Articles | Volume 25, issue 11
https://doi.org/10.5194/nhess-25-4713-2025
© 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/nhess-25-4713-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Nov 2025
Research article |
| 28 Nov 2025
| 28 Nov 2025
Assessing human-caused wildfire ignition likelihood across Europe
Pere Joan Gelabert
Department of Agriculture and Forest Engineering, University of Lleida, Alcalde Rovira Roure 191, 25198 Lleida, Spain
Adrián Jiménez-Ruano
Department of Agriculture and Forest Engineering, University of Lleida, Alcalde Rovira Roure 191, 25198 Lleida, Spain
GEOFOREST Group, University Institute of Research in Environmental Sciences (IUCA), University of Zaragoza, Zaragoza, Spain
University Institute of Research in Environmental Sciences (IUCA), University of Zaragoza, Zaragoza, Spain
Clara Ochoa
Department of Geology, Geography and the Environment, University of Alcalá, Alcalá de Henares, Madrid, Spain
Fermín Alcasena
Institute for Sustainability & Food Chain Innovation, Department of Engineering, Public University of Navarre, Campus Arrosadia, 31006 Pamplona, Spain
Johan Sjöström
Research Institutes of Sweden (RISE), Göteborg, Sweden
Christopher Marrs
Technische Universität Dresden, Dresden, Germany
Luís Mário Ribeiro
Department of Mechanical Engineering, Universidade de Coimbra, ADI, Coimbra, Portugal
Palaiologos Palaiologou
Department of Forestry and Natural Resources Management, Agricultural University of Athens, Karpenisi, Greece
Carmen Bentué Martínez
GEOT Group, University Institute of Research in Environmental Sciences (IUCA), University of Zaragoza, Zaragoza, Spain
Emilio Chuvieco
Department of Geology, Geography and the Environment, University of Alcalá, Alcalá de Henares, Madrid, Spain
Cristina Vega-García
Department of Agriculture and Forest Engineering, University of Lleida, Alcalde Rovira Roure 191, 25198 Lleida, Spain
Marcos Rodrigues
CORRESPONDING AUTHOR
Department of Agriculture and Forest Engineering, University of Lleida, Alcalde Rovira Roure 191, 25198 Lleida, Spain
GEOFOREST Group, University Institute of Research in Environmental Sciences (IUCA), University of Zaragoza, Zaragoza, Spain
University Institute of Research in Environmental Sciences (IUCA), University of Zaragoza, Zaragoza, Spain
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Wildfires are increasing in Central Europe. We studied how they could threaten settlements in the Saxon–Czech border region. Using satellite information, local data, and computer simulations, we mapped where fires are most likely and how intense they could be. We tested the model against a destructive fire that occurred in 2022. The results are shared in an interactive web map with the aim of helping residents and agencies improve preparedness and coordinate cross-border disaster response.
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, Cong Gao, Carlos M. Di Bella, Kebonye Dintwe, Victoria M. Donovan, Sarah Harris, Elena A. Kukavskaya, Aya Brigitte N'Dri, Cristina Santín, Galia Selaya, Johan Sjöström, John T. 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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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.
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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.
Elena Aragoneses, Mariano García, Michele Salis, Luís M. Ribeiro, and Emilio Chuvieco
Earth Syst. Sci. Data, 15, 1287–1315, https://doi.org/10.5194/essd-15-1287-2023, https://doi.org/10.5194/essd-15-1287-2023, 2023
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We present a new hierarchical fuel classification system with a total of 85 fuels that is useful for preventing fire risk at different spatial scales. Based on this, we developed a European fuel map (1 km resolution) using land cover datasets, biogeographic datasets, and bioclimatic modelling. We validated the map by comparing it to high-resolution data, obtaining high overall accuracy. Finally, we developed a crosswalk for standard fuel models as a first assignment of fuel parameters.
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We show that ecological value might be reduced by 50 % due to fire perturbation in ecosystems that have not developed in the presence of fire and/or that present changes in the fire regime. The biomes most affected are tropical and subtropical forests, tundra, and mangroves. Integration of biotic and abiotic fire regime and regeneration factors resulted in a powerful way to map ecological vulnerability to fire and develop assessments to generate adaptation plans of management in forest masses.
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2020-399, https://doi.org/10.5194/essd-2020-399, 2021
Preprint withdrawn
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The article presents the burned area product of the Copernicus Climate Change Service, called C3SBA10. It is the adaptation to Sentinel-3 OLCI data of the FireCCI51 global BA product. The paper shows how C3SBA10 is fully consistent with its predecessor, ensuring an uninterrupted provision of global burned area data from 2001 to present. The product is freely available in two monthly formats: in continental tiles at 300m spatial resolution, and globally at 0.25 degrees.
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Short summary
Wildfires threaten ecosystems and communities across Europe. Our study developed models to predict where and why these ignitions occur in different European environments. We found that weather anomalies and human factors, like proximity to urban areas and roads, are key drivers. Using Machine Learning our models achieved strong predictive accuracy. These insights help design better wildfire prevention strategies, ensuring safer landscapes and communities as fire risks grow with climate change.
Wildfires threaten ecosystems and communities across Europe. Our study developed models to...
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