1. Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks
- Author
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Gustau Camps-Valls, G. Tramontana, Trevor F. Keenan, Markus Reichstein, Dario Papale, Mirco Migliavacca, Jérôme Ogée, Martin Jung, Jochem Verrelst, Department for Innovation in Biological, Agro-Food and Forest Systems, University of Tuscia, Department of Biogeochemical Integration [Jena], Max Planck Institute for Biogeochemistry (MPI-BGC), Max-Planck-Gesellschaft-Max-Planck-Gesellschaft, Max-Planck-Gesellschaft, Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Universitat de València (UV), Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Image Processing Laboratory (IPL), and DIBAF, University of Tuscia, Via S.C. de Lellis, 01100 Viterbo, Italy
- Subjects
0106 biological sciences ,ecosystem respiration ,010504 meteorology & atmospheric sciences ,net ecosystem exchange ,neural network ,Eddy covariance ,Climate change ,Atmospheric sciences ,Photosynthesis ,01 natural sciences ,7. Clean energy ,Carbon Cycle ,Atmosphere ,Flux (metallurgy) ,FluxNet ,Respiration ,eddy covariance ,Environmental Chemistry ,Ecosystem ,Primary Research Article ,0105 earth and related environmental sciences ,General Environmental Science ,Global and Planetary Change ,Ecology ,carbon dioxide fluxes partitioning ,gross primary production (GPP) ,Carbon Dioxide ,Biological Sciences ,15. Life on land ,gross primary production ,machine learning ,13. Climate action ,[SDE]Environmental Sciences ,Environmental science ,Neural Networks, Computer ,Seasons ,ecosystem respiration (RECO) ,Environmental Sciences ,010606 plant biology & botany - Abstract
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 (NNC‐part). NNC‐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., In this paper, we present an innovative neural network (NNC‐part) for partitioning net ecosystem exchange of CO2, measured by eddy covariance over land ecosystems, into ecosystem respiration (RECO) and gross primary production (GPP). NNC‐part has a hybrid nature for assimilating the light‐use efficiency (LUE) concept. The GPP and RECO produced by NNC‐part resulted consistent with estimates provided in FLUXNET2015 and patterns of fluxes very realistic, being the method able to reproduce: the GPP and RECO response to meteorological forces; hysteresis in the diel cycle of fluxes; effect of diffuse to direct radiation ratio on LUE, respiration pulse.
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