Publication
VPD-based models of dead fine fuel moisture provide best estimates in a global dataset
| dc.contributor.author | Rodrigues, Marcos | |
| dc.contributor.author | Dios, Víctor Resco de | |
| dc.contributor.author | Sil, Ângelo Filipe | |
| dc.contributor.author | Cunill Camprubí, Àngel | |
| dc.contributor.author | Fernandes, Paulo M. | |
| dc.date.accessioned | 2024-01-24T17:31:51Z | |
| dc.date.available | 2024-01-24T17:31:51Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | 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/. | pt_PT |
| dc.description.sponsorship | This work was supported by the Portuguese Foundation for Science and Technology (projects UIDB/04033/2020 and PTDC/AAG-MAA/ 2656/2014), the Spanish MICINN (RTI2018-094691-B-C31, PID2020- 116556RA-I00) and EU H2020 (grant agreements 101003890-FirEUrisk, and 101037419-FireRES). | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Rodrigues, Marcos; Dios, Víctor Resco de; Sil, Ângelo Filipe; Cunill Camprubí, Àngel; Fernandes, Paulo M. (2024). VPD-based models of dead fine fuel moisture provide best estimates in a global dataset. Agricultural and Forest Meteorology. ISSN 0168-1923. 346, p. 1-10 | pt_PT |
| dc.identifier.doi | 10.1016/j.agrformet.2023.109868 | pt_PT |
| dc.identifier.issn | 0168-1923 | |
| dc.identifier.uri | http://hdl.handle.net/10198/29370 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Elsevier | pt_PT |
| dc.relation | Centre for the Research and Technology of Agro-Environmental and Biological Sciences | |
| dc.relation | Global-scale analysis and modelling of fire behaviour potential | |
| dc.relation | FIREURISK - DEVELOPING A HOLISTIC, RISK-WISE STRATEGY FOR EUROPEAN WILDFIRE MANAGEMENT | |
| dc.relation | Innovative technologies and socio-ecological-economic solutions for fire resilient territories in Europe. | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Dead fine fuel moisture content | pt_PT |
| dc.subject | Models | pt_PT |
| dc.subject | Vapor pressure deficit | pt_PT |
| dc.subject | Vegetation type | pt_PT |
| dc.title | VPD-based models of dead fine fuel moisture provide best estimates in a global dataset | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Centre for the Research and Technology of Agro-Environmental and Biological Sciences | |
| oaire.awardTitle | Global-scale analysis and modelling of fire behaviour potential | |
| oaire.awardTitle | FIREURISK - DEVELOPING A HOLISTIC, RISK-WISE STRATEGY FOR EUROPEAN WILDFIRE MANAGEMENT | |
| oaire.awardTitle | Innovative technologies and socio-ecological-economic solutions for fire resilient territories in Europe. | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04033%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/Projetos de Investigação Científica e Desenvolvimento Tecnológico - 2014 (P2020)/PTDC%2FAAG-MAA%2F2656%2F2014/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/EC/H2020/101003890/EU | |
| oaire.awardURI | info:eu-repo/grantAgreement/EC/H2020/101037419/EU | |
| oaire.citation.endPage | 10 | pt_PT |
| oaire.citation.startPage | 1 | pt_PT |
| oaire.citation.title | Agricultural and Forest Meteorology | pt_PT |
| oaire.citation.volume | 346 | pt_PT |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | Projetos de Investigação Científica e Desenvolvimento Tecnológico - 2014 (P2020) | |
| oaire.fundingStream | H2020 | |
| oaire.fundingStream | H2020 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100008530 | |
| project.funder.identifier | http://doi.org/10.13039/501100008530 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | European Commission | |
| project.funder.name | European Commission | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
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