Analysis of fire patterns and drivers with global SEVER-FIRE v1.0 model incorporated into dynamic global vegetation model and satellite and on-ground observations

Detalhes bibliográficos
Autor(a) principal: Venevsky, Sergey
Data de Publicação: 2019
Outros Autores: Le Page, Yannick, Cardoso Pereira, José Miguel, Wu, Chao
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.5/17313
Resumo: Biomass burning is an important environmental process with a strong influence on vegetation and on the atmospheric composition. It competes with microbes and herbivores to convert biomass to CO2 and it is a major contributor of gases and aerosols to the atmosphere. To better understand and predict global fire occurrence, fire models have been developed and coupled to dynamic global vegetation models (DGVMs) and Earth system models (ESMs). We present SEVER-FIRE v1.0 (Socio-Economic and natural Vegetation ExpeRimental global fire model version 1.0), which is incorporated into the SEVER DGVM. One of the major focuses of SEVER-FIRE is an implementation of pyrogenic behavior of humans (timing of their activities and their willingness and necessity to ignite or suppress fire), related to socioeconomic and demographic conditions in a geographical domain of the model application. Burned areas and emissions from the SEVER model are compared to the Global Fire Emission Database version 2 (GFED), derived from satellite observations, while number of fires is compared with regional historical fire statistics.We focus on both the model output accuracy and its assumptions regarding fire drivers and perform (1) an evaluation of the predicted spatial and temporal patterns, focusing on fire incidence, seasonality and interannual variability; (2) analysis to evaluate the assumptions concerning the etiology, or causation, of fire, including climatic and anthropogenic drivers, as well as the type and amount of vegetation. SEVER reproduces the main features of climate-driven interannual fire variability at a regional scale, for example the large fires associated with the 1997–1998 El Niño event in Indonesia and Central and South America, which had critical ecological and atmospheric impacts. Spatial and seasonal patterns of fire incidence reveal some model inaccuracies, and we discuss the implications of the distribution of vegetation types inferred by the DGVM and of assumed proxies of human fire practices.We further suggest possible development directions to enable such models to better project future fire activity
id RCAP_5adedc9f25232b803c36e0ba3023ffdb
oai_identifier_str oai:www.repository.utl.pt:10400.5/17313
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Analysis of fire patterns and drivers with global SEVER-FIRE v1.0 model incorporated into dynamic global vegetation model and satellite and on-ground observationsfiremodelSEVER-FIREvegetationbiomassBiomass burning is an important environmental process with a strong influence on vegetation and on the atmospheric composition. It competes with microbes and herbivores to convert biomass to CO2 and it is a major contributor of gases and aerosols to the atmosphere. To better understand and predict global fire occurrence, fire models have been developed and coupled to dynamic global vegetation models (DGVMs) and Earth system models (ESMs). We present SEVER-FIRE v1.0 (Socio-Economic and natural Vegetation ExpeRimental global fire model version 1.0), which is incorporated into the SEVER DGVM. One of the major focuses of SEVER-FIRE is an implementation of pyrogenic behavior of humans (timing of their activities and their willingness and necessity to ignite or suppress fire), related to socioeconomic and demographic conditions in a geographical domain of the model application. Burned areas and emissions from the SEVER model are compared to the Global Fire Emission Database version 2 (GFED), derived from satellite observations, while number of fires is compared with regional historical fire statistics.We focus on both the model output accuracy and its assumptions regarding fire drivers and perform (1) an evaluation of the predicted spatial and temporal patterns, focusing on fire incidence, seasonality and interannual variability; (2) analysis to evaluate the assumptions concerning the etiology, or causation, of fire, including climatic and anthropogenic drivers, as well as the type and amount of vegetation. SEVER reproduces the main features of climate-driven interannual fire variability at a regional scale, for example the large fires associated with the 1997–1998 El Niño event in Indonesia and Central and South America, which had critical ecological and atmospheric impacts. Spatial and seasonal patterns of fire incidence reveal some model inaccuracies, and we discuss the implications of the distribution of vegetation types inferred by the DGVM and of assumed proxies of human fire practices.We further suggest possible development directions to enable such models to better project future fire activityEuropean Geosciences UnionRepositório da Universidade de LisboaVenevsky, SergeyLe Page, YannickCardoso Pereira, José MiguelWu, Chao2019-02-05T09:58:54Z20192019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/17313engGeosci. Model Dev., 12, 89–110, 2019https://doi.org/10.5194/gmd-12-89-2019info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-06T14:46:59Zoai:www.repository.utl.pt:10400.5/17313Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:02:32.830065Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Analysis of fire patterns and drivers with global SEVER-FIRE v1.0 model incorporated into dynamic global vegetation model and satellite and on-ground observations
