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

doi:10.1111/gcb.15203

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

Journal Article · Thu Jul 02 00:00:00 EDT 2020 · Global Change Biology

DOI:https://doi.org/10.1111/gcb.15203· OSTI ID:1816106

^[1]; Migliavacca, Mirco ^[2]; Jung, Martin ^[2]; Reichstein, Markus ^[2]; Keenan, Trevor F. ^[3]; Camps‐Valls, Gustau ^[4];

^[5]; Verrelst, Jochem ^[4]; Papale, Dario ^[6]

Univ. of Tuscia, Viterbo (Italy). DIBAF Department for Innovation in Biological Agro‐food and Forestry Systems; Universitat de València, Paterna (Spain). Parc Científic. Image Processing Laboratory (IPL)
Max Planck Inst. for Biogeochemistry, Jena (Germany)
Univ. of California, Berkeley, CA (United States). Dept. of Environmental Science, Policy and Management; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Earth and Environmental Sciences Area
Universitat de València, Paterna (Spain). Parc Científic. Image Processing Laboratory (IPL)
INRAE, UMR, Villenaved’Ornon (France)
Univ. of Tuscia, Viterbo (Italy). DIBAF Department for Innovation in Biological Agro‐food and Forestry Systems; Euro‐Mediterranean Center on Climate Change (CMCC), Viterbo (Italy)

The 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 (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 (R² > .94) and RECO (R² > .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, 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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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1816106

Journal Information:: Global Change Biology, Vol. 26, Issue 9; ISSN 1354-1013

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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

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