Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests

Besnard Simon, Carvalhais Nuno, Arain M. Altaf, Black Andrew, Brede Benjamin, Buchmann Nina, Chen Jiquan, Clevers Jan G.P.W., Dutrieux Loïc, Gans Fabian, Herold Martin, Jung Martin, Kosugi Yoshiko, Knohl Alexander, Law Beverly, Paul-Limoges Eugénie, Lohila Annalea, Merbold Lutz, Roupsard Olivier, Valentini Riccardo, Wolf Sebastian, Zhang Xudong, Reichstein Markus. 2019. Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests. PloS One, 14 (2):e0211510, 22 p.

Url - jeu de données - Entrepôt autre : https://fluxnet.fluxdata.org/data/data-policy/

Quartile : Q2, Sujet : MULTIDISCIPLINARY SCIENCES

Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Psychologie-éthologie-ergonomie; Staps

Note générale : Corrigendum paru dans PloS One, (2019) 1 p. https://doi.org/10.1371/journal.pone.0213467

Résumé : Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate's temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE.

Mots-clés Agrovoc : forêt, climat, séquestration du carbone, cycle du carbone, saison, couverture végétale, radiation solaire, écosystème forestier

Mots-clés libres : Seasons, Forests, Forest ecology, Carbon dioxide, Ecosystems, Memory, Solar radiation, Vapor pressure

Classification Agris : K01 - Foresterie - Considérations générales
F40 - Écologie végétale
P40 - Météorologie et climatologie
P33 - Chimie et physique du sol
U10 - Informatique, mathématiques et statistiques

Champ stratégique Cirad : CTS 6 (2019-) - Changement climatique

Auteurs et affiliations

• Besnard Simon, Max Planck Institut für Biogeochemie (DEU) - auteur correspondant
• Carvalhais Nuno, Max Planck Institut für Biogeochemie (DEU)
• Arain M. Altaf, McMaster University (CAN)
• Black Andrew, University of British Columbia (CAN)
• Brede Benjamin, Wageningen University (NLD)
• Buchmann Nina, ETH (CHE)
• Chen Jiquan, MSU (USA)
• Clevers Jan G.P.W., Wageningen University (NLD)
• Dutrieux Loïc, CONABIO (MEX) ORCID: 0000-0002-5058-2526
• Gans Fabian, Max Planck Institut für Biogeochemie (DEU)
• Herold Martin, Wageningen University and Research Centre (NLD)
• Jung Martin, Max Planck Institut für Biogeochemie (DEU)
• Kosugi Yoshiko, Kyoto University (JPN)
• Knohl Alexander, Georg-August University of Göttingen (DEU)
• Law Beverly, Oregon State University (USA)
• Paul-Limoges Eugénie, ETH (CHE)
• Lohila Annalea, Finnish meteorological institute (FIN)
• Merbold Lutz, ILRI (KEN)
• Roupsard Olivier, CIRAD-PERSYST-UMR Eco&Sols (SEN) ORCID: 0000-0002-1319-142X
• Valentini Riccardo, Università degli studi della Tuscia (ITA)
• Wolf Sebastian, ETH (CHE)
• Zhang Xudong, Chinese Academy of Forestry (CHN)
• Reichstein Markus, Max Planck Institut für Biogeochemie (DEU)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/591569/)

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