title Analysis of fire patterns and drivers with global SEVER-FIRE v1.0 model incorporated into dynamic global vegetation model and satellite and on-ground observations
spellingShingle Analysis of fire patterns and drivers with global SEVER-FIRE v1.0 model incorporated into dynamic global vegetation model and satellite and on-ground observations
Venevsky, Sergey
fire
model
SEVER-FIRE
vegetation
biomass
title_short Analysis of fire patterns and drivers with global SEVER-FIRE v1.0 model incorporated into dynamic global vegetation model and satellite and on-ground observations
title_full Analysis of fire patterns and drivers with global SEVER-FIRE v1.0 model incorporated into dynamic global vegetation model and satellite and on-ground observations
title_fullStr Analysis of fire patterns and drivers with global SEVER-FIRE v1.0 model incorporated into dynamic global vegetation model and satellite and on-ground observations
title_full_unstemmed Analysis of fire patterns and drivers with global SEVER-FIRE v1.0 model incorporated into dynamic global vegetation model and satellite and on-ground observations
title_sort Analysis of fire patterns and drivers with global SEVER-FIRE v1.0 model incorporated into dynamic global vegetation model and satellite and on-ground observations
author Venevsky, Sergey
author_facet Venevsky, Sergey
Le Page, Yannick
Cardoso Pereira, José Miguel
Wu, Chao
author_role author
author2 Le Page, Yannick
Cardoso Pereira, José Miguel
Wu, Chao
author2_role author
author
author
dc.contributor.none.fl_str_mv Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Venevsky, Sergey
Le Page, Yannick
Cardoso Pereira, José Miguel
Wu, Chao
dc.subject.por.fl_str_mv fire
model
SEVER-FIRE
vegetation
biomass
topic fire
model
SEVER-FIRE
vegetation
biomass
description Biomass burning is an important environmental process with a strong influence on vegetation and on the atmospheric composition. It competes with microbes and herbivores to convert biomass to CO2 and it is a major contributor of gases and aerosols to the atmosphere. To better understand and predict global fire occurrence, fire models have been developed and coupled to dynamic global vegetation models (DGVMs) and Earth system models (ESMs). We present SEVER-FIRE v1.0 (Socio-Economic and natural Vegetation ExpeRimental global fire model version 1.0), which is incorporated into the SEVER DGVM. One of the major focuses of SEVER-FIRE is an implementation of pyrogenic behavior of humans (timing of their activities and their willingness and necessity to ignite or suppress fire), related to socioeconomic and demographic conditions in a geographical domain of the model application. Burned areas and emissions from the SEVER model are compared to the Global Fire Emission Database version 2 (GFED), derived from satellite observations, while number of fires is compared with regional historical fire statistics.We focus on both the model output accuracy and its assumptions regarding fire drivers and perform (1) an evaluation of the predicted spatial and temporal patterns, focusing on fire incidence, seasonality and interannual variability; (2) analysis to evaluate the assumptions concerning the etiology, or causation, of fire, including climatic and anthropogenic drivers, as well as the type and amount of vegetation. SEVER reproduces the main features of climate-driven interannual fire variability at a regional scale, for example the large fires associated with the 1997–1998 El Niño event in Indonesia and Central and South America, which had critical ecological and atmospheric impacts. Spatial and seasonal patterns of fire incidence reveal some model inaccuracies, and we discuss the implications of the distribution of vegetation types inferred by the DGVM and of assumed proxies of human fire practices.We further suggest possible development directions to enable such models to better project future fire activity
publishDate 2019
dc.date.none.fl_str_mv 2019-02-05T09:58:54Z
2019
2019-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.5/17313
url http://hdl.handle.net/10400.5/17313
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Geosci. Model Dev., 12, 89–110, 2019
https://doi.org/10.5194/gmd-12-89-2019
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv European Geosciences Union
publisher.none.fl_str_mv European Geosciences Union
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799131115257069568