Numerical Simulation of Surface Forest Fires and Probability of Surface Fire Spread in the Brazilian Amazon

Detalhes bibliográficos
Autor(a) principal: Bufacchi, Paulo
Data de Publicação: 2019
Outros Autores: Krieger Filho, Guenther Carlos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Biodiversidade Brasileira
Texto Completo: https://revistaeletronica.icmbio.gov.br/BioBR/article/view/1085
Resumo: Although climate changes affect the whole planet, human intervention is the cause for most fires in the Brazilian Amazon. Fires initiated to clean fields either for livestock or agriculture can propagate to the forest floor, burning the litter in a surface fire and influencing forest regeneration. This study aims to describe the use of numerical simulation to assess fire spread through litter fuels in the Brazilian Amazon by using a three-dimensional, fully transient, physics-based computer simulation approach. It also describes the development of a logistic model to predict the probability of surface fire spread. Numerical simulations solve the governing equations of vegetation thermal degradation, solid and gaseous phases combustion, fluid dynamics and heat transfer, in order to assess the fire rate of spread. Outdoor experiments carried out in the States of Mato Grosso, Acre and Rondonia provide a way to compare numerical simulation results to actual fire scenarios. Parametric variation of input variables to the numerical simulation assessed the importance of vegetation moisture content, temperature, bulk density, surface to volume ratio and air humidity. For the assessment of probability of surface fire spread, experimental results were classified into two groups: one for which the fire propagated and the other one for which the fire self-extinguished. The relevant parameters for fire propagation using a logistic regression model are litter height and litter moisture content. Conclusions show that in the range of parameter variation considered, vegetation initial temperature and air humidity does not influence the fire rate of spread. On the other hand, the most important parameters to fire spread are vegetation moisture content, surface area to volume ratio, and bulk density. Because of the absence of external wind in the forest floor, radiation is a more important process than convection, and directly affects the fire rate of spread. Regarding the probability of successful fire propagation, the logistic model showed a true positive rate of 71% and a true negative rate of 84%.
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spelling Numerical Simulation of Surface Forest Fires and Probability of Surface Fire Spread in the Brazilian Amazon Numerical Simulation of Surface Forest Fires and Probability of Surface Fire Spread in the Brazilian Amazon Numerical Simulation of Surface Forest Fires and Probability of Surface Fire Spread in the Brazilian Amazon Although climate changes affect the whole planet, human intervention is the cause for most fires in the Brazilian Amazon. Fires initiated to clean fields either for livestock or agriculture can propagate to the forest floor, burning the litter in a surface fire and influencing forest regeneration. This study aims to describe the use of numerical simulation to assess fire spread through litter fuels in the Brazilian Amazon by using a three-dimensional, fully transient, physics-based computer simulation approach. It also describes the development of a logistic model to predict the probability of surface fire spread. Numerical simulations solve the governing equations of vegetation thermal degradation, solid and gaseous phases combustion, fluid dynamics and heat transfer, in order to assess the fire rate of spread. Outdoor experiments carried out in the States of Mato Grosso, Acre and Rondonia provide a way to compare numerical simulation results to actual fire scenarios. Parametric variation of input variables to the numerical simulation assessed the importance of vegetation moisture content, temperature, bulk density, surface to volume ratio and air humidity. For the assessment of probability of surface fire spread, experimental results were classified into two groups: one for which the fire propagated and the other one for which the fire self-extinguished. The relevant parameters for fire propagation using a logistic regression model are litter height and litter moisture content. Conclusions show that in the range of parameter variation considered, vegetation initial temperature and air humidity does not influence the fire rate of spread. On the other hand, the most important parameters to fire spread are vegetation moisture content, surface area to volume ratio, and bulk density. Because of the absence of external wind in the forest floor, radiation is a more important process than convection, and directly affects the fire rate of spread. Regarding the probability of successful fire propagation, the logistic model showed a true positive rate of 71% and a true negative rate of 84%.Although climate changes affect the whole planet, human intervention is the cause for most fires in the Brazilian Amazon. Fires initiated to clean fields either for livestock or agriculture