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.
Highlights
The eddy covariance (EC) technique offers a unique opportunity for monitoring carbon and energy exchanges between land ecosystems and the atmosphere (Baldocchi, 2003) allowing near-continuous measurements integrated at the ecosystem scale
The EC method allows for the measurement of the net ecosystem exchange (NEE) which is the difference between two larger flux components: gross primary production (GPP) and ecosystem respiration (RECO)
The largest mismatch we found between NNC-part and the two standard methods of partitioning (NT and daytime method (DT)) is in late spring/early summer, with lower fluxes of daytime RECO predicted by NNC-part compared to both standard methods
Summary
The eddy covariance (EC) technique offers a unique opportunity for monitoring carbon and energy exchanges between land ecosystems and the atmosphere (Baldocchi, 2003) allowing near-continuous measurements integrated at the ecosystem scale. The estimation of gross CO2 fluxes from NEE has been approached using the EC method in combination with parallel measurements of a trace gas such as carbonyl sulfide (COS; Commane et al, 2015) or 13C isotopes (Ogée, Peylin, et al, 2003; Oikawa et al, 2017; Wehr et al, 2016; Wehr & Saleska, 2015) with the aim to disentangle the photosynthesis signals from respiration in daytime NEE measurements Both methods are promising and are starting to be applied in the field; they currently require extensive and expensive instrumentation and the uncertainty in the results is still large (Oikawa et al, 2017; Whelan et al, 2018), limiting, for their application to a restricted number of study sites.
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