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771 results on '"Migliavacca, Mirco"'

1. Physics Informed Modeling of Ecosystem Respiration via Dynamic Mode Decomposition with Control Input

Author

Shadaydeh, Maha, Denzler, Joachim, and Migliavacca, Mirco

Subjects
Mathematics - Dynamical Systems
Abstract

Ecosystem respiration (Reco) represents a major component of the global carbon cycle, and accurate characterization of its dynamics is essential for a comprehensive understanding of ecosystem-climate interactions and the impacts of climate extremes on the ecosystem. This paper presents a novel data-driven and physics-aware method for estimating Reco dynamics using the dynamic mode decomposition with control input (DMDc) technique, an emerging tool for analyzing nonlinear dynamical systems. The proposed model represents Reco as a state space model with an autonomous component and an exogenous control input. The control input can be any ecosystem driver(s), such as air temperature, soil temperature, or soil water content. This unique modeling approach allows controlled intervention to study the effects of different inputs on the system. Experimental results using Fluxnet2015 data show that the prediction accuracy of Reco dynamics achieved with DMDc is comparable to state-of-the-art methods, making it a promising tool for analyzing the dynamic behavior of different vegetation ecosystems on multi-temporal scales in response to different climatic drivers.

Published
2024

2. Leaf-level coordination principles propagate to the ecosystem scale.

Author

Gomarasca, Ulisse, Migliavacca, Mirco, Kattge, Jens, Nelson, Jacob, Niinemets, Ülo, Wirth, Christian, Cescatti, Alessandro, Bahn, Michael, Nair, Richard, Acosta, Alicia, Arain, M, Beloiu, Mirela, Black, T, Bruun, Hans, Bucher, Solveig, Buchmann, Nina, Byun, Chaeho, Carrara, Arnaud, Conte, Adriano, da Silva, Ana, Duveiller, Gregory, Fares, Silvano, Ibrom, Andreas, Knohl, Alexander, Komac, Benjamin, Limousin, Jean-Marc, Lusk, Christopher, Mahecha, Miguel, Martini, David, Minden, Vanessa, Montagnani, Leonardo, Mori, Akira, Onoda, Yusuke, Peñuelas, Josep, Perez-Priego, Oscar, Poschlod, Peter, Powell, Thomas, Reich, Peter, Šigut, Ladislav, van Bodegom, Peter, Walther, Sophia, Wohlfahrt, Georg, Wright, Ian, and Reichstein, Markus

Subjects
Ecosystem, Plants, Climate Change, Plant Leaves, Phenotype
Abstract

Fundamental axes of variation in plant traits result from trade-offs between costs and benefits of resource-use strategies at the leaf scale. However, it is unclear whether similar trade-offs propagate to the ecosystem level. Here, we test whether trait correlation patterns predicted by three well-known leaf- and plant-level coordination theories - the leaf economics spectrum, the global spectrum of plant form and function, and the least-cost hypothesis - are also observed between community mean traits and ecosystem processes. We combined ecosystem functional properties from FLUXNET sites, vegetation properties, and community mean plant traits into three corresponding principal component analyses. We find that the leaf economics spectrum (90 sites), the global spectrum of plant form and function (89 sites), and the least-cost hypothesis (82 sites) all propagate at the ecosystem level. However, we also find evidence of additional scale-emergent properties. Evaluating the coordination of ecosystem functional properties may aid the development of more realistic global dynamic vegetation models with critical empirical data, reducing the uncertainty of climate change projections.

Published
2023

3. The effect of relative humidity on eddy covariance latent heat flux measurements and its implication for partitioning into transpiration and evaporation

Author

Zhang, Weijie, Jung, Martin, Migliavacca, Mirco, Poyatos, Rafael, Miralles, Diego G, El-Madany, Tarek S, Galvagno, Marta, Carrara, Arnaud, Arriga, Nicola, Ibrom, Andreas, Mammarella, Ivan, Papale, Dario, Cleverly, Jamie R, Liddell, Michael, Wohlfahrt, Georg, Markwitz, Christian, Mauder, Matthias, Paul-Limoges, Eugenie, Schmidt, Marius, Wolf, Sebastian, Brümmer, Christian, Arain, M Altaf, Fares, Silvano, Kato, Tomomichi, Ardö, Jonas, Oechel, Walter, Hanson, Chad, Korkiakoski, Mika, Biraud, Sébastien, Steinbrecher, Rainer, Billesbach, Dave, Montagnani, Leonardo, Woodgate, William, Shao, Changliang, Carvalhais, Nuno, Reichstein, Markus, and Nelson, Jacob A

Subjects
Earth Sciences, Atmospheric Sciences, Evapotranspiration, Energy balance closure, Latent energy, FLUXNET, Eddy covariance, Biological Sciences, Agricultural and Veterinary Sciences, Meteorology & Atmospheric Sciences, Agricultural, veterinary and food sciences, Biological sciences, Earth sciences
Abstract

