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1. Upscaling Wetland Methane Emissions From the FLUXNET‐CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison

Author

McNicol, Gavin, Fluet‐Chouinard, Etienne, Ouyang, Zutao, Knox, Sara, Zhang, Zhen, Aalto, Tuula, Bansal, Sheel, Chang, Kuang‐Yu, Chen, Min, Delwiche, Kyle, Feron, Sarah, Goeckede, Mathias, Liu, Jinxun, Malhotra, Avni, Melton, Joe R, Riley, William, Vargas, Rodrigo, Yuan, Kunxiaojia, Ying, Qing, Zhu, Qing, Alekseychik, Pavel, Aurela, Mika, Billesbach, David P, Campbell, David I, Chen, Jiquan, Chu, Housen, Desai, Ankur R, Euskirchen, Eugenie, Goodrich, Jordan, Griffis, Timothy, Helbig, Manuel, Hirano, Takashi, Iwata, Hiroki, Jurasinski, Gerald, King, John, Koebsch, Franziska, Kolka, Randall, Krauss, Ken, Lohila, Annalea, Mammarella, Ivan, Nilson, Mats, Noormets, Asko, Oechel, Walter, Peichl, Matthias, Sachs, Torsten, Sakabe, Ayaka, Schulze, Christopher, Sonnentag, Oliver, Sullivan, Ryan C, Tuittila, Eeva‐Stiina, Ueyama, Masahito, Vesala, Timo, Ward, Eric, Wille, Christian, Wong, Guan Xhuan, Zona, Donatella, Windham‐Myers, Lisamarie, Poulter, Benjamin, and Jackson, Robert B

Subjects
Earth Sciences, Atmospheric Sciences, Climate Action, Life on Land, Climate change science, Geology, Physical geography and environmental geoscience
Abstract

Wetlands are responsible for 20%–31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH4 emissions of 146 ± 43 TgCH4 y−1 for 2001–2018 which agrees closely with current bottom-up land surface models (102–181 TgCH4 y−1) and overlaps with top-down atmospheric inversion models (155–200 TgCH4 y−1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253).

Published
2023

2. Causality guided machine learning model on wetland CH4 emissions across global wetlands

Author

Yuan, Kunxiaojia, Zhu, Qing, Li, Fa, Riley, William J, Torn, Margaret, Chu, Housen, McNicol, Gavin, Chen, Min, Knox, Sara, Delwiche, Kyle, Wu, Huayi, Baldocchi, Dennis, Ma, Hongxu, Desai, Ankur R, Chen, Jiquan, Sachs, Torsten, Ueyama, Masahito, Sonnentag, Oliver, Helbig, Manuel, Tuittila, Eeva-Stiina, Jurasinski, Gerald, Koebsch, Franziska, Campbell, David, Schmid, Hans Peter, Lohila, Annalea, Goeckede, Mathias, Nilsson, Mats B, Friborg, Thomas, Jansen, Joachim, Zona, Donatella, Euskirchen, Eugenie, Ward, Eric J, Bohrer, Gil, Jin, Zhenong, Liu, Licheng, Iwata, Hiroki, Goodrich, Jordan, and Jackson, Robert

Subjects
Earth Sciences, Machine Learning and Artificial Intelligence, Climate Action, Eddy covariance CH4 emission, Wetlands, Causal inference, Machine learning, Biological Sciences, Agricultural and Veterinary Sciences, Meteorology & Atmospheric Sciences, Agricultural, veterinary and food sciences, Biological sciences, Earth sciences
Abstract

Wetland CH₄ emissions are among the most uncertain components of the global CH₄ budget. The complex nature of wetland CH₄ processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH₄ emissions. In this study, we used the flux measurements of CH₄ from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH₄ emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH₄ emissions in all studied wetland types. Ecosystem respiration (CO₂) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH₄ emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH₄ emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH₄ emissions within earth system land models.

