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1. Estimating tree biomass of sub-Saharan African forests: a review of available allometric equations

Author: Henry, Matieu, Picard, Nicolas, Trotta, Carlo, Manlay, Raphaël, Valentini, Riccardo, Bernoux, Martial, and Saint-André, Laurent
Subjects: Forestry, SD1-669.5
Abstract: In response to the growing interest in estimating carbon stocks in forests, available allometric equations have been compiled for sub-Saharan Africa. Tree, sprout and stand volume and biomass equations were reviewed. The 850 equations and 125 related references were incorporated into an open-access database on the Carboafrica website (http://www.carboafrica.net). The collected information provides a basic tool for the estimation of biomass and carbon stocks and other purposes, such as bioenergy and fodder supply assessment. A Tier-method approach was developed to illustrate the possible use of the equations. Current available biomass expansion factors that are used to convert a volume to the total aboveground biomass appear to be limited; incomplete species-specific allometric equations are preferred to generalised equations. The analysis of the database highlighted important gaps in available tools to assess forest carbon stocks and changes in these stocks. A quality control assessment revealed that 22% of the equations were misreported and recommendations were proposed to guide further research. Further statistical analyses, such as the Bayesian approach, would help to produce more accurate biomass estimates.
Published: 2011
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2. Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Author: Irvin, Jeremy, Zhou, Sharon, McNicol, Gavin, Lu, Fred, Liu, Vincent, Fluet-Chouinard, Etienne, Ouyang, Zutao, Knox, Sara Helen, Lucas-Moffat, Antje, Trotta, Carlo, Papale, Dario, Vitale, Domenico, Mammarella, Ivan, Alekseychik, Pavel, Aurela, Mika, Avati, Anand, Baldocchi, Dennis, Bansal, Sheel, Bohrer, Gil, Campbell, David I, Chen, Jiquan, Chu, Housen, Dalmagro, Higo J, Delwiche, Kyle B, Desai, Ankur R, Euskirchen, Eugenie, Feron, Sarah, Goeckede, Mathias, Heimann, Martin, Helbig, Manuel, Helfter, Carole, Hemes, Kyle S, Hirano, Takashi, Iwata, Hiroki, Jurasinski, Gerald, Kalhori, Aram, Kondrich, Andrew, Lai, Derrick YF, Lohila, Annalea, Malhotra, Avni, Merbold, Lutz, Mitra, Bhaskar, Ng, Andrew, Nilsson, Mats B, Noormets, Asko, Peichl, Matthias, Rey-Sanchez, A Camilo, Richardson, Andrew D, Runkle, Benjamin RK, Schäfer, Karina VR, Sonnentag, Oliver, Stuart-Haëntjens, Ellen, Sturtevant, Cove, Ueyama, Masahito, Valach, Alex C, Vargas, Rodrigo, Vourlitis, George L, Ward, Eric J, Wong, Guan Xhuan, Zona, Donatella, Alberto, Ma Carmelita R, Billesbach, David P, Celis, Gerardo, Dolman, Han, Friborg, Thomas, Fuchs, Kathrin, Gogo, Sébastien, Gondwe, Mangaliso J, Goodrich, Jordan P, Gottschalk, Pia, Hörtnagl, Lukas, Jacotot, Adrien, Koebsch, Franziska, Kasak, Kuno, Maier, Regine, Morin, Timothy H, Nemitz, Eiko, Oechel, Walter C, Oikawa, Patricia Y, Ono, Keisuke, Sachs, Torsten, Sakabe, Ayaka, Schuur, Edward A, Shortt, Robert, Sullivan, Ryan C, Szutu, Daphne J, Tuittila, Eeva-Stiina, Varlagin, Andrej, Verfaillie, Joeseph G, Wille, Christian, Windham-Myers, Lisamarie, Poulter, Benjamin, and Jackson, Robert B
Subjects: Earth Sciences, Agricultural, Veterinary and Food Sciences, Biological Sciences, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence, Bioengineering, Machine learning, time series, imputation, gap-filling, methane, flux, wetlands, Agricultural and Veterinary Sciences, Meteorology & Atmospheric Sciences, Agricultural, veterinary and food sciences, Biological sciences, Earth sciences
Abstract: Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).
Published: 2021

3. Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions.

Author: Chang, Kuang-Yu, Riley, William J, Knox, Sara H, Jackson, Robert B, McNicol, Gavin, Poulter, Benjamin, Aurela, Mika, Baldocchi, Dennis, Bansal, Sheel, Bohrer, Gil, Campbell, David I, Cescatti, Alessandro, Chu, Housen, Delwiche, Kyle B, Desai, Ankur R, Euskirchen, Eugenie, Friborg, Thomas, Goeckede, Mathias, Helbig, Manuel, Hemes, Kyle S, Hirano, Takashi, Iwata, Hiroki, Kang, Minseok, Keenan, Trevor, Krauss, Ken W, Lohila, Annalea, Mammarella, Ivan, Mitra, Bhaskar, Miyata, Akira, Nilsson, Mats B, Noormets, Asko, Oechel, Walter C, Papale, Dario, Peichl, Matthias, Reba, Michele L, Rinne, Janne, Runkle, Benjamin RK, Ryu, Youngryel, Sachs, Torsten, Schäfer, Karina VR, Schmid, Hans Peter, Shurpali, Narasinha, Sonnentag, Oliver, Tang, Angela CI, Torn, Margaret S, Trotta, Carlo, Tuittila, Eeva-Stiina, Ueyama, Masahito, Vargas, Rodrigo, Vesala, Timo, Windham-Myers, Lisamarie, Zhang, Zhen, and Zona, Donatella
Abstract: Wetland methane (CH4) emissions ([Formula: see text]) are important in global carbon budgets and climate change assessments. Currently, [Formula: see text] projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent [Formula: see text] temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that [Formula: see text] are often controlled by factors beyond temperature. Here, we evaluate the relationship between [Formula: see text] and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between [Formula: see text] and temperature, suggesting larger [Formula: see text] sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.
Published: 2021

4. Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data.

Author: Pastorello, Gilberto, Trotta, Carlo, Canfora, Eleonora, Chu, Housen, Christianson, Danielle, Cheah, You-Wei, Poindexter, Cristina, Chen, Jiquan, Elbashandy, Abdelrahman, Humphrey, Marty, Isaac, Peter, Polidori, Diego, Reichstein, Markus, Ribeca, Alessio, van Ingen, Catharine, Vuichard, Nicolas, Zhang, Leiming, Amiro, Brian, Ammann, Christof, Arain, M Altaf, Ardö, Jonas, Arkebauer, Timothy, Arndt, Stefan K, Arriga, Nicola, Aubinet, Marc, Aurela, Mika, Baldocchi, Dennis, Barr, Alan, Beamesderfer, Eric, Marchesini, Luca Belelli, Bergeron, Onil, Beringer, Jason, Bernhofer, Christian, Berveiller, Daniel, Billesbach, Dave, Black, Thomas Andrew, Blanken, Peter D, Bohrer, Gil, Boike, Julia, Bolstad, Paul V, Bonal, Damien, Bonnefond, Jean-Marc, Bowling, David R, Bracho, Rosvel, Brodeur, Jason, Brümmer, Christian, Buchmann, Nina, Burban, Benoit, Burns, Sean P, Buysse, Pauline, Cale, Peter, Cavagna, Mauro, Cellier, Pierre, Chen, Shiping, Chini, Isaac, Christensen, Torben R, Cleverly, James, Collalti, Alessio, Consalvo, Claudia, Cook, Bruce D, Cook, David, Coursolle, Carole, Cremonese, Edoardo, Curtis, Peter S, D'Andrea, Ettore, da Rocha, Humberto, Dai, Xiaoqin, Davis, Kenneth J, De Cinti, Bruno, de Grandcourt, Agnes, De Ligne, Anne, De Oliveira, Raimundo C, Delpierre, Nicolas, Desai, Ankur R, Di Bella, Carlos Marcelo, di Tommasi, Paul, Dolman, Han, Domingo, Francisco, Dong, Gang, Dore, Sabina, Duce, Pierpaolo, Dufrêne, Eric, Dunn, Allison, Dušek, Jiří, Eamus, Derek, Eichelmann, Uwe, ElKhidir, Hatim Abdalla M, Eugster, Werner, Ewenz, Cacilia M, Ewers, Brent, Famulari, Daniela, Fares, Silvano, Feigenwinter, Iris, Feitz, Andrew, Fensholt, Rasmus, Filippa, Gianluca, Fischer, Marc, Frank, John, Galvagno, Marta, and Gharun, Mana
Abstract: The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions.
Published: 2021

5. FLUXNET-CH4: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands

Author: Delwiche, Kyle B, Knox, Sara Helen, Malhotra, Avni, Fluet-Chouinard, Etienne, McNicol, Gavin, Feron, Sarah, Ouyang, Zutao, Papale, Dario, Trotta, Carlo, Canfora, Eleonora, Cheah, You-Wei, Christianson, Danielle, Alberto, Ma Carmelita R, Alekseychik, Pavel, Aurela, Mika, Baldocchi, Dennis, Bansal, Sheel, Billesbach, David P, Bohrer, Gil, Bracho, Rosvel, Buchmann, Nina, Campbell, David I, Celis, Gerardo, Chen, Jiquan, Chen, Weinan, Chu, Housen, Dalmagro, Higo J, Dengel, Sigrid, Desai, Ankur R, Detto, Matteo, Dolman, Han, Eichelmann, Elke, Euskirchen, Eugenie, Famulari, Daniela, Fuchs, Kathrin, Goeckede, Mathias, Gogo, Sébastien, Gondwe, Mangaliso J, Goodrich, Jordan P, Gottschalk, Pia, Graham, Scott L, Heimann, Martin, Helbig, Manuel, Helfter, Carole, Hemes, Kyle S, Hirano, Takashi, Hollinger, David, Hörtnagl, Lukas, Iwata, Hiroki, Jacotot, Adrien, Jurasinski, Gerald, Kang, Minseok, Kasak, Kuno, King, John, Klatt, Janina, Koebsch, Franziska, Krauss, Ken W, Lai, Derrick YF, Lohila, Annalea, Mammarella, Ivan, Marchesini, Luca Belelli, Manca, Giovanni, Matthes, Jaclyn Hatala, Maximov, Trofim, Merbold, Lutz, Mitra, Bhaskar, Morin, Timothy H, Nemitz, Eiko, Nilsson, Mats B, Niu, Shuli, Oechel, Walter C, Oikawa, Patricia Y, Ono, Keisuke, Peichl, Matthias, Peltola, Olli, Reba, Michele L, Richardson, Andrew D, Riley, William, Runkle, Benjamin RK, Ryu, Youngryel, Sachs, Torsten, Sakabe, Ayaka, Sanchez, Camilo Rey, Schuur, Edward A, Schäfer, Karina VR, Sonnentag, Oliver, Sparks, Jed P, Stuart-Haëntjens, Ellen, Sturtevant, Cove, Sullivan, Ryan C, Szutu, Daphne J, Thom, Jonathan E, Torn, Margaret S, Tuittila, Eeva-Stiina, Turner, Jessica, Ueyama, Masahito, Valach, Alex C, Vargas, Rodrigo, Varlagin, Andrej, and Vazquez-Lule, Alma
Subjects: Earth Sciences, Atmospheric Sciences, Climate Action, Geochemistry, Physical Geography and Environmental Geoscience, Atmospheric sciences, Geoinformatics, Physical geography and environmental geoscience
Abstract: Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20g g€¯S to 20g g€¯N) the spring onset of elevated CH4 emissions starts 3g€¯d earlier, and the CH4 emission season lasts 4g€¯d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at 10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.
Published: 2021

6. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data.

Author: Pastorello, Gilberto, Trotta, Carlo, Canfora, Eleonora, Chu, Housen, Christianson, Danielle, Cheah, You-Wei, Poindexter, Cristina, Chen, Jiquan, Elbashandy, Abdelrahman, Humphrey, Marty, Isaac, Peter, Polidori, Diego, Reichstein, Markus, Ribeca, Alessio, van Ingen, Catharine, Vuichard, Nicolas, Zhang, Leiming, Amiro, Brian, Ammann, Christof, Arain, M Altaf, Ardö, Jonas, Arkebauer, Timothy, Arndt, Stefan K, Arriga, Nicola, Aubinet, Marc, Aurela, Mika, Baldocchi, Dennis, Barr, Alan, Beamesderfer, Eric, Marchesini, Luca Belelli, Bergeron, Onil, Beringer, Jason, Bernhofer, Christian, Berveiller, Daniel, Billesbach, Dave, Black, Thomas Andrew, Blanken, Peter D, Bohrer, Gil, Boike, Julia, Bolstad, Paul V, Bonal, Damien, Bonnefond, Jean-Marc, Bowling, David R, Bracho, Rosvel, Brodeur, Jason, Brümmer, Christian, Buchmann, Nina, Burban, Benoit, Burns, Sean P, Buysse, Pauline, Cale, Peter, Cavagna, Mauro, Cellier, Pierre, Chen, Shiping, Chini, Isaac, Christensen, Torben R, Cleverly, James, Collalti, Alessio, Consalvo, Claudia, Cook, Bruce D, Cook, David, Coursolle, Carole, Cremonese, Edoardo, Curtis, Peter S, D'Andrea, Ettore, da Rocha, Humberto, Dai, Xiaoqin, Davis, Kenneth J, Cinti, Bruno De, Grandcourt, Agnes de, Ligne, Anne De, De Oliveira, Raimundo C, Delpierre, Nicolas, Desai, Ankur R, Di Bella, Carlos Marcelo, Tommasi, Paul di, Dolman, Han, Domingo, Francisco, Dong, Gang, Dore, Sabina, Duce, Pierpaolo, Dufrêne, Eric, Dunn, Allison, Dušek, Jiří, Eamus, Derek, Eichelmann, Uwe, ElKhidir, Hatim Abdalla M, Eugster, Werner, Ewenz, Cacilia M, Ewers, Brent, Famulari, Daniela, Fares, Silvano, Feigenwinter, Iris, Feitz, Andrew, Fensholt, Rasmus, Filippa, Gianluca, Fischer, Marc, Frank, John, Galvagno, Marta, and Gharun, Mana
Abstract: The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.
Published: 2020

7. FLUXNET-CH4 Synthesis Activity: Objectives, Observations, and Future Directions FLUXNET-CH4 Synthesis Activity: Objectives, Observations, and Future Directions

Author: Knox, Sara H, Jackson, Robert B, Poulter, Benjamin, McNicol, Gavin, Fluet-Chouinard, Etienne, Zhang, Zhen, Hugelius, Gustaf, Bousquet, Philippe, Canadell, Josep G, Saunois, Marielle, Papale, Dario, Chu, Housen, Keenan, Trevor F, Baldocchi, Dennis, Torn, Margaret S, Mammarella, Ivan, Trotta, Carlo, Aurela, Mika, Bohrer, Gil, Campbell, David I, Cescatti, Alessandro, Chamberlain, Samuel, Chen, Jiquan, Chen, Weinan, Dengel, Sigrid, Desai, Ankur R, Euskirchen, Eugenie, Friborg, Thomas, Gasbarra, Daniele, Goded, Ignacio, Goeckede, Mathias, Heimann, Martin, Helbig, Manuel, Hirano, Takashi, Hollinger, David Y, Iwata, Hiroki, Kang, Minseok, Klatt, Janina, Krauss, Ken W, Kutzbach, Lars, Lohila, Annalea, Mitra, Bhaskar, Morin, Timothy H, Nilsson, Mats B, Niu, Shuli, Noormets, Asko, Oechel, Walter C, Peichl, Matthias, Peltola, Olli, Reba, Michele L, Richardson, Andrew D, Runkle, Benjamin RK, Ryu, Youngryel, Sachs, Torsten, Schäfer, Karina VR, Schmid, Hans Peter, Shurpali, Narasinha, Sonnentag, Oliver, Tang, Angela CI, Ueyama, Masahito, Vargas, Rodrigo, Vesala, Timo, Ward, Eric J, Windham-Myers, Lisamarie, Wohlfahrt, Georg, and Zona, Donatella
Subjects: Earth Sciences, Atmospheric Sciences, Climate Change Science, Astronomical and Space Sciences, Physical Geography and Environmental Geoscience, Meteorology & Atmospheric Sciences, Atmospheric sciences, Climate change science
Abstract: We describe a new coordination activity and initial results for a global synthesis of eddy covariance CH4 flux measurements.
Published: 2019

8. Thinning Can Reduce Losses in Carbon Use Efficiency and Carbon Stocks in Managed Forests Under Warmer Climate.

Author: Collalti, Alessio, Trotta, Carlo, Keenan, Trevor F, Ibrom, Andreas, Bond-Lamberty, Ben, Grote, Ruediger, Vicca, Sara, Reyer, Christopher PO, Migliavacca, Mirco, Veroustraete, Frank, Anav, Alessandro, Campioli, Matteo, Scoccimarro, Enrico, Šigut, Ladislav, Grieco, Elisa, Cescatti, Alessandro, and Matteucci, Giorgio
Subjects: CO2 fertilization, ISIMIP, carbon sequestration, climate change, forest management, forest model, Atmospheric Sciences
Abstract: Forest carbon use efficiency (CUE, the ratio of net to gross primary productivity) represents the fraction of photosynthesis that is not used for plant respiration. Although important, it is often neglected in climate change impact analyses. Here we assess the potential impact of thinning on projected carbon cycle dynamics and implications for forest CUE and its components (i.e., gross and net primary productivity and plant respiration), as well as on forest biomass production. Using a detailed process-based forest ecosystem model forced by climate outputs of five Earth System Models under four representative climate scenarios, we investigate the sensitivity of the projected future changes in the autotrophic carbon budget of three representative European forests. We focus on changes in CUE and carbon stocks as a result of warming, rising atmospheric CO2 concentration, and forest thinning. Results show that autotrophic carbon sequestration decreases with forest development, and the decrease is faster with warming and in unthinned forests. This suggests that the combined impacts of climate change and changing CO2 concentrations lead the forests to grow faster, mature earlier, and also die younger. In addition, we show that under future climate conditions, forest thinning could mitigate the decrease in CUE, increase carbon allocation into more recalcitrant woody pools, and reduce physiological-climate-induced mortality risks. Altogether, our results show that thinning can improve the efficacy of forest-based mitigation strategies and should be carefully considered within a portfolio of mitigation options.
Published: 2018

9. Simulating the effects of thinning and species mixing on stands of oak (Quercus petraea (Matt.) Liebl./Quercus robur L.) and pine (Pinus sylvestris L.) across Europe

Author: Engel, Markus, Vospernik, Sonja, Toïgo, Maude, Morin, Xavier, Tomao, Antonio, Trotta, Carlo, Steckel, Mathias, Barbati, Anna, Nothdurft, Arne, Pretzsch, Hans, del Rio, Miren, Skrzyszewski, Jerzy, Ponette, Quentin, Löf, Magnus, Jansons, Āris, and Brazaitis, Gediminas
Published: 2021
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10. Global FLUXNET Datasets: Past Usage, Opportunities, and New Data

