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Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks

hal.structure.identifierDepartment for Innovation in Biological, Agro-Food and Forest Systems
dc.contributor.authorTRAMONTANA, Gianluca
hal.structure.identifierDepartment of Biogeochemical Integration [Jena]
dc.contributor.authorMIGLIAVACCA, Mirco
hal.structure.identifierDepartment of Biogeochemical Integration [Jena]
dc.contributor.authorJUNG, Martin
hal.structure.identifierMax Planck Institute for Biogeochemistry [MPI-BGC]
dc.contributor.authorREICHSTEIN, Markus
hal.structure.identifierLawrence Berkeley National Laboratory [Berkeley] [LBNL]
dc.contributor.authorKEENAN, Trevor
hal.structure.identifierUniversitat de València [UV]
dc.contributor.authorCAMPS‐VALLS, Gustau
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorOGÉE, Jérôme
hal.structure.identifierImage Processing Laboratory [IPL]
dc.contributor.authorVERRELST, Jochem
hal.structure.identifierDIBAF, University of Tuscia, Via S.C. de Lellis, 01100 Viterbo, Italy
dc.contributor.authorPAPALE, Dario
dc.date.accessioned2024-04-08T11:52:09Z
dc.date.available2024-04-08T11:52:09Z
dc.date.issued2020-07-02
dc.identifier.issn1354-1013
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/195469
dc.description.abstractEnThe eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO2 between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO2 release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO2 fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co-acting factors that modulate GPP and RECO flux dynamics. To overcome this limitation, we developed a hybrid data-driven approach based on combined neural networks (NNC-part). NNIC-part part incorporates process knowledge by introducing a photosynthetic response based on the light-use efficiency (LUE) concept, and uses a comprehensive dataset of soil and micrometeorological variables as fluxes drivers. We applied the method to 36 sites from the FLUXNET2015 dataset and found a high consistency in the results with those derived from other standard partitioning methods for both GPP (R-2 > .94) and RECO (R-2 > .8). High consistency was also found for (a) the diurnal and seasonal patterns of fluxes and (b) the ecosystem functional responses. NNC-part performed more realistic than the traditional methods for predicting additional patterns of gross CO2 fluxes, such as: (a) the GPP response to VPD, (b) direct effects of air temperature on GPP dynamics, (c) hysteresis in the diel cycle of gross CO2 fluxes, (d) the sensitivity of LUE to the diffuse to direct radiation ratio, and (e) the post rain respiration pulse after a long dry period. In conclusion, NNC-part is a valid data-driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods.
dc.language.isoen
dc.publisherWiley
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.encarbon dioxide fluxes partitioning
dc.subject.enecosystem respiration (RECO)
dc.subject.eneddy covariance
dc.subject.engross primary production (GPP)
dc.subject.enmachine learning
dc.subject.ennet ecosystem exchange
dc.subject.enneural network
dc.title.enPartitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks
dc.typeArticle de revue
dc.identifier.doi10.1111/gcb.15203
dc.subject.halSciences de l'environnement
bordeaux.journalGlobal Change Biology
bordeaux.page5235 - 5253
bordeaux.volume26
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.issue9
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-03173753
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03173753v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Global%20Change%20Biology&rft.date=2020-07-02&rft.volume=26&rft.issue=9&rft.spage=5235%20-%205253&rft.epage=5235%20-%205253&rft.eissn=1354-1013&rft.issn=1354-1013&rft.au=TRAMONTANA,%20Gianluca&MIGLIAVACCA,%20Mirco&JUNG,%20Martin&REICHSTEIN,%20Markus&KEENAN,%20Trevor&rft.genre=article


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