg-2023-136, 2023
Revised manuscript not accepted
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Representing socio-economic impacts on fires is crucial to underpin the confidence in global fire models. Introducing these into INFERNO, reduces biases and improves the modelled burnt area (BA) trends when compared to observations. Including socio-economic factors in the representation of fires in Earth System Models is important for realistically simulating BA, quantifying trends in the recent past, and for understanding the main drivers of those at regional scales.
Camilla Mathison, Eleanor Burke, Andrew J. Hartley, Douglas I. Kelley, Chantelle Burton, Eddy Robertson, Nicola Gedney, Karina Williams, Andy Wiltshire, Richard J. Ellis, Alistair A. Sellar, and Chris D. Jones
Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, https://doi.org/10.5194/gmd-16-4249-2023, 2023
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This paper describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle. We have created a new configuration and set-up for the JULES-ES land surface model, driven by bias-corrected historical and future climate model output provided by the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This allows us to compare projections of the impacts of climate change across multiple impact models and multiple sectors.
Jane P. Mulcahy, Colin G. Jones, Steven T. Rumbold, Till Kuhlbrodt, Andrea J. Dittus, Edward W. Blockley, Andrew Yool, Jeremy Walton, Catherine Hardacre, Timothy Andrews, Alejandro Bodas-Salcedo, Marc Stringer, Lee de Mora, Phil Harris, Richard Hill, Doug Kelley, Eddy Robertson, and Yongming Tang
Geosci. Model Dev., 16, 1569–1600, https://doi.org/10.5194/gmd-16-1569-2023, https://doi.org/10.5194/gmd-16-1569-2023, 2023
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Recent global climate models simulate historical global mean surface temperatures which are too cold, possibly to due to excessive aerosol cooling. This raises questions about the models' ability to simulate important climate processes and reduces confidence in future climate predictions. We present a new version of the UK Earth System Model, which has an improved aerosols simulation and a historical temperature record. Interestingly, the long-term response to CO2 remains largely unchanged.
Rahayu Adzhar, Douglas I. Kelley, Ning Dong, Charles George, Mireia Torello Raventos, Elmar Veenendaal, Ted R. Feldpausch, Oliver L. Phillips, Simon L. Lewis, Bonaventure Sonké, Herman Taedoumg, Beatriz Schwantes Marimon, Tomas Domingues, Luzmila Arroyo, Gloria Djagbletey, Gustavo Saiz, and France Gerard
Biogeosciences, 19, 1377–1394, https://doi.org/10.5194/bg-19-1377-2022, https://doi.org/10.5194/bg-19-1377-2022, 2022
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The MODIS Vegetation Continuous Fields (VCF) product underestimates tree cover compared to field data and could be underestimating tree cover significantly across the tropics. VCF is used to represent land cover or validate model performance in many land surface and global vegetation models and to train finer-scaled Earth observation products. Because underestimation in VCF may render it unsuitable for training data and bias model predictions, it should be calibrated before use in the tropics.
Douglas I. Kelley, Chantelle Burton, Chris Huntingford, Megan A. J. Brown, Rhys Whitley, and Ning Dong
Biogeosciences, 18, 787–804, https://doi.org/10.5194/bg-18-787-2021, https://doi.org/10.5194/bg-18-787-2021, 2021
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Initial evidence suggests human ignitions or landscape changes caused most Amazon fires during August 2019. However, confirmation is needed that meteorological conditions did not have a substantial role. Assessing the influence of historical weather on burning in an uncertainty framework, we find that 2019 meteorological conditions alone should have resulted in much less fire than observed. We conclude socio-economic factors likely had a strong role in the high recorded 2019 fire activity.
Cited articles
Abril-Pla, O., Andreani, V., Carroll, C., Dong, L., Fonnesbeck, C. J., Kochurov, M., Kumar, R., Lao, J., Luhmann, C. C., Martin, O. A., Osthege, M., Vieira, R., Wiecki, T., and Zinkov, R.: PyMC: A Modern and Comprehensive Probabilistic Programming Framework in Python, Comput. Sci., 9, e1516, https://doi.org/10.7717/peerj-cs.1516, 2023.
