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

The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO(2) between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO(2) release due to all respiration processes...

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Autores principales: Tramontana, Gianluca, Migliavacca, Mirco, Jung, Martin, Reichstein, Markus, Keenan, Trevor F., Camps‐Valls, Gustau, Ogee, Jerome, Verrelst, Jochem, Papale, Dario
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7496462/
https://www.ncbi.nlm.nih.gov/pubmed/32497360
http://dx.doi.org/10.1111/gcb.15203
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author Tramontana, Gianluca
Migliavacca, Mirco
Jung, Martin
Reichstein, Markus
Keenan, Trevor F.
Camps‐Valls, Gustau
Ogee, Jerome
Verrelst, Jochem
Papale, Dario
author_facet Tramontana, Gianluca
Migliavacca, Mirco
Jung, Martin
Reichstein, Markus
Keenan, Trevor F.
Camps‐Valls, Gustau
Ogee, Jerome
Verrelst, Jochem
Papale, Dario
author_sort Tramontana, Gianluca
collection PubMed
description The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO(2) between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO(2) release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO(2) 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 (NN(C‐part)). NN(C‐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. NN(C‐part) performed more realistic than the traditional methods for predicting additional patterns of gross CO(2) 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 CO(2) 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, NN(C‐part) is a valid data‐driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods.
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spelling pubmed-74964622020-09-25 Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks Tramontana, Gianluca Migliavacca, Mirco Jung, Martin Reichstein, Markus Keenan, Trevor F. Camps‐Valls, Gustau Ogee, Jerome Verrelst, Jochem Papale, Dario Glob Chang Biol Primary Research Article The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO(2) between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO(2) release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO(2) 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 (NN(C‐part)). NN(C‐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. NN(C‐part) performed more realistic than the traditional methods for predicting additional patterns of gross CO(2) 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 CO(2) 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, NN(C‐part) is a valid data‐driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods. John Wiley and Sons Inc. 2020-07-02 2020-09 /pmc/articles/PMC7496462/ /pubmed/32497360 http://dx.doi.org/10.1111/gcb.15203 Text en © 2020 The Authors. Global Change Biology published by John Wiley & Sons Ltd This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Primary Research Article
Tramontana, Gianluca
Migliavacca, Mirco
Jung, Martin
Reichstein, Markus
Keenan, Trevor F.
Camps‐Valls, Gustau
Ogee, Jerome
Verrelst, Jochem
Papale, Dario
Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks
title Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks
title_full Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks
title_fullStr Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks
title_full_unstemmed Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks
title_short Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks
title_sort partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks
topic Primary Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7496462/
https://www.ncbi.nlm.nih.gov/pubmed/32497360
http://dx.doi.org/10.1111/gcb.15203
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