Author: Pastorello, Gilberto, primary, Beringer, Jason, additional, Durden, David, additional, Trotta, Carlo, additional, Cheah, You-Wei, additional, Isaac, Peter, additional, Sturtevant, Cove, additional, and Papale, Dario, additional
Published: 2024
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11. X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X

Author: Nelson, Jacob A., primary, Walther, Sophia, additional, Gans, Fabian, additional, Kraft, Basil, additional, Weber, Ulrich, additional, Novick, Kimberly, additional, Buchmann, Nina, additional, Migliavacca, Mirco, additional, Wohlfahrt, Georg, additional, Šigut, Ladislav, additional, Ibrom, Andreas, additional, Papale, Dario, additional, Göckede, Mathias, additional, Duveiller, Gregory, additional, Knohl, Alexander, additional, Hörtnagl, Lukas, additional, Scott, Russell L., additional, Zhang, Weijie, additional, Hamdi, Zayd Mahmoud, additional, Reichstein, Markus, additional, Aranda-Barranco, Sergio, additional, Ardö, Jonas, additional, Op de Beeck, Maarten, additional, Billdesbach, Dave, additional, Bowling, David, additional, Bracho, Rosvel, additional, Brümmer, Christian, additional, Camps-Valls, Gustau, additional, Chen, Shiping, additional, Cleverly, Jamie Rose, additional, Desai, Ankur, additional, Dong, Gang, additional, El-Madany, Tarek S., additional, Euskirchen, Eugenie Susanne, additional, Feigenwinter, Iris, additional, Galvagno, Marta, additional, Gerosa, Giacomo, additional, Gielen, Bert, additional, Goded, Ignacio, additional, Goslee, Sarah, additional, Gough, Christopher Michael, additional, Heinesch, Bernard, additional, Ichii, Kazuhito, additional, Jackowicz-Korczynski, Marcin Antoni, additional, Klosterhalfen, Anne, additional, Knox, Sara, additional, Kobayashi, Hideki, additional, Kohonen, Kukka-Maaria, additional, Korkiakoski, Mika, additional, Mammarella, Ivan, additional, Mana, Gharun, additional, Marzuoli, Riccardo, additional, Matamala, Roser, additional, Metzger, Stefan, additional, Montagnani, Leonardo, additional, Nicolini, Giacomo, additional, O'Halloran, Thomas, additional, Ourcival, Jean-Marc, additional, Peichl, Matthias, additional, Pendall, Elise, additional, Ruiz Reverter, Borja, additional, Roland, Marilyn, additional, Sabbatini, Simone, additional, Sachs, Torsten, additional, Schmidt, Marius, additional, Schwalm, Christopher R., additional, Shekhar, Ankit, additional, Silberstein, Richard, additional, Silveira, Maria Lucia, additional, Spano, Donatella, additional, Tagesson, Torbern, additional, Tramontana, Gianluca, additional, Trotta, Carlo, additional, Turco, Fabio, additional, Vesala, Timo, additional, Vincke, Caroline, additional, Vitale, Domenico, additional, Vivoni, Enrique R., additional, Wang, Yi, additional, Woodgate, William, additional, Yepez, Enrico A., additional, Zhang, Junhui, additional, Zona, Donatella, additional, and Jung, Martin, additional
Published: 2024
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12. The sensitivity of the forest carbon budget shifts across processes along with stand development and climate change

Author: Collalti, Alessio, Thornton, Peter E., Cescatti, Alessandro, Rita, Angelo, Borghetti, Marco, Nolè, Angelo, Trotta, Carlo, Ciais, Philippe, and Matteucci, Giorgio
Published: 2019

13. The potential carbon neutrality of sustainable viticulture showed through a comprehensive assessment of the greenhouse gas (GHG) budget of wine production

Author: Chiriacò, Maria Vincenza, Belli, Claudio, Chiti, Tommaso, Trotta, Carlo, and Sabbatini, Simone
Published: 2019
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14. The ecosystem carbon sink implications of mountain forest expansion into abandoned grazing land: The role of subsoil and climatic factors

Author: Pellis, Guido, Chiti, Tommaso, Rey, Ana, Curiel Yuste, Jorge, Trotta, Carlo, and Papale, Dario
Published: 2019
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15. Normospermic Patients Infected With Ureaplasma parvum: Role of Dysregulated miR-122-5p, miR-34c-5, and miR-141-3p

Author: Galdiero, Marilena, primary, Trotta, Carlo, additional, Schettino, Maria Teresa, additional, Cirillo, Luigi, additional, Sasso, Francesca Paola, additional, Petrillo, Francesco, additional, and Petrillo, Arianna, additional
Published: 2024
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16. Fostering collaboration through improved software development practices for the ONEFlux eddy covariance data processing pipeline

Author: Pastorello, Gilberto, primary, Trotta, Carlo, additional, Ribeca, Alessio, additional, Beattie, Keith, additional, Ngo, Sy-Toan, additional, Chu, Housen, additional, Cheah, You-Wei, additional, Christianson, Danielle, additional, Nicolini, Giacomo, additional, Dengel, Sigrid, additional, Polidori, Diego, additional, Isaac, Peter, additional, Archer, Matthew, additional, Orchard, Dominic, additional, Agarwal, Deb, additional, Biraud, Sebastien, additional, Torn, Margaret, additional, and Papale, Dario, additional
Published: 2023
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17. From raw data to standardized, fully corrected, quality ensured eddy covariance flux data: the ICOS Ecosystem processing pipeline

Author: Nicolini, Giacomo, primary, Sabbatini, Simone, additional, Canfora, Eleonora, additional, Polidori, Diego, additional, Ribeca, Alessio, additional, Trotta, Carlo, additional, Vitale, Domenico, additional, Gielen, Bert, additional, Iserbyt, Arne, additional, Loubet, Benjamin, additional, Michilsens, Fana, additional, Op de Beeck, Maarten, additional, and Papale, Dario, additional
Published: 2023
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18. Latest developments in the long-term monitoring of vegetation characteristics in the ICOS ecosystem network.

Author: Gielen, Bert, primary, Op de Beeck, Maarten, additional, Nicolini, Giacomo, additional, Sabbatini, Simone, additional, Michilsens, Fana, additional, Trotta, Carlo, additional, Iserbyt, Arne, additional, and Papale, Dario, additional
Published: 2023
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19. Use of global climate and weather reanalysis to fill meteorological time series gaps

Author: Trotta, Carlo, primary, Vuichard, Nicolas, additional, Pastorello, Gilberto, additional, Manco, Ilenia, additional, Mancini, Marco, additional, Mercogliano, Paola, additional, and Papale, Dario, additional
Published: 2023
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20. FLUXNET-CH4 Synthesis Activity: Objectives, Observations, and Future Directions

Author: Knox, Sara H, Jackskson, Robert B, Poulter, Benjamin, McNicol, Gavin, Fluet-Chouinard, Etienne, Zhang, Zhen, Hugelius, Gustaf, Bousquet, Philippppe, Canadell, Josep G, Saunois, Marielle, Papale, Dario, Chu, Housen, Keenan, Trevor F, Baldocchi, Dennis, Torn, Margaret S, Mammarella, Ivan, Trotta, Carlo, Aurela, Mika, Bohrer, Gil, Campbell, David I, Cescatti, Alessssandro, Chamberlain, Samuel, Chen, Jiquan, Chen, Weinan, Dengel, Sigrid, Desai, Ankur R, Euskskirchen, Eugenie, Friborg, Thomas, Gasbarra, Daniele, Goded, Ignacio, Goeckede, Mathias, Heimann, Martin, Helbig, Manuel, Hirano, Takashshi, Hollinger, David Y, Iwata, Hiroki, Kang, Minseok, Klatt, Janina, Kraussss, Ken W, Kutzbach, Lars, Lohila, Annalea, Mitra, Bhaskskar, Morin, Timothyhy H, Nilsssson, Mats B, Niu, Shuli, Noormets, Asksko, Oechel, Walter C, Peichl, Matthias, Peltola, Olli, Reba, Michele L, Richardson, Andrew D, Runkle, Benjamin R. K, Ryu, Youngryel, Sachshs, Torsten, Schäfer, Karina V. R, Schmid, Hans Peter, Shurpali, Narasinha, Sonnentag, Oliver, Tang, Angela C. I, Ueyama, Masahito, Vargas, Rodrigo, Vesala, Timo, Ward, Eric J, Windham-Myers, Lisamarie, Wohlfahrt, Georg, and Zona, Donatella
Subjects: Meteorology And Climatology
Abstract: This paper describes the formation of, and initial results for, a new FLUXNET coordination network for ecosystem-scale methane (CH4) measurements at 60 sites globally, organized by the Global Carbon Project in partnership with other initiatives and regional flux tower networks. The objectives of the effort are presented along with an overview of the coverage of eddy covariance (EC) CH4 flux measurements globally, initial results comparing CH4 fluxes across the sites, and future research directions and needs. Annual estimates of net CH4 fluxes across sites ranged from −0.2 ± 0.02 g C m(exp -2) yr(exp -1) for an upland forest site to 114.9 ± 13.4 g C m(exp -2) yr(exp -1) for an estuarine freshwater marsh, with fluxes exceeding 40 g C m(exp -2) yr(exp -1) at multiple sites. Average annual soil and air temperatures were found to be the strongest predictor of annual CH4 flux across wetland sites globally. Water table position was positively correlated with annual CH4 emissions, although only for wetland sites that were not consistently inundated throughout the year. The ratio of annual CH4 fluxes to ecosystem respiration increased significantly with mean site temperature. Uncertainties in annual CH4 estimates due to gap-filling and random errors were on average ±1.6 g C m(exp -2) yr(exp -1) at 95% confidence, with the relative error decreasing exponentially with increasing flux magnitude across sites. Through the analysis and synthesis of a growing EC CH4 flux database, the controls on ecosystem CH4 fluxes can be better understood, used to inform and validate Earth system models, and reconcile differences between land surface model- and atmospheric-based estimates of CH4 emissions.
Published: 2020
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21. Biomass and volume modeling in Olea europaea L. cv “Leccino”

Author: Brunori, Antonio, Dini, Francesca, Cantini, Claudio, Sala, Giovanna, La Mantia, Tommaso, Caruso, Tiziano, Marra, Francesco Paolo, Trotta, Carlo, Nasini, Luigi, Regni, Luca, and Proietti, Primo
Published: 2017
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22. Error in the estimation of emission factors for forest degradation in central Africa

Author: Picard, Nicolas, Henry, Matieu, Fonton, Noël H., Kondaoulé, Josiane, Fayolle, Adeline, Birigazzi, Luca, Sola, Gaël, Poultouchidou, Anatoli, Trotta, Carlo, and Maïdou, Hervé
Published: 2016
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23. Simulating the effects of thinning and species mixing on stands of oak (Quercus petraea (Matt.) Liebl./Quercus robur L.) and pine (Pinus sylvestris L.) across Europe

Author: European Commission, Federal Ministry of Agriculture, Forestry, Regions and Water Management (Austria), Engel, Markus [0000-0001-6991-9021], Vospernik, Sonja [0000-0002-4201-6444], Toïgo, Maude [0000-0002-4910-2214], Tomao, Antonio [0000-0001-6656-400X], Trotta, Carlo [0000-0001-6377-0262], Steckel, Mathias [0000-0002-1940-5441], Barbati, Anna [0000-0002-9064-0903], Nothdurft, Arne [0000-0002-7065-7601], Pretzsch, Hans [0000-0002-4958-1868], del Rio, Miren [0000-0001-7496-3713], Skrzyszewski, Jerzy [0000-0003-4330-5827], Ponette, Quentin [0000-0002-2726-7392], Löf, Magnus [0000-0002-9173-2156], Jansons, Aris [0000-0001-7981-4346], Brazaitis, Gediminas [0000-0003-0234-9292], Engel, Markus, Vospernik, Sonja, Toïgo, Maude, Morin, Xavier, Tomao, Antonio, Trotta, Carlo, Steckel, Mathias, Barbati, Anna, Nothdurft, Arne, Pretzsch, Hans, Río, Miren del, Skrzyszewski, Jerzy, Ponette, Quentin, Löf, Magnus, Jansons, Aris, Brazaitis, Gediminas, European Commission, Federal Ministry of Agriculture, Forestry, Regions and Water Management (Austria), Engel, Markus [0000-0001-6991-9021], Vospernik, Sonja [0000-0002-4201-6444], Toïgo, Maude [0000-0002-4910-2214], Tomao, Antonio [0000-0001-6656-400X], Trotta, Carlo [0000-0001-6377-0262], Steckel, Mathias [0000-0002-1940-5441], Barbati, Anna [0000-0002-9064-0903], Nothdurft, Arne [0000-0002-7065-7601], Pretzsch, Hans [0000-0002-4958-1868], del Rio, Miren [0000-0001-7496-3713], Skrzyszewski, Jerzy [0000-0003-4330-5827], Ponette, Quentin [0000-0002-2726-7392], Löf, Magnus [0000-0002-9173-2156], Jansons, Aris [0000-0001-7981-4346], Brazaitis, Gediminas [0000-0003-0234-9292], Engel, Markus, Vospernik, Sonja, Toïgo, Maude, Morin, Xavier, Tomao, Antonio, Trotta, Carlo, Steckel, Mathias, Barbati, Anna, Nothdurft, Arne, Pretzsch, Hans, Río, Miren del, Skrzyszewski, Jerzy, Ponette, Quentin, Löf, Magnus, Jansons, Aris, and Brazaitis, Gediminas
Abstract: Tree species mixing of oak (Quercus petraea (Matt.) Liebl./Quercus robur L.) and pine (Pinus sylvestris L.) has been shown to have positive effects on ecosystem service provision. From a management perspective, however, it is still uncertain which thinning regime provides the highest possible productivity of mixed oak–pine forests in the long term. Because of a lack of empirical studies dealing with thinning and species mixing effects on oak–pine forests, we simulated forest growth in order to test which thinning type and intensity may provide the highest productivity in the long-term. To achieve this, we simulated the growth of pure and mixed stands of oak and pine for 100 years in 23 triplets located on an ecological gradient across Europe. For this purpose, we applied four different growth simulators and compared their results: the distance-independent single-tree simulator PROGNAUS, the distance-dependent single-tree simulator SILVA, the gap model ForCEEPS, and the process-based simulator 3D-CMCC-FEM. We investigated the effects of species mixing and thinning from the upper (thinning from above) and lower tail (thinning from below) of the diameter distribution by reducing the stand basal area to 50 and 80% of the maximum basal area. We compared simulated results of the relative volume productivity of mixed versus pure stands and of thinned versus unthinned stands to empirical results previously obtained on the same set of triplets. Simulated relative volume productivity ranged between 61 and 156%, although extremes of 10% and of 300% could be observed. We found the relative volume productivity to be influenced by stand age, but not by stand density, except for PROGNAUS. Relative volume productivity did not increase with the site water supply of the triplet location. Highest long-term productivity for oak, pine and oak–pine stands can be expected in consequence of thinning from above, but the effect of thinning intensity differed between simulators. Thinning effe
Published: 2021

24. Accuracy, realism and general applicability of European forest models

Author: Mahnken, Mats, primary, Cailleret, Maxime, additional, Collalti, Alessio, additional, Trotta, Carlo, additional, Biondo, Corrado, additional, D'Andrea, Ettore, additional, Dalmonech, Daniela, additional, Marano, Gina, additional, Mäkelä, Annikki, additional, Minunno, Francesco, additional, Peltoniemi, Mikko, additional, Trotsiuk, Volodymyr, additional, Nadal‐Sala, Daniel, additional, Sabaté, Santiago, additional, Vallet, Patrick, additional, Aussenac, Raphaël, additional, Cameron, David R., additional, Bohn, Friedrich J., additional, Grote, Rüdiger, additional, Augustynczik, Andrey L. D., additional, Yousefpour, Rasoul, additional, Huber, Nica, additional, Bugmann, Harald, additional, Merganičová, Katarina, additional, Merganic, Jan, additional, Valent, Peter, additional, Lasch‐Born, Petra, additional, Hartig, Florian, additional, Vega del Valle, Iliusi D., additional, Volkholz, Jan, additional, Gutsch, Martin, additional, Matteucci, Giorgio, additional, Krejza, Jan, additional, Ibrom, Andreas, additional, Meesenburg, Henning, additional, Rötzer, Thomas, additional, van der Maaten‐Theunissen, Marieke, additional, van der Maaten, Ernst, additional, and Reyer, Christopher P. O., additional
Published: 2022
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25. Accuracy, realism and general applicability of European forest models

Author: Mahnken, Mats, Cailleret, Maxime, Collalti, Alessio, Trotta, Carlo, Biondo, Corrado, D'Andrea, Ettore, Dalmonech, Daniela, Marano, Gina, Mäkelä, Annikki, Minunno, Francesco, Peltoniemi, Mikko, Trotsiuk, Volodymyr, Nadal‐Sala, Daniel, Sabaté, Santiago, Vallet, Patrick, Aussenac, Raphaël, Cameron, David R., Bohn, Friedrich J., Grote, Rüdiger, Augustynczik, Andrey L.D., Yousefpour, Rasoul, Huber, Nica, Bugmann, Harald, Merganičová, Katarina, Merganic, Jan, Valent, Peter, Lasch‐Born, Petra, Hartig, Florian, Vega del Valle, Iliusi D., Volkholz, Jan, Gutsch, Martin, Matteucci, Giorgio, Krejza, Jan, Ibrom, Andreas, Meesenburg, Henning, Rötzer, Thomas, van der Maaten‐Theunissen, Marieke, van der Maaten, Ernst, Reyer, Christopher P.O., Mahnken, Mats, Cailleret, Maxime, Collalti, Alessio, Trotta, Carlo, Biondo, Corrado, D'Andrea, Ettore, Dalmonech, Daniela, Marano, Gina, Mäkelä, Annikki, Minunno, Francesco, Peltoniemi, Mikko, Trotsiuk, Volodymyr, Nadal‐Sala, Daniel, Sabaté, Santiago, Vallet, Patrick, Aussenac, Raphaël, Cameron, David R., Bohn, Friedrich J., Grote, Rüdiger, Augustynczik, Andrey L.D., Yousefpour, Rasoul, Huber, Nica, Bugmann, Harald, Merganičová, Katarina, Merganic, Jan, Valent, Peter, Lasch‐Born, Petra, Hartig, Florian, Vega del Valle, Iliusi D., Volkholz, Jan, Gutsch, Martin, Matteucci, Giorgio, Krejza, Jan, Ibrom, Andreas, Meesenburg, Henning, Rötzer, Thomas, van der Maaten‐Theunissen, Marieke, van der Maaten, Ernst, and Reyer, Christopher P.O.
Abstract: Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models' performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe's common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at
Published: 2022

26. Preeclampsia: state of art and future perspectives. A special focus on possible preventions

Author: Kahramanoglu, Özge, primary, Schiattarella, Antonio, additional, Demirci, Oya, additional, Sisti, Giovanni, additional, Ammaturo, Franco Pietro, additional, Trotta, Carlo, additional, Ferrari, Federico, additional, and Rapisarda, Agnese Maria Chiara, additional
Published: 2022
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27. Author Correction:The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data (Scientific Data, (2020), 7, 1, (225), 10.1038/s41597-020-0534-3)