While the eddy covariance (EC) technique is a well-established method for measuring water fluxes (i.e., evaporation or 'evapotranspiration’, ET), the measurement is susceptible to many uncertainties. One such issue is the potential underestimation of ET when relative humidity (RH) is high (>70%), due to low-pass filtering with some EC systems. Yet, this underestimation for different types of EC systems (e.g. open-path or closed-path sensors) has not been characterized for synthesis datasets such as the widely used FLUXNET2015 dataset. Here, we assess the RH-associated underestimation of latent heat fluxes (LE, or ET) from different EC systems for 163 sites in the FLUXNET2015 dataset. We found that the LE underestimation is most apparent during hours when RH is higher than 70%, predominantly observed at sites using closed-path EC systems, but the extent of the LE underestimation is highly site-specific. We then propose a machine learning based method to correct for this underestimation, and compare it to two energy balance closure based LE correction approaches (Bowen ratio correction, BRC, and attributing all errors to LE). Our correction increases LE by 189% for closed-path sites at high RH (>90%), while BRC increases LE by around 30% for all RH conditions. Additionally, we assess the influence of these corrections on ET-based transpiration (T) estimates using two different ET partitioning methods. Results show opposite responses (increasing vs. slightly decreasing T-to-ET ratios, T/ET) between the two methods when comparing T based on corrected and uncorrected LE. Overall, our results demonstrate the existence of a high RH bias in water fluxes in the FLUXNET2015 dataset and suggest that this bias is a pronounced source of uncertainty in ET measurements to be considered when estimating ecosystem T/ET and WUE.

Published
2023

4. Dryland evapotranspiration from remote sensing solar-induced chlorophyll fluorescence: constraining an optimal stomatal model within a two-source energy balance model

Author

Bu, Jingyi, Gan, Guojing, Chen, Jiahao, Su, Yanxin, Yuan, Mengjia, Gao, Yanchun, Domingo, Francisco, Migliavacca, Mirco, El-Madany, Tarek S., Gentine, Pierre, and Garcia, Monica

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Evapotranspiration (ET) represents the largest water loss flux in drylands, but ET and its partition into plant transpiration (T) and soil evaporation (E) are poorly quantified, especially at fine temporal scales. Physically-based remote sensing models relying on sensible heat flux estimates, like the two-source energy balance model, could benefit from considering more explicitly the key effect of stomatal regulation on dryland ET. The objective of this study is to assess the value of solar-induced chlorophyll fluorescence (SIF), a proxy for photosynthesis, to constrain the canopy conductance (Gc) of an optimal stomatal model within a two-source energy balance model in drylands. We assessed our ET estimation using in situ eddy covariance GPP as a benchmark, and compared with results from using the Contiguous solar-induced chlorophyll fluorescence (CSIF) remote sensing product instead of GPP, with and without the effect of root-zone soil moisture on the Gc. The estimated ET was robust across four steppes and two tree-grass dryland ecosystem. Comparison of ET simulated against in situ GPP yielded an average R2 of 0.73 (0.86) and RMSE of 0.031 (0.36) mm at half-hourly (daily) timescale. Including explicitly the soil moisture effect on Gc, increased the R2 to 0.76 (0.89). For the CSIF model, the average R2 for ET estimates also improved when including the effect of soil moisture: from 0.65 (0.79) to 0.71 (0.84), with RMSE ranging between 0.023 (0.22) and 0.043 (0.54) mm depending on the site. Our results demonstrate the capacity of SIF to estimate subdaily and daily ET fluxes under very low ET conditions. SIF can provide effective vegetation signals to constrain stomatal conductance and partition ET into T and E in drylands. This approach could be extended for regional estimates using remote sensing SIF estimates such as CSIF, TROPOMI-SIF, or the upcoming FLEX mission, among others.

Published
2022

6. Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects

Author

Graf, Alexander, Wohlfahrt, Georg, Aranda-Barranco, Sergio, Arriga, Nicola, Brümmer, Christian, Ceschia, Eric, Ciais, Philippe, Desai, Ankur R., Di Lonardo, Sara, Gharun, Mana, Grünwald, Thomas, Hörtnagl, Lukas, Kasak, Kuno, Klosterhalfen, Anne, Knohl, Alexander, Kowalska, Natalia, Leuchner, Michael, Lindroth, Anders, Mauder, Matthias, Migliavacca, Mirco, Morel, Alexandra C., Pfennig, Andreas, Poorter, Hendrik, Terán, Christian Poppe, Reitz, Oliver, Rebmann, Corinna, Sanchez-Azofeifa, Arturo, Schmidt, Marius, Šigut, Ladislav, Tomelleri, Enrico, Yu, Ke, Varlagin, Andrej, and Vereecken, Harry