Published
2022

3. Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing

Author

Xie, Mingjuan, Ma, Xiaofei, Wang, Yuangang, Li, Chaofan, Shi, Haiyang, Yuan, Xiuliang, Hellwich, Olaf, Chen, Chunbo, Zhang, Wenqiang, Zhang, Chen, Ling, Qing, Gao, Ruixiang, Zhang, Yu, Ochege, Friday Uchenna, Frankl, Amaury, De Maeyer, Philippe, Buchmann, Nina, Feigenwinter, Iris, Olesen, Jørgen E., Juszczak, Radoslaw, Jacotot, Adrien, Korrensalo, Aino, Pitacco, Andrea, Varlagin, Andrej, Shekhar, Ankit, Lohila, Annalea, Carrara, Arnaud, Brut, Aurore, Kruijt, Bart, Loubet, Benjamin, Heinesch, Bernard, Chojnicki, Bogdan, Helfter, Carole, Vincke, Caroline, Shao, Changliang, Bernhofer, Christian, Brümmer, Christian, Wille, Christian, Tuittila, Eeva-Stiina, Nemitz, Eiko, Meggio, Franco, Dong, Gang, Lanigan, Gary, Niedrist, Georg, Wohlfahrt, Georg, Zhou, Guoyi, Goded, Ignacio, Gruenwald, Thomas, Olejnik, Janusz, Jansen, Joachim, Neirynck, Johan, Tuovinen, Juha-Pekka, Zhang, Junhui, Klumpp, Katja, Pilegaard, Kim, Šigut, Ladislav, Klemedtsson, Leif, Tezza, Luca, Hörtnagl, Lukas, Urbaniak, Marek, Roland, Marilyn, Schmidt, Marius, Sutton, Mark A., Hehn, Markus, Saunders, Matthew, Mauder, Matthias, Aurela, Mika, Korkiakoski, Mika, Du, Mingyuan, Vendrame, Nadia, Kowalska, Natalia, Leahy, Paul G., Alekseychik, Pavel, Shi, Peili, Weslien, Per, Chen, Shiping, Fares, Silvano, Friborg, Thomas, Tallec, Tiphaine, Kato, Tomomichi, Sachs, Torsten, Maximov, Trofim, di Cella, Umberto Morra, Moderow, Uta, Li, Yingnian, He, Yongtao, Kosugi, Yoshiko, and Luo, Geping

Published
2023
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4. The ABCflux database: Arctic–boreal CO2 flux observations and ancillary information aggregated to monthly time steps across terrestrial ecosystems

Author

Virkkala, Anna-Maria, Natali, Susan M, Rogers, Brendan M, Watts, Jennifer D, Savage, Kathleen, Connon, Sara June, Mauritz, Marguerite, Schuur, Edward AG, Peter, Darcy, Minions, Christina, Nojeim, Julia, Commane, Roisin, Emmerton, Craig A, Goeckede, Mathias, Helbig, Manuel, Holl, David, Iwata, Hiroki, Kobayashi, Hideki, Kolari, Pasi, López-Blanco, Efrén, Marushchak, Maija E, Mastepanov, Mikhail, Merbold, Lutz, Parmentier, Frans-Jan W, Peichl, Matthias, Sachs, Torsten, Sonnentag, Oliver, Ueyama, Masahito, Voigt, Carolina, Aurela, Mika, Boike, Julia, Celis, Gerardo, Chae, Namyi, Christensen, Torben R, Bret-Harte, M Syndonia, Dengel, Sigrid, Dolman, Han, Edgar, Colin W, Elberling, Bo, Euskirchen, Eugenie, Grelle, Achim, Hatakka, Juha, Humphreys, Elyn, Järveoja, Järvi, Kotani, Ayumi, Kutzbach, Lars, Laurila, Tuomas, Lohila, Annalea, Mammarella, Ivan, Matsuura, Yojiro, Meyer, Gesa, Nilsson, Mats B, Oberbauer, Steven F, Park, Sang-Jong, Petrov, Roman, Prokushkin, Anatoly S, Schulze, Christopher, St. Louis, Vincent L, Tuittila, Eeva-Stiina, Tuovinen, Juha-Pekka, Quinton, William, Varlagin, Andrej, Zona, Donatella, and Zyryanov, Viacheslav I

Subjects
Earth Sciences, Physical Geography and Environmental Geoscience, Atmospheric Sciences, Geoinformatics, Geochemistry, Atmospheric sciences, Physical geography and environmental geoscience
Abstract