Author: Pastorello, Gilberto, Trotta, Carlo, Canfora, Eleonora, Chu, Housen, Christianson, Danielle, Cheah, You-Wei, Poindexter, Cristina, Chen, Jiquan, Elbashandy, Abdelrahman, Humphrey, Marty, Isaac, Peter, Polidori, Diego, Reichstein, Markus, Ribeca, Alessio, van Ingen, Catharine, Vuichard, Nicolas, Zhang, Leiming, Amiro, Brian, Ammann, Christof, Arain, M. Altaf, Ardo, Jonas, Arkebauer, Timothy, Arndt, Stefan K., Arriga, Nicola, Aubinet, Marc, Aurela, Mika, Baldocchi, Dennis, Barr, Alan, Beamesderfer, Eric, Marchesini, Luca Belelli, Bergeron, Onil, Beringer, Jason, Bernhofer, Christian, Berveiller, Daniel, Billesbach, Dave, Black, Thomas Andrew, Blanken, Peter D., Bohrer, Gil, Boike, Julia, Bolstad, Paul V., Bonal, Damien, Bonnefond, Jean-Marc, Bowling, David R., Bracho, Rosvel, Brodeur, Jason, Brummer, Christian, Buchmann, Nina, Burban, Benoit, Burns, Sean P., Buysse, Pauline, Cale, Peter, Cavagna, Mauro, Cellier, Pierre, Chen, Shiping, Chini, Isaac, Christensen, Torben R., Cleverly, James, Collalti, Alessio, Consalvo, Claudia, Cook, Bruce D., Cook, David, Coursolle, Carole, Cremonese, Edoardo, Curtis, Peter S., D'Andrea, Ettore, da Rocha, Humberto, Dai, Xiaoqin, Davis, Kenneth J., De Cinti, Bruno, de Grandcourt, Agnes, De Ligne, Anne, De Oliveira, Raimundo C., Delpierre, Nicolas, Desai, Ankur R., Di Bella, Carlos Marcelo, di Tommasi, Paul, Dolman, Han, Domingo, Francisco, Dong, Gang, Dore, Sabina, Duce, Pierpaolo, Dufrene, Eric, Dunn, Allison, Dusek, Jiri, Eamus, Derek, Eichelmann, Uwe, ElKhidir, Hatim Abdalla M., Eugster, Werner, Ewenz, Cacilia M., Ewers, Brent, Famulari, Daniela, Fares, Silvano, Feigenwinter, Iris, Feitz, Andrew, Fensholt, Rasmus, Filippa, Gianluca, Fischer, Marc, Frank, John, Galvagno, Marta, Gharun, Mana, Gianelle, Damiano, Gielen, Bert, Gioli, Beniamino, Gitelson, Anatoly, Goded, Ignacio, Goeckede, Mathias, Goldstein, Allen H., Gough, Christopher M., Goulden, Michael L., Graf, Alexander, Griebel, Anne, Gruening, Carsten, Grunwald, Thomas, Hammerle, Albin, Han, Shijie, Han, Xingguo, Hansen, Birger Ulf, Hanson, Chad, Hatakka, Juha, He, Yongtao, Hehn, Markus, Heinesch, Bernard, Hinko-Najera, Nina, Hortnagl, Lukas, Hutley, Lindsay, Ibrom, Andreas, Ikawa, Hiroki, Jackowicz-Korczynski, Marcin, Janous, Dalibor, Jans, Wilma, Jassal, Rachhpal, Jiang, Shicheng, Kato, Tomomichi, Khomik, Myroslava, Klatt, Janina, Knohl, Alexander, Knox, Sara, Kobayashi, Hideki, Koerber, Georgia, Kolle, Olaf, Kosugi, Yoshiko, Kotani, Ayumi, Kowalski, Andrew, Kruijt, Bart, Kurbatova, Julia, Kutsch, Werner L., Kwon, Hyojung, Launiainen, Samuli, Laurila, Tuomas, Law, Bev, Leuning, Ray, Li, Yingnian, Liddell, Michael, Limousin, Jean-Marc, Lion, Marryanna, Liska, Adam J., Lohila, Annalea, Lopez-Ballesteros, Ana, Lopez-Blanco, Efren, Loubet, Benjamin, Loustau, Denis, Lucas-Moffat, Antje, Luers, Johannes, Ma, Siyan, Macfarlane, Craig, Magliulo, Vincenzo, Maier, Regine, Mammarella, Ivan, Manca, Giovanni, Marcolla, Barbara, Margolis, Hank A., Marras, Serena, Massman, William, Mastepanov, Mikhail, Matamala, Roser, Matthes, Jaclyn Hatala, Mazzenga, Francesco, McCaughey, Harry, McHugh, Ian, McMillan, Andrew M. S., Merbold, Lutz, Meyer, Wayne, Meyers, Tilden, Miller, Scott D., Minerbi, Stefano, Moderow, Uta, Monson, Russell K., Montagnani, Leonardo, Moore, Caitlin E., Moors, Eddy, Moreaux, Virginie, Moureaux, Christine, Munger, J. William, Nakai, Taro, Neirynck, Johan, Nesic, Zoran, Nicolini, Giacomo, Noormets, Asko, Northwood, Matthew, Nosetto, Marcelo, Nouvellon, Yann, Novick, Kimberly, Oechel, Walter, Olesen, Jorgen Eivind, Ourcival, Jean-Marc, Papuga, Shirley A., Parmentier, Frans-Jan, Paul-Limoges, Eugenie, Pavelka, Marian, Peichl, Matthias, Pendall, Elise, Phillips, Richard P., Pilegaard, Kim, Pirk, Norbert, Posse, Gabriela, Powell, Thomas, Prasse, Heiko, Prober, Suzanne M., Rambal, Serge, Rannik, Ullar, Raz-Yaseef, Naama, Rebmann, Corinna, Reed, David, de Dios, Victor Resco, Restrepo-Coupe, Natalia, Reverter, Borja R., Roland, Marilyn, Sabbatini, Simone, Sachs, Torsten, Saleska, Scott R., Sanchez-Canete, Enrique P., Sanchez-Mejia, Zulia M., Schmid, Hans Peter, Schmidt, Marius, Schneider, Karl, Schrader, Frederik, Schroder, Ivan, Scott, Russell L., Sedlak, Pavel, Serrano-Ortiz, Penelope, Shao, Changliang, Shi, Peili, Shironya, Ivan, Siebicke, Lukas, Sigut, Ladislav, Silberstein, Richard, Sirca, Costantino, Spano, Donatella, Steinbrecher, Rainer, Stevens, Robert M., Sturtevant, Cove, Suyker, Andy, Tagesson, Torbern, Takanashi, Satoru, Tang, Yanhong, Tapper, Nigel, Thom, Jonathan, Tomassucci, Michele, Tuovinen, Juha-Pekka, Urbanski, Shawn, Valentini, Riccardo, van der Molen, Michiel, van Gorsel, Eva, van Huissteden, Ko, Varlagin, Andrej, Verfaillie, Joseph, Vesala, Timo, Vincke, Caroline, Vitale, Domenico, Vygodskaya, Natalia, Walker, Jeffrey P., Walter-Shea, Elizabeth, Wang, Huimin, Weber, Robin, Westermann, Sebastian, Wille, Christian, Wofsy, Steven, Wohlfahrt, Georg, Wolf, Sebastian, Woodgate, William, Li, Yuelin, Zampedri, Roberto, Zhang, Junhui, Zhou, Guoyi, Zona, Donatella, Agarwal, Deb, Biraud, Sebastien, Torn, Margaret, and Papale, Dario
Abstract: The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions.
Published: 2021
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28. Ecosystem Photosynthesis in Land‐Surface Models: A First‐Principles Approach Incorporating Acclimation

Author: Mengoli, Giulia, primary, Agustí‐Panareda, Anna, additional, Boussetta, Souhail, additional, Harrison, Sandy P., additional, Trotta, Carlo, additional, and Prentice, I. Colin, additional
Published: 2022
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29. FLUXNET-CH4 : a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands

Author: Delwiche, Kyle B., Knox, Sara Helen, Malhotra, Avni, Fluet-Chouinard, Etienne, McNicol, Gavin, Feron, Sarah, Ouyang, Zutao, Papale, Dario, Trotta, Carlo, Canfora, Eleonora, Cheah, You Wei, Christianson, Danielle, Alberto, Ma Carmelita R., Alekseychik, Pavel, Aurela, Mika, Baldocchi, Dennis, Bansal, Sheel, Billesbach, David P., Bohrer, Gil, Bracho, Rosvel, Buchmann, Nina, Campbell, David I., Celis, Gerardo, Chen, Jiquan, Chen, Weinan, Chu, Housen, Dalmagro, Higo J., Dengel, Sigrid, Desai, Ankur R., Detto, Matteo, Dolman, Han, Eichelmann, Elke, Euskirchen, Eugenie, Famulari, Daniela, Fuchs, Kathrin, Goeckede, Mathias, Gogo, Sébastien, Gondwe, Mangaliso J., Goodrich, Jordan P., Gottschalk, Pia, Graham, Scott L., Heimann, Martin, Helbig, Manuel, Helfter, Carole, Hemes, Kyle S., Hirano, Takashi, Hollinger, David, Hörtnagl, Lukas, Iwata, Hiroki, Jacotot, Adrien, Jurasinski, Gerald, Kang, Minseok, Kasak, Kuno, King, John, Klatt, Janina, Koebsch, Franziska, Krauss, Ken W., Lai, Derrick Y.F., Lohila, Annalea, Mammarella, Ivan, Belelli Marchesini, Luca, Manca, Giovanni, Matthes, Jaclyn Hatala, Maximov, Trofim, Merbold, Lutz, Mitra, Bhaskar, Morin, Timothy H., Nemitz, Eiko, Nilsson, Mats B., Niu, Shuli, Oechel, Walter C., Oikawa, Patricia Y., Ono, Keisuke, Peichl, Matthias, Peltola, Olli, Reba, Michele L., Richardson, Andrew D., Riley, William, Runkle, Benjamin R.K., Ryu, Youngryel, Sachs, Torsten, Sakabe, Ayaka, Sanchez, Camilo Rey, Schuur, Edward A., Schäfer, Karina V.R., Sonnentag, Oliver, Sparks, Jed P., Stuart-Haëntjens, Ellen, Sturtevant, Cove, Sullivan, Ryan C., Szutu, Daphne J., Thom, Jonathan E., Torn, Margaret S., Tuittila, Eeva Stiina, Turner, Jessica, Ueyama, Masahito, Valach, Alex C., Vargas, Rodrigo, Varlagin, Andrej, Vazquez-Lule, Alma, Verfaillie, Joseph G., Vesala, Timo, Vourlitis, George L., Ward, Eric J., Wille, Christian, Wohlfahrt, Georg, Wong, Guan Xhuan, Zhang, Zhen, Zona, Donatella, Windham-Myers, Lisamarie, Poulter, Benjamin, Jackson, Robert B., Institute for Atmospheric and Earth System Research (INAR), Micrometeorology and biogeochemical cycles, Viikki Plant Science Centre (ViPS), Ecosystem processes (INAR Forest Sciences), Earth Sciences, and Earth and Climate
Subjects: 1171 Geosciences, Earth sciences, SDG 17 - Partnerships for the Goals, Physical Geography, ddc:550, 114 Physical sciences, 1172 Environmental sciences, Atmospheric Sciences
Abstract: Methane (CH$_{4}$) emissions from natural landscapes constitute roughly half of global CH$_{4}$ contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH$_{4}$ flux are ideal for constraining ecosystem-scale CH$_{4}$ emissions due to quasi-continuous and high-temporal-resolution CH$_{4}$ flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH$_{4}$ Version 1.0, the first open-source global dataset of CH$_{4}$ EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH$_{4}$ fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH$_{4}$ representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH$_{4}$ Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH$_{4}$ emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH$_{4}$ fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH$_{4}$ emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20° S to 20° N) the spring onset of elevated CH$_{4}$ emissions starts 3 d earlier, and the CH$_{4}$ emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH$_{4}$ emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH$_{4}$ emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH$_{4}$ emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH$_{4}$ emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH$_{4}$ modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH$_{4}$ peak). FLUXNET-CH$_{4}$ is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH$_{4}$ cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH$_{4}$ data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.
Published: 2021
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30. Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Author: Irvin, Jeremy, primary, Zhou, Sharon, additional, McNicol, Gavin, additional, Lu, Fred, additional, Liu, Vincent, additional, Fluet-Chouinard, Etienne, additional, Ouyang, Zutao, additional, Knox, Sara Helen, additional, Lucas-Moffat, Antje, additional, Trotta, Carlo, additional, Papale, Dario, additional, Vitale, Domenico, additional, Mammarella, Ivan, additional, Alekseychik, Pavel, additional, Aurela, Mika, additional, Avati, Anand, additional, Baldocchi, Dennis, additional, Bansal, Sheel, additional, Bohrer, Gil, additional, Campbell, David I, additional, Chen, Jiquan, additional, Chu, Housen, additional, Dalmagro, Higo J, additional, Delwiche, Kyle B, additional, Desai, Ankur R, additional, Euskirchen, Eugenie, additional, Feron, Sarah, additional, Goeckede, Mathias, additional, Heimann, Martin, additional, Helbig, Manuel, additional, Helfter, Carole, additional, Hemes, Kyle S, additional, Hirano, Takashi, additional, Iwata, Hiroki, additional, Jurasinski, Gerald, additional, Kalhori, Aram, additional, Kondrich, Andrew, additional, Lai, Derrick YF, additional, Lohila, Annalea, additional, Malhotra, Avni, additional, Merbold, Lutz, additional, Mitra, Bhaskar, additional, Ng, Andrew, additional, Nilsson, Mats B, additional, Noormets, Asko, additional, Peichl, Matthias, additional, Rey-Sanchez, A. Camilo, additional, Richardson, Andrew D, additional, Runkle, Benjamin RK, additional, Schäfer, Karina VR, additional, Sonnentag, Oliver, additional, Stuart-Haëntjens, Ellen, additional, Sturtevant, Cove, additional, Ueyama, Masahito, additional, Valach, Alex C, additional, Vargas, Rodrigo, additional, Vourlitis, George L, additional, Ward, Eric J, additional, Wong, Guan Xhuan, additional, Zona, Donatella, additional, Alberto, Ma. Carmelita R, additional, Billesbach, David P, additional, Celis, Gerardo, additional, Dolman, Han, additional, Friborg, Thomas, additional, Fuchs, Kathrin, additional, Gogo, Sébastien, additional, Gondwe, Mangaliso J, additional, Goodrich, Jordan P, additional, Gottschalk, Pia, additional, Hörtnagl, Lukas, additional, Jacotot, Adrien, additional, Koebsch, Franziska, additional, Kasak, Kuno, additional, Maier, Regine, additional, Morin, Timothy H, additional, Nemitz, Eiko, additional, Oechel, Walter C, additional, Oikawa, Patricia Y, additional, Ono, Keisuke, additional, Sachs, Torsten, additional, Sakabe, Ayaka, additional, Schuur, Edward A, additional, Shortt, Robert, additional, Sullivan, Ryan C, additional, Szutu, Daphne J, additional, Tuittila, Eeva-Stiina, additional, Varlagin, Andrej, additional, Verfaillie, Joeseph G, additional, Wille, Christian, additional, Windham-Myers, Lisamarie, additional, Poulter, Benjamin, additional, and Jackson, Robert B, additional
Published: 2021
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31. FLUXNET-CH4: a global, multi-ecosystem datasetand analysis of methane seasonalityfrom freshwater wetlands

Author: Knox, Sara, Alberto, Ma, Matthes, Jaclyn, Sanchez, Camilo, Wong, Guan, Delwiche, Kyle, Knox, Sara Helen, Malhotra, Avni, Fluet-Chouinard, Etienne, McNicol, Gavin, Feron, Sarah, Ouyang, Zutao, Papale, Dario, Trotta, Carlo, Canfora, Eleonora, Cheah, You-Wei, Christianson, Danielle, Alberto, Ma. Carmelita R., Alekseychik, Pavel, Aurela, Mika, Baldocchi, Dennis, Bansal, Sheel, Billesbach, David, Bohrer, Gil, Bracho, Rosvel, Buchmann, Nina, Campbell, David, Celis, Gerardo, Chen, Jiquan, Chen, Weinan, Chu, Housen, Dalmagro, Higo, Dengel, Sigrid, Desai, Ankur, Detto, Matteo, Dolman, Han, Eichelmann, Elke, Euskirchen, Eugenie, Famulari, Daniela, Fuchs, Kathrin, Goeckede, Mathias, Gogo, Sébastien, Gondwe, Mangaliso, Goodrich, Jordan, Gottschalk, Pia, Graham, Scott, Heimann, Martin, Helbig, Manuel, Helfter, Carole, Hemes, Kyle, Hirano, Takashi, Hollinger, David, Hörtnagl, Lukas, Iwata, Hiroki, Jacotot, Adrien, Jurasinski, Gerald, Kang, Minseok, Kasak, Kuno, King, John, Klatt, Janina, Koebsch, Franziska, Krauss, Ken, Lai, Derrick, Lohila, Annalea, Mammarella, Ivan, Belelli Marchesini, Luca, Manca, Giovanni, Matthes, Jaclyn Hatala, Maximov, Trofim, Merbold, Lutz, Mitra, Bhaskar, Morin, Timothy, Nemitz, Eiko, Nilsson, Mats, Niu, Shuli, Oechel, Walter, Oikawa, Patricia, Ono, Keisuke, Peichl, Matthias, Peltola, Olli, Reba, Michele, Richardson, Andrew, Riley, William, Runkle, Benjamin, Ryu, Youngryel, Sachs, Torsten, Sakabe, Ayaka, Sanchez, Camilo Rey, Schuur, Edward, Schäfer, Karina, Sonnentag, Oliver, Sparks, Jed, Stuart-Haëntjens, Ellen, Sturtevant, Cove, Sullivan, Ryan, Szutu, Daphne, Thom, Jonathan, Torn, Margaret, Tuittila, Eeva-Stiina, Turner, Jessica, Ueyama, Masahito, Valach, Alex, Vargas, Rodrigo, Varlagin, Andrej, Vazquez-Lule, Alma, Verfaillie, Joseph, Vesala, Timo, Vourlitis, George, Ward, Eric, Wille, Christian, Wohlfahrt, Georg, Wong, Guan Xhuan, Zhang, Zhen, Zona, Donatella, Windham-Myers, Lisamarie, Poulter, Benjamin, Jackson, Robert, Institut des Sciences de la Terre d'Orléans - UMR7327 (ISTO), Centre National de la Recherche Scientifique (CNRS)-Université d'Orléans (UO)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers en région Centre (OSUC), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Bureau de Recherches Géologiques et Minières (BRGM) (BRGM), Biogéosystèmes Continentaux - UMR7327, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Bureau de Recherches Géologiques et Minières (BRGM) (BRGM)-Centre National de la Recherche Scientifique (CNRS)-Université d'Orléans (UO)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers en région Centre (OSUC)
Subjects: [SDU]Sciences of the Universe [physics], [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment
Abstract: International audience; Abstract. Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20∘ S to 20∘ N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.
Published: 2021
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32. Ecosystem photosynthesis in land-surface models: a first-principles approach

