Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms
Abstract. 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 method...
| Autores principales: | , , , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Copernicus GmbH
2016
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| Acceso en línea: | https://hdl.handle.net/10568/129400 |
| _version_ | 1855542148786552832 |
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| author | 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 |
| author_browse | Arain, M. Altaf Camps-Valls, Gustau Cescatti, Alessandro Ichii, Kazuhito Jung, Martin Kiely, Gerard Merbold, Lutz Papale, Dario Reichstein, Markus Ráduly, Botond Schwalm, Christopher R. Serrano-Ortiz, Penelope Sickert, Sven Tramontana, Gianluca Wolf, Sebastian |
| author_facet | 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 |
| author_sort | Tramontana, Gianluca |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Abstract. 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). The evaluated large ensemble of ML-based models will be the basis of new global flux products. |
| format | Journal Article |
| id | CGSpace129400 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | Copernicus GmbH |
| publisherStr | Copernicus GmbH |
| record_format | dspace |
| spelling | CGSpace1294002025-12-08T10:11:39Z 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 carbon energy carbon dioxide algorithms Abstract. 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). The evaluated large ensemble of ML-based models will be the basis of new global flux products. 2016-07-29 2023-03-10T14:34:29Z 2023-03-10T14:34:29Z Journal Article https://hdl.handle.net/10568/129400 en Open Access Copernicus GmbH 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. 2016. Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. Biogeosciences 13: 4291-4313 |
| spellingShingle | carbon energy carbon dioxide 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 Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms |
| title | Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms |
| title_full | Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms |
| title_fullStr | Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms |
| title_full_unstemmed | Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms |
| title_short | Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms |
| title_sort | predicting carbon dioxide and energy fluxes across global fluxnet sites with regression algorithms |
| topic | carbon energy carbon dioxide algorithms |
| url | https://hdl.handle.net/10568/129400 |
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