Performance of operational fire spread models in California

Background. Wildfire simulators allow estimating fire spread and behaviour in complex environments, supporting planning and analysis of incidents in real time. However, uncertainty derived from input data quality and model inherent inaccuracies may undermine the utility of such predictions.Aims. We...

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Autores: Cardil Forradellas, Adrián, Monedero, Santiago, Selegue, Phillip, Navarrete, Miguel Angel, Miguel Magaña, Sergio de, Purdy, Scott, Marshall, Geoff, Chavez, Tim, Allison, Kristen, Quilez, Raul, Ortega, Macarena, Silva, Carlos A., Ramírez, Joaquin
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/463852
Acceso en línea:https://doi.org/10.1071/WF22128
https://hdl.handle.net/10459.1/463852
Access Level:acceso abierto
Palabra clave:Fire behaviour
Fire simulation modelling
Wildfire Analyst
Rothermel
Ecologia del foc
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spelling Performance of operational fire spread models in CaliforniaCardil Forradellas, AdriánMonedero, SantiagoSelegue, PhillipNavarrete, Miguel AngelMiguel Magaña, Sergio dePurdy, ScottMarshall, GeoffChavez, TimAllison, KristenQuilez, RaulOrtega, MacarenaSilva, Carlos A.Ramírez, JoaquinFire behaviourFire simulation modellingWildfire AnalystRothermelEcologia del focBackground. Wildfire simulators allow estimating fire spread and behaviour in complex environments, supporting planning and analysis of incidents in real time. However, uncertainty derived from input data quality and model inherent inaccuracies may undermine the utility of such predictions.Aims. We assessed the performance of fire spread models for initial attack incidents used in California through the analysis of the rate of spread (ROS) of 1853 wildfires.Methods. We retrieved observed fire growth from the FireGuard (FG) database, ran an automatic simulation with Wildfire Analyst Enterprise and assessed the accuracy of the simulations by comparing observed and predicted ROS with well-known error and bias metrics, analysing the main factors influencing accuracy.Key results. The model errors and biases were reasonable for simulations performed automatically. We identified environmental variables that may bias ROS predictions, especially in timber areas where some fuel models underestimated ROS.Conclusions. The fire spread models' performance for California is in line with studies developed in other regions and the models are accurate enough to be used in real time to assess initial attack fires.Implications. This work allows users to better understand the performance of fire spread models in operational environments and opens new research lines to further improve the performance of current operational models.CSIRO PUBLISHING2023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.1071/WF22128https://hdl.handle.net/10459.1/463852reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)InglésReproducció del document publicat a https://doi.org/10.1071/WF22128International Journal of Wildland Fire, 2023, vol. 32, núm. 11, p. 1492-1502cc-by-nc (c) Cardil et al., 2023Attribution-NonCommercial-NoDerivatives 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:repositori.udl.cat:10459.1/4638522026-06-24T12:42:17Z
dc.title.none.fl_str_mv Performance of operational fire spread models in California
title Performance of operational fire spread models in California
spellingShingle Performance of operational fire spread models in California
Cardil Forradellas, Adrián
Fire behaviour
Fire simulation modelling
Wildfire Analyst
Rothermel
Ecologia del foc
title_short Performance of operational fire spread models in California
title_full Performance of operational fire spread models in California
title_fullStr Performance of operational fire spread models in California
title_full_unstemmed Performance of operational fire spread models in California
title_sort Performance of operational fire spread models in California
dc.creator.none.fl_str_mv Cardil Forradellas, Adrián
Monedero, Santiago
Selegue, Phillip
Navarrete, Miguel Angel
Miguel Magaña, Sergio de
Purdy, Scott
Marshall, Geoff
Chavez, Tim
Allison, Kristen
Quilez, Raul
Ortega, Macarena
Silva, Carlos A.
Ramírez, Joaquin
author Cardil Forradellas, Adrián
author_facet Cardil Forradellas, Adrián
Monedero, Santiago
Selegue, Phillip
Navarrete, Miguel Angel
Miguel Magaña, Sergio de
Purdy, Scott
Marshall, Geoff
Chavez, Tim
Allison, Kristen
Quilez, Raul
Ortega, Macarena
Silva, Carlos A.
Ramírez, Joaquin
author_role author
author2 Monedero, Santiago
Selegue, Phillip
Navarrete, Miguel Angel
Miguel Magaña, Sergio de
Purdy, Scott
Marshall, Geoff
Chavez, Tim
Allison, Kristen
Quilez, Raul
Ortega, Macarena
Silva, Carlos A.
Ramírez, Joaquin
author2_role author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Fire behaviour
Fire simulation modelling
Wildfire Analyst
Rothermel
Ecologia del foc
topic Fire behaviour
Fire simulation modelling
Wildfire Analyst
Rothermel
Ecologia del foc
description Background. Wildfire simulators allow estimating fire spread and behaviour in complex environments, supporting planning and analysis of incidents in real time. However, uncertainty derived from input data quality and model inherent inaccuracies may undermine the utility of such predictions.Aims. We assessed the performance of fire spread models for initial attack incidents used in California through the analysis of the rate of spread (ROS) of 1853 wildfires.Methods. We retrieved observed fire growth from the FireGuard (FG) database, ran an automatic simulation with Wildfire Analyst Enterprise and assessed the accuracy of the simulations by comparing observed and predicted ROS with well-known error and bias metrics, analysing the main factors influencing accuracy.Key results. The model errors and biases were reasonable for simulations performed automatically. We identified environmental variables that may bias ROS predictions, especially in timber areas where some fuel models underestimated ROS.Conclusions. The fire spread models' performance for California is in line with studies developed in other regions and the models are accurate enough to be used in real time to assess initial attack fires.Implications. This work allows users to better understand the performance of fire spread models in operational environments and opens new research lines to further improve the performance of current operational models.
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1071/WF22128
https://hdl.handle.net/10459.1/463852
url https://doi.org/10.1071/WF22128
https://hdl.handle.net/10459.1/463852
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a https://doi.org/10.1071/WF22128
International Journal of Wildland Fire, 2023, vol. 32, núm. 11, p. 1492-1502
dc.rights.none.fl_str_mv cc-by-nc (c) Cardil et al., 2023
Attribution-NonCommercial-NoDerivatives 4.0 International
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
rights_invalid_str_mv cc-by-nc (c) Cardil et al., 2023
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv CSIRO PUBLISHING
publisher.none.fl_str_mv CSIRO PUBLISHING
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
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