Past efforts to synthesize and quantify the magnitude and change in carbon dioxide (CO2) fluxes in terrestrial ecosystems across the rapidly warming Arctic-boreal zone (ABZ) have provided valuable information but were limited in their geographical and temporal coverage. Furthermore, these efforts have been based on data aggregated over varying time periods, often with only minimal site ancillary data, thus limiting their potential to be used in large-scale carbon budget assessments. To bridge these gaps, we developed a standardized monthly database of Arctic-boreal CO2 fluxes (ABCflux) that aggregates in situ measurements of terrestrial net ecosystem CO2 exchange and its derived partitioned component fluxes: gross primary productivity and ecosystem respiration. The data span from 1989 to 2020 with over 70 supporting variables that describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. Here, we describe these variables, the spatial and temporal distribution of observations, the main strengths and limitations of the database, and the potential research opportunities it enables. In total, ABCflux includes 244 sites and 6309 monthly observations; 136 sites and 2217 monthly observations represent tundra, and 108 sites and 4092 observations represent the boreal biome. The database includes fluxes estimated with chamber (19% of the monthly observations), snow diffusion (3%) and eddy covariance (78%) techniques. The largest number of observations were collected during the climatological summer (June-August; 32%), and fewer observations were available for autumn (September-October; 25%), winter (December-February; 18%), and spring (March-May; 25%). ABCflux can be used in a wide array of empirical, remote sensing and modeling studies to improve understanding of the regional and temporal variability in CO2 fluxes and to better estimate the terrestrial ABZ CO2 budget. ABCflux is openly and freely available online (Virkkala et al., 2021b, 10.3334/ORNLDAAC/1934). Copyright:

Published
2022

5. Earlier snowmelt may lead to late season declines in plant productivity and carbon sequestration in Arctic tundra ecosystems

Author

Zona, Donatella, Lafleur, Peter M, Hufkens, Koen, Bailey, Barbara, Gioli, Beniamino, Burba, George, Goodrich, Jordan P, Liljedahl, Anna K, Euskirchen, Eugénie S, Watts, Jennifer D, Farina, Mary, Kimball, John S, Heimann, Martin, Göckede, Mathias, Pallandt, Martijn, Christensen, Torben R, Mastepanov, Mikhail, López-Blanco, Efrén, Jackowicz-Korczynski, Marcin, Dolman, Albertus J, Marchesini, Luca Belelli, Commane, Roisin, Wofsy, Steven C, Miller, Charles E, Lipson, David A, Hashemi, Josh, Arndt, Kyle A, Kutzbach, Lars, Holl, David, Boike, Julia, Wille, Christian, Sachs, Torsten, Kalhori, Aram, Song, Xia, Xu, Xiaofeng, Humphreys, Elyn R, Koven, Charles D, Sonnentag, Oliver, Meyer, Gesa, Gosselin, Gabriel H, Marsh, Philip, and Oechel, Walter C

Subjects
Biological Sciences, Ecology, Life on Land, Arctic Regions, Carbon Dioxide, Carbon Sequestration, Climate Change, Ecosystem, Plants, Seasons, Soil, Tundra
Abstract

Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO2 later in the season.

Published
2022

6. 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, Machine Learning and Artificial Intelligence, Bioengineering, Networking and Information Technology R&D (NITRD), 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

7. Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales

Author

Knox, Sara H, Bansal, Sheel, McNicol, Gavin, Schafer, Karina, Sturtevant, Cove, Ueyama, Masahito, Valach, Alex C, Baldocchi, Dennis, Delwiche, Kyle, Desai, Ankur R, Euskirchen, Eugenie, Liu, Jinxun, Lohila, Annalea, Malhotra, Avni, Melling, Lulie, Riley, William, Runkle, Benjamin RK, Turner, Jessica, Vargas, Rodrigo, Zhu, Qing, Alto, Tuula, Fluet‐Chouinard, Etienne, Goeckede, Mathias, Melton, Joe R, Sonnentag, Oliver, Vesala, Timo, Ward, Eric, Zhang, Zhen, Feron, Sarah, Ouyang, Zutao, Alekseychik, Pavel, Aurela, Mika, Bohrer, Gil, Campbell, David I, Chen, Jiquan, Chu, Housen, Dalmagro, Higo J, Goodrich, Jordan P, Gottschalk, Pia, Hirano, Takashi, Iwata, Hiroki, Jurasinski, Gerald, Kang, Minseok, Koebsch, Franziska, Mammarella, Ivan, Nilsson, Mats B, Ono, Keisuke, Peichl, Matthias, Peltola, Olli, Ryu, Youngryel, Sachs, Torsten, Sakabe, Ayaka, Sparks, Jed P, Tuittila, Eeva‐Stiina, Vourlitis, George L, Wong, Guan X, Windham‐Myers, Lisamarie, Poulter, Benjamin, and Jackson, Robert B

Subjects
Earth Sciences, Climate Change Impacts and Adaptation, Environmental Sciences, Carbon Dioxide, Ecosystem, Fresh Water, Methane, Seasons, Wetlands, eddy covariance, generalized additive modeling, lags, methane, mutual information, predictors, random forest, synthesis, time scales, wetlands, Biological Sciences, Ecology, Biological sciences, Earth sciences, Environmental sciences
Abstract

While wetlands are the largest natural source of methane (CH4 ) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17 ± 11 days, and lagged air and soil temperature by median values of 8 ± 16 and 5 ± 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4 . At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.