Alencar, A. A., Arruda, V. L., Silva, W. V. D., Conciani, D. E., Costa, D. P., Crusco, N., Duverger, S. G., Ferreira, N. C., Franca-Rocha, W., Hasenack, H., and Martenexen, L. F. M.: Long-term Landsat-based monthly burned area dataset for the Brazilian biomes using deep learning, Remote Sens., 14, 2510, https://doi.org/10.3390/rs14112510, 2022.
Alvarado, S. T., Andela, N., Silva, T. S. F., and Archibald, S.: Thresholds of fire response to moisture and fuel load differ between tropical savannas and grasslands across continents, Global Ecol. Biogeogr., 29, 331–344, https://doi.org/10.1111/geb.13034, 2020.
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., Kloster, S., and Bachelet, D.: A human-driven decline in global burned area, Science, 356, 1356–1362, https://doi.org/10.1126/science.aal4108, 2017.
Antongiovanni, M., Venticinque, E. M., Matsumoto, M., and Fonseca, C. R.: Chronic anthropogenic disturbance on Caatinga dry forest fragments, J. Appl. Ecol., 57, 2064–2074, https://doi.org/10.1111/1365-2664.13686, 2020.
Aragão, L. E. O., Malhi, Y., Barbier, N., Lima, A., Shimabukuro, Y., Anderson, L., and Saatchi, S.: Interactions between rainfall, deforestation and fires during recent years in the Brazilian Amazonia, Philos. Trans. R. Soc. B Biol. Sci., 363, 1779–1785, https://doi.org/10.1098/rstb.2007.0026, 2008.
Armenteras, D., González, T. M., and Retana, J.: Forest fragmentation and edge influence on fire occurrence and intensity under different management types in Amazon forests, Biol. Conserv., 159, 73–79, https://doi.org/10.1016/j.biocon.2012.10.026, 2013.
Armenteras, D., Barreto, J. S., Tabor, K., Molowny-Horas, R., and Retana, J.: Changing patterns of fire occurrence in proximity to forest edges, roads and rivers between NW Amazonian countries, Biogeosciences, 14, 2755–2765, https://doi.org/10.5194/bg-14-2755-2017, 2017.
Banerjee, P.: Maximum entropy-based forest fire likelihood mapping: analysing the trends, distribution, and drivers of forest fires in Sikkim Himalaya, Scand. J. For. Res., 36, 275–288, https://doi.org/10.1080/02827581.2021.1918239, 2021.
Barbosa, M. L. F., Delgado, R. C., Teodoro, P. E., Pereira, M. G., Correia, T. P., de Mendonça, B. A. F., and Ávila Rodrigues, R. D.: Occurrence of fire foci under different land uses in the State of Amazonas during the 2005 drought, Environ. Develop. Sustain., 21, 2707–2720, https://doi.org/10.1007/s10668-018-0157-4, 2019.
Barbosa, M. L. F., Haddad, I., da Silva Nascimento, A. L., Maximo da Silva, G., Moura da Veiga, R., Hoffmann, T. B., Rosane de Souza, A., Dalagnol, R., Susin Streher, A., Souza Pereira, F. R., and Oliveira e Cruz de Aragão, L. E.: Compound impact of land use and extreme climate on the 2020 fire record of the Brazilian Pantanal, Global Ecol. Biogeogr., 31, 1960–1975, https://doi.org/10.1111/geb.13563, 2022.
Barbosa, M. L. F., Kelley, D., Burton, C., and Anderson, L.: FLAME 1.0: Fogo local analisado pela Máxima Entropia (v0.1), Zenodo [code], https://doi.org/10.5281/zenodo.13367375, 2024a.
Barbosa, M. L. F, Kelley, D., Bradley, A., and Burton, C.: FLAME 1.0: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept, Zenodo [data set], https://doi.org/10.5281/zenodo.11491125, 2024b.
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.
Burton, C., Betts, R., Cardoso, M., Feldpausch, T. R., Harper, A., Jones, C. D., Kelley, D. I., Robertson, E., and Wiltshire, A.: Representation of fire, land-use change and vegetation dynamics in the Joint UK Land Environment Simulator vn4.9 (JULES), Geosci. Model Dev., 12, 179–193, https://doi.org/10.5194/gmd-12-179-2019, 2019.
Burton, C., Kelley, D. I., Jones, C. D., Betts, R. A., Cardoso, M., and Anderson, L.: South American fires and their impacts on ecosystems increase with continued emissions, Clim. Resil. Sustain., 1, e8, https://doi.org/10.1002/cli2.8, 2022.