Author: Mengoli, Giulia, primary, Agustí-Panareda, Anna, additional, Boussetta, Souhail, additional, Harrison, Sandy P., additional, Trotta, Carlo, additional, and Prentice, I. Colin, additional
Published: 2021
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33. The FLUXNET 20165 dataset and the ONEFlux processing pipeline for eddy covariance data

Author: Pastorello, Gilberto, Trotta, Carlo, Canfora, Eleonora, Chu, Housen, Christianson, Danielle, Cheah, You - Wei, Poindexter, Cristina, Chen, Jiquan, Elbashandy, Abdelrahman, Humphrey, Marty, Isaac, Peter, Polidori, Diego, Riveca, Alessio, van Ingen, Catharine, Zhang, Leiming, Amiro, Brian, Ammann, Christof, Altaf Arain, M., Ardo, Jonas, Arkebauer, Timothy, Arndt, Stefan K., Arriga, Nicola, Aubinet, Marc, Aurela, Mika, Baldocchi, Dennis, Barr, Alan, Beamesderfer, Eric, Belelli Marchesini, Luca, Bergeron, Onil, Beringer, Jason, Bernhofer, Christian, Berveiller, Daniel, Billesbach, Dave, Black, Thomas Andrew, Blanken, Peter D., Bohrer, Gil, Boike, Julia, Bolstad, Paul V., Bonal, Damien, Bonnefond, Jean - Marc, Bowling, David R., Bracho, Rosuel, Brodeur, Jason, Brummer, Christian, Buchmann, Nina, Burban, Benoit, Burns, Sean P., Buysse, Pauline, Cale, Peter, Cavagna, Mauro, Cellier, Pierre, Chen, Shiping, Chini, Issac, Christensen, Storben, Cleverly, James, Collatti, Alessio, Consalvo, Claudia, Cook, Bruce, Cook, David, Coversolle, Carole, Cremonese, Edoardo, Curtis, Peter, D'Andrea, Ettore, da Rocha, Humberto, Dai, Xiaoqin, Davis, Kenneth, De Cinti, Bruno, de Grandcourt, agnes, De Ligne, Anne, De Oliveira, Raimundo C., Delpierre, Nicolas, Desai, Ankur R., Di Bella, Carlos Marcelo, di Tommasi, Paul, Dolman, Han, Domingo, Francisco, Dong, Gang, Dore, Sabina, Duce, Pierpaolo, Dufrêne, Eric, Dunn, Allison, Dušek, Jiří, Eamus, Derek, Eichelmann, Uwe, ElKhidir, Hatim abdalla M., Eugster, Werner, Ewenz, Cacilia M., Ewers, Brent, Famulari, Daniela, Fares, Silvano, Feigenwinter, Iris, Feitz, Andrew, Fensholt, Rasmus, Filippa, Gianluca, Fischer, Marc, Frank, John, Galvagno, Marta, Gharun, Mana, Gianelle, Damiano, Gielen, Bert, Gioli, Beniamino, Gitelson, Anatoly, Goded, Ignacio, Goeckede, Mathias, Goldstein, Allen H., Gough, Christopher M., Goulden, Michael L., Graf, Alexander, Griebel, Anne, Gruening, Carsten, Grünwald, Thomas, Hammerle, Albin, Han, Shijie, Han, Xingguo, Hansen, Birger Ulf, Hanson, Chad, Hatakka, Juha, He, Yongtao, Hehn, Markus, Heinesch, Bernard, Hinko-Najera, Nina, Hörtnagl, Lukas, Hutley, Lindsay, Ibrom, Andreas, Ikawa, Hiroki, Jackowicz-Korczynski, Marcin, Janouš, Dalibor, Jans, Wilma, Jassal, Rachhpal, Jiang, Shicheng, Kato, Tomomichi, Khomik, Myroslava, Klatt, Janina, Knohl, Alexander, Knox, Sara, Kobayashi, Hideki, Koerber, Georgia, Kolle, Olaf, Kosugi, Yoshiko, Kotani, Ayumi, Kowalski, Andrew, Kruijt, Bart, Kurbatova, Julia, Kutsch, Werner L., Kwon, Hyojung, Launiainen, Samuli, Laurila, Tuomas, Law, Bev, Leuning, Ray, Li, Yingnian, Liddell, Michael, Limousin, Jean-Marc, Lion, Marryanna, Liska, Adam J., Lohila, Annalea, López-Ballesteros, Ana, López-Blanco, Efrén, Loubet, Benjamin, Loustau, Denis, Lucas-Moffat, Antje, Lüers, Johannes, Ma, Siyan, Macfarlane, Craig, Magliulo, Vincenzo, Maier, Regine, Mammarella, Ivan, Manca, Giovanni, Marcolla, Barbara, Margolis, Hank A., Marras, Serena, Massman, William, Mastepanov, Mikhail, Matamala, Roser, Matthes, Jaclyn Hatala, Mazzenga, Francesco, McCaughey, Harry, McHugh, Ian, McMillan, Andrew M. S., Merbold, Lutz, Meyer, Wayne, Meyers, Tilden, Miller, Scott D., Minerbi, Stefano, Moderow, Uta, Monson, Russell K., Montagnani, Leonardo, Moore, Caitlin E., Moors, Eddy, Moreaux, Virginie, Moureaux, Christine, Munger, J. William, Nakai, Taro, Neirynck, Johan, Nesic, Zoran, Nicolini, Giacomo, Noormets, Asko, Northwood, Matthew, Nosetto, Marcelo, Nouvellon, Yann, Novick, Kimberly, Oechel, Walter, Olesen, Jørgen Eivind, Ourcival, Jean-Marc, Papuga, Shirley A., Parmentier, Frans-Jan, Paul-Limoges, Eugenie, Pavelka, Marian, Peichl, Matthias, Pendall, Elise, Phillips, Richard P., Pilegaard, Kim, Pirk, Norbert, Posse Beaulieu, Gabriela, Powell, Thomas, Prasse, Heiko, Prober, Suzanne M., Rambal, Serge, Rannik, Üllar, Raz-Yaseef, Naama, Reed, David, Resco de Dios, Victor, Restrepo-Coupe, Natalia, Reverter, Borja R., Roland, Marilyn, Sabbatini, Simone, Sachs, Torsten, Saleska, Scott R., Sánchez-Cañete, Enrique P., Sanchez-Mejia, Zulia M., Schmid, Hans Peter, Schmidt, Marius, Schneider, Karl, Schrader, Frederik, Schroder, Ivan, Scott, Russell L., Sedlák, Pavel, Serrano-Ortíz, Penélope, Shao, Changliang, Shi, Peili, Shironya, Ivan, Siebicke, Lukas, Šigut, Ladislav, Silberstein, Richard, Sirca, Costantino, Spano, Donatella, Steinbrecher, Rainer, Stevens, Robert M., Sturtevant, Cove, Suyker, Andy, Tagesson, Torbern, Takanashi, Satoru, Tang, Yanhong, Tapper, Nigel, Thom, Jonathan, Tiedemann, Frank, Tomassucci, Michele, Tuovinen, Juha-Pekka, Urbanski, Shawn, Valentini, Riccardo, van der Molen, Michiel, van Gorsel, Eva, van Huissteden, Ko, Varlagin, Andrej, Verfaillie, Joseph, Vesala, Timo, Vincke, Caroline, Vitale, Domenico, Vygodskaya, Natalia, Walker, Jeffrey P., Walter-Shea, Elizabeth, Wang, Huimin, Weber, Robin, Westermann, Sebastian, Wille, Christian, Wofsy, Steven, Wohlfahrt, Georg, Wolf, Sebastian, Woodgate, William, Li, Yuelin, Zampedri, Roberto, Zhang, Junhui, Zhou, Guoyi, Zona, Donatella, Agarwal, Deb, Biraud, Sebastien, Torn, Margaret, and Papale, Dario
Subjects: Remote Sensing, Red de Datos, Tratados, Treaties, Teledetección, Covarianza de Remolinos, Garantía de Calidad, Quality Assurance, Eddy Covariance, Data Network
Abstract: The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible. Fil: Pastorello, Gilberto. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Trotta, Carlo. University of Tuscia. DiBAf; Italia Fil: Canfora, Eleonora. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: Chu, Housen. Lawrence Berkeley national Laboratory. Climate & Ecosystem Sciences Division; Estados Unidos Fil: Christianson, Danielle. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Cheah, You - Wei. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Poindexter, Cristina. California State University. Department of Civil Engineering; Estados Unidos Fil: Chen, Jiquan. Michigan State University. Department of Geography, Environment, and Spatial Sciences; Estados Unidos Fil: Elbashandy, Abdelrah man. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Humphrey, Marty. University of Virginia. Department of Computer Science; Estados Unidos Fil: Isaac, Peter. TeRn Ecosystrem Processes; Australia Fil: Polidori, Diego. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: Riveca, Alessio. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: van Ingen, Catharine. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Zhang, Leiming. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China Fil: Amiro, Brian. University of Manitoba. Department of Soil Science; Canadá Fil: Ammann, Christof. Agroscope Research Institute. Department of Agroecology and Environment; Suiza Fil: Altaf Arain, M. McMaster University. School of Geography and Earth Sciences; Canadá. Fil: Ardo, Jonas. Lund University. Department of Physical Geography and Ecosystem Science; Suecia Fil: Arkebauer, Timothy. University of Nebraska-Lincoln. Department of Agronomy and Horticulture; Estados Unidos Fil: Arndt, Stefan K. The University of Melbourne. School of Ecosystem and Forest Sciences; Australia Fil: Arriga, Nicola. University of Antwerp. Department of Biology, Research Group PLECO; Bélgica. European Commission. Joint Research Centre; Italia Fil: Aubinet, Marc. University of Liege. TeRRA Teaching and Research Center; Bélgica Fil: Aurela, Mika. Finnish Meteorological Institute; Finlandia Fil: Baldocchi, Dennis. University of California Berkeley. ESPM; Estados Unidos Fil: Barr, Alan. University of Saskatchewan. Global Institute for Water Security; Canadá. Environment and Climate Change Canada. Climate Research Division; Canadá. Fil: Beamesderfer, Eric. McMaster University. School of Geography and Earth Sciences; Canadá. Fil: Belelli Marchesini, Luca. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia. RUDN University. Agrarian-Technological Institute. Department of Landscape Design and Sustainable Ecosystems; Rusia Fil: Bergeron, Onil. Ministère du Développement durable de l’Environnement et de la Lutte contre les Changements Climatiques. Direction du Marché du Carbone; Canadá. Fil: Beringer, Jason. University of Western Australia. School of Agriculture and Environment; Australia. Fil: Bernhofer, Christia. Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: Berveiller, Daniel. Université Paris-Saclay, CNRS, AgroParisTech, Ecologie Systématique et Evolution; Francia Fil: Billesbach, Dave. University of Nebraska-Lincoln. Biological Systems Engineering; Estados Unidos Fil: Black, Tomas Andrew. University of British Columbia. Faculty of Land and Food Systems; Canadá. Fil: Blanken, Peter D. University of Colorado. Department of Geography; Estados Unidos Fil: Bohrer, Gil. Ohio State University. Department of Civil, Environmental & Geodetic Engineering; Estados Unidos Fil: Boike, Julia Alfred. Wegener Institute. Helmholtz Centre for Polar and Marine Research; Alemania. Humboldt-Universität zu Berlin. Geography Department; Alemania Fil: Bolstad, Paul V. University of Minnesota. Forest Resources; Estados Unidos Fil: Bonal, Damien. Université de Lorraine, AgroParisTech, INRAE, UMR Silva; Francia Fil: Bonnefond, Jean - Marc. ISPA, Bordeaux Sciences Agro, INRAE; Francia Fil: Bowling, David R. University of Utah. School of Biological Sciences; Estados Unidos Fil: Bracho, Rosuel. University of Florida.School of Forest Resources and Conservation; Estados Unidos Fil: Brodeur, Jason. McMaster University. McMaster University Library; Estados Unidos Fil: Brummer, Christian. Federal Research Institute of Rural Areas, Forestry and Fisheries. Thünen Institute of Climate-Smart Agriculture; Alemania Fil: Buchmann, Nina. ETH. Department of Environmental Systems Science; Suiza Fil: Burban, Benoit. INRAE UMR ECOFOG; Guyana Francesa Fil: Burns, Sean P. University of Colorado. Department of Geography; Estados Unidos. National Center for Atmospheric Research. Mesoscale and Microscale Meteorology Laboratory; Estados Unidos Fil: Buysse, Pauline. Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS; Francia Fil: Cale, Peter. Australian Landscape Trust; Australia Fil: Cavagna, Mauro. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia Fil: Cellier, Pierre. Université Paris-Saclay. INRAE, AgroParisTech, UMR ECOSYS; Francia Fil: Che, Shiping. Chinese Academy of Sciences. Institute of Botany. State Key Laboratory of Vegetation and Environmental Change; China Fil: Chini, Issac. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia Fil: Christensen, Storben. Aarhus University. Arctic Research Center. Department of Bioscience; Dinamarca Fil: Cleverly, James. University of Technology. School of Life Sciences; Australia. University of Technology. Terrestrial Ecosystem Research Network; Australia. Fil: Collalti, Alessio. University of Tuscia. DiBAf; Italia. National Research Council of Italy. Institute for Agricultural and Forestry Systems in the Mediterranean; Italia Fil: Consalvo, Claudia. University of Tuscia. DiBAf; Italia. National Research Council of Italy. Research Institute on Terrestrial Ecosystems; Italia Fil: Cook, Bruce. NASA Goddard Space Flight Center. Biospheric Sciences Laboratory; Estados Unidos Fil: Cook, David. Argonne National Laboratory. Environmental Science Division; Estados Unidos Fil: Coursolle, Carole. Natural Resources Canada. Canadian Forest Service; Canadá. Université Laval. Faculté de Foresterie, de Géographie et de Géomatique. Centre d’étude de la Forêt; Canadá Fil: Cremonose, Edoardo. Environmental Protection Agency of Aosta Valley. Climate Change Unit; Italia Fil: Curtis, Peter. Ohio State University. Department of Evolution, Ecology, and Organismal Biology; Estados Unidos Fil: D'Andrea, Ettore. National Research Council of Italy. Institute for Agricultural and Forestry Systems in the Mediterranean; Italia Fil: da Rocha, Humberto. Universidade de São Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; Brasil Fil: Dai, Xiaoqin. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China. Fil: Davis, Kenneth. The Pennsylvania State University. Department of Meteorology and Atmospheric Science; Estados Unidos Fil: De Cinti, Bruno. National Research Council of Italy. Institute of Research on Terrestrial Ecosystems; Italia Fil: de Grandcourt, Agnes. UMR Eco&Sols, CIRAD; Francia Fil: De Ligne, Anne. University of Liege. TeRRA Teaching and Research Center; Bélgica Fil: De Oliveira, Raimundo C. Pedology, Embrapa Amazonia Oriental; Brasil. Fil: Delpierre, Nicolas. Université Paris-Saclay. CNRS, AgroParisTech, Ecologie Systématique et Evolution; Francia Fil: Desai, Ankur R. University of Wisconsin-Madison. Atmospheric and Oceanic Sciences; Estados Unidos Fil: Di Bella, Carlos Marcelo. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; Argentina Fil: di Tommasi, Paul. National Research Council of Italy. Institute for Agricultural and Forestry Systems in the Mediterranean; Italia Fil: Dolman, Han. Vrije Universiteit Amsterdam. Department of Earth Sciences; Holanda Fil: Domingo, Francisco. CSIC. Experimental Station of Arid Zones. Desertification and Geoecology Department; España Fil: Dong, Gang. Shanxi University. School of Life Science; China. Fil: Dore, Sabina. HydroFocus; Estados Unidos Fil: Duce, Pierpaolo. National Research Council of Italy. Institute of Bioeconomy; Italia Fil: Dufrêne, Eric. Université Paris-Saclay, CNRS., AgroParisTech. Ecologie Systématique et Evolution; Francia Fil: Dunn, Allison. Worcester State University. Department of Earth, Environment, and Physics; Estados Unidos Fil: Dušek, Jiri. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa Fil: Eamus Derek. University of Technology. School of Life Sciences; Australia. Fil: Eichelmann, Uwe.Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: ElKhidir, Hatim abdalla M. elObeid. Agricultural Research Corporation. Research Station; Sudán Fil: Eugster, Wener. ETH. Department of Environmental Systems Science; Suiza Fil: Ewenz, Cacilia M. Airborne Research Australia. TERN Ecosystem Processes Central Node; Australia Fil: Ewers, Brent. University of Wyoming. Department of Botany. Program in Ecology; Estados Unidos Fil: Famulari, Daniela. National Research Council of Italy. Institute for Agricultural and Forestry Systems in the Mediterranean; Italia Fil: Fares, Silvano. National Research Council of Italy. Institute of BioEconomy; Italia Fil: Feigenwinter, Iris. ETH. Department of Environmental Systems Science; Suiza Fil: Feitz, Andrew. Geoscience Australia; Australia Fil: Fensholt, Rasmus. University of Copenhagen. Department of Geosciences and Natural Resource Management; Dinamarca Fil: Filippa, Gianluca. Environmental Protection Agency of Aosta Valley. Climate Change Unit; Italia Fil: Fischer, Marc. Lawrence Berkeley National Laboratory. Energy Analysis & Environmental Impacts Division; Estados Unidos Fil: Frank, John. USDA Forest Service. Rocky Mountain Research Station; Estados Unidos Fil: Galvagno, Marta. Environmental Protection Agency of Aosta Valley. Climate Change Unit; Italia Fil: Gharun, Mana. ETH. Department of Environmental Systems Science; Suiza Fil: Gianelle, Damiano. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia Fil: Gielen, Bert. University of Antwerp. Department of Biology. Research Group PLECO; Bélgica Fil: Gioli, Beniamino. National Research Council of Italy. Institute of BioEconomy; Italia Fil: Gitelson, Anatoly. University of Nebraska-Lincoln. School of natural Resources; Estados Unidos Fil: Goded, Ignacio. Joint Research Centre, European Commission; Italia Fil: Goeckede, Mathias. Max Planck Institute for Biogeochemistry; Alemania Fil: Goldstein. Allen H. University of California Berkeley. ESPM; Estados Unidos Fil: Gough, Christopher M. Virginia Commonwealth University. Department of Biology; Estados Unidos Fil: Goulden, Michael L. University of California Irvine. Department of Earth System Science; Estados Unidos Fil: Graf, Alexander. Agrosphere. (IBG3), Forschungszentrum Jülich; Alemania Fil: Griebel, Anne. The University of Melbourne. School of Ecosystem and Forest Sciences; Australia Fil: Gruening, Carsten. Joint Research Centre, European Commission; Italia Fil: Grünwald, Thomas. Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: Hammerle, Albin. University of Innsbruck. Department of Ecology; Austria. Fil: Han, Shijie. Henan University. School of Life Sciences. International Joint Research Laboratory for Global Change Ecology; China. Chinese Academy of Sciences. Institute of Applied Ecology; China. Fil: Han, Xingguo. Chinese Academy of Sciences.Institute of Botany. State Key Laboratory of Vegetation and Environmental Change; China Fil: Hansen, Birger Ulf. University of Copenhagen. Department of Geosciences and Natural Resource Management; Dinamarca Fil: Hanson, Chad. Oregon State University. Department of Forest Ecosystems and Society; Estados Unidos Fil: , Juha Hatakka, Juha. Finnish Meteorological Institute; Finlandia Fil: He, Yongtao. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China. University of Chinese Academy of Sciences. College of Resources and Environment; China Fil: Hehn, Markus. Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: Heinesch, Bernard. University of Liege. TeRRA Teaching and Research Center; Bélgica Fil: Hinko-Najera, Nina. The University of Melbourne. School of Ecosystem and Forest Sciences; Australia. Fil: Hörtnagl, Lukas. ETH. Department of Environmental Systems Science; Suiza. Fil: Hutley, Lindsay. Charles Darwin University. Research Institute for the Environment and Livelihoods; Australia Fil: Ibrom, Andreas. Technical University of Denmark. Department of Environmental Engineering; Dinamarca Fil: Ikawa, Hiroki. National Agriculture and Food Research Organization. Institute for Agro-Environmental Sciences; Japón Fil: Jackowicz-Korczynski, Marcin. Lund University. Department of Physical Geography and Ecosystem Science; Suecia. Aarhus University. Arctic Research Center. Department of Bioscience; Dinamarca Fil: Janouš, Dalibor. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa Fil: Jans, Wilma. Wageningen University and Research. Wageningen Environmental Research; Holanda Fil: Jassal, Rachhpal. University of British Columbia. Faculty of Land and Food Systems; Canadá. Fil: Jiang, Shicheng. Ministry of Education, Northeast Normal University. Key Laboratory of Vegetation Ecology; China Fil: Kato, Tomomichi. Hokkaido University. Research Faculty of Agriculture; Japón. Hokkaido University. GICore; Japón Fil: Khomik, Myroslava. Geography and Environmental Management; Canadá. Fil: Klatt, Janina. Karlsruhe Institute of Technology. Institute of Meteorology and Climate Research; Alemania Fil: Knohl, Alexander. University of Goettingen. Bioclimatology; Alelmania. University of Goettingen. Centre of Biodiversity and Sustainable Land Use; Alemania Fil: Knox, Sara. The University of British Columbia. Department of Geography; Canadá Fil: Kobayashi, Hideki. Japan Agency for Marine-Earth Science and Technology. Research Institute for Global change, Institute of Arctic Climate and Environment Research; Japón Fil: Koerber, Georgia. University of Adelaide. Biological Sciences; Australia. Fil: Kolle, Olaf. Max Planck Institute for Biogeochemistry; Alemania Fil: Kosugi, Yoshiko. Kyoto University. Graduate School of Agriculture; Japón Fil: Kotani, Ayumi. Nagoya University. Graduate School of Bioagricultural Sciences; Japón Fil: Kowalski, Andrew. University of Granada. Department of Applied Physics; España Fil: Kruijt, Bart. Wageningen University, Wageningen. Water systems and Global Change group; Holanda Fil: Kurbatova; Julia A. Russian Academy of Sciences. Severtsov institute of Ecology and Evolution; Rusia Fil: Kutsch, Werner L. Integrated Carbon Observation System (ICOS ERIC). Head Office; Finlandia Fil: Kwon, Hyojung. Oregon State University. Department of Forest Ecosystems and Society; Estados Unidos Fil: Launiainen, Samuli. Natural Resources Institute Finland; Finlandia Fil: Laurila, Tuomas. Finnish Meteorological Institute; Finlandia Fil: Law, Bev. Oregon State University. Department of Forest Ecosystems and Society; Estados Unidos Fil: Leuning. Ray. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Li, Yingnian. Chinese Academy of Sciences. Northwest institute of Plateau Biology. Key Laboratory of Adaptation and Evolution of Plateau Biota; China Fil: Liddell, Michael. James Cook University. Centre for Tropical Environmental Sustainability Studies; Australia. Fil: Limousin, Jean-Marc. CEFE, CNRS, Univ Montpellier: Francia Fil: Lion, Marryanna. Forest Research Institute Malaysia. Forestry and Environment Division; Malasia Fil: Lohila, Annalea. University of Helsinki. Institute for Atmosphere and Earth System Research/Physics; Finlandia Fil: López-Ballesteros, Ana. Trinity College Dublin. School of Natural Sciences. Department of Botany; Irlanda Fil: López-Blanco, Efren. Aarhus University. Arctic Research Center. Department of Bioscience; Dinamarca Fil: Loubet, Benjamin. Université Paris-Saclay. INRAE, AgroParisTech, UMR ECOSYS; Francia Fil: Lucas-Moffat, Antje. Centre for Agrometeorological Research. German Meteorological Service; Alemania Fil: Lüers, Johannes. University of Bayreuth. Micrometeorology; Alemania. Bayreuth Center of Ecology and Environmental Research; Alemania Fil: Ma, Siyan. University of California Berkeley. ESPM; Estados Unidos Fil: Macfarlane, Craig. CSIRO Land and Water; Australia Fi: Magliulo, Vincenzo. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Mammarella, Ivan. University of Helsinki . Institute for Atmosphere and Earth System Research/Physics; Finlandia Fil: Manca, Giovanni. Joint Research Centre, European Commission; Italia Fil: Marras, Serena. University of Sassari. Department of Agriculture; Italia Fil: Massman, William. USDA Forest Service. Rocky Mountain Research Station; Estados Unidos Fil: Mastepanov, Mikhail. University of Oulu. Oulanka Research Station; Finlandia Fil: Matamala, Roser. Argonne National Laboratory. Environmental Science Division; Estados Unidos Fil: Matthes, Jaclyn Hatala. Wellesley College. Department of Biological Sciences; Estados Unidos Fil: , Mazzenga, Francesco. National Research Council of Italy. Research Institute on Terrestrial Ecosystems; Italia Fil: McCaughey, Harry. Queen’s University. Department of Geography and Planning; Canadá. Fil: McHugh, Ian. The University of Melbourne. School of Ecosystem and Forest Sciences; Australia. Fil: McMillan, Andrew M.S. Environmental Analytics nZ, Ltd.; Nueva Zelanda Fil: Merbold, Lutz. International Livestock Research Institute. Mazingira Centre; Kenia. Fil: Meyer, Wayne. University of Adelaide. Biological Sciences; Australia. Fil: Meyers, Tilden. NOAA/OAR/Air Resources Laboratory; Estados Unidos Fil: Miller, Scott D. State University of New York at Albany. Atmospheric Sciences Research Center; Estados Unidos Fil: Minerbi, Stefano. Forest Department of South Tyrol; Italia Fil: Monson, Russell K. University of Arizona. Department of Ecology and Evolutionary Biology; Estados Unidos Fil: Montagnani, Leonardo. Forest Department of South Tyrol; Italia. Free University of Bolzano. Faculty of Science and Technology; Italia Fil: Moore, Caitlin E. University of Illinois at Urbana-Champaign. Department of Plant Biology; Estados Unidos Fil: Moors, Eddy. IHE Delft: Holanda. VU Amsterdam. Faculty of Science; Holanda Fil: Moreaux, Virginie. University Grenoble Alpes; Francia Fil: Moureaux, Christine. University of Liege. TeRRA Teaching and Research Center; Bélgica Fil: Munger, J. William. Harvard University. School of engineering and Applied Sciences; Estados Unidos. Harvard University. Department of Earth and Planetary Sciences; Estados Unidos Fil: Nakai, Taro. National Taiwan University. School of forestry and Resource conservation; Taiwan. University of Alaska Fairbanks. International Arctic Research Center; Estados Unidos Fil: Neirynck, Johan. Research Institute for Nature and Forest. Environment and Climate; Bélgica Fil: Nesic, Zoran. University of British Columbia. Faculty of Land and Food Systems; Canadá Fil: Nicolini, Giacomo. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: Noormets, Asko. Texas A&M University. College Station. Department of Ecosystem Science and Management; Estados Unidos Fil: Northwood, Matthew. Charles Darwin University. Research Institute for the Environment and Livelihoods; Australia Fil: Nosetto, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Matemática Aplicada San Luis. Grupo de Estudios Ambientales. Universidad Nacional de San Luis; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentina Fil: Nouvellon, Yann. Université Montpellier-CIRAD-INRA-IRD-Montpellier SupAgro. Eco&Sols; Francia Fil: Novick, Kimberly O’Neill. Indiana University Bloomington. School of Public and Environmental Affairs; Estados Unidos Fil: Oechel, Walter. San Diego State University. Department of Biology. Global Change Research Group; Estados Unidos. University of Exeter. College of Life and Environmental Sciences. Department of Geography; Reino Unido Fil: Olesen, Jørgen Eivind. Aarhus University. Department of Agroecology; Dinamarca. Aarhus University. iCLIMATE; Dinamarca Fil: Ourcival, Jean-Marc. CEFE, CNRS, Université Montpellier; Francia Fil: Papuga, Shirley A. Wayne State University. Department of Geology; Estados Unidos Fil: Parmentier, Frans-Jan. University of Oslo. Department of Geosciences; Noruega Fil: Paul-Limoges, Eugenie. University of Zurich. Department of Geography; Suiza Fil: Pavelka, Marian. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa Fil: Peichl, Matthias. Swedish University of Agricultural Sciences. Department of Forest Ecology and Management; Suecia Fil: Pendall, Elise. Western Sydney University. Hawkesbury Institute for the Environment; Australia Fil: Phillips, Richard P. Indiana University Bloomington. Department of Biology; Estados Unidos Fil: Pilegaard, Kim. Technical University of Denmark. Department of Environmental Engineering; Dinamarca Fil: Pirk, Norbert. CSiRO Land and Water; Australia. Fil: Posse Beaulieu, Gabriela. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina Fil: Powell, Thomas. Lawrence Berkeley National Laboratory. Climate & Ecosystem Sciences Division; Estados Unidos Fil: Prasse, Heiko. Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: Reed, David. Michigan State University. Center for Global Change & Earth Observations: Estados Unidos. Michigan State University. Center for Global Change & Earth Observations; Estados Unidos Fil: Resco de Dios, Víctor. Western Sydney University. Hawkesbury Institute for the Environment; Australia. Southwest University of Science and Technology. School of Life Science and Engineering; China Fil: Restrepo-Coupe, Natalia. University of Arizona. Department of Ecology and Evolutionary Biology; Estados Unidos Fil: Reverter, Borja R. Universidade Federal da Paraiba. Departamento de Química e Física; Brasil Fil: Roland, Marilyn. University of Antwerp. Department of Biology. Research Group PLECO; Bélgica Fil: Sabbatini, Simone. University of Tuscia. DiBAf; Italia. Fil: Sachs, Torsten. GfZ German Research Centre for Geosciences. Remote Sensing and Geoinformatics; Alemania Fil: Saleska, Scott R. University of Arizona. Department of Ecology and Evolutionary Biology; Estados Unidos Fil: Sánchez-Cañete, Enrique P. University of Granada. Department of Applied Physics; España. Andalusian Institute for Earth System Research (CEAMA-IISTA); España Fil: Sanchez-Mejia, Zulia M. Instituto Tecnológico de Sonora. Ciencias del Agua y Medioambiente; México Fil: Schmid, Hans Peter. Karlsruhe Institute of Technology. Institute of Meteorology and Climate Research; Alemania Fil: Schmidt, Marius. Agrosphere (IBG3), Forschungszentrum Jülich; Alemania Fil: Schneider, Karl. University of Cologne. Geographical Institute; Alemania Fil: Schrader, Frederik Thünen. Federal Research Institute of Rural Areas, Forestry and Fisheries. Institute of Climate-Smart Agriculture; Alemania Fil: Schroder, Ivan. Geoscience Australia. Department of industry, Innovation and Science; Australia. Fil: Scott, Russell L. USDA-ARS. Southwest Watershed Research Center; Estados Unidos Fil: Sedlák, Pavel. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa. Institute of Atmospheric Physics of the Czech Academy of Sciences; República Checa Fil: Serrano-Ortíz, Penélope. Andalusian Institute for Earth System Research (CEAMA-IISTA); España. University of Granada. Department of Ecology; España Fil: Shao, Changliang. Chinese Academy of Agricultural Sciences. National Hulunber Grassland Ecosystem Observation and Research Station & Institute of Agricultural Resources and Regional Planning; China. Fil: Shi, Peili. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China. Fil: Shironya, Ivan A.n. Russian Academy of Sciences. Severtsov Institute of Ecology and Evolution; Rusia Fil: Siebicke, Lukas. University of Goettingen. Bioclimatology; Alemania Fil: Šigut, Ladislav. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa Fil: Silberstein, Richard. University of Western Australia. School of Agriculture and Environment; Australia. Edith Cowan University. School of Science; Australia. Fil: Sirca, Costantino. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia. University of Sassari. Department of Agriculture; Italia Fil: Spano, Donatella. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia. University of Sassari. Department of Agriculture; Italia Fil: Steinbrecher, Rainer. Karlsruhe Institute of Technology. Institute of Meteorology and Climate Research; Alemania Fil: Stevens, Robert M. Sentek Pty Ltd.; Australia Fil: Sturtevant, Cove. National ecological Observatory Network Program; Estados Unidos Fil: Suyker, Andy. University of Nebraska-Lincoln. School of Natural Resources; Estados Unidos Fil: Tagesson, Torbem. Lund University. Department of Physical Geography and Ecosystem Science; Suecia. University of Copenhagen. Department of Geosciences and Natural Resource Management; Dinamarca Fil: Takanashi, Satoru. Forestry and Forest Products Research Institute. Kansai Research Center; Japón Fil: Tang, Yanhong. Peking University. College of Urban and Environmental Sciences; China. Fil: Tapper, Nigel. Monash University. School of Earth, Atmosphere and Environment; Australia Fil: Thom, Jonathan. University of Wisconsin-Madison. Space Science and Engineering Center; Estados Unidos Fil: Tiedemann, Frank. University of Goettingen. Bioclimatology; Alemania Fil: Tomassucci, Michele. University of Tuscia. DiBAf; Italia. Terrasystem srl; Italia Fil: Tuovinen, Juha-Pekka. Finnish Meteorological Institute; Finlandia Fil: Urbanski, Shawn. USDA Forest Service. Rocky Mountain Research Station; Estados Unidos Fil: Valentini, Riccardo. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: van der Molen, Michiel. Wageningen University. Meteorology and Air Quality Group; Holanda Fil: van Gorsel, Eva. Australian National University Canberra. Fenner School of Environment and Society; Australia. Fil: van Huissteden, Ko. Vrije Universiteit Amsterdam. Department of Earth Sciences; Holanda Fil: Varlagin, Andrej. Agroscope Research Institute. Department of Agroecology and Environment; Suiza Fil: Verfaillie, Joseph. University of California Berkeley. ESPM; Estados Unidos Fil: Vesala, Timo. University of Helsinki. Institute for Atmosphere and Earth System Research/Physics; Finlandia Fil: Vincke, Caroline. Chinese Academy of Sciences. South China Botanical Garden; China. Fil: Vitale, Domenico. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: Vygodskaya, Natalia. University of Bayreuth. Micrometeorology; Alemania Fil: Walker, Jeffrey P. Monash University. Department of Civil Engineering; Australia Fil: Walter-Shea, Elizabeth. University of Nebraska-Lincoln. School of natural Resources; Estados Unidos Fil: Wang, Huimin. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China Fil: Weber, Robin. University of California Berkeley. ESPM; Estados Unidos Fil: Westermann, Sebastian. Instituto Nacional de Tecnologia Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Fil: Wille, Christian. GfZ German Research centre for Geosciences. Remote Sensing and Geoinformatics; Alemania Fil: Wofsy, Steven. Harvard University. School of engineering and Applied Sciences; Estados Unidos. Harvard University. Department of Earth and Planetary Sciences; Estados Unidos Fil: Wohlfahrt, Georg. University of Innsbruck. Department of Ecology; Austria. Fil: Woodgate, William. CSIRO Land and Water; Australia. Fil: Li, Yuelin. Chinese Academy of Sciences. South China Botanical Garden; China. Fil: Zampedri, Roberto. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia Fil: Zhang, Junhui. Chinese Academy of Sciences. Institute of Applied Ecology; China. Fil: Zhou, Guoyi. Nanjing University of Information Science & Technology. College of Applied Meteorology; China. Fil: Zona, Donatella. San Diego State University. Department of Biology. Global Change Research Group; Estados Unidos. University of Sheffield. Department of Animal and Plant Sciences; Reino Unido Fil: Agarwal, Deb. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Biraud, Sebastien. Lawrence Berkeley National Laboratory. Climate & Ecosystem Sciences Division; Estados Unidos Fil: Torn, Margaret. Lawrence Berkeley National Laboratory. Climate & Ecosystem Sciences Division; Estados Unidos Fil: Papale, Dario. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia
Published: 2020