Published
2021

9. 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

11. 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

14. Large loss of CO2 in winter observed across the northern permafrost region

Author

Natali, Susan M, Watts, Jennifer D, Rogers, Brendan M, Potter, Stefano, Ludwig, Sarah M, Selbmann, Anne-Katrin, Sullivan, Patrick F, Abbott, Benjamin W, Arndt, Kyle A, Birch, Leah, Björkman, Mats P, Bloom, A Anthony, Celis, Gerardo, Christensen, Torben R, Christiansen, Casper T, Commane, Roisin, Cooper, Elisabeth J, Crill, Patrick, Czimczik, Claudia, Davydov, Sergey, Du, Jinyang, Egan, Jocelyn E, Elberling, Bo, Euskirchen, Eugenie S, Friborg, Thomas, Genet, Hélène, Göckede, Mathias, Goodrich, Jordan P, Grogan, Paul, Helbig, Manuel, Jafarov, Elchin E, Jastrow, Julie D, Kalhori, Aram AM, Kim, Yongwon, Kimball, John S, Kutzbach, Lars, Lara, Mark J, Larsen, Klaus S, Lee, Bang-Yong, Liu, Zhihua, Loranty, Michael M, Lund, Magnus, Lupascu, Massimo, Madani, Nima, Malhotra, Avni, Matamala, Roser, McFarland, Jack, McGuire, A David, Michelsen, Anders, Minions, Christina, Oechel, Walter C, Olefeldt, David, Parmentier, Frans-Jan W, Pirk, Norbert, Poulter, Ben, Quinton, William, Rezanezhad, Fereidoun, Risk, David, Sachs, Torsten, Schaefer, Kevin, Schmidt, Niels M, Schuur, Edward AG, Semenchuk, Philipp R, Shaver, Gaius, Sonnentag, Oliver, Starr, Gregory, Treat, Claire C, Waldrop, Mark P, Wang, Yihui, Welker, Jeffrey, Wille, Christian, Xu, Xiaofeng, Zhang, Zhen, Zhuang, Qianlai, and Zona, Donatella

Subjects
Agricultural, Veterinary and Food Sciences, Biological Sciences, Forestry Sciences, Climate Action, Atmospheric Sciences, Physical Geography and Environmental Geoscience, Environmental Science and Management
Abstract

Recent warming in the Arctic, which has been amplified during the winter1-3, greatly enhances microbial decomposition of soil organic matter and subsequent release of carbon dioxide (CO2)4. However, the amount of CO2 released in winter is highly uncertain and has not been well represented by ecosystem models or by empirically-based estimates5,6. Here we synthesize regional in situ observations of CO2 flux from arctic and boreal soils to assess current and future winter carbon losses from the northern permafrost domain. We estimate a contemporary loss of 1662 Tg C yr-1 from the permafrost region during the winter season (October through April). This loss is greater than the average growing season carbon uptake for this region estimated from process models (-1032 Tg C yr-1). Extending model predictions to warmer conditions in 2100 indicates that winter CO2 emissions will increase 17% under a moderate mitigation scenario-Representative Concentration Pathway (RCP) 4.5-and 41% under business-as-usual emissions scenario-RCP 8.5. Our results provide a new baseline for winter CO2 emissions from northern terrestrial regions and indicate that enhanced soil CO2 loss due to winter warming may offset growing season carbon uptake under future climatic conditions.

