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Probabilistic fire spread forecast as a management tool in an operational setting
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 asso...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4965362/ https://www.ncbi.nlm.nih.gov/pubmed/27516943 http://dx.doi.org/10.1186/s40064-016-2842-9 |
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author | Pinto, Renata M.S. Benali, Akli Sá, Ana C. L. Fernandes, Paulo M. Soares, Pedro M. M. Cardoso, Rita M. Trigo, Ricardo M. Pereira, José M. C. |
author_facet | Pinto, Renata M.S. Benali, Akli Sá, Ana C. L. Fernandes, Paulo M. Soares, Pedro M. M. Cardoso, Rita M. Trigo, Ricardo M. Pereira, José M. C. |
author_sort | Pinto, Renata M.S. |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40064-016-2842-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4965362 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-49653622016-08-11 Probabilistic fire spread forecast as a management tool in an operational setting Pinto, Renata M.S. Benali, Akli Sá, Ana C. L. Fernandes, Paulo M. Soares, Pedro M. M. Cardoso, Rita M. Trigo, Ricardo M. Pereira, José M. C. Springerplus Research 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40064-016-2842-9) contains supplementary material, which is available to authorized users. Springer International Publishing 2016-07-28 /pmc/articles/PMC4965362/ /pubmed/27516943 http://dx.doi.org/10.1186/s40064-016-2842-9 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Research Pinto, Renata M.S. Benali, Akli Sá, Ana C. L. Fernandes, Paulo M. Soares, Pedro M. M. Cardoso, Rita M. Trigo, Ricardo M. Pereira, José M. C. Probabilistic fire spread forecast as a management tool in an operational setting |
title | Probabilistic fire spread forecast as a management tool in an operational setting |
title_full | Probabilistic fire spread forecast as a management tool in an operational setting |
title_fullStr | Probabilistic fire spread forecast as a management tool in an operational setting |
title_full_unstemmed | Probabilistic fire spread forecast as a management tool in an operational setting |
title_short | Probabilistic fire spread forecast as a management tool in an operational setting |
title_sort | probabilistic fire spread forecast as a management tool in an operational setting |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4965362/ https://www.ncbi.nlm.nih.gov/pubmed/27516943 http://dx.doi.org/10.1186/s40064-016-2842-9 |
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