34. Simulating the effects of thinning and species mixing on stands of oak (Quercus petraea (Matt.) Liebl./Quercus robur L.) and pine (Pinus sylvestris L.) across Europe

Author: UCL - SST/ELI/ELIE - Environmental Sciences, Engel, Markus, Vospernik, Sonja, Toïgo, Maude, Morin, Xavier, Tomao, Antonio, Trotta, Carlo, Steckel, Mathias, Barbati, Anna, Nothdurft, Arne, Pretzsch, Hans, del Rio, Miren, Skrzyszewski, Jerzy, Ponette, Quentin, Löf, Magnus, Jansons, Āris, Brazaitis, Gediminas, UCL - SST/ELI/ELIE - Environmental Sciences, Engel, Markus, Vospernik, Sonja, Toïgo, Maude, Morin, Xavier, Tomao, Antonio, Trotta, Carlo, Steckel, Mathias, Barbati, Anna, Nothdurft, Arne, Pretzsch, Hans, del Rio, Miren, Skrzyszewski, Jerzy, Ponette, Quentin, Löf, Magnus, Jansons, Āris, and Brazaitis, Gediminas
Abstract: Tree species mixing of oak (Quercus petraea (Matt.) Liebl./Quercus robur L.) and pine (Pinus sylvestris L.) has been shown to have positive effects on ecosystem service provision. From a management perspective, however, it is still uncertain which thinning regime provides the highest possible productivity of mixed oak–pine forests in the long term. Because of a lack of empirical studies dealing with thinning and species mixing effects on oak–pine forests, we simulated forest growth in order to test which thinning type and intensity may provide the highest productivity in the long-term. To achieve this, we simulated the growth of pure and mixed stands of oak and pine for 100 years in 23 triplets located on an ecological gradient across Europe. For this purpose, we applied four different growth simulators and compared their results: the distance-independent single-tree simulator PROGNAUS, the distance-dependent single-tree simulator SILVA, the gap model ForCEEPS, and the process-based simulator 3D-CMCC-FEM. We investigated the effects of species mixing and thinning from the upper (thinning from above) and lower tail (thinning from below) of the diameter distribution by reducing the stand basal area to 50 and 80% of the maximum basal area. We compared simulated results of the relative volume productivity of mixed versus pure stands and of thinned versus unthinned stands to empirical results previously obtained on the same set of triplets. Simulated relative volume productivity ranged between 61 and 156%, although extremes of 10% and of 300% could be observed. We found the relative volume productivity to be influenced by stand age, but not by stand density, except for PROGNAUS. Relative volume productivity did not increase with the site water supply of the triplet location. Highest long-term productivity for oak, pine and oak–pine stands can be expected in consequence of thinning from above, but the effect of thinning intensity differed between simulators. Thinning effe
Published: 2021

35. Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Author: Irvin, Jeremy, Zhou, Sharon, Mcnicol, Gavin, Lu, Fred, Liu, Vincent, Fluet-chouinard, Etienne, Ouyang, Zutao, Knox, Sara Helen, Lucas-moffat, Antje, Trotta, Carlo, Papale, Dario, Vitale, Domenico, Mammarella, Ivan, Alekseychik, Pavel, Aurela, Mika, Avati, Anand, Baldocchi, Dennis, Bansal, Sheel, Bohrer, Gil, Campbell, David I, Chen, Jiquan, Chu, Housen, Dalmagro, Higo J, Delwiche, Kyle B, Desai, Ankur R, Euskirchen, Eugenie, Feron, Sarah, Goeckede, Mathias, Heimann, Martin, Helbig, Manuel, Helfter, Carole, Hemes, Kyle S, Hirano, Takashi, Iwata, Hiroki, Jurasinski, Gerald, Kalhori, Aram, Kondrich, Andrew, Lai, Derrick Yf, Lohila, Annalea, Malhotra, Avni, Merbold, Lutz, Mitra, Bhaskar, Ng, Andrew, Nilsson, Mats B, Noormets, Asko, Peichl, Matthias, Rey-sanchez, A. Camilo, Richardson, Andrew D, Runkle, Benjamin Rk, Schäfer, Karina Vr, Sonnentag, Oliver, Stuart-haëntjens, Ellen, Sturtevant, Cove, Ueyama, Masahito, Valach, Alex C, Vargas, Rodrigo, Vourlitis, George L, Ward, Eric J, Wong, Guan Xhuan, Zona, Donatella, Alberto, Ma. Carmelita R, Billesbach, David P, Celis, Gerardo, Dolman, Han, Friborg, Thomas, Fuchs, Kathrin, Gogo, Sébastien, Gondwe, Mangaliso J, Goodrich, Jordan P, Gottschalk, Pia, Hörtnagl, Lukas, Jacotot, Adrien, Koebsch, Franziska, Kasak, Kuno, Maier, Regine, Morin, Timothy H, Nemitz, Eiko, Oechel, Walter C, Oikawa, Patricia Y, Ono, Keisuke, Sachs, Torsten, Sakabe, Ayaka, Schuur, Edward A, Shortt, Robert, Sullivan, Ryan C, Szutu, Daphne J, Tuittila, Eeva-stiina, Varlagin, Andrej, Verfaillie, Joeseph G, Wille, Christian, Windham-myers, Lisamarie, Poulter, Benjamin, Jackson, Robert B, Irvin, Jeremy, Zhou, Sharon, Mcnicol, Gavin, Lu, Fred, Liu, Vincent, Fluet-chouinard, Etienne, Ouyang, Zutao, Knox, Sara Helen, Lucas-moffat, Antje, Trotta, Carlo, Papale, Dario, Vitale, Domenico, Mammarella, Ivan, Alekseychik, Pavel, Aurela, Mika, Avati, Anand, Baldocchi, Dennis, Bansal, Sheel, Bohrer, Gil, Campbell, David I, Chen, Jiquan, Chu, Housen, Dalmagro, Higo J, Delwiche, Kyle B, Desai, Ankur R, Euskirchen, Eugenie, Feron, Sarah, Goeckede, Mathias, Heimann, Martin, Helbig, Manuel, Helfter, Carole, Hemes, Kyle S, Hirano, Takashi, Iwata, Hiroki, Jurasinski, Gerald, Kalhori, Aram, Kondrich, Andrew, Lai, Derrick Yf, Lohila, Annalea, Malhotra, Avni, Merbold, Lutz, Mitra, Bhaskar, Ng, Andrew, Nilsson, Mats B, Noormets, Asko, Peichl, Matthias, Rey-sanchez, A. Camilo, Richardson, Andrew D, Runkle, Benjamin Rk, Schäfer, Karina Vr, Sonnentag, Oliver, Stuart-haëntjens, Ellen, Sturtevant, Cove, Ueyama, Masahito, Valach, Alex C, Vargas, Rodrigo, Vourlitis, George L, Ward, Eric J, Wong, Guan Xhuan, Zona, Donatella, Alberto, Ma. Carmelita R, Billesbach, David P, Celis, Gerardo, Dolman, Han, Friborg, Thomas, Fuchs, Kathrin, Gogo, Sébastien, Gondwe, Mangaliso J, Goodrich, Jordan P, Gottschalk, Pia, Hörtnagl, Lukas, Jacotot, Adrien, Koebsch, Franziska, Kasak, Kuno, Maier, Regine, Morin, Timothy H, Nemitz, Eiko, Oechel, Walter C, Oikawa, Patricia Y, Ono, Keisuke, Sachs, Torsten, Sakabe, Ayaka, Schuur, Edward A, Shortt, Robert, Sullivan, Ryan C, Szutu, Daphne J, Tuittila, Eeva-stiina, Varlagin, Andrej, Verfaillie, Joeseph G, Wille, Christian, Windham-myers, Lisamarie, Poulter, Benjamin, and Jackson, Robert B
Abstract: Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an impro
Published: 2021

36. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author: Pastorello, Gilberto, Trotta, Carlo, Canfora, Eleonora, Chu, Housen, Christianson, Danielle, Cheah, You Wei, Poindexter, Cristina, Chen, Jiquan, Elbashandy, Abdelrahman, Humphrey, Marty, Isaac, Peter, Polidori, Diego, Reichstein, Markus, Ribeca, Alessio, van Ingen, Catharine, Vuichard, Nicolas, Zhang, Leiming, Amiro, Brian, Ammann, Christof, Arain, M.A., Ardö, Jonas, Arkebauer, Timothy, Arndt, Stefan K., Arriga, Nicola, Aubinet, Marc, Aurela, Mika, Baldocchi, Dennis, Barr, Alan, Beamesderfer, Eric, Marchesini, Luca Belelli, Bergeron, Onil, Beringer, Jason, Bernhofer, Christian, Berveiller, Daniel, Billesbach, Dave, Black, Thomas Andrew, Blanken, Peter D., Bohrer, Gil, Boike, Julia, Bolstad, Paul V., Bonal, Damien, Bonnefond, Jean Marc, Bowling, David R., Bracho, Rosvel, Brodeur, Jason, Brümmer, Christian, Buchmann, Nina, Burban, Benoit, Burns, Sean P., Buysse, Pauline, Cale, Peter, Cavagna, Mauro, Cellier, Pierre, Chen, Shiping, Chini, Isaac, Christensen, Torben R., Cleverly, James, Collalti, Alessio, Consalvo, Claudia, Cook, Bruce D., Cook, David, Coursolle, Carole, Cremonese, Edoardo, Curtis, Peter S., D’Andrea, Ettore, da Rocha, Humberto, Dai, Xiaoqin, Davis, Kenneth J., De Cinti, Bruno, de Grandcourt, Agnes, De Ligne, Anne, De Oliveira, Raimundo C., Delpierre, Nicolas, Desai, Ankur R., Di Bella, Carlos Marcelo, di Tommasi, Paul, Dolman, Han, Domingo, Francisco, Dong, Gang, Dore, Sabina, Duce, Pierpaolo, Dufrêne, Eric, Dunn, Allison, Dušek, Jiří, Eamus, Derek, Eichelmann, Uwe, ElKhidir, Hatim Abdalla M., Eugster, Werner, Ewenz, Cacilia M., Ewers, Brent, Famulari, Daniela, Fares, Silvano, Feigenwinter, Iris, Feitz, Andrew, Fensholt, Rasmus, Filippa, Gianluca, Fischer, Marc, Frank, John, Galvagno, Marta, Gharun, Mana, Gianelle, Damiano, Gielen, Bert, Gioli, Beniamino, Gitelson, Anatoly, Goded, Ignacio, Goeckede, Mathias, Goldstein, Allen H., Gough, Christopher M., Goulden, Michael L., Graf, Alexander, Griebel, Anne, Gruening, Carsten, Grünwald, Thomas, Hammerle, Albin, Han, Shijie, Han, Xingguo, Hansen, Birger Ulf, Hanson, Chad, Hatakka, Juha, He, Yongtao, Hehn, Markus, Heinesch, Bernard, Hinko-Najera, Nina, Hörtnagl, Lukas, Hutley, Lindsay, Ibrom, Andreas, Ikawa, Hiroki, Jackowicz-Korczynski, Marcin, Janouš, Dalibor, Jans, Wilma, Jassal, Rachhpal, Jiang, Shicheng, Kato, Tomomichi, Khomik, Myroslava, Klatt, Janina, Knohl, Alexander, Knox, Sara, Kobayashi, Hideki, Koerber, Georgia, Kolle, Olaf, Kosugi, Yoshiko, Kotani, Ayumi, Kowalski, Andrew, Kruijt, Bart, Kurbatova, Julia, Kutsch, Werner L., Kwon, Hyojung, Launiainen, Samuli, Laurila, Tuomas, Law, Bev, Leuning, Ray, Li, Yingnian, Liddell, Michael, Limousin, Jean Marc, Lion, Marryanna, Liska, Adam J., Lohila, Annalea, López-Ballesteros, Ana, López-Blanco, Efrén, Loubet, Benjamin, Loustau, Denis, Lucas-Moffat, Antje, Lüers, Johannes, Ma, Siyan, Macfarlane, Craig, Magliulo, Vincenzo, Maier, Regine, Mammarella, Ivan, Manca, Giovanni, Marcolla, Barbara, Margolis, Hank A., Marras, Serena, Massman, William, Mastepanov, Mikhail, Matamala, Roser, Matthes, Jaclyn Hatala, Mazzenga, Francesco, McCaughey, Harry, McHugh, Ian, McMillan, Andrew M.S., Merbold, Lutz, Meyer, Wayne, Meyers, Tilden, Miller, Scott D., Minerbi, Stefano, Moderow, Uta, Monson, Russell K., Montagnani, Leonardo, Moore, Caitlin E., Moors, Eddy, Moreaux, Virginie, Moureaux, Christine, Munger, J.W., Nakai, Taro, Neirynck, Johan, Nesic, Zoran, Nicolini, Giacomo, Noormets, Asko, Northwood, Matthew, Nosetto, Marcelo, Nouvellon, Yann, Novick, Kimberly, Oechel, Walter, Olesen, Jørgen Eivind, Ourcival, Jean Marc, Papuga, Shirley A., Parmentier, Frans Jan, Paul-Limoges, Eugenie, Pavelka, Marian, Peichl, Matthias, Pendall, Elise, Phillips, Richard P., Pilegaard, Kim, Pirk, Norbert, Posse, Gabriela, Powell, Thomas, Prasse, Heiko, Prober, Suzanne M., Rambal, Serge, Rannik, Üllar, Raz-Yaseef, Naama, Rebmann, Corinna, Reed, David, de Dios, Victor Resco, Restrepo-Coupe, Natalia, Reverter, Borja R., Roland, Marilyn, Sabbatini, Simone, Sachs, Torsten, Saleska, Scott R., Sánchez-Cañete, Enrique P., Sanchez-Mejia, Zulia M., Schmid, Hans Peter, Schmidt, Marius, Schneider, Karl, Schrader, Frederik, Schroder, Ivan, Scott, Russell L., Sedlák, Pavel, Serrano-Ortíz, Penélope, Shao, Changliang, Shi, Peili, Shironya, Ivan, Siebicke, Lukas, Šigut, Ladislav, Silberstein, Richard, Sirca, Costantino, Spano, Donatella, Steinbrecher, Rainer, Stevens, Robert M., Sturtevant, Cove, Suyker, Andy, Tagesson, Torbern, Takanashi, Satoru, Tang, Yanhong, Tapper, Nigel, Thom, Jonathan, Tomassucci, Michele, Tuovinen, Juha Pekka, Urbanski, Shawn, Valentini, Riccardo, van der Molen, Michiel, van Gorsel, Eva, van Huissteden, Ko, Varlagin, Andrej, Verfaillie, Joseph, Vesala, Timo, Vincke, Caroline, Vitale, Domenico, Vygodskaya, Natalia, Walker, Jeffrey P., Walter-Shea, Elizabeth, Wang, Huimin, Weber, Robin, Westermann, Sebastian, Wille, Christian, Wofsy, Steven, Wohlfahrt, Georg, Wolf, Sebastian, Woodgate, William, Li, Yuelin, Zampedri, Roberto, Zhang, Junhui, Zhou, Guoyi, Zona, Donatella, Agarwal, Deb, Biraud, Sebastien, Torn, Margaret, Papale, Dario, Pastorello, Gilberto, Trotta, Carlo, Canfora, Eleonora, Chu, Housen, Christianson, Danielle, Cheah, You Wei, Poindexter, Cristina, Chen, Jiquan, Elbashandy, Abdelrahman, Humphrey, Marty, Isaac, Peter, Polidori, Diego, Reichstein, Markus, Ribeca, Alessio, van Ingen, Catharine, Vuichard, Nicolas, Zhang, Leiming, Amiro, Brian, Ammann, Christof, Arain, M.A., Ardö, Jonas, Arkebauer, Timothy, Arndt, Stefan K., Arriga, Nicola, Aubinet, Marc, Aurela, Mika, Baldocchi, Dennis, Barr, Alan, Beamesderfer, Eric, Marchesini, Luca Belelli, Bergeron, Onil, Beringer, Jason, Bernhofer, Christian, Berveiller, Daniel, Billesbach, Dave, Black, Thomas Andrew, Blanken, Peter D., Bohrer, Gil, Boike, Julia, Bolstad, Paul V., Bonal, Damien, Bonnefond, Jean Marc, Bowling, David R., Bracho, Rosvel, Brodeur, Jason, Brümmer, Christian, Buchmann, Nina, Burban, Benoit, Burns, Sean P., Buysse, Pauline, Cale, Peter, Cavagna, Mauro, Cellier, Pierre, Chen, Shiping, Chini, Isaac, Christensen, Torben R., Cleverly, James, Collalti, Alessio, Consalvo, Claudia, Cook, Bruce D., Cook, David, Coursolle, Carole, Cremonese, Edoardo, Curtis, Peter S., D’Andrea, Ettore, da Rocha, Humberto, Dai, Xiaoqin, Davis, Kenneth J., De Cinti, Bruno, de Grandcourt, Agnes, De Ligne, Anne, De Oliveira, Raimundo C., Delpierre, Nicolas, Desai, Ankur R., Di Bella, Carlos Marcelo, di Tommasi, Paul, Dolman, Han, Domingo, Francisco, Dong, Gang, Dore, Sabina, Duce, Pierpaolo, Dufrêne, Eric, Dunn, Allison, Dušek, Jiří, Eamus, Derek, Eichelmann, Uwe, ElKhidir, Hatim Abdalla M., Eugster, Werner, Ewenz, Cacilia M., Ewers, Brent, Famulari, Daniela, Fares, Silvano, Feigenwinter, Iris, Feitz, Andrew, Fensholt, Rasmus, Filippa, Gianluca, Fischer, Marc, Frank, John, Galvagno, Marta, Gharun, Mana, Gianelle, Damiano, Gielen, Bert, Gioli, Beniamino, Gitelson, Anatoly, Goded, Ignacio, Goeckede, Mathias, Goldstein, Allen H., Gough, Christopher M., Goulden, Michael L., Graf, Alexander, Griebel, Anne, Gruening, Carsten, Grünwald, Thomas, Hammerle, Albin, Han, Shijie, Han, Xingguo, Hansen, Birger Ulf, Hanson, Chad, Hatakka, Juha, He, Yongtao, Hehn, Markus, Heinesch, Bernard, Hinko-Najera, Nina, Hörtnagl, Lukas, Hutley, Lindsay, Ibrom, Andreas, Ikawa, Hiroki, Jackowicz-Korczynski, Marcin, Janouš, Dalibor, Jans, Wilma, Jassal, Rachhpal, Jiang, Shicheng, Kato, Tomomichi, Khomik, Myroslava, Klatt, Janina, Knohl, Alexander, Knox, Sara, Kobayashi, Hideki, Koerber, Georgia, Kolle, Olaf, Kosugi, Yoshiko, Kotani, Ayumi, Kowalski, Andrew, Kruijt, Bart, Kurbatova, Julia, Kutsch, Werner L., Kwon, Hyojung, Launiainen, Samuli, Laurila, Tuomas, Law, Bev, Leuning, Ray, Li, Yingnian, Liddell, Michael, Limousin, Jean Marc, Lion, Marryanna, Liska, Adam J., Lohila, Annalea, López-Ballesteros, Ana, López-Blanco, Efrén, Loubet, Benjamin, Loustau, Denis, Lucas-Moffat, Antje, Lüers, Johannes, Ma, Siyan, Macfarlane, Craig, Magliulo, Vincenzo, Maier, Regine, Mammarella, Ivan, Manca, Giovanni, Marcolla, Barbara, Margolis, Hank A., Marras, Serena, Massman, William, Mastepanov, Mikhail, Matamala, Roser, Matthes, Jaclyn Hatala, Mazzenga, Francesco, McCaughey, Harry, McHugh, Ian, McMillan, Andrew M.S., Merbold, Lutz, Meyer, Wayne, Meyers, Tilden, Miller, Scott D., Minerbi, Stefano, Moderow, Uta, Monson, Russell K., Montagnani, Leonardo, Moore, Caitlin E., Moors, Eddy, Moreaux, Virginie, Moureaux, Christine, Munger, J.W., Nakai, Taro, Neirynck, Johan, Nesic, Zoran, Nicolini, Giacomo, Noormets, Asko, Northwood, Matthew, Nosetto, Marcelo, Nouvellon, Yann, Novick, Kimberly, Oechel, Walter, Olesen, Jørgen Eivind, Ourcival, Jean Marc, Papuga, Shirley A., Parmentier, Frans Jan, Paul-Limoges, Eugenie, Pavelka, Marian, Peichl, Matthias, Pendall, Elise, Phillips, Richard P., Pilegaard, Kim, Pirk, Norbert, Posse, Gabriela, Powell, Thomas, Prasse, Heiko, Prober, Suzanne M., Rambal, Serge, Rannik, Üllar, Raz-Yaseef, Naama, Rebmann, Corinna, Reed, David, de Dios, Victor Resco, Restrepo-Coupe, Natalia, Reverter, Borja R., Roland, Marilyn, Sabbatini, Simone, Sachs, Torsten, Saleska, Scott R., Sánchez-Cañete, Enrique P., Sanchez-Mejia, Zulia M., Schmid, Hans Peter, Schmidt, Marius, Schneider, Karl, Schrader, Frederik, Schroder, Ivan, Scott, Russell L., Sedlák, Pavel, Serrano-Ortíz, Penélope, Shao, Changliang, Shi, Peili, Shironya, Ivan, Siebicke, Lukas, Šigut, Ladislav, Silberstein, Richard, Sirca, Costantino, Spano, Donatella, Steinbrecher, Rainer, Stevens, Robert M., Sturtevant, Cove, Suyker, Andy, Tagesson, Torbern, Takanashi, Satoru, Tang, Yanhong, Tapper, Nigel, Thom, Jonathan, Tomassucci, Michele, Tuovinen, Juha Pekka, Urbanski, Shawn, Valentini, Riccardo, van der Molen, Michiel, van Gorsel, Eva, van Huissteden, Ko, Varlagin, Andrej, Verfaillie, Joseph, Vesala, Timo, Vincke, Caroline, Vitale, Domenico, Vygodskaya, Natalia, Walker, Jeffrey P., Walter-Shea, Elizabeth, Wang, Huimin, Weber, Robin, Westermann, Sebastian, Wille, Christian, Wofsy, Steven, Wohlfahrt, Georg, Wolf, Sebastian, Woodgate, William, Li, Yuelin, Zampedri, Roberto, Zhang, Junhui, Zhou, Guoyi, Zona, Donatella, Agarwal, Deb, Biraud, Sebastien, Torn, Margaret, and Papale, Dario
Abstract: The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions.
Published: 2021