Published
2019

15. Supplementary material to "WetCH4: A Machine Learning-based Upscaling of Methane Fluxes of Northern Wetlands during 2016–2022"

Author

Ying, Qing, primary, Poulter, Benjamin, additional, Watts, Jennifer D., additional, Arndt, Kyle A., additional, Virkkala, Anna-Maria, additional, Bruhwiler, Lori, additional, Oh, Youmi, additional, Rogers, Brendan M., additional, Natali, Susan M., additional, Sullivan, Hilary, additional, Schiferl, Luke D., additional, Elder, Clayton, additional, Peltola, Olli, additional, Bartsch, Annett, additional, Armstrong, Amanda, additional, Desai, Ankur R., additional, Euskirchen, Eugénie, additional, Göckede, Mathias, additional, Lehner, Bernhard, additional, Nilsson, Mats B., additional, Peichl, Matthias, additional, Sonnentag, Oliver, additional, Tuittila, Eeva-Stiina, additional, Sachs, Torsten, additional, Kalhori, Aram, additional, Ueyama, Masahito, additional, and Zhang, Zhen, additional

Published
2024
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16. WetCH4: A Machine Learning-based Upscaling of Methane Fluxes of Northern Wetlands during 2016–2022

Author

Ying, Qing, primary, Poulter, Benjamin, additional, Watts, Jennifer D., additional, Arndt, Kyle A., additional, Virkkala, Anna-Maria, additional, Bruhwiler, Lori, additional, Oh, Youmi, additional, Rogers, Brendan M., additional, Natali, Susan M., additional, Sullivan, Hilary, additional, Schiferl, Luke D., additional, Elder, Clayton, additional, Peltola, Olli, additional, Bartsch, Annett, additional, Armstrong, Amanda, additional, Desai, Ankur R., additional, Euskirchen, Eugénie, additional, Göckede, Mathias, additional, Lehner, Bernhard, additional, Nilsson, Mats B., additional, Peichl, Matthias, additional, Sonnentag, Oliver, additional, Tuittila, Eeva-Stiina, additional, Sachs, Torsten, additional, Kalhori, Aram, additional, Ueyama, Masahito, additional, and Zhang, Zhen, additional

Published
2024
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18. Remote sensing supporting the Arctic Methane and Permafrost Challenge (AMPAC)

Author

Bartsch, Annett, primary, Hugelius, Gustaf, additional, Grosse, Guido, additional, Hashemi, Joshua, additional, Treat, Clair, additional, Goeckede, Mathias, additional, Tamminen, Johanna, additional, Fix, Andreas, additional, Sachs, Torsten, additional, Houweling, Sander, additional, Bergstedt, Helena, additional, and Widhalm, Barbara, additional

Published
2024
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19. 15 years of Integrated Terrestrial Environmental Observatories (TERENO) in Germany: Functions, Services and Lessons Learned

Author

Zacharias, Steffen, primary, Loescher, Henry W, additional, Bogena, Heye, additional, Kiese, Ralf, additional, Schrön, Martin, additional, Attinger, Sabine, additional, Blume, Theresa, additional, Borchardt, Dietrich, additional, Borg, Erik, additional, Bumberger, Jan, additional, Chwala, Christian, additional, Dietrich, Peter, additional, Fersch, Benjamin, additional, Frenzel, Mark, additional, Gaillardet, Jerome, additional, Groh, Jannis, additional, Hajnsek, Irena, additional, Itzerott, Sibylle, additional, Kunkel, Ralf, additional, Kunstmann, Harald Günter, additional, Kunz, Matthias, additional, Liebner, Susanne, additional, Mirtl, Michael, additional, Montzka, Carsten, additional, Musolff, Andreas, additional, Pütz, Thomas, additional, Rebmann, Corinna, additional, Rinke, Karsten, additional, Rode, Michael, additional, Sachs, Torsten, additional, Samaniego, Luis E., additional, Schmid, Hans-Peter (HaPe), additional, Vogel, Hans-Jörg, additional, Weber, Ute, additional, Wollschläger, Ute, additional, and Vereecken, Harry, additional

Published
2024
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20. 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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21. Vegetation type is an important predictor of the arctic summer land surface energy budget

