Assessing human-caused wildfire ignition likelihood across Europe

dc.contributor.author
Gelabert Vadillo, Pere Joan
dc.contributor.author
Jiménez-Ruano, Adrián
dc.contributor.author
Ochoa, Clara
dc.contributor.author
Alcasena Urdíroz, Fermín J.
dc.contributor.author
Sjöström, Johan
dc.contributor.author
Marrs, Christopher
dc.contributor.author
Ribeiro, Luís Mário
dc.contributor.author
Palaiologou, Palaiologos
dc.contributor.author
Bentué Martínez, Carmen
dc.contributor.author
Chuvieco, Emilio
dc.contributor.author
Vega García, Cristina
dc.contributor.author
Rodrigues, Marcos
dc.date.accessioned
2026-03-09T19:15:08Z
dc.date.available
2026-03-09T19:15:08Z
dc.date.issued
2025
dc.identifier
https://doi.org/10.5194/nhess-25-4713-2025
dc.identifier
1561-8633
dc.identifier
https://hdl.handle.net/10459.1/469722
dc.identifier.uri
https://hdl.handle.net/10459.1/469722
dc.description.abstract
This study features a cohesive modelling approach of human-caused wildfire ignitions applied to a set of representative regions in terms of fire activity across Europe (pilot sites, PS). Our main goal was to develop a common approach to model human-caused ignition probability at a fine-grained spatial resolution (100 m) and identify the main drivers of ignitions. Specifically, we (i) ascertain which factors influence ignitions in each PS; (ii) deliver a spatial-explicit representation of ignition probability, and (iii) provide a framework for comparison with regional-scale models among PS. To do so, we calibrated Random Forest models from historical fire records compiled by local fire agencies, and geospatial layers of land cover, accessibility, population density and dead fine-fuel moisture content (DFMC). Models were built individually for each PS, comparing them with a full model constructed from all PS. Furthermore, special attention was given to the effect of spatial autocorrelation in model performance. All models achieved sufficient predictive performance (Areas Under the Receiver Operating Characteristic Curve (AUCs) from 0.70 to 0.89). For all PS models, the yearly anomaly in DFMC was the most influential variable. Among human-related factors, distance to the Wildland Urban Interface emerged as the most relevant variable, followed by proximity to roads, population density, and the fraction of wildland coverage. The performance of the full model achieved an AUC value of 0.81, with mean DFMC and anomaly being the main ignition factors, modulated by distance to roads and population density. The local performance of the full model dropped by 0.10 for AUC in both Southern Sweden and Attica (Greece) regions. The wildfire occurrence models developed in this study are essential for understanding wildfire ignition hazard and may help implement integrated wildfire risk management strategies and mitigation policies in fire-prone EU landscapes.
dc.language
eng
dc.publisher
Copernicus Publications
dc.relation
info:eu-repo/grantAgreement/ES/MICINN/CNS2023-144228
dc.relation
info:eu-repo/grantAgreement/ES/MICINN/PID2020-116556RA-I00
dc.relation
info:eu-repo/grantAgreement/ES/MS/240621
dc.relation
Reproducció del document publicat a https://doi.org/10.5194/nhess-25-4713-2025
dc.relation
Natural Hazards and Earth System Sciences, 2025, vol. 25, núm. 11, p. 4713-4729
dc.relation
info:eu-repo/grantAgreement/EC/H2020/101003890/EU/FIREURISK - DEVELOPING A HOLISTIC, RISK-WISE STRATEGY FOR EUROPEAN WILDFIRE MANAGEMENT/FirEUrisk
dc.rights
cc-by (c) Gelabert et al., 2025
dc.rights
Attribution 4.0 International
dc.rights
info:eu-repo/semantics/openAccess
dc.rights
http://creativecommons.org/licenses/by/4.0/
dc.subject
Human-caused wildfires
dc.subject
Spatial modelling
dc.subject
Random Forest
dc.subject
Ignition probability
dc.title
Assessing human-caused wildfire ignition likelihood across Europe
dc.type
info:eu-repo/semantics/article
dc.type
info:eu-repo/semantics/publishedVersion


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