Burton, C., Lampe, S., Kelley, D. I., Thiery, W., Hantson, S., Christidis, N., Gudmundsson, L., Forrest, M., Burke, E., Chang, J., and Huang, H.: Global burned area increasingly explained by climate change, Nat. Clim. Chang., 14, 1186–1192, https://doi.org/10.1038/s41558-024-02140-w, 2024.
Campanharo, W. A., Lopes, A. P., Anderson, L. O., da Silva, T. F., and Aragão, L. E.: Translating fire impacts in Southwestern Amazonia into economic costs, Remote Sens., 11, 764, https://doi.org/10.3390/rs11070764, 2019.
Cano-Crespo, A., Oliveira, P. J., Boit, A., Cardoso, M., and Thonicke, K.: Forest edge burning in the Brazilian Amazon promoted by escaping fires from managed pastures, J. Geophys. Res.-Biogeo., 120, 2095–2107, https://doi.org/10.1002/2015JG002914, 2015.
Cano-Crespo, A., Traxl, D., Prat-Ortega, G., Rolinski, S., and Thonicke, K.: Characterization of land cover-specific fire regimes in the Brazilian Amazon, Reg. Environ. Change, 23, 19, https://doi.org/10.1007/s10113-022-02012-z, 2023.
Cecil, D. J.: LIS/OTD 0.5 degree high resolution monthly climatology, NASA Global Hydrometeorology Resource Center DAAC [data set], https://doi.org/10.5067/LIS/LIS-OTD/DATA311, 2006.
Chen, F., Du, Y., Niu, S., and Zhao, J.: Modeling forest lightning fire occurrence in the Daxinganling Mountains of Northeastern China with MAXENT, Forests, 6, 1422–1438, https://doi.org/10.3390/f6051422, 2015.
Chen, X., Dimitrov, N. B., and Meyers, L. A.: Uncertainty analysis of species distribution models, PLoS One, 14, e0214190, https://doi.org/10.1371/journal.pone.0214190, 2019.
Chiaravalloti, R. M., Tomas, W. M., Akre, T., Morato, R. G., Camilo, A. R., Giordano, A. J., and Leimgruber, P.: Achieving conservation through cattle ranching: the case of the Brazilian Pantanal, Conservation Science and Practice, 1–11, https://doi.org/10.1111/csp2.13006, 2023.
Damasceno-Junior, G. A., Pereira, A. d. M. M., Oldeland, J., Parolin, P., and Pott, A.: Fire, Flood and Pantanal Vegetation, in: Flora and Vegetation of the Pantanal Wetland, Plant and Vegetation, edited by: Damasceno-Junior, G. A. and Pott, A., Springer, Cham, vol. 18, https://doi.org/10.1007/978-3-030-83375-6_18, 2022.
De Araújo, F. M., Ferreira, L. G., and Arantes, A. E.: Distribution patterns of burned areas in the Brazilian biomes: An analysis based on satellite data for the 2002–2010 period, Remote Sens., 4, 1929–1946, 2012.
De Oliveira, G., Mataveli, G., Stark, S. C., Jones, M. W., Carmenta, R., Brunsell, N. A., Santos, C. A., da Silva Junior, C. A., Cunha, H. F., da Cunha, A. C., and dos Santos, C. A.: Increasing wildfires threaten progress on halting deforestation in Brazilian Amazonia, Nat. Ecol. Evol., 7, 1945–1946, https://doi.org/10.1038/s41559-023-02233-3, 2023.
De Praga Baião, C. F., Santos, F. C., Ferreira, M. P., Bignotto, R. B., da Silva, R. F. G., and Massi, K. G.: The relationship between forest fire and deforestation in the southeast Atlantic rainforest, PLoS One, 18, e0286754, https://doi.org/10.1371/journal.pone.0286754, 2023.
Dos Reis, M., de Alencastro Graça, P. M. L., Yanai, A. M., Ramos, C. J. P., and Fearnside, P. M.: Forest fires and deforestation in the central Amazon: effects of landscape and climate on spatial and temporal dynamics, J. Environ. Manage., 288, 112310, https://doi.org/10.1016/j.jenvman.2021.112310, 2021.
