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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: William Marchand; Martin P. Girardin; Sylvie Gauthier; Henrik Hartmann; +3 Authors

    In view of the economic, social and ecological importance of Canada's forest ecosystems, there is a growing interest in studying the response of these ecosystems to climate change. Accurate knowledge regarding growth trajectories is needed for both policy makers and forest managers to ensure sustainability of the forest resource. However, results of previous analyses regarding the sign and magnitude of trends have often diverged. The main objective of this paper was to analyze the current state of scientific knowledge on growth and productivity trends in Canada's forests and provide some explanatory elements for contrasting observations. The three methods that are commonly used for assessments of tree growth and forest productivity (i.e. forest inventory data, tree-ring records, and satellite observations) have different underlying physiological assumptions and operate on different spatiotemporal scales, which complicates direct comparisons of trend values between studies. Within our systematic review of 44 peer-reviewed studies, half identified increasing trends for tree growth or forest productivity, while the other half showed negative trends. Biases and uncertainties associated with the three methods may explain some of the observed discrepancies. Given the complexity of interactions and feedbacks between ecosystem processes at different scales, researchers should consider the different approaches as complementary, rather than contradictory. Here, we propose the integration of these different approaches into a single framework that capitalizes on their respective advantages while limiting associated biases. Harmonization of sampling protocols and improvement of data processing and analyses would allow for more consistent trend estimations, thereby providing greater insight into climate-change related trends in forest growth and productivity. Similarly, a more open data-sharing culture should speed-up progress in this field of research.

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    Article . 2018
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    Environmental Research Letters
    Other literature type . Article . 2018 . Peer-reviewed
    License: CC BY
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    Environmental Research Letters
    Article . 2018
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    Article . 2018
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ MPG.PuRearrow_drop_down
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      Environmental Research Letters
      Other literature type . Article . 2018 . Peer-reviewed
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      Environmental Research Letters
      Article . 2018
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      Article . 2018
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Tramontana; G. and Jung; M. and Camps-Valls; G. and Ichii; +12 Authors

    Gianluca Tramontana was supported by the GEOCARBON EU FP7 project (GA 283080). Dario Papale, Martin Jung and Markus Reichstein acknowledge funding from the EU FP7 project GEOCARBON (grant agreement no. 283080) and the EU H2020 BACI project (grant agreement no. 640176). Gustau Camps-Valls wants to acknowledge the support by an ERC Consolidator Grant with grant agreement 647423 (SEDAL). Kazuhito Ichii was supported by Environment Research and Technology Development Funds (2-1401) from the Ministry of the Environment of Japan and the JAXA Global Change Observation Mission (GCOM) project (no. 115). Christopher R. Schwalm was supported by National Aeronautics and Space Administration (NASA) grants nos. NNX12AP74G, NNX10AG01A, and NNX11AO08A. M. Altaf Arain thanks the support of Natural Sciences and Engineering Research Council (NSREC) of Canada. Penelope Serrano Ortiz was partially supported by the GEISpain project (CGL2014-52838-C2-1-R) funded by the Spanish Ministry of Economy and Competitiveness and the European Union ERDF funds. Sebastian Wolf acknowledges support from a Marie Curie International Outgoing Fellowship (European Commission, grant 300083). The FLUXCOM initiative is coordinated by Martin Jung, Max Planck Institute for Biogeochemistry (Jena, Germany). This work used eddy-covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, FluxnetCanada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy-covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, the Max Planck Institute for Biogeochemistry, the National Science Foundation, the University of Tuscia and the US Department of Energy, and the databasing and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, the University of California - Berkeley, and the University of Virginia. 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  0.6), 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). The evaluated large ensemble of ML-based models will be the basis of new global flux products. GEISpain project - Spanish Ministry of Economy and Competitiveness CGL2014-52838-C2-1-R National Aeronautics & Space Administration (NASA) NNX12AP74G NNX10AG01A NNX11AO08A United States Department of Energy (DOE) DE-FG02-04ER63917 DE-FG02-04ER63911 Natural Sciences and Engineering Research Council of Canada JAXA Global Change Observation Mission (GCOM) project 115 European Commission Joint Research Centre 300083 European Union (EU) GA 283080 283080 640176 Ministry of the Environment, Japan 2-1401 Max Planck Institute for Biogeochemistry European Research Council (ERC) 647423 National Science Foundation (NSF) University of Tuscia FAO-GTOS-TCO iLEAPS