can propagate to the forest floor, burning the litter in a surface fire and influencing forest regeneration. This study aims to describe the use of numerical simulation to assess fire spread through litter fuels in the Brazilian Amazon by using a three-dimensional, fully transient, physics-based computer simulation approach. It also describes the development of a logistic model to predict the probability of surface fire spread. Numerical simulations solve the governing equations of vegetation thermal degradation, solid and gaseous phases combustion, fluid dynamics and heat transfer, in order to assess the fire rate of spread. Outdoor experiments carried out in the States of Mato Grosso, Acre and Rondonia provide a way to compare numerical simulation results to actual fire scenarios. Parametric variation of input variables to the numerical simulation assessed the importance of vegetation moisture content, temperature, bulk density, surface to volume ratio and air humidity. For the assessment of probability of surface fire spread, experimental results were classified into two groups: one for which the fire propagated and the other one for which the fire self-extinguished. The relevant parameters for fire propagation using a logistic regression model are litter height and litter moisture content. Conclusions show that in the range of parameter variation considered, vegetation initial temperature and air humidity does not influence the fire rate of spread. On the other hand, the most important parameters to fire spread are vegetation moisture content, surface area to volume ratio, and bulk density. Because of the absence of external wind in the forest floor, radiation is a more important process than convection, and directly affects the fire rate of spread. Regarding the probability of successful fire propagation, the logistic model showed a true positive rate of 71% and a true negative rate of 84%.Although climate changes affect the whole planet, human intervention is the cause for most fires in the Brazilian Amazon. Fires initiated to clean fields either for livestock or agriculture can propagate to the forest floor, burning the litter in a surface fire and influencing forest regeneration. This study aims to describe the use of numerical simulation to assess fire spread through litter fuels in the Brazilian Amazon by using a three-dimensional, fully transient, physics-based computer simulation approach. It also describes the development of a logistic model to predict the probability of surface fire spread. Numerical simulations solve the governing equations of vegetation thermal degradation, solid and gaseous phases combustion, fluid dynamics and heat transfer, in order to assess the fire rate of spread. Outdoor experiments carried out in the States of Mato Grosso, Acre and Rondonia provide a way to compare numerical simulation results to actual fire scenarios. Parametric variation of input variables to the numerical simulation assessed the importance of vegetation moisture content, temperature, bulk density, surface to volume ratio and air humidity. For the assessment of probability of surface fire spread, experimental results were classified into two groups: one for which the fire propagated and the other one for which the fire self-extinguished. The relevant parameters for fire propagation using a logistic regression model are litter height and litter moisture content. Conclusions show that in the range of parameter variation considered, vegetation initial temperature and air humidity does not influence the fire rate of spread. On the other hand, the most important parameters to fire spread are vegetation moisture content, surface area to volume ratio, and bulk density. Because of the absence of external wind in the forest floor, radiation is a more important process than convection, and directly affects the fire rate of spread. Regarding the probability of successful fire propagation, the logistic model showed a true positive rate of 71% and a true negative rate of 84%.Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio)2019-11-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistaeletronica.icmbio.gov.br/BioBR/article/view/108510.37002/biodiversidadebrasileira.v9i1.1085Biodiversidade Brasileira ; v. 9 n. 1 (2019): Wildfire Conference: Resumos; 173Biodiversidade Brasileira ; Vol. 9 No. 1 (2019): Wildfire Conference: Resumos; 173Biodiversidade Brasileira ; Vol. 9 Núm. 1 (2019): Wildfire Conference: Resumos; 1732236-288610.37002/biodiversidadebrasileira.v9i1reponame:Biodiversidade Brasileirainstname:Instituto Chico Mendes de Conservação da Biodiversidade (ICMBIO)instacron:ICMBIOenghttps://revistaeletronica.icmbio.gov.br/BioBR/article/view/1085/821Copyright (c) 2021 Biodiversidade Brasileira - BioBrasilhttps://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessBufacchi, PauloKrieger Filho, Guenther Carlos2023-05-09T12:56:02Zoai:revistaeletronica.icmbio.gov.br:article/1085Revistahttps://revistaeletronica.icmbio.gov.br/BioBRPUBhttps://revistaeletronica.icmbio.gov.br/BioBR/oaifernanda.oliveto@icmbio.gov.br || katia.ribeiro@icmbio.gov.br2236-28862236-2886opendoar:2023-05-09T12:56:02Biodiversidade Brasileira - Instituto Chico Mendes de Conservação da Biodiversidade (ICMBIO)false
