Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1111/gcb.15203 |
Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks | |
Gianluca Tramontana; Mirco Migliavacca; Martin Jung; Markus Reichstein; Trevor F. Keenan; Gustau Camps‐; Valls; Jerome Ogee; Jochem Verrelst; Dario Papale | |
2020-07-02 | |
发表期刊 | Global Change Biology
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出版年 | 2020 |
英文摘要 | 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. |
领域 | 气候变化 ; 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/281733 |
专题 | 气候变化 资源环境科学 |
推荐引用方式 GB/T 7714 | Gianluca Tramontana,Mirco Migliavacca,Martin Jung,et al. Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks[J]. Global Change Biology,2020. |
APA | Gianluca Tramontana.,Mirco Migliavacca.,Martin Jung.,Markus Reichstein.,Trevor F. Keenan.,...&Dario Papale.(2020).Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks.Global Change Biology. |
MLA | Gianluca Tramontana,et al."Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks".Global Change Biology (2020). |
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