37. Factors affecting Hamamatsu H8500 flat panel PMT calibration for gamma ray imaging

Author: Pani, Roberto, Pellegrini, Rosanna, Cinti, Maria Nerina, Trotta, Carlo, Trotta, Gianfranco, Scafe, Raffaele, D'Addio, Lorenzo, Iurlaro, Giorgia, Montani, Livia, Bennati, Paolo, Ridolfi, Stefano, Cuzanno, Francesco, and Garibaldi, Franco
Subjects: Photoelectric multipliers -- Research, Diagnostic imaging -- Research, Business, Electronics, Electronics and electrical industries, Hamamatsu H8500 (Electronic equipment) -- Research
Abstract: The Hamamatsu H8500 Flat Panel PMT is the latest technological advancement in gamma ray imaging. Its compact size makes it attractive for medical imaging applications. To study and compare image performance a Flat Panel PMT, representing the present production, was coupled to CsI(TI) and NaI(Ti) scintillation arrays with 3 mm and 1.8 mm pixel size respectively and connected to a multi-anode electronic readout. Furthermore, a pulsed blue LED coupled to an optical fiber was utilized to scan the tube with different light distributions. This study investigated how PMT anode gain uniformity response, scintillation light distribution and intensity influence the spatial resolution, the position linearity and the image noise. Each crystal in the scintillation array produces a different charge distribution, which depends on the match between the anode size and the scintillation array lattice. The NaI(TI) array demonstrated to fit both PMT characteristics and dynamic range of electronic read out, due to the charge distribution adequate to the anode size. For CsI(TI) crystal, the pulse height calibration resulted more critical, due to the narrow light distribution. In conclusion, the use of Flat Panel tube with selected anode gain uniformity could represent the cheapest and easiest solution to obtain the best image quality, in particular for scintillator array with smaller pixel size. Index Terms--Nuclear imaging, photomultipliers, scintillation detectors.
Published: 2007

38. Application of an optimality-based model to operate at half-hourly timestep to implement plant acclimation within a land-surface modelling framework

Author: Mengoli, Giulia, primary, Agustí-Panareda, Anna, additional, Boussetta, Souhail, additional, Harrison, Sandy P., additional, Trotta, Carlo, additional, and Prentice, Iain Colin, additional
Published: 2021
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39. FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands

Author: Delwiche, Kyle B., primary, Knox, Sara Helen, additional, Malhotra, Avni, additional, Fluet-Chouinard, Etienne, additional, McNicol, Gavin, additional, Feron, Sarah, additional, Ouyang, Zutao, additional, Papale, Dario, additional, Trotta, Carlo, additional, Canfora, Eleonora, additional, Cheah, You-Wei, additional, Christianson, Danielle, additional, Alberto, M. Carmelita R., additional, Alekseychik, Pavel, additional, Aurela, Mika, additional, Baldocchi, Dennis, additional, Bansal, Sheel, additional, Billesbach, David P., additional, Bohrer, Gil, additional, Bracho, Rosvel, additional, Buchmann, Nina, additional, Campbell, David I., additional, Celis, Gerardo, additional, Chen, Jiquan, additional, Chen, Weinan, additional, Chu, Housen, additional, Dalmagro, Higo J., additional, Dengel, Sigrid, additional, Desai, Ankur R., additional, Detto, Matteo, additional, Dolman, Han, additional, Eichelmann, Elke, additional, Euskirchen, Eugenie, additional, Famulari, Daniela, additional, Friborg, Thomas, additional, Fuchs, Kathrin, additional, Goeckede, Mathias, additional, Gogo, Sébastien, additional, Gondwe, Mangaliso J., additional, Goodrich, Jordan P., additional, Gottschalk, Pia, additional, Graham, Scott L., additional, Heimann, Martin, additional, Helbig, Manuel, additional, Helfter, Carole, additional, Hemes, Kyle S., additional, Hirano, Takashi, additional, Hollinger, David, additional, Hörtnagl, Lukas, additional, Iwata, Hiroki, additional, Jacotot, Adrien, additional, Jansen, Joachim, additional, Jurasinski, Gerald, additional, Kang, Minseok, additional, Kasak, Kuno, additional, King, John, additional, Klatt, Janina, additional, Koebsch, Franziska, additional, Krauss, Ken W., additional, Lai, Derrick Y. F., additional, Mammarella, Ivan, additional, Manca, Giovanni, additional, Marchesini, Luca Belelli, additional, Matthes, Jaclyn Hatala, additional, Maximon, Trofim, additional, Merbold, Lutz, additional, Mitra, Bhaskar, additional, Morin, Timothy H., additional, Nemitz, Eiko, additional, Nilsson, Mats B., additional, Niu, Shuli, additional, Oechel, Walter C., additional, Oikawa, Patricia Y., additional, Ono, Keisuke, additional, Peichl, Matthias, additional, Peltola, Olli, additional, Reba, Michele L., additional, Richardson, Andrew D., additional, Riley, William, additional, Runkle, Benjamin R. K., additional, Ryu, Youngryel, additional, Sachs, Torsten, additional, Sakabe, Ayaka, additional, Sanchez, Camilo Rey, additional, Schuur, Edward A., additional, Schäfer, Karina V. R., additional, Sonnentag, Oliver, additional, Sparks, Jed P., additional, Stuart-Haëntjens, Ellen, additional, Sturtevant, Cove, additional, Sullivan, Ryan C., additional, Szutu, Daphne J., additional, Thom, Jonathan E., additional, Torn, Margaret S., additional, Tuittila, Eeva-Stiina, additional, Turner, Jessica, additional, Ueyama, Masahito, additional, Valach, Alex C., additional, Vargas, Rodrigo, additional, Varlagin, Andrej, additional, Vazquez-Lule, Alma, additional, Verfaillie, Joseph G., additional, Vesala, Timo, additional, Vourlitis, George L., additional, Ward, Eric J., additional, Wille, Christian, additional, Wohlfahrt, Georg, additional, Wong, Guan Xhuan, additional, Zhang, Zhen, additional, Zona, Donatella, additional, Windham-Myers, Lisamarie, additional, Poulter, Benjamin, additional, and Jackson, Robert B., additional
Published: 2021
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40. The PROFOUND Database for evaluating vegetation models and simulating climate impacts on European forests

Author: Reyer, Christopher P. O., primary, Silveyra Gonzalez, Ramiro, additional, Dolos, Klara, additional, Hartig, Florian, additional, Hauf, Ylva, additional, Noack, Matthias, additional, Lasch-Born, Petra, additional, Rötzer, Thomas, additional, Pretzsch, Hans, additional, Meesenburg, Henning, additional, Fleck, Stefan, additional, Wagner, Markus, additional, Bolte, Andreas, additional, Sanders, Tanja G. M., additional, Kolari, Pasi, additional, Mäkelä, Annikki, additional, Vesala, Timo, additional, Mammarella, Ivan, additional, Pumpanen, Jukka, additional, Collalti, Alessio, additional, Trotta, Carlo, additional, Matteucci, Giorgio, additional, D'Andrea, Ettore, additional, Foltýnová, Lenka, additional, Krejza, Jan, additional, Ibrom, Andreas, additional, Pilegaard, Kim, additional, Loustau, Denis, additional, Bonnefond, Jean-Marc, additional, Berbigier, Paul, additional, Picart, Delphine, additional, Lafont, Sébastien, additional, Dietze, Michael, additional, Cameron, David, additional, Vieno, Massimo, additional, Tian, Hanqin, additional, Palacios-Orueta, Alicia, additional, Cicuendez, Victor, additional, Recuero, Laura, additional, Wiese, Klaus, additional, Büchner, Matthias, additional, Lange, Stefan, additional, Volkholz, Jan, additional, Kim, Hyungjun, additional, Horemans, Joanna A., additional, Bohn, Friedrich, additional, Steinkamp, Jörg, additional, Chikalanov, Alexander, additional, Weedon, Graham P., additional, Sheffield, Justin, additional, Babst, Flurin, additional, Vega del Valle, Iliusi, additional, Suckow, Felicitas, additional, Martel, Simon, additional, Mahnken, Mats, additional, Gutsch, Martin, additional, and Frieler, Katja, additional
Published: 2020
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41. The PROFOUND database for evaluating vegetation models and simulating climate impacts on European forests

Author: Reyer, Christopher P.O., Silveyra Gonzalez, Ramiro, Dolos, Klara, Hartig, Florian, Hauf, Ylva, Noack, Matthias, Lasch-Born, Petra, Rötzer, Thomas, Pretzsch, Hans, Meesenburg, Henning, Fleck, Stefan, Wagner, Markus, Bolte, Andreas, Sanders, Tanja G.M., Kolari, Pasi, Mäkelä, Annikki, Vesala, Timo, Mammarella, Ivan, Pumpanen, Jukka, Collalti, Alessio, Trotta, Carlo, Matteucci, Giorgio, D'Andrea, Ettore, Foltýnová, Lenka, Krejza, Jan, Ibrom, Andreas, Pilegaard, Kim, Loustau, Denis, Bonnefond, Jean-Marc, Berbigier, Paul, Picart, Delphine, Lafont, Sébastien, Dietze, Michael, Cameron, David, Vieno, Massimo, Tian, Hanqin, Palacios-Orueta, Alicia, Cicuendez, Victor, Recuero, Laura, Wiese, Klaus, Büchner, Matthias, Lange, Stefan, Volkholz, Jan, Kim, Hyungjun, Horemans, Joanna A., Bohn, Friedrich, Steinkamp, Jörg, Chikalanov, Alexander, Weedon, Graham P., Sheffield, Justin, Babst, Flurin, Vega del Valle, Iliusi, Suckow, Felicitas, Martel, Simon, Mahnken, Mats, Gutsch, Martin, Frieler, Katja, Reyer, Christopher P.O., Silveyra Gonzalez, Ramiro, Dolos, Klara, Hartig, Florian, Hauf, Ylva, Noack, Matthias, Lasch-Born, Petra, Rötzer, Thomas, Pretzsch, Hans, Meesenburg, Henning, Fleck, Stefan, Wagner, Markus, Bolte, Andreas, Sanders, Tanja G.M., Kolari, Pasi, Mäkelä, Annikki, Vesala, Timo, Mammarella, Ivan, Pumpanen, Jukka, Collalti, Alessio, Trotta, Carlo, Matteucci, Giorgio, D'Andrea, Ettore, Foltýnová, Lenka, Krejza, Jan, Ibrom, Andreas, Pilegaard, Kim, Loustau, Denis, Bonnefond, Jean-Marc, Berbigier, Paul, Picart, Delphine, Lafont, Sébastien, Dietze, Michael, Cameron, David, Vieno, Massimo, Tian, Hanqin, Palacios-Orueta, Alicia, Cicuendez, Victor, Recuero, Laura, Wiese, Klaus, Büchner, Matthias, Lange, Stefan, Volkholz, Jan, Kim, Hyungjun, Horemans, Joanna A., Bohn, Friedrich, Steinkamp, Jörg, Chikalanov, Alexander, Weedon, Graham P., Sheffield, Justin, Babst, Flurin, Vega del Valle, Iliusi, Suckow, Felicitas, Martel, Simon, Mahnken, Mats, Gutsch, Martin, and Frieler, Katja
Abstract: Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data on European forests to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple data sources at different hierarchical and temporal scales, together with environmental driving data as well as the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate, CO2, nitrogen deposition, tree and forest stand level, and remote sensing data for nine contrasting forest stands distributed across Europe. Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat conduction and soil water are also available. The climate and nitrogen deposition data contain several datasets for the historic period and a wide range of future climate change scenarios following the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND DB is available freely as a “SQLite” relational database or “ASCII” flat file version (at https://doi.org/10.5880/PIK.2020.006/; Reyer et al., 2020). The data policies of the individual contributing datasets are provided in the metadata of each data file. The PROFOUND DB can also be accessed via the ProfoundData R package (https://CRAN.R-project.org/package=ProfoundData; Silveyra Gonzalez et al., 2020), which provides basic functions to explore, plot and extract the data for model set-up, calibration and evaluation.
Published: 2020

42. The PROFOUND Database for evaluating vegetation models and simulating climate impacts on European forests

Author: Reyer, Christopher P. O., Gonzalez, Ramiro Silveyra, Dolos, Klara, Hartig, Florian, Hauf, Ylva, Noack, Matthias, Lasch-Born, Petra, Roetzer, Thomas, Pretzsch, Hans, Meesenburg, Henning, Fleck, Stefan, Wagner, Markus, Bolte, Andreas, Sanders, Tanja G. M., Kolari, Pasi, Makela, Annikki, Vesala, Timo, Mammarella, Ivan, Pumpanen, Jukka, Collalti, Alessio, Trotta, Carlo, Matteucci, Giorgio, D'Andrea, Ettore, Foltynova, Lenka, Krejza, Jan, Ibrom, Andreas, Pilegaard, Kim, Loustau, Denis, Bonnefond, Jean-Marc, Berbigier, Paul, Picart, Delphine, Lafont, Sebastien, Dietze, Michael, Cameron, David, Vieno, Massimo, Tian, Hanqin, Palacios-Orueta, Alicia, Cicuendez, Victor, Recuero, Laura, Wiese, Klaus, Buechner, Matthias, Lange, Stefan, Volkholz, Jan, Kim, Hyungjun, Horemans, Joanna A., Bohn, Friedrich, Steinkamp, Joerg, Chikalanov, Alexander, Weedon, Graham P., Sheffield, Justin, Babst, Flurin, del Valle, Iliusi Vega, Suckow, Felicitas, Martel, Simon, Mahnken, Mats, Gutsch, Martin, Frieler, Katja, Reyer, Christopher P. O., Gonzalez, Ramiro Silveyra, Dolos, Klara, Hartig, Florian, Hauf, Ylva, Noack, Matthias, Lasch-Born, Petra, Roetzer, Thomas, Pretzsch, Hans, Meesenburg, Henning, Fleck, Stefan, Wagner, Markus, Bolte, Andreas, Sanders, Tanja G. M., Kolari, Pasi, Makela, Annikki, Vesala, Timo, Mammarella, Ivan, Pumpanen, Jukka, Collalti, Alessio, Trotta, Carlo, Matteucci, Giorgio, D'Andrea, Ettore, Foltynova, Lenka, Krejza, Jan, Ibrom, Andreas, Pilegaard, Kim, Loustau, Denis, Bonnefond, Jean-Marc, Berbigier, Paul, Picart, Delphine, Lafont, Sebastien, Dietze, Michael, Cameron, David, Vieno, Massimo, Tian, Hanqin, Palacios-Orueta, Alicia, Cicuendez, Victor, Recuero, Laura, Wiese, Klaus, Buechner, Matthias, Lange, Stefan, Volkholz, Jan, Kim, Hyungjun, Horemans, Joanna A., Bohn, Friedrich, Steinkamp, Joerg, Chikalanov, Alexander, Weedon, Graham P., Sheffield, Justin, Babst, Flurin, del Valle, Iliusi Vega, Suckow, Felicitas, Martel, Simon, Mahnken, Mats, Gutsch, Martin, and Frieler, Katja
Abstract: Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data on European forests to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple data sources at different hierarchical and temporal scales, together with environmental driving data as well as the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate, CO2, nitrogen deposition, tree and forest stand level, and remote sensing data for nine contrasting forest stands distributed across Europe. Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat conduction and soil water are also available. The climate and nitrogen deposition data contain several datasets for the historic period and a wide range of future climate change scenarios following the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND DB is available freely as a "SQLite" relational database or "ASCII" flat file version (at https://doi.org/10.5880/PIK.2020.006/; Reyer et al., 2020). The data policies of the individual contributing datasets are provided in the metadata of each data file. The PROFOUND DB can also be accessed via the ProfoundData R package (https://CRAN.R- project.org/package=ProfoundData; Silveyra Gonzalez et al., 2020), which provides basic functions to explore, plot and extract the data for model set-up, calibration and evaluation.
Published: 2020

43. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author: Pastorello, Gilberto; https://orcid.org/0000-0002-9387-3702, Trotta, Carlo; https://orcid.org/0000-0001-6377-0262, Canfora, Eleonora, Chu, Housen; https://orcid.org/0000-0002-8131-4938, Christianson, Danielle; https://orcid.org/0000-0002-8663-7701, Cheah, You-Wei, Poindexter, Cristina, Chen, Jiquan; https://orcid.org/0000-0003-0761-9458, Elbashandy, Abdelrahman, Humphrey, Marty, Isaac, Peter, Polidori, Diego, Ribeca, Alessio, van Ingen, Catharine, Zhang, Leiming, Amiro, Brian; https://orcid.org/0000-0002-9969-0050, Ammann, Christof; https://orcid.org/0000-0002-0783-5444, Arain, M Altaf; https://orcid.org/0000-0002-1433-5173, Ardö, Jonas; https://orcid.org/0000-0002-9318-0973, Arkebauer, Timothy, Arndt, Stefan K; https://orcid.org/0000-0001-7086-9375, Arriga, Nicola, Aubinet, Marc, Aurela, Mika; https://orcid.org/0000-0002-4046-7225, Baldocchi, Dennis, Barr, Alan, Beamesderfer, Eric; https://orcid.org/0000-0002-7513-7349, Marchesini, Luca Belelli; https://orcid.org/0000-0001-8408-4675, Bergeron, Onil, Beringer, Jason; https://orcid.org/0000-0002-4619-8361, Paul-Limoges, Eugénie, et al, Pastorello, Gilberto; https://orcid.org/0000-0002-9387-3702, Trotta, Carlo; https://orcid.org/0000-0001-6377-0262, Canfora, Eleonora, Chu, Housen; https://orcid.org/0000-0002-8131-4938, Christianson, Danielle; https://orcid.org/0000-0002-8663-7701, Cheah, You-Wei, Poindexter, Cristina, Chen, Jiquan; https://orcid.org/0000-0003-0761-9458, Elbashandy, Abdelrahman, Humphrey, Marty, Isaac, Peter, Polidori, Diego, Ribeca, Alessio, van Ingen, Catharine, Zhang, Leiming, Amiro, Brian; https://orcid.org/0000-0002-9969-0050, Ammann, Christof; https://orcid.org/0000-0002-0783-5444, Arain, M Altaf; https://orcid.org/0000-0002-1433-5173, Ardö, Jonas; https://orcid.org/0000-0002-9318-0973, Arkebauer, Timothy, Arndt, Stefan K; https://orcid.org/0000-0001-7086-9375, Arriga, Nicola, Aubinet, Marc, Aurela, Mika; https://orcid.org/0000-0002-4046-7225, Baldocchi, Dennis, Barr, Alan, Beamesderfer, Eric; https://orcid.org/0000-0002-7513-7349, Marchesini, Luca Belelli; https://orcid.org/0000-0001-8408-4675, Bergeron, Onil, Beringer, Jason; https://orcid.org/0000-0002-4619-8361, Paul-Limoges, Eugénie, and et al
Abstract: The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.
Published: 2020