Published
2023
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12. Automatic Plant Cover Estimation with Convolutional Neural Networks

Author

Körschens, Matthias, Bodesheim, Paul, Römermann, Christine, Bucher, Solveig Franziska, Migliavacca, Mirco, Ulrich, Josephine, and Denzler, Joachim

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Monitoring the responses of plants to environmental changes is essential for plant biodiversity research. This, however, is currently still being done manually by botanists in the field. This work is very laborious, and the data obtained is, though following a standardized method to estimate plant coverage, usually subjective and has a coarse temporal resolution. To remedy these caveats, we investigate approaches using convolutional neural networks (CNNs) to automatically extract the relevant data from images, focusing on plant community composition and species coverages of 9 herbaceous plant species. To this end, we investigate several standard CNN architectures and different pretraining methods. We find that we outperform our previous approach at higher image resolutions using a custom CNN with a mean absolute error of 5.16%. In addition to these investigations, we also conduct an error analysis based on the temporal aspect of the plant cover images. This analysis gives insight into where problems for automatic approaches lie, like occlusion and likely misclassifications caused by temporal changes.

Published
2021

14. Towards a standardized, ground-based network of hyperspectral measurements: Combining time series from autonomous field spectrometers with Sentinel-2

Author

Naethe, Paul, De Sanctis, Andrea, Burkart, Andreas, Campbell, Petya K.E., Colombo, Roberto, Di Mauro, Biagio, Damm, Alexander, El-Madany, Tarek, Fava, Francesco, Gamon, John A., Huemmrich, Karl F., Migliavacca, Mirco, Paul-Limoges, Eugenie, Rascher, Uwe, Rossini, Micol, Schüttemeyer, Dirk, Tagliabue, Giulia, Zhang, Yongguang, and Julitta, Tommaso

Published
2024
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15. The three major axes of terrestrial ecosystem function

Author

Migliavacca, Mirco, Musavi, Talie, Mahecha, Miguel D, Nelson, Jacob A, Knauer, Jürgen, Baldocchi, Dennis D, Perez-Priego, Oscar, Christiansen, Rune, Peters, Jonas, Anderson, Karen, Bahn, Michael, Black, T Andrew, Blanken, Peter D, Bonal, Damien, Buchmann, Nina, Caldararu, Silvia, Carrara, Arnaud, Carvalhais, Nuno, Cescatti, Alessandro, Chen, Jiquan, Cleverly, Jamie, Cremonese, Edoardo, Desai, Ankur R, El-Madany, Tarek S, Farella, Martha M, Fernández-Martínez, Marcos, Filippa, Gianluca, Forkel, Matthias, Galvagno, Marta, Gomarasca, Ulisse, Gough, Christopher M, Göckede, Mathias, Ibrom, Andreas, Ikawa, Hiroki, Janssens, Ivan A, Jung, Martin, Kattge, Jens, Keenan, Trevor F, Knohl, Alexander, Kobayashi, Hideki, Kraemer, Guido, Law, Beverly E, Liddell, Michael J, Ma, Xuanlong, Mammarella, Ivan, Martini, David, Macfarlane, Craig, Matteucci, Giorgio, Montagnani, Leonardo, Pabon-Moreno, Daniel E, Panigada, Cinzia, Papale, Dario, Pendall, Elise, Penuelas, Josep, Phillips, Richard P, Reich, Peter B, Rossini, Micol, Rotenberg, Eyal, Scott, Russell L, Stahl, Clement, Weber, Ulrich, Wohlfahrt, Georg, Wolf, Sebastian, Wright, Ian J, Yakir, Dan, Zaehle, Sönke, and Reichstein, Markus

Subjects
Life on Land, Carbon Cycle, Carbon Dioxide, Climate, Datasets as Topic, Ecosystem, Humidity, Plants, Principal Component Analysis, Water Cycle, General Science & Technology
Abstract

The leaf economics spectrum1,2 and the global spectrum of plant forms and functions3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species2. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities4. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability4,5. Here we derive a set of ecosystem functions6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems7,8.