Dos Santos, A. C., da Rocha Montenegro, S., Ferreira, M. C., Barradas, A. C. S., and Schmidt, I. B.: Managing fires in a changing world: fuel and weather determine fire behavior and safety in the neotropical savannas, J. Environ. Manage., 289, 112508, https://doi.org/10.1016/j.jenvman.2021.112508, 2021.
Driscoll, D. A., Armenteras, D., Bennett, A. F., Brotons, L., Clarke, M. F., Doherty, T. S., Haslem, A., Kelly, L. T., Sato, C. F., Sitters, H., and Aquilué, N.: How fire interacts with habitat loss and fragmentation, Biol. Rev., 96, 976–998, https://doi.org/10.1111/brv.12687, 2021.
Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., and Yates, C. J.: A statistical explanation of MaxEnt for ecologists, Divers. Distrib., 17, 43–57, https://doi.org/10.1111/j.1472-4642.2010.00725.x, 2011.
Ferreira, I. J., Campanharo, W. A., Barbosa, M. L., Silva, S. S. D., Selaya, G., Aragão, L. E., and Anderson, L. O.: Assessment of fire hazard in Southwestern Amazon, Front. For. Global Change, 6, 1107417, https://doi.org/10.3389/ffgc.2023.1107417, 2023.
Fidelis, A., Schmidt, I. B., Furquim, F. F., and Overbeck, G. E.: Burning in the Pampa and Cerrado in Brazil, Global Application of Prescribed Fire, CSIRO Publishing, 38 pp., ISBN 9781486312481, 2022.
Flores, B. M., Montoya, E., Sakschewski, B., Nascimento, N., Staal, A., Betts, R. A., Levis, C., Lapola, D. M., Esquível-Muelbert, A., Jakovac, C., and Nobre, C. A.: Critical transitions in the Amazon Forest system, Nature, 626, 555–564, https://doi.org/10.1038/s41586-023-06970-0, 2024.
Fonseca, M. G., Anderson, L. O., Arai, E., Shimabukuro, Y. E., Xaud, H. A., Xaud, M. R., Madani, N., Wagner, F. H., and Aragão, L. E.: Climatic and anthropogenic drivers of northern Amazon fires during the 2015–2016 El Niño event, Ecol. Appl., 27, 2514–2527, https://doi.org/10.1002/eap.1628, 2017.
Fonseca, M. G., Alves, L. M., Aguiar, A. P. D., Arai, E., Anderson, L. O., Rosan, T. M., Shimabukuro, Y. E., and de Aragão, L. E. O. E. C.: Effects of climate and land-use change scenarios on fire probability during the 21st century in the Brazilian Amazon, Glob. Change Biol., 25, 2931–2946, https://doi.org/10.1111/gcb.14709, 2019.
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.
Frieler, K., Volkholz, J., Lange, S., Schewe, J., Mengel, M., del Rocío Rivas López, M., Otto, C., Reyer, C. P. O., Karger, D. N., Malle, J. T., Treu, S., Menz, C., Blanchard, J. L., Harrison, C. S., Petrik, C. M., Eddy, T. D., Ortega-Cisneros, K., Novaglio, C., Rousseau, Y., Watson, R. A., Stock, C., Liu, X., Heneghan, R., Tittensor, D., Maury, O., Büchner, M., Vogt, T., Wang, T., Sun, F., Sauer, I. J., Koch, J., Vanderkelen, I., Jägermeyr, J., Müller, C., Rabin, S., Klar, J., Vega del Valle, I. D., Lasslop, G., Chadburn, S., Burke, E., Gallego-Sala, A., Smith, N., Chang, J., Hantson, S., Burton, C., Gädeke, A., Li, F., Gosling, S. N., Müller Schmied, H., Hattermann, F., Wang, J., Yao, F., Hickler, T., Marcé, R., Pierson, D., Thiery, W., Mercado-Bettín, D., Ladwig, R., Ayala-Zamora, A. I., Forrest, M., and Bechtold, M.: Scenario setup and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a), Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, 2024.
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B.: Bayesian Data Analysis, Third Edition, Chapman and Hall/CRC, ISBN 978-1-4398-4095-5, 2013.
Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L., and Justice, C. O.: The Collection 6 MODIS burned area mapping algorithm and product, Remote Sens. Environ., 217, 72–85, https://doi.org/10.1016/j.rse.2018.08.005, 2018.
