Publicação
VPD-based models of dead fine fuel moisture provide best estimates in a global dataset
| Resumo: | Dead fine fuel moisture content (FM) is one of the most important determinants of fire behavior. Fire scientists have attempted to effectively estimate FM for nearly a century, but we are still lacking broad scale evaluations of the different approaches for prediction. Here we tackle this problem by taking advantage or a recently compiled global fire behavior database (BONFIRE) gathering 1603 records of 1h (i.e., <6 mm diameter or thickness) dead fuel moisture content from measurements before experimental fires. We compared the results of models routinely used by different agencies worldwide, empirical models, semi-mechanistic models and also non-linear and machine learning approaches based on either temperature and relative humidity or vapor pressure deficit (VPD). A semi-mechanistic model based on VPD showed the best performance across all FM ranges and a historical model developed in Australia (MK5) was additionally recommended for low fuel moisture estimations. We also observed significant differences in FM dynamics between vegetation types with FM in grasslands more responsive to changes in atmospheric dryness than woody ecosystems. The addition of computational complexity through machine learning is not recommended since the gain in model fit is small relative to the increase in complexity. Future research efforts should concentrate on predictions at low FM (<10 %) as this is the range most significant for fire behavior and where the poorest model performance was observed. Model predictions are available from https://hcfm.shinyapps.io/shinyfmd/. |
|---|---|
| Autores principais: | Rodrigues, Marcos |
| Outros Autores: | Dios, Víctor Resco de; Sil, Ângelo Filipe; Cunill Camprubí, Àngel; Fernandes, Paulo M. |
| Assunto: | Dead fine fuel moisture content Models Vapor pressure deficit Vegetation type |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| _version_ | 1867172999823622144 |
|---|---|
| author | Rodrigues, Marcos |
| author2 | Dios, Víctor Resco de Sil, Ângelo Filipe Cunill Camprubí, Àngel Fernandes, Paulo M. |
| author2_role | author author author author |
| author_facet | Rodrigues, Marcos Dios, Víctor Resco de Sil, Ângelo Filipe Cunill Camprubí, Àngel Fernandes, Paulo M. |
| author_role | author |
| contributor_name_str_mv | Biblioteca Digital do IPB |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Rodrigues, Marcos\"},{\"Person.name\":\"Dios, Víctor Resco de\"},{\"Person.name\":\"Sil, Ângelo Filipe\"},{\"Person.name\":\"Cunill Camprubí, Àngel\"},{\"Person.name\":\"Fernandes, Paulo M.\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Biblioteca Digital do IPB |
| datacite.creators.creator.creatorName.fl_str_mv | Rodrigues, Marcos Dios, Víctor Resco de Sil, Ângelo Filipe Cunill Camprubí, Àngel Fernandes, Paulo M. |
| datacite.date.Accepted.fl_str_mv | 2024-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2024-01-24T17:31:51Z |
| datacite.date.embargoed.fl_str_mv | 2024-01-24T17:31:51Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Dead fine fuel moisture content Models Vapor pressure deficit Vegetation type |
| datacite.titles.title.fl_str_mv | VPD-based models of dead fine fuel moisture provide best estimates in a global dataset |
| dc.contributor.none.fl_str_mv | Biblioteca Digital do IPB |
| dc.creator.none.fl_str_mv | Rodrigues, Marcos Dios, Víctor Resco de Sil, Ângelo Filipe Cunill Camprubí, Àngel Fernandes, Paulo M. |
| dc.date.Accepted.fl_str_mv | 2024-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2024-01-24T17:31:51Z |
| dc.date.embargoed.fl_str_mv | 2024-01-24T17:31:51Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/29370 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Dead fine fuel moisture content Models Vapor pressure deficit Vegetation type |
| dc.title.fl_str_mv | VPD-based models of dead fine fuel moisture provide best estimates in a global dataset |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | Dead fine fuel moisture content (FM) is one of the most important determinants of fire behavior. Fire scientists have attempted to effectively estimate FM for nearly a century, but we are still lacking broad scale evaluations of the different approaches for prediction. Here we tackle this problem by taking advantage or a recently compiled global fire behavior database (BONFIRE) gathering 1603 records of 1h (i.e., <6 mm diameter or thickness) dead fuel moisture content from measurements before experimental fires. We compared the results of models routinely used by different agencies worldwide, empirical models, semi-mechanistic models and also non-linear and machine learning approaches based on either temperature and relative humidity or vapor pressure deficit (VPD). A semi-mechanistic model based on VPD showed the best performance across all FM ranges and a historical model developed in Australia (MK5) was additionally recommended for low fuel moisture estimations. We also observed significant differences in FM dynamics between vegetation types with FM in grasslands more responsive to changes in atmospheric dryness than woody ecosystems. The addition of computational complexity through machine learning is not recommended since the gain in model fit is small relative to the increase in complexity. Future research efforts should concentrate on predictions at low FM (<10 %) as this is the range most significant for fire behavior and where the poorest model performance was observed. Model predictions are available from https://hcfm.shinyapps.io/shinyfmd/. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://bibliotecadigital.ipb.pt/bitstreams/1f1c5b32-a405-4917-b411-139b60c69d2b/download |