dc.title.none.fl_str_mv Numerical Simulation of Surface Forest Fires and Probability of Surface Fire Spread in the Brazilian Amazon
Numerical Simulation of Surface Forest Fires and Probability of Surface Fire Spread in the Brazilian Amazon
Numerical Simulation of Surface Forest Fires and Probability of Surface Fire Spread in the Brazilian Amazon
title Numerical Simulation of Surface Forest Fires and Probability of Surface Fire Spread in the Brazilian Amazon
spellingShingle Numerical Simulation of Surface Forest Fires and Probability of Surface Fire Spread in the Brazilian Amazon
Bufacchi, Paulo
title_short Numerical Simulation of Surface Forest Fires and Probability of Surface Fire Spread in the Brazilian Amazon
title_full Numerical Simulation of Surface Forest Fires and Probability of Surface Fire Spread in the Brazilian Amazon
title_fullStr Numerical Simulation of Surface Forest Fires and Probability of Surface Fire Spread in the Brazilian Amazon
title_full_unstemmed Numerical Simulation of Surface Forest Fires and Probability of Surface Fire Spread in the Brazilian Amazon
title_sort Numerical Simulation of Surface Forest Fires and Probability of Surface Fire Spread in the Brazilian Amazon
author Bufacchi, Paulo
author_facet Bufacchi, Paulo
Krieger Filho, Guenther Carlos
author_role author
author2 Krieger Filho, Guenther Carlos
author2_role author
dc.contributor.author.fl_str_mv Bufacchi, Paulo
Krieger Filho, Guenther Carlos
description Although climate changes affect the whole planet, human intervention is the cause for most fires in the Brazilian Amazon. Fires initiated to clean fields either for livestock or agriculture can propagate to the forest floor, burning the litter in a surface fire and influencing forest regeneration. This study aims to describe the use of numerical simulation to assess fire spread through litter fuels in the Brazilian Amazon by using a three-dimensional, fully transient, physics-based computer simulation approach. It also describes the development of a logistic model to predict the probability of surface fire spread. Numerical simulations solve the governing equations of vegetation thermal degradation, solid and gaseous phases combustion, fluid dynamics and heat transfer, in order to assess the fire rate of spread. Outdoor experiments carried out in the States of Mato Grosso, Acre and Rondonia provide a way to compare numerical simulation results to actual fire scenarios. Parametric variation of input variables to the numerical simulation assessed the importance of vegetation moisture content, temperature, bulk density, surface to volume ratio and air humidity. For the assessment of probability of surface fire spread, experimental results were classified into two groups: one for which the fire propagated and the other one for which the fire self-extinguished. The relevant parameters for fire propagation using a logistic regression model are litter height and litter moisture content. Conclusions show that in the range of parameter variation considered, vegetation initial temperature and air humidity does not influence the fire rate of spread. On the other hand, the most important parameters to fire spread are vegetation moisture content, surface area to volume ratio, and bulk density. Because of the absence of external wind in the forest floor, radiation is a more important process than convection, and directly affects the fire rate of spread. Regarding the probability of successful fire propagation, the logistic model showed a true positive rate of 71% and a true negative rate of 84%.
publishDate 2019
dc.date.none.fl_str_mv 2019-11-15
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://revistaeletronica.icmbio.gov.br/BioBR/article/view/1085
10.37002/biodiversidadebrasileira.v9i1.1085
url https://revistaeletronica.icmbio.gov.br/BioBR/article/view/1085
identifier_str_mv 10.37002/biodiversidadebrasileira.v9i1.1085
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://revistaeletronica.icmbio.gov.br/BioBR/article/view/1085/821
dc.rights.driver.fl_str_mv Copyright (c) 2021 Biodiversidade Brasileira - BioBrasil
https://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2021 Biodiversidade Brasileira - BioBrasil
https://creativecommons.org/licenses/by-nc-nd/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio)
publisher.none.fl_str_mv Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio)
dc.source.none.fl_str_mv Biodiversidade Brasileira ; v. 9 n. 1 (2019): Wildfire Conference: Resumos; 173
Biodiversidade Brasileira ; Vol. 9 No. 1 (2019): Wildfire Conference: Resumos; 173
Biodiversidade Brasileira ; Vol. 9 Núm. 1 (2019): Wildfire Conference: Resumos; 173
2236-2886
10.37002/biodiversidadebrasileira.v9i1
reponame:Biodiversidade Brasileira
instname:Instituto Chico Mendes de Conservação da Biodiversidade (ICMBIO)
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repository.name.fl_str_mv Biodiversidade Brasileira - Instituto Chico Mendes de Conservação da Biodiversidade (ICMBIO)
repository.mail.fl_str_mv fernanda.oliveto@icmbio.gov.br || katia.ribeiro@icmbio.gov.br
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