Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

Tramontana, Gianluca; Jung, Martin; Schwalm, Christopher R.; Ichii, Kazuhito; Camps-Valls, Gustau; Ráduly, Botond; Reichstein, Markus; Arain, M. Altaf; Cescatti, Alessandro; Kiely, Gerard; Merbold, Lutz; Serrano-Ortiz, Penelope; Sickert, Sven; Wolf, Sebastian; Papale, Dario

doi:10.5194/bg-13-4291-2016

Title: Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

Journal Article · Fri Jul 29 00:00:00 EDT 2016 · Biogeosciences (Online)

DOI:https://doi.org/10.5194/bg-13-4291-2016· OSTI ID:1361528

Tramontana, Gianluca ^[1]; Jung, Martin ^[2]; Schwalm, Christopher R. ^[3]; Ichii, Kazuhito ^[4]; Camps-Valls, Gustau ^[5]; Ráduly, Botond ^[6]; Reichstein, Markus ^[2]; Arain, M. Altaf ^[7]; Cescatti, Alessandro ^[8]; Kiely, Gerard ^[9]; Merbold, Lutz ^[10]; Serrano-Ortiz, Penelope ^[11]; Sickert, Sven ^[12]; Wolf, Sebastian ^[13]; Papale, Dario ^[1]

Univ. of Tuscia, Viterbo (Italy)
Max Planck Institute for Biogeochemistry, Jena (Germany)
Woods Hole Research Center, Falmouth, MA (United States)
Japan Agency for Marine-Earth Science and Technology, Yokohama (Japan); National Institute for Environmental Studies, Tsukuba (Japan)
Univ. de Valencia, Paterna (Spain)
Univ. of Tuscia, Viterbo (Italy); Sapientia Hungarian Univ. of Transylvania, Miercurea Ciuc (Romania)
McMaster Univ., Hamilton, ON (Canada)
European Commission, Ispra (Italy)
Univ. College, Cork (Ireland)
ETH Zurich, Zurich (Switzerland); International Livestock Research Institute (ILRI), Nairobi (Kenya)
Univ. of Granada, Granada (Spain)
Friedrich Schiller Univ. Jena, Jena (Germany)
ETH Zurich, Zurich (Switzerland)

Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R² < 0.5), ecosystem respiration (R² > 0.6), gross primary production (R²> 0.7), latent heat (R² > 0.7), sensible heat (R² > 0.7), and net radiation (R² > 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R² > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R² < 0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). Finally, the evaluated large ensemble of ML-based models will be the basis of new global flux products.

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Research Organization:: Oregon State Univ., Corvallis, OR (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: FG02-04ER63917; FG02-04ER63911

OSTI ID:: 1361528

Journal Information:: Biogeosciences (Online), Vol. 13, Issue 14; ISSN 1726-4189

Publisher:: European Geosciences UnionCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 398 works

