Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , , , , , , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/107435 |
Resumo: | Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate’s temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE. |
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Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forestsBiochemistry, Genetics and Molecular Biology(all)Agricultural and Biological Sciences(all)GeneralSDG 13 - Climate ActionSDG 15 - Life on LandForests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate’s temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE.CENSE - Centro de Investigação em Ambiente e SustentabilidadeDCEA - Departamento de Ciências e Engenharia do AmbienteRUNBesnard, SimonCarvalhais, NunoAltaf Arain, M.Black, AndrewBrede, BenjaminBuchmann, NinaChen, JiquanClevers, Jan G.P.W.Dutrieux, Loïc P.Gans, FabianHerold, MartinJung, MartinKosugi, YoshikoKnohl, AlexanderLaw, Beverly E.Paul-Limoges, EugénieLohila, AnnaleaMerbold, LutzRoupsard, OlivierValentini, RiccardoWolf, SebastianZhang, XudongReichstein, Markus2020-11-18T23:59:03Z2019-022019-02-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/107435eng1932-6203PURE: 18729447https://doi.org/10.1371/journal.pone.0211510info: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:RCAAP2024-03-11T04:52:10Zoai:run.unl.pt:10362/107435Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:40:58.616772Repositório 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 |
Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests |
title |
Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests |
spellingShingle |
Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests Besnard, Simon Biochemistry, Genetics and Molecular Biology(all) Agricultural and Biological Sciences(all) General SDG 13 - Climate Action SDG 15 - Life on Land |
title_short |
Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests |
title_full |
Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests |
title_fullStr |
Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests |
title_full_unstemmed |
Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests |
title_sort |
Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests |
author |
Besnard, Simon |
author_facet |
Besnard, Simon Carvalhais, Nuno Altaf Arain, M. Black, Andrew Brede, Benjamin Buchmann, Nina Chen, Jiquan Clevers, Jan G.P.W. Dutrieux, Loïc P. Gans, Fabian Herold, Martin Jung, Martin Kosugi, Yoshiko Knohl, Alexander Law, Beverly E. Paul-Limoges, Eugénie Lohila, Annalea Merbold, Lutz Roupsard, Olivier Valentini, Riccardo Wolf, Sebastian Zhang, Xudong Reichstein, Markus |
author_role |
author |
author2 |
Carvalhais, Nuno Altaf Arain, M. Black, Andrew Brede, Benjamin Buchmann, Nina Chen, Jiquan Clevers, Jan G.P.W. Dutrieux, Loïc P. Gans, Fabian Herold, Martin Jung, Martin Kosugi, Yoshiko Knohl, Alexander Law, Beverly E. Paul-Limoges, Eugénie Lohila, Annalea Merbold, Lutz Roupsard, Olivier Valentini, Riccardo Wolf, Sebastian Zhang, Xudong Reichstein, Markus |
author2_role |
author author author author author author author author author author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
CENSE - Centro de Investigação em Ambiente e Sustentabilidade DCEA - Departamento de Ciências e Engenharia do Ambiente RUN |
dc.contributor.author.fl_str_mv |
Besnard, Simon Carvalhais, Nuno Altaf Arain, M. Black, Andrew Brede, Benjamin Buchmann, Nina Chen, Jiquan Clevers, Jan G.P.W. Dutrieux, Loïc P. Gans, Fabian Herold, Martin Jung, Martin Kosugi, Yoshiko Knohl, Alexander Law, Beverly E. Paul-Limoges, Eugénie Lohila, Annalea Merbold, Lutz Roupsard, Olivier Valentini, Riccardo Wolf, Sebastian Zhang, Xudong Reichstein, Markus |
dc.subject.por.fl_str_mv |
Biochemistry, Genetics and Molecular Biology(all) Agricultural and Biological Sciences(all) General SDG 13 - Climate Action SDG 15 - Life on Land |
topic |
Biochemistry, Genetics and Molecular Biology(all) Agricultural and Biological Sciences(all) General SDG 13 - Climate Action SDG 15 - Life on Land |
description |
Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate’s temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-02 2019-02-01T00:00:00Z 2020-11-18T23:59:03Z |
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/10362/107435 |
url |
http://hdl.handle.net/10362/107435 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1932-6203 PURE: 18729447 https://doi.org/10.1371/journal.pone.0211510 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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application/pdf |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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