Published
2021

16. Harmonised statistics and maps of forest biomass and increment in Europe

Author

Avitabile, Valerio, Pilli, Roberto, Migliavacca, Mirco, Duveiller, Gregory, Camia, Andrea, Blujdea, Viorel, Adolt, Radim, Alberdi, Iciar, Barreiro, Susana, Bender, Susann, Borota, Dragan, Bosela, Michal, Bouriaud, Olivier, Breidenbach, Johannes, Cañellas, Isabel, Čavlović, Jura, Colin, Antoine, Di Cosmo, Lucio, Donis, Janis, Fischer, Christoph, Freudenschuss, Alexandra, Fridman, Jonas, Gasparini, Patrizia, Gschwantner, Thomas, Hernández, Laura, Korhonen, Kari, Kulbokas, Gintaras, Kvist, Vivian, Latte, Nicolas, Lazdins, Andis, Lejeune, Philippe, Makovskis, Kristaps, Marin, Gheorghe, Maslo, Jan, Michorczyk, Artur, Mionskowski, Marcin, Morneau, François, Myszkowski, Marcin, Nagy, Kinga, Nilsson, Mats, Nord-Larsen, Thomas, Pantic, Damjan, Perin, Jerôme, Redmond, John, Rizzo, Maria, Šebeň, Vladimír, Skudnik, Mitja, Snorrason, Arnor, Sroga, Radosław, Stoyanov, Todor, Svensson, Arvid, Talarczyk, Andrzej, Teeuwen, Sander, Thürig, Esther, Uva, José, and Mubareka, Sarah

Published
2024
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18. COSORE: A community database for continuous soil respiration and other soil‐atmosphere greenhouse gas flux data

Author

Bond‐Lamberty, Ben, Christianson, Danielle S, Malhotra, Avni, Pennington, Stephanie C, Sihi, Debjani, AghaKouchak, Amir, Anjileli, Hassan, Arain, M Altaf, Armesto, Juan J, Ashraf, Samaneh, Ataka, Mioko, Baldocchi, Dennis, Black, Thomas Andrew, Buchmann, Nina, Carbone, Mariah S, Chang, Shih‐Chieh, Crill, Patrick, Curtis, Peter S, Davidson, Eric A, Desai, Ankur R, Drake, John E, El‐Madany, Tarek S, Gavazzi, Michael, Görres, Carolyn‐Monika, Gough, Christopher M, Goulden, Michael, Gregg, Jillian, del Arroyo, Omar Gutiérrez, He, Jin‐Sheng, Hirano, Takashi, Hopple, Anya, Hughes, Holly, Järveoja, Järvi, Jassal, Rachhpal, Jian, Jinshi, Kan, Haiming, Kaye, Jason, Kominami, Yuji, Liang, Naishen, Lipson, David, Macdonald, Catriona A, Maseyk, Kadmiel, Mathes, Kayla, Mauritz, Marguerite, Mayes, Melanie A, McNulty, Steve, Miao, Guofang, Migliavacca, Mirco, Miller, Scott, Miniat, Chelcy F, Nietz, Jennifer G, Nilsson, Mats B, Noormets, Asko, Norouzi, Hamidreza, O’Connell, Christine S, Osborne, Bruce, Oyonarte, Cecilio, Pang, Zhuo, Peichl, Matthias, Pendall, Elise, Perez‐Quezada, Jorge F, Phillips, Claire L, Phillips, Richard P, Raich, James W, Renchon, Alexandre A, Ruehr, Nadine K, Sánchez‐Cañete, Enrique P, Saunders, Matthew, Savage, Kathleen E, Schrumpf, Marion, Scott, Russell L, Seibt, Ulli, Silver, Whendee L, Sun, Wu, Szutu, Daphne, Takagi, Kentaro, Takagi, Masahiro, Teramoto, Munemasa, Tjoelker, Mark G, Trumbore, Susan, Ueyama, Masahito, Vargas, Rodrigo, Varner, Ruth K, Verfaillie, Joseph, Vogel, Christoph, Wang, Jinsong, Winston, Greg, Wood, Tana E, Wu, Juying, Wutzler, Thomas, Zeng, Jiye, Zha, Tianshan, Zhang, Quan, and Zou, Junliang

Subjects
Climate Change Impacts and Adaptation, Environmental Sciences, Climate Action, Atmosphere, Carbon Dioxide, Ecosystem, Greenhouse Gases, Methane, Nitrous Oxide, Reproducibility of Results, Respiration, Soil, carbon dioxide, greenhouse gases, methane, open data, open science, soil respiration, Biological Sciences, Ecology, Biological sciences, Earth sciences, Environmental sciences
Abstract

Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil-to-atmosphere CO2 flux, commonly though imprecisely termed soil respiration (RS ), is one of the largest carbon fluxes in the Earth system. An increasing number of high-frequency RS measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open-source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long-term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured RS , the database design accommodates other soil-atmosphere measurements (e.g. ecosystem respiration, chamber-measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.