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Contrasting biosphere responses to hydrometeorological extremes: revisiting the 2010 western Russian heatwave Flach, Milan; Sippel, Sebastian; Gans, Fabian Biogeosciences, Vol. 15, Issue 20 https://doi.org/10.5194/bg-15-6067-2018	journal	January 2018
Assessing the dynamics of vegetation productivity in circumpolar regions with different satellite indicators of greenness and photosynthesis Walther, Sophia; Guanter, Luis; Heim, Birgit Biogeosciences, Vol. 15, Issue 20 https://doi.org/10.5194/bg-15-6221-2018	journal	January 2018
Carbon–water flux coupling under progressive drought Boese, Sven; Jung, Martin; Carvalhais, Nuno Biogeosciences, Vol. 16, Issue 13 https://doi.org/10.5194/bg-16-2557-2019	journal	January 2019
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Summarizing the state of the terrestrial biosphere in few dimensions Kraemer, Guido; Camps-Valls, Gustau; Reichstein, Markus Biogeosciences, Vol. 17, Issue 9 https://doi.org/10.5194/bg-17-2397-2020	journal	January 2020
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Monitoring Drought Effects on Vegetation Productivity Using Satellite Solar-Induced Chlorophyll Fluorescence Zhang, Lifu; Qiao, Na; Huang, Changping Remote Sensing, Vol. 11, Issue 4 https://doi.org/10.3390/rs11040378	journal	February 2019
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Detecting impacts of extreme events with ecological in situ monitoring networks M., Mahecha, Miguel; Fabian, Gans,; Sebastian, Sippel, ETH Zurich https://doi.org/10.3929/ethz-b-000192472	text	January 2017
Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests Besnard, Simon; Carvalhais, Nuno; Arain, M. Altaf Public Library of Science (PLoS) https://doi.org/10.5167/uzh-171422	text	January 2019
Global satellite-driven estimates of heterotrophic respiration Konings, Alexandra G.; Bloom, A. Anthony; Liu, Junjie Biogeosciences, Vol. 16, Issue 11 https://doi.org/10.5194/bg-16-2269-2019	journal	January 2019
Estimating global gross primary productivity using chlorophyll fluorescence and a data assimilation system with the BETHY-SCOPE model Norton, Alexander J.; Rayner, Peter J.; Koffi, Ernest N. Biogeosciences, Vol. 16, Issue 15 https://doi.org/10.5194/bg-16-3069-2019	journal	January 2019
Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques Flach, Milan; Gans, Fabian; Brenning, Alexander Earth System Dynamics, Vol. 8, Issue 3 https://doi.org/10.5194/esd-8-677-2017	journal	January 2017
Coupling the Canadian Terrestrial Ecosystem Model (CTEM v. 2.0) to Environment and Climate Change Canada's greenhouse gas forecast model (v.107-glb) Badawy, Bakr; Polavarapu, Saroja; Jones, Dylan B. A. Geoscientific Model Development, Vol. 11, Issue 2 https://doi.org/10.5194/gmd-11-631-2018	journal	January 2018
P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production Stocker, Benjamin D.; Wang, Han; Smith, Nicholas G. Geoscientific Model Development, Vol. 13, Issue 3 https://doi.org/10.5194/gmd-13-1545-2020	journal	January 2020
Accounting for forest age in the tile-based dynamic global vegetation model JSBACH4 (4.20p7; git feature/forests) – a land surface model for the ICON-ESM Nabel, Julia E. M. S.; Naudts, Kim; Pongratz, Julia Geoscientific Model Development, Vol. 13, Issue 1 https://doi.org/10.5194/gmd-13-185-2020	journal	January 2020
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Impact of the 2015/2016 El Niño on the terrestrial carbon cycle constrained by bottom-up and top-down approaches Bastos, Ana; Friedlingstein, Pierre; Sitch, Stephen Royal Society of London https://doi.org/10.7892/boris.128667	text	January 2018
Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations Peltola, Olli; Vesala, Timo; Gao, Yao Copernicus Publications https://doi.org/10.7892/boris.135334	text	January 2019
Changes in productivity and carbon storage of grasslands in China under future global warming scenarios of 1.5°C and 2°C Wang, Zhaoqi; Chang, Jinfeng; Peng, Shushi Journal of Plant Ecology, Vol. 12, Issue 5 https://doi.org/10.1093/jpe/rtz024	journal	May 2019
Impact of the 2015/2016 El Niño on the terrestrial carbon cycle constrained by bottom-up and top-down approaches Bastos, Ana; Friedlingstein, Pierre; Sitch, Stephen Philosophical Transactions of the Royal Society B: Biological Sciences, Vol. 373, Issue 1760 https://doi.org/10.1098/rstb.2017.0304	journal	October 2018
Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence Alemohammad, Seyed Hamed; Fang, Bin; Konings, Alexandra G. Biogeosciences, Vol. 14, Issue 18 https://doi.org/10.5194/bg-14-4101-2017	journal	January 2017
Maximum carbon uptake rate dominates the interannual variability of global net ecosystem exchange Fu, Zheng; Stoy, Paul C.; Poulter, Benjamin Global Change Biology, Vol. 25, Issue 10 https://doi.org/10.1111/gcb.14731	journal	July 2019
Understanding terrestrial water storage variations in northern latitudes across scales Trautmann, Tina; Koirala, Sujan; Carvalhais, Nuno Hydrology and Earth System Sciences, Vol. 22, Issue 7 https://doi.org/10.5194/hess-22-4061-2018	journal	January 2018
Land use change and El Niño-Southern Oscillation drive decadal carbon balance shifts in Southeast Asia Kondo, Masayuki; Ichii, Kazuhito; Patra, Prabir K. Karlsruhe https://doi.org/10.5445/ir/1000082232	text	January 2018
ORCHIDEE-PEAT (revision 4596), a model for northern peatland CO2, water, and energy fluxes on daily to annual scales Qiu, Chunjing; Zhu, Dan; Ciais, Philippe Karlsruhe https://doi.org/10.5445/ir/1000083904	text	January 2018
Emergent relationships with respect to burned area in global satellite observations and fire-enabled vegetation models Forkel, Matthias; Andela, Niels; Harrison, Sandy P. Karlsruhe https://doi.org/10.5445/ir/1000090447	text	January 2019