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    https://doi.org/10.5194/bg-201...
    Other literature type . Article . Preprint . 2016 . Peer-reviewed
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    Biogeosciences
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    Article . 2016
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    Unitus DSpace
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    Research . 2016
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    Article . 2016
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    Biogeosciences (BG)
    Other literature type . 2018
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    Research Collection
    Article . 2016
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    DOAJ
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    Research . 2016
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    ETH Zürich Research Collection
    Article . 2016
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    Other literature type . 2018
    ETH Zürich Research Collection
    Research . 2016
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      Other literature type . Article . Preprint . 2016 . Peer-reviewed
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      Research . 2016
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      Article . 2016
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      ETH Zürich Research Collection
      Article . 2016
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      Other literature type . 2018
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      Research . 2016
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    Authors: Dario Papale; T. Andrew Black; Nuno Carvalhais; Alessandro Cescatti; +15 Authors

    Empirical modeling approaches are frequently used to upscale local eddy covariance observations of carbon, water, and energy fluxes to regional and global scales. The predictive capacity of such models largely depends on the data used for parameterization and identification of input-output relationships, while prediction for conditions outside the training domain is generally uncertain. In this work, artificial neural networks (ANNs) were used for the prediction of gross primary production (GPP) and latent heat flux (LE) on local and European scales with the aim to assess the portion of uncertainties in extrapolation due to sample selection. ANNs were found to be a useful tool for GPP and LE prediction, in particular for extrapolation in time (mean absolute error MAE for GPP between 0.53 and 1.56 gC m-2 d-1). Extrapolation in space in similar climatic and vegetation conditions also gave good results (GPP MAE 0.7-1.41 gC m-2 d-1), while extrapolation in areas with different seasonal cycles and controlling factors (e.g., the tropical regions) showed noticeably higher errors (GPP MAE 0.8-2.09 gC m-2 d-1). The distribution and the number of sites used for ANN training had a remarkable effect on prediction uncertainty in both, regional GPP and LE budgets and their interannual variability. Results obtained show that for ANN upscaling for continents with relatively small networks of sites, the error due to the sampling can be large and needs to be considered and quantified. The analysis of the spatial variability of the uncertainty helped to identify the meteorological drivers driving the uncertainty. Key Points Uncertainty due to spatial sampling is evaluated using ANNs and FLUXNET data GPP and LE budgets and IAV are analyzed with different site networks The uncertainty in upscaling due to spatial sampling is highly heterogeneous

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    Article . 2015
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    Article . 2015
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: William Marchand; Martin P. Girardin; Sylvie Gauthier; Henrik Hartmann; +3 Authors

    In view of the economic, social and ecological importance of Canada's forest ecosystems, there is a growing interest in studying the response of these ecosystems to climate change. Accurate knowledge regarding growth trajectories is needed for both policy makers and forest managers to ensure sustainability of the forest resource. However, results of previous analyses regarding the sign and magnitude of trends have often diverged. The main objective of this paper was to analyze the current state of scientific knowledge on growth and productivity trends in Canada's forests and provide some explanatory elements for contrasting observations. The three methods that are commonly used for assessments of tree growth and forest productivity (i.e. forest inventory data, tree-ring records, and satellite observations) have different underlying physiological assumptions and operate on different spatiotemporal scales, which complicates direct comparisons of trend values between studies. Within our systematic review of 44 peer-reviewed studies, half identified increasing trends for tree growth or forest productivity, while the other half showed negative trends. Biases and uncertainties associated with the three methods may explain some of the observed discrepancies. Given the complexity of interactions and feedbacks between ecosystem processes at different scales, researchers should consider the different approaches as complementary, rather than contradictory. Here, we propose the integration of these different approaches into a single framework that capitalizes on their respective advantages while limiting associated biases. Harmonization of sampling protocols and improvement of data processing and analyses would allow for more consistent trend estimations, thereby providing greater insight into climate-change related trends in forest growth and productivity. Similarly, a more open data-sharing culture should speed-up progress in this field of research.