Published
2020

20. Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks

Author

Tramontana, Gianluca, Migliavacca, Mirco, Jung, Martin, Reichstein, Markus, Keenan, Trevor F, Camps‐Valls, Gustau, Ogee, Jerome, Verrelst, Jochem, and Papale, Dario

Subjects
Plant Biology, Biological Sciences, Environmental Sciences, Carbon Cycle, Carbon Dioxide, Ecosystem, Neural Networks, Computer, Photosynthesis, Respiration, Seasons, carbon dioxide fluxes partitioning, ecosystem respiration, eddy covariance, gross primary production, machine learning, net ecosystem exchange, neural network, Ecology, Biological sciences, Earth sciences, Environmental sciences
Abstract

The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO2 between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO2 release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO2 fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co-acting factors that modulate GPP and RECO flux dynamics. To overcome this limitation, we developed a hybrid data-driven approach based on combined neural networks (NNC-part ). NNC-part incorporates process knowledge by introducing a photosynthetic response based on the light-use efficiency (LUE) concept, and uses a comprehensive dataset of soil and micrometeorological variables as fluxes drivers. We applied the method to 36 sites from the FLUXNET2015 dataset and found a high consistency in the results with those derived from other standard partitioning methods for both GPP (R2  > .94) and RECO (R2  > .8). High consistency was also found for (a) the diurnal and seasonal patterns of fluxes and (b) the ecosystem functional responses. NNC-part performed more realistic than the traditional methods for predicting additional patterns of gross CO2 fluxes, such as: (a) the GPP response to VPD, (b) direct effects of air temperature on GPP dynamics, (c) hysteresis in the diel cycle of gross CO2 fluxes, (d) the sensitivity of LUE to the diffuse to direct radiation ratio, and (e) the post rain respiration pulse after a long dry period. In conclusion, NNC-part is a valid data-driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods.

Published
2020

21. Biodiversity loss and climate extremes — study the feedbacks

Author

Mahecha, Miguel D., Bastos, Ana, Bohn, Friedrich J., Eisenhauer, Nico, Feilhauer, Hannes, Hartmann, Henrik, Hickler, Thomas, Kalesse-Los , Heike, Migliavacca, Mirco, Otto, Friederike E. L., Peng, Jian, Quaas, Johannes, Tegen, Ina, Weigelt, Alexandra, Wendisch, Manfred, and Wirth , Christian

Published
2022
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25. EXPLAINING EXTREME EVENTS OF 2016 : From A Climate Perspective

Author

Herring, Stephanie C., Christidi, Nikolaos, Hoell, Andrew, Kossin, James P., Schreck, Carl J., Stott, Peter A., Webb, Robert S., Werner, Francisco E., Knutson, Thomas R., Kam, Jonghun, Zeng, Fanrong, Wittenberg, Andrew T., Brainard, Russell E., Oliver, Thomas, McPhaden, Michael J., Cohen, Anne, Venegas, Robert o, Heenan, Adel, Vargas-Ángel, Bernardo, Rotjan, Randi, Mangubhai, Sangeeta, Flint, Elizabeth, Hunter, Susan A., Oliver, Eric C. J., Perkins-Kirkpatrick, Sarah E., Holbrook, Neil J., Bindoff, Nathaniel L., Tett, Simon F. B., Falk, Alexander, Rogers, Megan, Spuler, Fiona, Turner, Calum, Wainwright, Joshua, Dimdore-Miles, Oscar, Knight, Sam, Freychet, Nicolas, Mineter, Michael J., Lehmann, Caroline E. R., Martins, Eduardo S. P. R., Coelho, Caio A. S., Haarsma, Rein, Otto, Friederike E. L., King, Andrew D., van Oldenborgh, Geert Jan, Kew, Sarah, Philip, Sjoukje, Vasconcelos, Francisco C., Cullen, Heidi, Jézéquel, Aglaé, Yiou, Pascal, Radanovics, Sabine, Vautard, Robert, Sippel, Sebastian, El-Madany, Tarek S., Migliavacca, Mirco, Mahecha, Miguel D., Carrara, Arnaud, Flach, Milan, Kaminski, Thomas, Otto, Friederike E. L., Thonicke, Kirsten, Vossbeck, Michael, Reichstein, Markus, Yuan, Xing, Wang, Linying, Wood, Eric F., Funk, Chris, Davenport, Frank, Harrison, Laura, Magadzire, Tamuka, Galu, Gideon, Artan, Guleid A., Shukla, Shraddhanand, Korecha, Diriba, Indeje, Matayo, Pomposi, Catherine, Macharia, Denis, Husak, Gregory, Nsadisa, Faka Dieudonne, Imada, Yukiko, Shiogama, Hideo, Takahashi, Chiharu, Watanabe, Masahiro, Mori, Masato, Kamae, Youichi, Maeda, Shuhei, Sun, Qiaohong, Miao, Chiyuan, Zhou, Chunlüe, Wang, Kaicun, Qi, Dan, Yuan, Xing, Wang, Shanshan, Hu, Zeng-Zhen, Qian, Cheng, Wang, Jun, Dong, Siyan, Yin, Hong, Burke, Claire, Ciavarella, Andrew, Dong, Buwen, Freychet, Nicolas, Lott, Fraser C., Tett, Simon F. B., Sun, Ying, Hu, Ting, Zhang, Xuebin, Wan, Hui, Stott, Peter, Lu, Chunhui, Christidis, Nikolaos, Manomaiphiboon, Kasemsan, Ciavarella, Andrew, Stott, Peter A., Hope, Pandora, Lim, Eun-Pa, Hendon, Harry, Wang, Guomin, Lewis, Sophie C., Mallela, Jennie, Grose, Michael R., Black, Mitchell, Risbey, James S., Uhe, Peter, Hope, Pandora K., Haustein, Karsten, Mitchell, Dann, Stott, Peter A., Christidis, Nikos, Herring, Stephanie C., Hoell, Andrew, Kossin, James P., and Schreck, Carl J.