44. The PROFOUND database for evaluating vegetation models and simulating climate impacts on forests

Author: Reyer, Christopher P. O., Silveyra Gonzalez, Ramiro, Dolos, Klara, Hartig, Florian, Hauf, Ylva, Noack, Matthias, Lasch-Born, Petra, Rötzer, Thomas, Pretzsch, Hans, Mesenburg, Henning, Fleck, Stefan, Wagner, Markus, Bolte, Andreas, Sanders, Tanja G. M., Kolari, Pasi, Mäkelä, Annikki, Vesala, Timo, Mammarella, Ivan, Pumpanen, Jukka, Collalti, Alessio, Trotta, Carlo, Matteucci, Giorgio, D'Andrea, Ettore, Foltýnová, Lenka, Krejza, Jan, Ibrom, Andreas, Pilegaard, Kim, Loustau, Denis, Bonnefond, Jean-Marc, Berbigier, Paul, Picart, Delphine, Lafont, Sebastien, Dietze, Michael, Cameron, David, Vieno, Massimo, Tian, Hanqin, Palacios-Orueta, Alicia, Cicuendez, Victor, Recuero, Laura, Wiese, Klaus, Büchner, Matthias, Lange, Stefan, Volkholz, Jan, Kim, Hyungjun, Weedon, Graham P., Sheffield, Justin, Vega del Valle, Iliusi, Suckow, Felicitas, Horemans, Joanna A., Martel, Simon, Bohn, Friedrich, Steinkamp, Jörg, Chikalanov, Alexander, Mahnken, Mats, Gutsch, Martin, and Frieler, Katja
Abstract: Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple data sources at different hierarchical and temporal scales, together with environmental driving data as well as the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate, CO2, nitrogen deposition, tree and forest stand-level, as well as remote sensing data for nine contrasting forest stands distributed across Europe. Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat conduction, and soil water are also available. The climate and nitrogen deposition data contain several datasets for the historic period and a wide range of future climate change scenarios following the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND DB is available freely as a SQLite relational database or ASCII flat file version (at https://doi.org/10.5880/PIK.2019.008). The data policies of the individual, contributing datasets are provided in the metadata of each data file. The PROFOUND DB can also be accessed via the ProfoundData R-package (https://github.com/COST-FP1304-PROFOUND/ProfoundData), which provides basic functions to explore, plot, and extract the data for model set-up, calibration and evaluation.
Published: 2019

45. Supplementary material to "The PROFOUND database for evaluating vegetation models and simulating climate impacts on forests"

Author: Reyer, Christopher P. O., primary, Silveyra Gonzalez, Ramiro, additional, Dolos, Klara, additional, Hartig, Florian, additional, Hauf, Ylva, additional, Noack, Matthias, additional, Lasch-Born, Petra, additional, Rötzer, Thomas, additional, Pretzsch, Hans, additional, Mesenburg, Henning, additional, Fleck, Stefan, additional, Wagner, Markus, additional, Bolte, Andreas, additional, Sanders, Tanja G. M., additional, Kolari, Pasi, additional, Mäkelä, Annikki, additional, Vesala, Timo, additional, Mammarella, Ivan, additional, Pumpanen, Jukka, additional, Collalti, Alessio, additional, Trotta, Carlo, additional, Matteucci, Giorgio, additional, D'Andrea, Ettore, additional, Foltýnová, Lenka, additional, Krejza, Jan, additional, Ibrom, Andreas, additional, Pilegaard, Kim, additional, Loustau, Denis, additional, Bonnefond, Jean-Marc, additional, Berbigier, Paul, additional, Picart, Delphine, additional, Lafont, Sébastien, additional, Dietze, Michael, additional, Cameron, David, additional, Vieno, Massimo, additional, Tian, Hanqin, additional, Palacios-Orueta, Alicia, additional, Cicuendez, Victor, additional, Recuero, Laura, additional, Wiese, Klaus, additional, Büchner, Matthias, additional, Lange, Stefan, additional, Volkholz, Jan, additional, Kim, Hyungjun, additional, Weedon, Graham P., additional, Sheffield, Justin, additional, Vega del Valle, Iliusi, additional, Suckow, Felicitas, additional, Horemans, Joanna A., additional, Martel, Simon, additional, Bohn, Friedrich, additional, Steinkamp, Jörg, additional, Chikalanov, Alexander, additional, Mahnken, Mats, additional, Gutsch, Martin, additional, and Frieler, Katja, additional
Published: 2019
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46. The PROFOUND database for evaluating vegetation models and simulating climate impacts on forests

Author: Reyer, Christopher P. O., primary, Silveyra Gonzalez, Ramiro, additional, Dolos, Klara, additional, Hartig, Florian, additional, Hauf, Ylva, additional, Noack, Matthias, additional, Lasch-Born, Petra, additional, Rötzer, Thomas, additional, Pretzsch, Hans, additional, Mesenburg, Henning, additional, Fleck, Stefan, additional, Wagner, Markus, additional, Bolte, Andreas, additional, Sanders, Tanja G. M., additional, Kolari, Pasi, additional, Mäkelä, Annikki, additional, Vesala, Timo, additional, Mammarella, Ivan, additional, Pumpanen, Jukka, additional, Collalti, Alessio, additional, Trotta, Carlo, additional, Matteucci, Giorgio, additional, D'Andrea, Ettore, additional, Foltýnová, Lenka, additional, Krejza, Jan, additional, Ibrom, Andreas, additional, Pilegaard, Kim, additional, Loustau, Denis, additional, Bonnefond, Jean-Marc, additional, Berbigier, Paul, additional, Picart, Delphine, additional, Lafont, Sébastien, additional, Dietze, Michael, additional, Cameron, David, additional, Vieno, Massimo, additional, Tian, Hanqin, additional, Palacios-Orueta, Alicia, additional, Cicuendez, Victor, additional, Recuero, Laura, additional, Wiese, Klaus, additional, Büchner, Matthias, additional, Lange, Stefan, additional, Volkholz, Jan, additional, Kim, Hyungjun, additional, Weedon, Graham P., additional, Sheffield, Justin, additional, Vega del Valle, Iliusi, additional, Suckow, Felicitas, additional, Horemans, Joanna A., additional, Martel, Simon, additional, Bohn, Friedrich, additional, Steinkamp, Jörg, additional, Chikalanov, Alexander, additional, Mahnken, Mats, additional, Gutsch, Martin, additional, and Frieler, Katja, additional
Published: 2019
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47. FLUXNET-CH4 Synthesis Activity : Objectives, Observations, and Future Directions

Author: Knox, Sara H., Jackson, Robert B., Poulter, Benjamin, McNicol, Gavin, Fluet-Chouinard, Etienne, Zhang, Zhen, Hugelius, Gustaf, Bousquet, Philippe, Canadell, Josep G., Saunois, Marielle, Papale, Dario, Chu, Housen, Keenan, Trevor F., Baldocchi, Dennis, Torn, Margaret S., Mammarella, Ivan, Trotta, Carlo, Aurela, Mika, Bohrer, Gil, Campbell, David, Cescatti, Alessandro, Chamberlain, Samuel, Chen, Jiquan, Chen, Weinan, Dengel, Sigrid, Desai, Ankur R., Euskirchen, Eugenie, Friborg, Thomas, Gasbarra, Daniele, Goded, Ignacio, Goeckede, Mathias, Heimann, Martin, Helbig, Manuel, Hirano, Takashi, Hollinger, David Y., Iwata, Hiroki, Kang, Minseok, Klatt, Janina, Krauss, Ken W., Kutzbach, Lars, Lohila, Annalea, Mitra, Bhaskar, Morin, Timothy H., Nilsson, Mats B., Niu, Shuli, Noormets, Asko, Oechel, Walter C., Peichl, Matthias, Peltola, Olli, Reba, Michele L., Richardson, Andrew D., Runkle, Benjamin R. K., Ryu, Youngryel, Sachs, Torsten, Schaefer, Karina V. R., Schmid, Hans Peter, Shurpali, Narasinha, Sonnentag, Oliver, Tang, Angela C., Ueyama, Masahito, Vargas, Rodrigo, Vesala, Timo, Ward, Eric J., Windham-Myers, Lisamarie, Wohlfahrt, Georg, Zona, Donatella, Knox, Sara H., Jackson, Robert B., Poulter, Benjamin, McNicol, Gavin, Fluet-Chouinard, Etienne, Zhang, Zhen, Hugelius, Gustaf, Bousquet, Philippe, Canadell, Josep G., Saunois, Marielle, Papale, Dario, Chu, Housen, Keenan, Trevor F., Baldocchi, Dennis, Torn, Margaret S., Mammarella, Ivan, Trotta, Carlo, Aurela, Mika, Bohrer, Gil, Campbell, David, Cescatti, Alessandro, Chamberlain, Samuel, Chen, Jiquan, Chen, Weinan, Dengel, Sigrid, Desai, Ankur R., Euskirchen, Eugenie, Friborg, Thomas, Gasbarra, Daniele, Goded, Ignacio, Goeckede, Mathias, Heimann, Martin, Helbig, Manuel, Hirano, Takashi, Hollinger, David Y., Iwata, Hiroki, Kang, Minseok, Klatt, Janina, Krauss, Ken W., Kutzbach, Lars, Lohila, Annalea, Mitra, Bhaskar, Morin, Timothy H., Nilsson, Mats B., Niu, Shuli, Noormets, Asko, Oechel, Walter C., Peichl, Matthias, Peltola, Olli, Reba, Michele L., Richardson, Andrew D., Runkle, Benjamin R. K., Ryu, Youngryel, Sachs, Torsten, Schaefer, Karina V. R., Schmid, Hans Peter, Shurpali, Narasinha, Sonnentag, Oliver, Tang, Angela C., Ueyama, Masahito, Vargas, Rodrigo, Vesala, Timo, Ward, Eric J., Windham-Myers, Lisamarie, Wohlfahrt, Georg, and Zona, Donatella
Abstract: This paper describes the formation of, and initial results for, a new FLUXNET coordination network for ecosystem-scale methane (CH4) measurements at 60 sites globally, organized by the Global Carbon Project in partnership with other initiatives and regional flux tower networks. The objectives of the effort are presented along with an overview of the coverage of eddy covariance (EC) CH4 flux measurements globally, initial results comparing CH4 fluxes across the sites, and future research directions and needs. Annual estimates of net CH4 fluxes across sites ranged from -0.2 +/- 0.02 g C m(-2) yr(-1) for an upland forest site to 114.9 +/- 13.4 g C m(-2) yr(-1) for an estuarine freshwater marsh, with fluxes exceeding 40 g C m(-2) yr(-1) at multiple sites. Average annual soil and air temperatures were found to be the strongest predictor of annual CH4 flux across wetland sites globally. Water table position was positively correlated with annual CH4 emissions, although only for wetland sites that were not consistently inundated throughout the year. The ratio of annual CH4 fluxes to ecosystem respiration increased significantly with mean site temperature. Uncertainties in annual CH4 estimates due to gap-filling and random errors were on average +/- 1.6 g C m(-2) yr(-1) at 95% confidence, with the relative error decreasing exponentially with increasing flux magnitude across sites. Through the analysis and synthesis of a growing EC CH4 flux database, the controls on ecosystem CH4 fluxes can be better understood, used to inform and validate Earth system models, and reconcile differences between land surface model- and atmospheric-based estimates of CH4 emissions.
Published: 2019
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48. FLUXNET-CH4 Synthesis Activity:Objectives, Observations, and Future Directions

Author: Knox, Sara H., Jackson, Robert B., Poulter, Benjamin, McNicol, Gavin, Fluet-Chouinard, Etienne, Zhang, Zhen, Hugelius, Gustaf, Bousquet, Philippe, Canadell, Josep G., Saunois, Marielle, Papale, Dario, Chu, Housen, Keenan, Trevor F., Baldocchi, Dennis, Torn, Margaret S., Mammarella, Ivan, Trotta, Carlo, Aurela, Mika, Bohrer, Gil, Campbell, David, I, Cescatti, Alessandro, Chamberlain, Samuel, Chen, Jiquan, Chen, Weinan, Dengel, Sigrid, Desai, Ankur R., Euskirchen, Eugenie, Friborg, Thomas, Gasbarra, Daniele, Goded, Ignacio, Goeckede, Mathias, Heimann, Martin, Helbig, Manuel, Hirano, Takashi, Hollinger, David Y., Iwata, Hiroki, Kang, Minseok, Klatt, Janina, Krauss, Ken W., Kutzbach, Lars, Lohila, Annalea, Mitra, Bhaskar, Morin, Timothy H., Nilsson, Mats B., Niu, Shuli, Noormets, Asko, Oechel, Walter C., Peichl, Matthias, Peltola, Olli, Reba, Michele L., Richardson, Andrew D., Runkle, Benjamin R. K., Ryu, Youngryel, Sachs, Torsten, Schaefer, Karina V. R., Schmid, Hans Peter, Shurpali, Narasinha, Sonnentag, Oliver, Tang, Angela C., I, Ueyama, Masahito, Vargas, Rodrigo, Vesala, Timo, Ward, Eric J., Windham-Myers, Lisamarie, Wohlfahrt, Georg, Zona, Donatella, Knox, Sara H., Jackson, Robert B., Poulter, Benjamin, McNicol, Gavin, Fluet-Chouinard, Etienne, Zhang, Zhen, Hugelius, Gustaf, Bousquet, Philippe, Canadell, Josep G., Saunois, Marielle, Papale, Dario, Chu, Housen, Keenan, Trevor F., Baldocchi, Dennis, Torn, Margaret S., Mammarella, Ivan, Trotta, Carlo, Aurela, Mika, Bohrer, Gil, Campbell, David, I, Cescatti, Alessandro, Chamberlain, Samuel, Chen, Jiquan, Chen, Weinan, Dengel, Sigrid, Desai, Ankur R., Euskirchen, Eugenie, Friborg, Thomas, Gasbarra, Daniele, Goded, Ignacio, Goeckede, Mathias, Heimann, Martin, Helbig, Manuel, Hirano, Takashi, Hollinger, David Y., Iwata, Hiroki, Kang, Minseok, Klatt, Janina, Krauss, Ken W., Kutzbach, Lars, Lohila, Annalea, Mitra, Bhaskar, Morin, Timothy H., Nilsson, Mats B., Niu, Shuli, Noormets, Asko, Oechel, Walter C., Peichl, Matthias, Peltola, Olli, Reba, Michele L., Richardson, Andrew D., Runkle, Benjamin R. K., Ryu, Youngryel, Sachs, Torsten, Schaefer, Karina V. R., Schmid, Hans Peter, Shurpali, Narasinha, Sonnentag, Oliver, Tang, Angela C., I, Ueyama, Masahito, Vargas, Rodrigo, Vesala, Timo, Ward, Eric J., Windham-Myers, Lisamarie, Wohlfahrt, Georg, and Zona, Donatella
Published: 2019

49. The ecosystem carbon sink implications of mountain forest expansion into abandoned grazing land: The role of subsoil and climatic factors

Author: Università degli Studi della Tuscia, Università degli Studi del Molise, European Commission, Eusko Jaurlaritza, Ministerio de Economía y Competitividad (España), Pellis, Guido, Chiti, Tommaso, Rey, Ana, Curiel Yuste, Jorge, Trotta, Carlo, Papale, Dario, Università degli Studi della Tuscia, Università degli Studi del Molise, European Commission, Eusko Jaurlaritza, Ministerio de Economía y Competitividad (España), Pellis, Guido, Chiti, Tommaso, Rey, Ana, Curiel Yuste, Jorge, Trotta, Carlo, and Papale, Dario
Abstract: Woody encroachment is a widespread phenomenon resulting from the abandonment of mountain agricultural andpastoral practices during the last century. As a result, forests have expanded, increasing biomass and necromass car-bon(C) pools. However,the impact on soil organic carbon(SOC) isless clear.The main aim of this study wasto in-vestigate the effect of woody encroachment on SOC stocks and ecosystem C pools in six chronosequences located along the Italian peninsula, three in the Alps and three in the Apennines. Five stages along the chronosequences were identified in each site. Considering the topsoil (0–30 cm), subsoil (30 cm-bedrock) and whole soil profile, the temporal trend in SOC stocks was similar in all sites, with an initial increment and subsequent decrement inthe intermediate phase. However, thefinal phase of the woody encroachment differed significantly between the Alps (mainly conifers) and the Apennines (broadleaf forests) sites, with a much more pronounced increment inthe latter case. Compared to the previous pastures, after mature forest (N62 years old) establishment, SOC stocksincreased by: 2.1(mean) ± 18.1(sd) and 50.1 ± 25.2 Mg C·ha−1in the topsoil, 7.3 ± 17.4 and 93.2 ±29.7 Mg C·ha−1in the subsoil, and 9.4 ± 24.4 and 143.3 ± 51.0 Mg C·ha−1in the whole soil profile in Alps and Apennines, respectively. Changes in SOC stocks increased with mean annual air temperature and average minimum winter temperature, and were negatively correlated with the sum of summer precipitation. At the same time, all other C pools (biomass and necromass) increased by 179.1 ± 51.3 and 304.2 ± 67.6 Mg C·ha−1in the Alps and theApennines sites, respectively. This study highlights the importance of considering both the subsoil, since deep soillayers contributed 38% to the observed variations in carbon stocks after land use change, and the possible repercussions for the carbon balance of large areas where forestsare expanding, especially under pressing global warming scenarios.
Published: 2019

50. 3D-CMCC-FEM User's Guide (v.5.5-ISIMIP)

Author: Collalti, Alessio, Dalmonech, Daniela, Biondo, Corrado, Marano, Gina, and Trotta, Carlo
Published: 2018
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