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    MPG.PuRe
    Article . 2018
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    Environmental Research Letters
    Other literature type . Article . 2018 . Peer-reviewed
    License: CC BY
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    Environmental Research Letters
    Article . 2018
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      Environmental Research Letters
      Other literature type . Article . 2018 . Peer-reviewed
      License: CC BY
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      Environmental Research Letters
      Article . 2018
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      Article . 2018
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    Authors: Tramontana; G. and Jung; M. and Camps-Valls; G. and Ichii; +12 Authors

    Gianluca Tramontana was supported by the GEOCARBON EU FP7 project (GA 283080). Dario Papale, Martin Jung and Markus Reichstein acknowledge funding from the EU FP7 project GEOCARBON (grant agreement no. 283080) and the EU H2020 BACI project (grant agreement no. 640176). Gustau Camps-Valls wants to acknowledge the support by an ERC Consolidator Grant with grant agreement 647423 (SEDAL). Kazuhito Ichii was supported by Environment Research and Technology Development Funds (2-1401) from the Ministry of the Environment of Japan and the JAXA Global Change Observation Mission (GCOM) project (no. 115). Christopher R. Schwalm was supported by National Aeronautics and Space Administration (NASA) grants nos. NNX12AP74G, NNX10AG01A, and NNX11AO08A. M. Altaf Arain thanks the support of Natural Sciences and Engineering Research Council (NSREC) of Canada. Penelope Serrano Ortiz was partially supported by the GEISpain project (CGL2014-52838-C2-1-R) funded by the Spanish Ministry of Economy and Competitiveness and the European Union ERDF funds. Sebastian Wolf acknowledges support from a Marie Curie International Outgoing Fellowship (European Commission, grant 300083). The FLUXCOM initiative is coordinated by Martin Jung, Max Planck Institute for Biogeochemistry (Jena, Germany). This work used eddy-covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, FluxnetCanada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy-covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, the Max Planck Institute for Biogeochemistry, the National Science Foundation, the University of Tuscia and the US Department of Energy, and the databasing and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, the University of California - Berkeley, and the University of Virginia. 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  0.6), 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). The evaluated large ensemble of ML-based models will be the basis of new global flux products. GEISpain project - Spanish Ministry of Economy and Competitiveness CGL2014-52838-C2-1-R National Aeronautics & Space Administration (NASA) NNX12AP74G NNX10AG01A NNX11AO08A United States Department of Energy (DOE) DE-FG02-04ER63917 DE-FG02-04ER63911 Natural Sciences and Engineering Research Council of Canada JAXA Global Change Observation Mission (GCOM) project 115 European Commission Joint Research Centre 300083 European Union (EU) GA 283080 283080 640176 Ministry of the Environment, Japan 2-1401 Max Planck Institute for Biogeochemistry European Research Council (ERC) 647423 National Science Foundation (NSF) University of Tuscia FAO-GTOS-TCO iLEAPS

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    https://doi.org/10.5194/bg-201...
    Other literature type . Article . Preprint . 2016 . Peer-reviewed
    License: CC BY
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    Biogeosciences
    Article . Preprint
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    Article . 2016
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    Unitus DSpace
    Article . 2016
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    Research . 2016
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    MPG.PuRe
    Article . 2016
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    Biogeosciences (BG)
    Other literature type . 2018
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    Research Collection
    Article . 2016
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      Other literature type . Article . Preprint . 2016 . Peer-reviewed
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    Authors: Dario Papale; T. Andrew Black; Nuno Carvalhais; Alessandro Cescatti; +15 Authors

    Empirical modeling approaches are frequently used to upscale local eddy covariance observations of carbon, water, and energy fluxes to regional and global scales. The predictive capacity of such models largely depends on the data used for parameterization and identification of input-output relationships, while prediction for conditions outside the training domain is generally uncertain. In this work, artificial neural networks (ANNs) were used for the prediction of gross primary production (GPP) and latent heat flux (LE) on local and European scales with the aim to assess the portion of uncertainties in extrapolation due to sample selection. ANNs were found to be a useful tool for GPP and LE prediction, in particular for extrapolation in time (mean absolute error MAE for GPP between 0.53 and 1.56 gC m-2 d-1). Extrapolation in space in similar climatic and vegetation conditions also gave good results (GPP MAE 0.7-1.41 gC m-2 d-1), while extrapolation in areas with different seasonal cycles and controlling factors (e.g., the tropical regions) showed noticeably higher errors (GPP MAE 0.8-2.09 gC m-2 d-1). The distribution and the number of sites used for ANN training had a remarkable effect on prediction uncertainty in both, regional GPP and LE budgets and their interannual variability. Results obtained show that for ANN upscaling for continents with relatively small networks of sites, the error due to the sampling can be large and needs to be considered and quantified. The analysis of the spatial variability of the uncertainty helped to identify the meteorological drivers driving the uncertainty. Key Points Uncertainty due to spatial sampling is evaluated using ANNs and FLUXNET data GPP and LE budgets and IAV are analyzed with different site networks The uncertainty in upscaling due to spatial sampling is highly heterogeneous

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