Published
2018

26. Interpretability of negative latent heat fluxes from eddy covariance measurements in dry conditions

Author

Paulus, Sinikka J., primary, Orth, Rene, additional, Lee, Sung-Ching, additional, Hildebrandt, Anke, additional, Jung, Martin, additional, Nelson, Jacob A., additional, El-Madany, Tarek Sebastian, additional, Carrara, Arnaud, additional, Moreno, Gerardo, additional, Mauder, Matthias, additional, Groh, Jannis, additional, Graf, Alexander, additional, Reichstein, Markus, additional, and Migliavacca, Mirco, additional

Published
2024
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29. Supplementary material to "DeepPhenoMem V1.0: Deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology"

Author

Liu, Guohua, primary, Migliavacca, Mirco, additional, Reimers, Christian, additional, Kraft, Basil, additional, Reichstein, Markus, additional, Richardson, Andrew, additional, Wingate, Lisa, additional, Delpierre, Nicolas, additional, Yang, Hui, additional, and Winkler, Alexander, additional

Published
2024
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30. DeepPhenoMem V1.0: Deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology

Author

Liu, Guohua, primary, Migliavacca, Mirco, additional, Reimers, Christian, additional, Kraft, Basil, additional, Reichstein, Markus, additional, Richardson, Andrew, additional, Wingate, Lisa, additional, Delpierre, Nicolas, additional, Yang, Hui, additional, and Winkler, Alexander, additional

Published
2024
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31. 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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35. Widespread inhibition of daytime ecosystem respiration

Author

Keenan, Trevor F, Migliavacca, Mirco, Papale, Dario, Baldocchi, Dennis, Reichstein, Markus, Torn, Margaret, and Wutzler, Thomas

Subjects
Biological Sciences, Ecology, Circadian Rhythm, Ecosystem, Photosynthesis, Plant Leaves, Plant Transpiration, Evolutionary biology, Environmental management
Abstract

The global land surface absorbs about a third of anthropogenic emissions each year, due to the difference between two key processes: ecosystem photosynthesis and respiration. Despite the importance of these two processes, it is not possible to measure either at the ecosystem scale during the daytime. Eddy-covariance measurements are widely used as the closest 'quasi-direct' ecosystem-scale observation from which to estimate ecosystem photosynthesis and respiration. Recent research, however, suggests that current estimates may be biased by up to 25%, due to a previously unaccounted for process: the inhibition of leaf respiration in the light. Yet the extent of inhibition remains debated, and implications for estimates of ecosystem-scale respiration and photosynthesis remain unquantified. Here, we quantify an apparent inhibition of daytime ecosystem respiration across the global FLUXNET eddy-covariance network and identify a pervasive influence that varies by season and ecosystem type. We develop partitioning methods that can detect an apparent ecosystem-scale inhibition of daytime respiration and find that diurnal patterns of ecosystem respiration might be markedly different than previously thought. The results call for the re-evaluation of global terrestrial carbon cycle models and also suggest that current global estimates of photosynthesis and respiration may be biased, some on the order of magnitude of anthropogenic fossil fuel emissions.

Published
2019

36. Towards long-term standardised carbon and greenhouse gas observations for monitoring Europe’s terrestrial ecosystems: a review