| funding.funder.alternateName_str_mv | FCT FCT EC EC |
| funding.funder.identifier_str_mv | http://doi.org/10.13039/501100001871 http://doi.org/10.13039/501100001871 http://doi.org/10.13039/501100008530 http://doi.org/10.13039/501100008530 |
| funding.funder.name_str_mv | Fundação para a Ciência e a Tecnologia Fundação para a Ciência e a Tecnologia European Commission European Commission |
| funding.name_str_mv | 6817 - DCRRNI ID Projetos de Investigação Científica e Desenvolvimento Tecnológico - 2014 (P2020) H2020 H2020 |
| id | ipb_3bc2384ad39dec2e1ad89b42e0902407 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10198/29370 |
| instacron_str | ipb |
| institution | Instituto Politécnico de Bragança |
| instname_str | Instituto Politécnico de Bragança |
| language | eng |
| network_acronym_str | ipb |
| network_name_str | Biblioteca Digital do IPB |
| oai_identifier_str | oai:bibliotecadigital.ipb.pt:10198/29370 |
| organization_str_mv | urn:organizationAcronym:ipb |
| person_str_mv | Rodrigues, Marcos Dios, Víctor Resco de Sil, Ângelo Filipe Cunill Camprubí, Àngel Fernandes, Paulo M. |
| publishDate | 2024 |
| publisher.none.fl_str_mv | Elsevier |
| reponame_str | Biblioteca Digital do IPB |
| repository_id_str | urn:repositoryAcronym:ipb |
| service_str_mv | urn:repositoryAcronym:ipb |
| spelling | engElsevierpt_PTDead fine fuel moisture content (FM) is one of the most important determinants of fire behavior. Fire scientists have attempted to effectively estimate FM for nearly a century, but we are still lacking broad scale evaluations of the different approaches for prediction. Here we tackle this problem by taking advantage or a recently compiled global fire behavior database (BONFIRE) gathering 1603 records of 1h (i.e., <6 mm diameter or thickness) dead fuel moisture content from measurements before experimental fires. We compared the results of models routinely used by different agencies worldwide, empirical models, semi-mechanistic models and also non-linear and machine learning approaches based on either temperature and relative humidity or vapor pressure deficit (VPD). A semi-mechanistic model based on VPD showed the best performance across all FM ranges and a historical model developed in Australia (MK5) was additionally recommended for low fuel moisture estimations. We also observed significant differences in FM dynamics between vegetation types with FM in grasslands more responsive to changes in atmospheric dryness than woody ecosystems. The addition of computational complexity through machine learning is not recommended since the gain in model fit is small relative to the increase in complexity. Future research efforts should concentrate on predictions at low FM (<10 %) as this is the range most significant for fire behavior and where the poorest model performance was observed. Model predictions are available from https://hcfm.shinyapps.io/shinyfmd/.application/pdfpt_PTVPD-based models of dead fine fuel moisture provide best estimates in a global datasetRodrigues, MarcosDios, Víctor Resco deSil, Ângelo FilipeCunill Camprubí, ÀngelFernandes, Paulo M.HostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptISSNIsPartOf0168-1923DOIIsPartOf10.1016/j.agrformet.2023.1098682024-01-24T17:31:51Z20242024-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/29370http://purl.org/coar/access_right/c_abf2open accessDead fine fuel moisture contentModelsVapor pressure deficitVegetation type4756936 bytesFundação para a Ciência e a TecnologiaCentre for the Research and Technology of Agro-Environmental and Biological Sciences6817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871Fundação para a Ciência e a TecnologiaGlobal-scale analysis and modelling of fire behaviour potentialProjetos de Investigação Científica e Desenvolvimento Tecnológico - 2014 (P2020)Crossref Funder IDhttp://doi.org/10.13039/501100001871European CommissionFIREURISK - DEVELOPING A HOLISTIC, RISK-WISE STRATEGY FOR EUROPEAN WILDFIRE MANAGEMENTH2020Crossref Funder IDhttp://doi.org/10.13039/501100008530European CommissionInnovative technologies and socio-ecological-economic solutions for fire resilient territories in Europe.H2020Crossref Funder IDhttp://doi.org/10.13039/501100008530literaturehttp://purl.org/coar/resource_type/c_6501journal article2024http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/1f1c5b32-a405-4917-b411-139b60c69d2b/downloadAgricultural and Forest Meteorology346110 |
| spellingShingle | VPD-based models of dead fine fuel moisture provide best estimates in a global dataset Rodrigues, Marcos Dead fine fuel moisture content Models Vapor pressure deficit Vegetation type |
| status | SINGLETON |
| subject.fl_str_mv | Dead fine fuel moisture content Models Vapor pressure deficit Vegetation type |
| title | VPD-based models of dead fine fuel moisture provide best estimates in a global dataset |
| title_full | VPD-based models of dead fine fuel moisture provide best estimates in a global dataset |
| title_fullStr | VPD-based models of dead fine fuel moisture provide best estimates in a global dataset |
| title_full_unstemmed | VPD-based models of dead fine fuel moisture provide best estimates in a global dataset |
| title_short | VPD-based models of dead fine fuel moisture provide best estimates in a global dataset |
| title_sort | VPD-based models of dead fine fuel moisture provide best estimates in a global dataset |
| topic | Dead fine fuel moisture content Models Vapor pressure deficit Vegetation type |
| topic_facet | Dead fine fuel moisture content Models Vapor pressure deficit Vegetation type |
| url | http://hdl.handle.net/10198/29370 |
| visible | 1 |