Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests
Copyright policy )Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests
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.
PLoS ONE, 14 (2)
ISSN:1932-6203
- Michigan State University United States
- University of British Columbia Canada
- Kyoto University Japan
- Finnish Meteorological Institute Finland
- Max Planck Institute of Neurobiology Germany
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Saison, 550, échange atmosphère biosphère, Forests, Seasons; Forests; Forest ecology; Carbon dioxide; Ecosystems; Memory; Solar radiation; Vapor pressure, Laboratory of Geo-information Science and Remote Sensing, forêt, K01 - Foresterie - Considérations générales, SDG 13 - Climate Action, 910 Geography & travel, biodiversity, SDG 15 - Life on Land, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, Agricultural and Biological Sciences(all), effet mémoire, Q, R, F40 - Ecologie végétale, PE&RC, Agricultural sciences, séquestration du carbone, climate change, Forêt, Institute of Geography, Medicine, réseau neuromimetique, Seasons, Research Article, Environmental Monitoring, P33 - Chimie et physique du sol, cycle du carbone, 570, P40 - Météorologie et climatologie, Science, Climate Change, 333, Carbon Cycle, Radiation solaire, Life Science, Couverture végétale, Laboratorium voor Geo-informatiekunde en remote sensing, Laboratorium voor Geo-informatiekunde en Remote Sensing, approche statistique, General, Ecosystem, écosystème forestier, forests, U10 - Méthodes mathématiques et statistiques, Biochemistry, Genetics and Molecular Biology(all), Atmosphere, flux de co2, Climat, Carbon Dioxide, Models, Theoretical, Neural Networks, Computer, Cycle du carbone, Sciences agricoles, agrovoc: agrovoc:c_25409, agrovoc: agrovoc:c_331583, agrovoc: agrovoc:c_1665, agrovoc: agrovoc:c_6911, agrovoc: agrovoc:c_17299, agrovoc: agrovoc:c_1374842133961, agrovoc: agrovoc:c_3062, agrovoc: agrovoc:c_14415
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Saison, 550, échange atmosphère biosphère, Forests, Seasons; Forests; Forest ecology; Carbon dioxide; Ecosystems; Memory; Solar radiation; Vapor pressure, Laboratory of Geo-information Science and Remote Sensing, forêt, K01 - Foresterie - Considérations générales, SDG 13 - Climate Action, 910 Geography & travel, biodiversity, SDG 15 - Life on Land, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, Agricultural and Biological Sciences(all), effet mémoire, Q, R, F40 - Ecologie végétale, PE&RC, Agricultural sciences, séquestration du carbone, climate change, Forêt, Institute of Geography, Medicine, réseau neuromimetique, Seasons, Research Article, Environmental Monitoring, P33 - Chimie et physique du sol, cycle du carbone, 570, P40 - Météorologie et climatologie, Science, Climate Change, 333, Carbon Cycle, Radiation solaire, Life Science, Couverture végétale, Laboratorium voor Geo-informatiekunde en remote sensing, Laboratorium voor Geo-informatiekunde en Remote Sensing, approche statistique, General, Ecosystem, écosystème forestier, forests, U10 - Méthodes mathématiques et statistiques, Biochemistry, Genetics and Molecular Biology(all), Atmosphere, flux de co2, Climat, Carbon Dioxide, Models, Theoretical, Neural Networks, Computer, Cycle du carbone, Sciences agricoles, agrovoc: agrovoc:c_25409, agrovoc: agrovoc:c_331583, agrovoc: agrovoc:c_1665, agrovoc: agrovoc:c_6911, agrovoc: agrovoc:c_17299, agrovoc: agrovoc:c_1374842133961, agrovoc: agrovoc:c_3062, agrovoc: agrovoc:c_14415
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