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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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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. |
format | Online Article Text |
id | pubmed-7496462 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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