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Title: Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests
Authors: Besnard, Simon
Carvalhais, Nuno
Arain, M. Altaf
Black, Andrew
Brede, Benjamin
Buchmann, Nina
Chen, Jiquan
Clevers, Jan G. P. W
Dutrieux, Loïc P.
Gans, Fabian
Herold, Martin
Jung, Martin
Kosugi, Yoshiko
Knohl, Alexander
Law, Beverly E.
Paul-Limoges, Eugénie
Lohila, Annalea
Merbold, Lutz
Roupsard, Olivier
Valentini, Riccardo
Wolf, Sebastian
Zhang, Xudong
Reichstein, Markus
Author's alias: 小杉, 緑子
Issue Date: 6-Feb-2019
Publisher: Public Library of Science (PLoS)
Journal title: PLOS ONE
Volume: 14
Issue: 2
Thesis number: e0211510
Abstract: 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.
Description: 28 Feb 2019: The PLOS ONE Staff (2019) Correction: Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests. PLOS ONE 14(2): e0213467. https://doi.org/10.1371/journal.pone.0213467.
Rights: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
URI: http://hdl.handle.net/2433/241605
DOI(Published Version): 10.1371/journal.pone.0211510
PubMed ID: 30726269
Related Link: https://doi.org/10.1371/journal.pone.0213467
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