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Pinto RM, Benali A, Sá AC, et al. Probabilistic fire spread forecast as a management tool in an operational setting. Springerplus. 2016;5(1):1205doi: 10.1186/s40064-016-2842-9.
Pinto, R. M., Benali, A., Sá, A. C., Fernandes, P. M., Soares, P. M., Cardoso, R. M., Trigo, R. M., & Pereira, J. M. (2016). Probabilistic fire spread forecast as a management tool in an operational setting. SpringerPlus, 5(1), 1205. https://doi.org/10.1186/s40064-016-2842-9
Pinto, Renata M S, et al. "Probabilistic fire spread forecast as a management tool in an operational setting." SpringerPlus vol. 5,1 (2016): 1205. doi: https://doi.org/10.1186/s40064-016-2842-9
Pinto RM, Benali A, Sá AC, Fernandes PM, Soares PM, Cardoso RM, Trigo RM, Pereira JM. Probabilistic fire spread forecast as a management tool in an operational setting. Springerplus. 2016 Jul 28;5(1):1205. doi: 10.1186/s40064-016-2842-9. eCollection 2016. PMID: 27516943; PMCID: PMC4965362.
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Springerplus. 2016 Jul 28;5(1):1205. doi: 10.1186/s40064-016-2842-9. eCollection 2016.

Probabilistic fire spread forecast as a management tool in an operational setting.

SpringerPlus

Renata M S Pinto, Akli Benali, Ana C L Sá, Paulo M Fernandes, Pedro M M Soares, Rita M Cardoso, Ricardo M Trigo, José M C Pereira

Affiliations

  1. Centro de Estudos Florestais, Tapada da Ajuda, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisbon, Portugal.
  2. Centro de Investigação e de Tecnologias Agro-Ambientais e Biológicas, Universidade de Trás-os-Montes e Alto Douro (UTAD), Apartado 1013, 5001-801 Vila Real, Portugal.
  3. Instituto Dom Luiz, Faculdade de Ciências da Universidade de Lisboa, 1749-016 Lisbon, Portugal.

PMID: 27516943 PMCID: PMC4965362 DOI: 10.1186/s40064-016-2842-9

Abstract

BACKGROUND: An approach to predict fire growth in an operational setting, with the potential to be used as a decision-support tool for fire management, is described and evaluated. The operational use of fire behaviour models has mostly followed a deterministic approach, however, the uncertainty associated with model predictions needs to be quantified and included in wildfire planning and decision-making process during fire suppression activities. We use FARSITE to simulate the growth of a large wildfire. Probabilistic simulations of fire spread are performed, accounting for the uncertainty of some model inputs and parameters. Deterministic simulations were performed for comparison. We also assess the degree to which fire spread modelling and satellite active fire data can be combined, to forecast fire spread during large wildfires events.

RESULTS: Uncertainty was propagated through the FARSITE fire spread modelling system by randomly defining 100 different combinations of the independent input variables and parameters, and running the correspondent fire spread simulations in order to produce fire spread probability maps. Simulations were initialized with the reported ignition location and with satellite active fires. The probabilistic fire spread predictions show great potential to be used as a fire management tool in an operational setting, providing valuable information regarding the spatial-temporal distribution of burn probabilities. The advantage of probabilistic over deterministic simulations is clear when both are compared. Re-initializing simulations with satellite active fires did not improve simulations as expected.

CONCLUSION: This information can be useful to anticipate the growth of wildfires through the landscape with an associated probability of occurrence. The additional information regarding when, where and with what probability the fire might be in the next few hours can ultimately help minimize the negative environmental, social and economic impacts of these fires.

Keywords: FARSITE; Fire growth modelling; MODIS; Satellite active fires; Uncertainty assessment and propagation; VIIRS

References

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