Göltas, M., Ayberk, H., and Küçük, O.: Forest fire occurrence modeling in Southwest Turkey using MaxEnt machine learning technique, iForest-Biogeosci. Forest., 17, 10–18, https://doi.org/10.3832/ifor4321-016, 2024.
Haas, O., Prentice, I. C., and Harrison, S. P.: Global environmental controls on wildfire burnt area, size, and intensity, Environ. Res. Lett., 13, 065004, https://doi.org/10.1088/1748-9326/ac6a69, 2022.
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.
Hardesty, J., Myers, R., and Fulks, W.: Fire, ecosystems, and people: a preliminary assessment of fire as a global conservation issue, The George Wright Forum, 22, 78–87, https://www.jstor.org/stable/43597968 (last access: 7 March 2024), 2005.
Hesselbarth, M. H. K., Sciani, M., Nowosad, J., Hanss, S., Graham, L. J., Hollister, J., With, K. A., Privé, F., Project Nayuki, and Strimas-Mackey, M.: Landscape Metrics for Categorical Map Patterns, Comprehensive R Archive Network (CRAN), https://cran.r-project.org/web/packages/landscapemetrics/landscapemetrics.pdf (last access: 15 December 2024), 2024.
Hoffman, M. D. and Gelman, A.: The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo, J. Mach. Learn. Res., 15, 1593–1623, 2014.
Jaynes, E. T.: Information theory and statistical mechanics, Phys. Rev., 106, 620–630, https://doi.org/10.1103/PhysRev.106.620, 1957.
Jiménez-Valverde, A.: Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling, Global Ecol. Biogeogr., 21, 498–507, https://doi.org/10.1111/j.1466-8238.2011.00683.x, 2011.
Krawchuk, M. A. and Moritz, M. A.: Burning issues: statistical analyses of global fire data to inform assessments of environmental change, Environmetrics, 25, 472–481, https://doi.org/10.1002/env.2287, 2014.
Kelley, D. I. and Harrison, S. P.: Enhanced Australian carbon sink despite increased wildfire during the 21st century, Environ. Res. Lett., 9, 104015, https://doi.org/10.1088/1748-9326/9/10/104015, 2014.
Kelley, D. I., Bistinas, I., Whitley, R., Burton, C., Marthews, T. R., and Dong, N.: How contemporary bioclimatic and human controls change global fire regimes, Nat. Clim. Change, 9, 690–696, https://doi.org/10.1038/s41558-019-0540-7, 2019.
Kelley, D. I., Burton, C., Huntingford, C., Brown, M. A. J., Whitley, R., and Dong, N.: Technical note: Low meteorological influence found in 2019 Amazonia fires, Biogeosciences, 18, 787–804, https://doi.org/10.5194/bg-18-787-2021, 2021.
Laplace, P. S.: The Analytic Theory of Probabilities, Third Edition, Book II, Courcier, ISBN 978-1009549004, 1820.
Li, B., Liu, B., Guo, K., Li, C., and Wang, B.: Application of a maximum entropy model for mineral prospectivity maps, Minerals, 9, 556, https://doi.org/10.3390/min9090556, 2019.
Li, S., Rifai, S., Anderson, L. O., and Sparrow, S.: Identifying local-scale meteorological conditions favorable to large fires in Brazil, Clim. Resil. Sustain., 1, e11, https://doi.org/10.1002/cli2.11, 2022.
Libonati, R., Geirinhas, J. L., Silva, P. S., Monteiro dos Santos, D., Rodrigues, J. A., Russo, A., Peres, L. F., Narcizo, L., Gomes, M. E., Rodrigues, A. P., and Dacamara, C. C.: Drought–heatwave nexus in Brazil and related impacts on health and fires: a comprehensive review, Ann. N.Y. Acad. Sci., 1517, 44–62, https://doi.org/10.1111/nyas.14887, 2022a.
Libonati, R., Geirinhas, J. L., Silva, P. S., Russo, A., Rodrigues, J. A., Belém, L. B. C., Nogueira, J., Roque, F. O., Dacamara, C. C., Nunes, A. M. B., Marengo, J. A., and Trigo, R. M.: Assessing the role of compound drought and heatwave events on unprecedented 2020 wildfires in the Pantanal, Environ. Res. Lett., 17, 015005, https://doi.org/10.1088/1748-9326/ac462e, 2022b.