Author

Franz, Daniela, Acosta, Manuel, Altimir, Núria, Arriga, Nicola, Arrouays, Dominique, Aubinet, Marc, Aurela, Mika, Ayres, Edward, López-Ballesteros, Ana, Barbaste, Mireille, Berveiller, Daniel, Biraud, Sébastien, Boukir, Hakima, Brown, Timothy, Brümmer, Christian, Buchmann, Nina, Burba, George, Carrara, Arnaud, Cescatti, Allessandro, Ceschia, Eric, Clement, Robert, Cremonese, Edoardo, Crill, Patrick, Darenova, Eva, Dengel, Sigrid, D’Odorico, Petra, Filippa, Gianluca, Fleck, Stefan, Fratini, Gerardo, Fuß, Roland, Gielen, Bert, Gogo, Sébastien, Grace, John, Graf, Alexander, Grelle, Achim, Gross, Patrick, Grünwald, Thomas, Haapanala, Sami, Hehn, Markus, Heinesch, Bernard, Heiskanen, Jouni, Herbst, Mathias, Herschlein, Christine, Hörtnagl, Lukas, Hufkens, Koen, Ibrom, Andreas, Jolivet, Claudy, Joly, Lilian, Jones, Michael, Kiese, Ralf, Klemedtsson, Leif, Kljun, Natascha, Klumpp, Katja, Kolari, Pasi, Kolle, Olaf, Kowalski, Andrew, Kutsch, Werner, Laurila, Tuomas, de Ligne, Anne, Linder, Sune, Lindroth, Anders, Lohila, Annalea, Longdoz, Bernhard, Mammarella, Ivan, Manise, Tanguy, Jiménez, Sara Maraňón, Matteucci, Giorgio, Mauder, Matthias, Meier, Philip, Merbold, Lutz, Mereu, Simone, Metzger, Stefan, Migliavacca, Mirco, Mölder, Meelis, Montagnani, Leonardo, Moureaux, Christine, Nelson, David, Nemitz, Eiko, Nicolini, Giacomo, Nilsson, Mats B, de Beeck, Maarten Op, Osborne, Bruce, Löfvenius, Mikaell Ottosson, Pavelka, Marian, Peichl, Matthias, Peltola, Olli, Pihlatie, Mari, Pitacco, Andrea, Pokorný, Radek, Pumpanen, Jukka, Ratié, Céline, Rebmann, Corinna, Roland, Marilyn, Sabbatini, Simone, Saby, Nicolas PA, Saunders, Matthew, Schmid, Hans Peter, Schrumpf, Marion, Sedlák, Pavel, and Ortiz, Penelope Serrano

Subjects
Agriculture, Land and Farm Management, Agricultural, Veterinary and Food Sciences, Climate Action, ICOS, GHG exchange, carbon cycle, standardised monitoring, observational network, Agronomy & Agriculture, Agriculture, land and farm management
Abstract

Research infrastructures play a key role in launching a new generation of integrated long-Term, geographically distributed observation programmes designed to monitor climate change, better understand its impacts on global ecosystems, and evaluate possible mitigation and adaptation strategies. The pan-European Integrated Carbon Observation System combines carbon and greenhouse gas (GHG; CO 2 , CH 4 , N 2 O, H 2 O) observations within the atmosphere, terrestrial ecosystems and oceans. High-precision measurements are obtained using standardised methodologies, are centrally processed and openly available in a traceable and verifiable fashion in combination with detailed metadata. The Integrated Carbon Observation System ecosystem station network aims to sample climate and land-cover variability across Europe. In addition to GHG flux measurements, a large set of complementary data (including management practices, vegetation and soil characteristics) is collected to support the interpretation, spatial upscaling and modelling of observed ecosystem carbon and GHG dynamics. The applied sampling design was developed and formulated in protocols by the scientific community, representing a trade-off between an ideal dataset and practical feasibility. The use of open-Access, high-quality and multi-level data products by different user communities is crucial for the Integrated Carbon Observation System in order to achieve its scientific potential and societal value.

Published
2018

37. 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

39. Weakly Supervised Segmentation Pretraining for Plant Cover Prediction

Author

Körschens, Matthias, Bodesheim, Paul, Römermann, Christine, Bucher, Solveig Franziska, Migliavacca, Mirco, Ulrich, Josephine, Denzler, Joachim, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bauckhage, Christian, editor, Gall, Juergen, editor, and Schwing, Alexander, editor

Published
2021
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42. Altered energy partitioning across terrestrial ecosystems in the European drought year 2018

Author

Graf, Alexander, Klosterhalfen, Anne, Arriga, Nicola, Bernhofer, Christian, Bogena, Heye, Bornet, Frédéric, Brüggemann, Nicolas, Brümmer, Christian, Buchmann, Nina, Chi, Jinshu, Chipeaux, Christophe, Cremonese, Edoardo, Cuntz, Matthias, Dušek, Jiří, El-Madany, Tarek S., Fares, Silvano, Fischer, Milan, Foltýnová, Lenka, Gharun, Mana, Ghiasi, Shiva, Gielen, Bert, Gottschalk, Pia, Grünwald, Thomas, Heinemann, Günther, Heinesch, Bernard, Heliasz, Michal, Holst, Jutta, Hörtnagl, Lukas, Ibrom, Andreas, Ingwersen, Joachim, Jurasinski, Gerald, Klatt, Janina, Knohl, Alexander, Koebsch, Franziska, Konopka, Jan, Korkiakoski, Mika, Kowalska, Natalia, Kremer, Pascal, Kruijt, Bart, Lafont, Sebastien, Léonard, Joël, De Ligne, Anne, Longdoz, Bernard, Loustau, Denis, Magliulo, Vincenzo, Mammarella, Ivan, Manca, Giovanni, Mauder, Matthias, Migliavacca, Mirco, Mölder, Meelis, Neirynck, Johan, Ney, Patrizia, Nilsson, Mats, Paul-Limoges, Eugénie, Peichl, Matthias, Pitacco, Andrea, Poyda, Arne, Rebmann, Corinna, Roland, Marilyn, Sachs, Torsten, Schmidt, Marius, Schrader, Frederik, Siebicke, Lukas, Šigut, Ladislav, Tuittila, Eeva-Stiina, Varlagin, Andrej, Vendrame, Nadia, Vincke, Caroline, Völksch, Ingo, Weber, Stephan, Wille, Christian, Wizemann, Hans-Dieter, Zeeman, Matthias, and Vereecken, Harry

