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Climate Classification of the Fire-Spotting Generated Wildfires †

Departamento de Matemática Aplicada y Ciencias de la Computación, Universidad de Cantabria, 39005 Santander, Spain
BCAM—Basque Center for Applied Mathematics, 48009 Bilbao, Spain
Ikerbasque—Basque Foundation for Science, 48013 Bilbao, Spain
Author to whom correspondence should be addressed.
Presented at the Third International Conference on Fire Behavior and Risk, Sardinia, Italy, 3–6 May 2022.
Environ. Sci. Proc. 2022, 17(1), 13;
Published: 8 August 2022
(This article belongs to the Proceedings of The Third International Conference on Fire Behavior and Risk)
Several cross-sectional studies recognize that conductive climatic conditions, including grave weather conditions favorable for ignition, larger burned areas, increasing fuel load and longer fire season, can lead to extreme events and enable fires to spread faster. Thus, the knowledge of relevant climatic and biomass characteristics is necessary for a reliable modelling of the fire-spotting generated fires.
This work concerns fire-spotting generated fires that occur worldwide in any vegetated area and are impacted by numerous factors, such as wind velocity and the ambient air temperature. However, atmospheric stability [1] and fuel moisture [2] are also important for generation of the spotting fires. Moreover, biomass characteristics determine energy available for combustion, and thus can be considered as a primary driver of fire behavior and possibility of the fire-spotting.
In present study we are aimed to integrate the diversity of climate-dependent parameters of the fire propagation into a few differentiable regions basing on climate-biome classification. For that purpose, RandomFront2.3 routine described in [3] is considered and improved by inclusion of climate-dependent parameters. This improvement allows the modelling of various scenarios for the fire-spotting in changing climatic conditions.

Author Contributions

All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.


This research is supported by the Basque Government through the BERC 2018–2021 program; by the Spanish Ministry of Economy and Competitiveness MINECO through the BCAM Severo Ochoa excellence accreditation SEV-2017-0718 and also through the project PID2019-107685RB-I00, and by the European Regional Development Fund (ERDF) and the Department of Education of the regional government, the Junta of Castilla y Léon, (Grant contract SA089P20).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The study does not report any data.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Egorova, V.N.; Trucchia, A.; Pagnini, G. Fire-spotting generated fires. Part I: The role atmospheric stability. Appl. Math. Model. 2020, 84, 590–609. [Google Scholar] [CrossRef] [Green Version]
  2. Egorova, V.N.; Trucchia, A.; Pagnini, G. Fire-spotting generated fires. Part II: The role of flame geometry and slope. Appl. Math. Model. 2022, 104, 1–20. [Google Scholar] [CrossRef]
  3. Trucchia, A.; Egorova, V.N.; Butenko, A.; Kaur, I.; Pagnini, G. Random-Front 2.3: A physical parametrisation of fire spotting for operational fire spread models—Implementation in WRF-SFIRE and response analysis with LSFire+. Geosci. Model Dev. 2019, 12, 69–87. [Google Scholar] [CrossRef] [Green Version]
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MDPI and ACS Style

Egorova, V.; Pagnini, G. Climate Classification of the Fire-Spotting Generated Wildfires. Environ. Sci. Proc. 2022, 17, 13.

AMA Style

Egorova V, Pagnini G. Climate Classification of the Fire-Spotting Generated Wildfires. Environmental Sciences Proceedings. 2022; 17(1):13.

Chicago/Turabian Style

Egorova, Vera, and Gianni Pagnini. 2022. "Climate Classification of the Fire-Spotting Generated Wildfires" Environmental Sciences Proceedings 17, no. 1: 13.

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