Lobo, J. M., Jiménez-Valverde, A., and Real, R.: AUC: a misleading measure of the performance of predictive distribution models, Global Ecol. Biogeogr., 17, 145–151, https://doi.org/10.1111/j.1466-8238.2007.00358.x, 2008.
MapBiomas Fogo: Coleção 2 do mapeamento das cicatrizes de fogo do Brasil (1985–2022), MapBiomas, https://brasil.mapbiomas.org/wp-content/uploads/sites/4/2023/08/ATBD_-_MapBiomas_Fogo_-_Colecao_2.pdf (last access: 12 April 2024), 2023.
Mapbiomas Project: Coleção 7 da série anual de mapas de cobertura e uso da terra do Brasil, MapBiomas, https://brasil.mapbiomas.org/downloads/ (last access: 20 April 2024), 2022.
Mathison, C., Burke, E., Hartley, A. J., Kelley, D. I., Burton, C., Robertson, E., Gedney, N., Williams, K., Wiltshire, A., Ellis, R. J., Sellar, A. A., and Jones, C. D.: Description and evaluation of the JULES-ES set-up for ISIMIP2b, Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, 2023.
Meijer, J. R., Huijbregts, M. A. J., Schotten, C. G. J., and Schipper, A. M.: Global patterns of current and future road infrastructure, Environ. Res. Lett., 13, e064006, https://doi.org/10.1088/1748-9326/aabd42, 2018.
Met Office: Iris: a powerful, format-agnostic, and community-driven Python package for analysing and visualising Earth science data, v3.6, 2010–2023, Met Office, http://scitools.org.uk/ (last access: 20 June 2024), 2023.
Nogueira, J. M., Rambal, S., Barbosa, J. P. R., and Mouillot, F.: Spatial pattern of the seasonal drought/burned area relationship across Brazilian biomes: sensitivity to drought metrics and global remote-sensing fire products, Climate, 5, 42, https://doi.org/10.3390/cli5020042, 2017.
Numata, I., Silva, S. S., Cochrane, M. A., and D'Oliveira, M. V.: Fire and edge effects in a fragmented tropical forest landscape in the southwestern Amazon, For. Ecol. Manag., 401, 135–146, https://doi.org/10.1016/j.foreco.2017.07.010, 2017.
Oliveira, U., Soares-Filho, B., Bustamante, M., Gomes, L., Ometto, J. P., and Rajão, R.: Determinants of fire impact in the Brazilian biomes, Front. For. Glob. Change, 5, 735017, https://doi.org/10.3389/ffgc.2022.735017, 2022.
Penfield Jr., P.: Principle of maximum entropy: simple form, Massachusetts Institute of Technology, Massachusetts, Springer, https://dspace.mit.edu/bitstream/handle/1721.1/45591/6-050JSpring-2003/NR/rdonlyres/Electrical-Engineering-and-Computer-Science/6-050JInformation-and-EntropySpring2003/ECB9F73C-48E8-4528-B69E-F5CBAF886C5C/0/chapter9.pdf (last access: 12 June 2025), 2003.
Peterson, A. T., Soberón, J., Pearson, R. G., Anderson, R. P., Martinez-Meyer, E., Nakamura, M., and Araujo, M. B.: Ecological niches and geographic distributions, Princeton University Press, Princeton, 49, https://doi.org/10.1515/9781400840670, 2011.
Phillips, S. J. and Dudik, M.: Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation, Ecography, 31, 161–175, https://doi.org/10.1111/j.0906-7590.2008.5203.x, 2008.
Phillips, S. J., Anderson, R. P., and Schapire, R. E.: Maximum entropy modeling of species geographic distributions, Ecol. Model., 190, 231–259, https://doi.org/10.1016/j.ecolmodel.2005.03.026, 2006.
Radosavljevic, A. and Anderson, R. P.: Making better Maxent models of species distributions: complexity, overfitting and evaluation, J. Biogeogr., 41, 629–643, https://doi.org/10.1111/jbi.12227, 2013.
Rosan, T. M., Sitch, S., Mercado, L. M., Heinrich, V., Friedlingstein, P., and Aragão, L. E.: Fragmentation-driven divergent trends in burned area in Amazonia and Cerrado, Front. For. Glob. Change, 5, 801408, https://doi.org/10.3389/ffgc.2022.801408, 2022.