Reviews and syntheses: An empirical spatiotemporal description of the global surface-atmosphere carbon fluxes: Opportunities and data limitations Jakob, Zscheischler,; D., Mahecha, Miguel; Valerio, Avitabile, ETH Zurich https://doi.org/10.3929/ethz-b-000192415	text	January 2017
Impacts of droughts and extreme temperature events on gross primary production and ecosystem respiration: a systematic assessment across ecosystems and climate zones Von Buttlar, Jannis; Zscheischler, Jakob; Rammig, Anja ETH Zurich https://doi.org/10.3929/ethz-b-000319384	text	January 2017
Estimating global gross primary productivity using chlorophyll fluorescence and a data assimilation system with the BETHY-SCOPE model Norton, Alexander J.; Rayner, Peter J.; Koffi, Ernest N. Biogeosciences Discussions https://doi.org/10.5194/bg-2018-270	journal	June 2018
Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations Peltola, Olli; Vesala, Timo; Gao, Yao Karlsruhe https://doi.org/10.5445/ir/1000098212	text	January 2019
Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests Simon, Besnard,; Nuno, Carvalhais,; Altaf, Arain, M. ETH Zurich https://doi.org/10.3929/ethz-b-000325091	text	January 2019
Understanding terrestrial water storage variations in northern latitudes across scales Trautmann, Tina; Koirala, Sujan; Carvalhais, Nuno Hydrology and Earth System Sciences Discussions https://doi.org/10.5194/hess-2017-690	posted_content	November 2017
Evaluating the Interplay Between Biophysical Processes and Leaf Area Changes in Land Surface Models Forzieri, Giovanni; Duveiller, Gregory; Georgievski, Goran Karlsruhe https://doi.org/10.5445/ir/1000083682	text	January 2018
Quantifying the effect of forest age in annual net forest carbon balance Besnard, Simon; Carvalhais, Nuno; Arain, M. Altaf Karlsruhe https://doi.org/10.5445/ir/1000088924	text	January 2018
Seasonality of Tropical Photosynthesis: A Global Map of Drivers and Comparison to Model Outputs Uribe, Maria del Rosario; Sierra, Carlos A.; Dukes, Jeffrey https://doi.org/10.1002/essoar.10504778.1	preprint	January 2020
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ICOS eddy covariance flux-station site setup: a review Corinna, Rebmann,; Marc, Aubinet,; Hape, Schmid, ETH Zurich https://doi.org/10.3929/ethz-b-000313358	text	January 2018
Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciences Johnson, J. Emmanuel; Laparra, Valero; Pérez-Suay, Adrián arXiv https://doi.org/10.48550/arxiv.2007.14706	text	January 2020
Gaussianizing the Earth: Multidimensional Information Measures for Earth Data Analysis Johnson, J. Emmanuel; Laparra, Valero; Piles, Maria arXiv https://doi.org/10.48550/arxiv.2010.06476	preprint	January 2020
Vegetation greenness and land carbon-flux anomalies associated with climate variations: a focus on the year 2015 Yue, Chao; Ciais, Philippe; Bastos, Ana Atmospheric Chemistry and Physics, Vol. 17, Issue 22 https://doi.org/10.5194/acp-17-13903-2017	journal	January 2017
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Patterns and trends of the dominant environmental controls of net biome productivity Marcolla, Barbara; Migliavacca, Mirco; Rödenbeck, Christian Biogeosciences, Vol. 17, Issue 8 https://doi.org/10.5194/bg-17-2365-2020	journal	January 2020
Machine learning estimates of eddy covariance carbon flux in a scrub in the Mexican highland Guevara-Escobar, Aurelio; González-Sosa, Enrique; Cervantes-Jiménez, Mónica Biogeosciences, Vol. 18, Issue 2 https://doi.org/10.5194/bg-18-367-2021	journal	January 2021
The importance of tree demography and root water uptake for modelling the carbon and water cycles of Amazonia Joetzjer, Emilie; Maignan, Fabienne; Chave, Jérôme Biogeosciences Discussions https://doi.org/10.5194/bg-2018-308	journal	July 2018
Barium stable isotopes as a fingerprint of biological cycling in the Amazon River Basin Charbonnier, Quentin; Bouchez, Julien; Gaillardet, Jérôme Biogeosciences Discussions https://doi.org/10.5194/bg-2020-81	journal	April 2020
Nitrogen leaching from natural ecosystems under global change: a modelling study Braakhekke, Maarten C.; Rebel, Karin T.; Dekker, Stefan C. Earth System Dynamics Discussions https://doi.org/10.5194/esd-2017-7	journal	March 2017
Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project Hantson, Stijn; Kelley, Douglas I.; Arneth, Almut Geoscientific Model Development, Vol. 13, Issue 7 https://doi.org/10.5194/gmd-13-3299-2020	journal	January 2020
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Accounting for forest age in the tile-based dynamic global vegetation model JSBACH4 (4.20p7; git feature/forests) – a land surface model for the ICON-ESM Nabel, Julia E. M. S.; Naudts, Kim; Pongratz, Julia Geoscientific Model Development Discussions https://doi.org/10.5194/gmd-2019-68	journal	April 2019
Interactive comment on “ERA-5 and ERA-Interim driven ISBA land surface model simulations: Which one performs better?” by Clement Albergel et al. Albergel, Clement Hydrology and Earth System Sciences Discussions https://doi.org/10.5194/hess-2018-117-ac2	journal	June 2018
Assimilation of vegetation optical depth retrievals from passive microwave radiometry Kumar, Sujay V.; Holmes, Thomas R.; Bindlish, Rajat Hydrology and Earth System Sciences Discussions https://doi.org/10.5194/hess-2020-36	journal	March 2020
Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces Albergel, Clément; Zheng, Yongjun; Bonan, Bertrand Hydrology and Earth System Sciences, Vol. 24, Issue 9 https://doi.org/10.5194/hess-24-4291-2020	journal	January 2020
Imprints of evaporative conditions and vegetation type in diurnal temperature variations Panwar, Annu; Renner, Maik; Kleidon, Axel Hydrology and Earth System Sciences, Vol. 24, Issue 10 https://doi.org/10.5194/hess-24-4923-2020	journal	October 2020

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