Published
2020

43. Sensitivity of gross primary productivity to climatic drivers during the summer drought of 2018 in Europe

Author

Fu, Zheng, Ciais, Philippe, Bastos, Ana, Stoy, Paul C., Yang, Hui, Green, Julia K., Wang, Bingxue, Yu, Kailiang, Huang, Yuanyuan, Knohl, Alexander, Šigut, Ladislav, Gharun, Mana, Cuntz, Matthias, Arriga, Nicola, Roland, Marilyn, Peichl, Matthias, Migliavacca, Mirco, Cremonese, Edoardo, Varlagin, Andrej, Brümmer, Christian, de la Motte, Louis Gourlez, Fares, Silvano, Buchmann, Nina, El-Madany, Tarek S., Pitacco, Andrea, Vendrame, Nadia, Li, Zhaolei, Vincke, Caroline, Magliulo, Enzo, and Koebsch, Franziska

Published
2020

46. Evergreen broadleaf greenness and its relationship with leaf flushing, aging, and water fluxes

Author

Luo, Yunpeng, primary, Pacheco-Labrador, Javier, additional, Richardson, Andrew, additional, Seyednasrollah, Bijan, additional, Perez-Priego, Oscar, additional, Cascon, Rosario Gonzalez, additional, Martí, M. Pilar, additional, Moreno, Gerardo, additional, Nair, Richard, additional, Wutzler, Thomas, additional, Bucher, Solveig Franziska, additional, Carrara, Arnaud, additional, Cremonese, Edoardo, additional, El-Madany, Tarek, additional, Filippa, Gianluca, additional, Galvagno, Marta, additional, Hammer, Tiana, additional, Ma, Xuanlong, additional, Martini, David, additional, Zhang, Qian, additional, Reichstein, Markus, additional, Menzel, Annette, additional, Römermann, Christine, additional, and Migliavacca, Mirco, additional

Published
2024
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47. Divergent responses of evergreen needle-leaf forests in Europe to the 2020 warm winter

Author

Gharun, Mana, primary, Shekhar, Ankit, additional, Hörtnagl, Lukas, additional, Krebs, Luana, additional, Arriga, Nicola, additional, Migliavacca, Mirco, additional, Roland, Marilyn, additional, Gielen, Bert, additional, Montagnani, Leonardo, additional, Tomelleri, Enrico, additional, Šigut, Ladislav, additional, Peichl, Matthias, additional, Zhao, Peng, additional, Schmidt, Marius, additional, Grünwald, Thomas, additional, Korkiakoski, Mika, additional, Lohila, Annalea, additional, and Buchmann, Nina, additional

Published
2024
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48. Supplementary material to "Divergent responses of evergreen needle-leaf forests in Europe to the 2020 warm winter"

Author

Gharun, Mana, primary, Shekhar, Ankit, additional, Hörtnagl, Lukas, additional, Krebs, Luana, additional, Arriga, Nicola, additional, Migliavacca, Mirco, additional, Roland, Marilyn, additional, Gielen, Bert, additional, Montagnani, Leonardo, additional, Tomelleri, Enrico, additional, Šigut, Ladislav, additional, Peichl, Matthias, additional, Zhao, Peng, additional, Schmidt, Marius, additional, Grünwald, Thomas, additional, Korkiakoski, Mika, additional, Lohila, Annalea, additional, and Buchmann, Nina, additional

Published
2024
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50. Interpretability of negative latent heat fluxes from Eddy Covariance measurements during dry conditions

Author

Paulus, Sinikka J., primary, Orth, Rene, additional, Lee, Sung-Ching, additional, Hildebrandt, Anke, additional, Jung, Martin, additional, Nelson, Jacob A., additional, El-Madany, Tarek S., additional, Carrara, Arnaud, additional, Moreno, Gerardo, additional, Mauder, Matthias, additional, Groh, Jannis, additional, Graf, Alexander, additional, Reichstein, Markus, additional, and Migliavacca, Mirco, additional

Published
2023
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