Silva Junior, C. H., Buna, A. T., Bezerra, D. S., Costa Jr., O. S., Santos, A. L., Basson, L. O., Santos, A. L., Alvarado, S. T., Almeida, C. T., Freire, A. T., and Rousseau, G. X.: Forest fragmentation and fires in the eastern Brazilian Amazon–Maranhão State, Brazil, Fire, 5, 77, https://doi.org/10.3390/fire5030077, 2022.
Silveira, M. V., Petri, C. A., Broggio, I. S., Chagas, G. O., Macul, M. S., Leite, C. C., Ferrari, E. M., Amim, C. G., Freitas, A. L., Motta, A. Z., and Carvalho, L. M.: Drivers of fire anomalies in the Brazilian Amazon: Lessons learned from the 2019 fire crisis, Land, 9, 516, https://doi.org/10.3390/land9120516, 2020.
Silveira, M. V., Silva-Júnior, C. H., Anderson, L. O., and Aragão, L. E.: Amazon fires in the 21st century: the year of 2020 in evidence, Global Ecol. Biogeogr., 31, 2026–2040, https://doi.org/10.1111/geb.13577, 2022.
Singh, M. and Huang, Z.: Analysis of forest fire dynamics, distribution and main drivers in the Atlantic Forest, Sustainability, 14, 992, https://doi.org/10.3390/su14020992, 2022.
Spearman, C.: The proof and measurement of association between two things, in: Studies in Individual Differences: The Search for Intelligence, edited by: Jenkins, J. J. and Paterson, D. G., Appleton Century Crofts, 45–58, https://doi.org/10.1037/11491-005, 1961.
United Nations Environment Programme (UNEP): Spreading like wildfire: the rising threat of extraordinary landscape fires, A UNEP Rapid Response Assessment, Nairobi, United Nations Environment Programme (UNEP), 240827, https://www.unep.org/resources/report/spreading-wildfire-rising-threat-extraordinary-landscape-fires (last access: 18 December 2024), 2022.
Volkholz, J., Lange, S., and Geiger, T.: ISIMIP3a population input data (v1.2), ISIMIP Repository [data set], https://doi.org/10.48364/ISIMIP.822480.2, 2022.
Wan, J., Qi, G., Ma, J., Ren, Y., Wang, R., and McKirdy, S.: Predicting the potential geographic distribution of Bactrocera bryoniae and Bactrocera neohumeralis (Diptera: Tephritidae) in China using MaxEnt ecological niche modeling, J. Integr. Agric., 19, 2072–2082, https://doi.org/10.1016/S2095-3119(19)62840-6, 2020.
Wiltshire, A. J., Burke, E. J., Chadburn, S. E., Jones, C. D., Cox, P. M., Davies-Barnard, T., Friedlingstein, P., Harper, A. B., Liddicoat, S., Sitch, S., and Zaehle, S.: JULES-CN: a coupled terrestrial carbon–nitrogen scheme (JULES vn5.1), Geosci. Model Dev., 14, 2161–2186, https://doi.org/10.5194/gmd-14-2161-2021, 2021.
Wu, Y., Li, S., Xu, R., Chen, G., Yue, X., Yu, P., Ye, T., Wen, B., Coêlho, M. D. S. Z. S., Saldiva, P. H. N., and Guo, Y.: Wildfire-related PM2.5 and health economic loss of mortality in Brazil, Environ. Int., 174, 107906, https://doi.org/10.1016/j.envint.2023.107906, 2023.
Zacharakis, I. and Tsihrintzis, V. A.: Environmental forest fire danger rating systems and indices around the globe: a review, Land, 12, 194, https://doi.org/10.3390/land12010194, 2023.
Short summary
As fire seasons in Brazil become increasingly severe, confidently understanding the factors driving fires is more critical than ever. To address this challenge, we developed FLAME (Fire Landscape Analysis using Maximum Entropy), a new model designed to predict fires and to analyse the spatial influence of both environmental and human factors while accounting for uncertainties. By adapting the model to different regions, we can enhance fire management strategies, making FLAME a powerful tool for protecting landscapes in Brazil and beyond.
As fire seasons in Brazil become increasingly severe, confidently understanding the factors...