Author

Oehri, Jacqueline, Schaepman-Strub, Gabriela, Kim, Jin-Soo, Grysko, Raleigh, Kropp, Heather, Grünberg, Inge, Zemlianskii, Vitalii, Sonnentag, Oliver, Euskirchen, Eugénie S., Reji Chacko, Merin, Muscari, Giovanni, Blanken, Peter D., Dean, Joshua F., di Sarra, Alcide, Harding, Richard J., Sobota, Ireneusz, Kutzbach, Lars, Plekhanova, Elena, Riihelä, Aku, Boike, Julia, Miller, Nathaniel B., Beringer, Jason, López-Blanco, Efrén, Stoy, Paul C., Sullivan, Ryan C., Kejna, Marek, Parmentier, Frans-Jan W., Gamon, John A., Mastepanov, Mikhail, Wille, Christian, Jackowicz-Korczynski, Marcin, Karger, Dirk N., Quinton, William L., Putkonen, Jaakko, van As, Dirk, Christensen, Torben R., Hakuba, Maria Z., Stone, Robert S., Metzger, Stefan, Vandecrux, Baptiste, Frost, Gerald V., Wild, Martin, Hansen, Birger, Meloni, Daniela, Domine, Florent, te Beest, Mariska, Sachs, Torsten, Kalhori, Aram, Rocha, Adrian V., Williamson, Scott N., Morris, Sara, Atchley, Adam L., Essery, Richard, Runkle, Benjamin R. K., Holl, David, Riihimaki, Laura D., Iwata, Hiroki, Schuur, Edward A. G., Cox, Christopher J., Grachev, Andrey A., McFadden, Joseph P., Fausto, Robert S., Göckede, Mathias, Ueyama, Masahito, Pirk, Norbert, de Boer, Gijs, Bret-Harte, M. Syndonia, Leppäranta, Matti, Steffen, Konrad, Friborg, Thomas, Ohmura, Atsumu, Edgar, Colin W., Olofsson, Johan, and Chambers, Scott D.

Published
2022
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22. 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

23. 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

25. ORCHIDEE-PEAT (revision 4596), a model for northern peatland CO2, water, and energy fluxes on daily to annual scales

Author

Qiu, Chunjing, Zhu, Dan, Ciais, Philippe, Guenet, Bertrand, Krinner, Gerhard, Peng, Shushi, Aurela, Mika, Bernhofer, Christian, Brümmer, Christian, Bret-Harte, Syndonia, Chu, Housen, Chen, Jiquan, Desai, Ankur R, Dušek, Jiří, Euskirchen, Eugénie S, Fortuniak, Krzysztof, Flanagan, Lawrence B, Friborg, Thomas, Grygoruk, Mateusz, Gogo, Sébastien, Grünwald, Thomas, Hansen, Birger U, Holl, David, Humphreys, Elyn, Hurkuck, Miriam, Kiely, Gerard, Klatt, Janina, Kutzbach, Lars, Largeron, Chloé, Laggoun-Défarge, Fatima, Lund, Magnus, Lafleur, Peter M, Li, Xuefei, Mammarella, Ivan, Merbold, Lutz, Nilsson, Mats B, Olejnik, Janusz, Ottosson-Löfvenius, Mikaell, Oechel, Walter, Parmentier, Frans-Jan W, Peichl, Matthias, Pirk, Norbert, Peltola, Olli, Pawlak, Włodzimierz, Rasse, Daniel, Rinne, Janne, Shaver, Gaius, Schmid, Hans Peter, Sottocornola, Matteo, Steinbrecher, Rainer, Sachs, Torsten, Urbaniak, Marek, Zona, Donatella, and Ziemblinska, Klaudia

Subjects
Earth Sciences, Atmospheric Sciences, Earth sciences
Abstract

Peatlands store substantial amounts of carbon and are vulnerable to climate change. We present a modified version of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model for simulating the hydrology, surface energy, and CO2 fluxes of peatlands on daily to annual timescales. The model includes a separate soil tile in each 0.5° grid cell, defined from a global peatland map and identified with peat-specific soil hydraulic properties. Runoff from non-peat vegetation within a grid cell containing a fraction of peat is routed to this peat soil tile, which maintains shallow water tables. The water table position separates oxic from anoxic decomposition. The model was evaluated against eddy-covariance (EC) observations from 30 northern peatland sites, with the maximum rate of carboxylation (Vcmax) being optimized at each site. Regarding short-term day-to-day variations, the model performance was good for gross primary production (GPP) (r2 Combining double low line 0.76; Nash-Sutcliffe modeling efficiency, MEF Combining double low line 0.76) and ecosystem respiration (ER, r2 Combining double low line 0.78, MEF Combining double low line 0.75), with lesser accuracy for latent heat fluxes (LE, r2 Combining double low line 0.42, MEF Combining double low line 0.14) and and net ecosystem CO2 exchange (NEE, r2 Combining double low line 0.38, MEF Combining double low line 0.26). Seasonal variations in GPP, ER, NEE, and energy fluxes on monthly scales showed moderate to high r2 values (0.57-0.86). For spatial across-site gradients of annual mean GPP, ER, NEE, and LE, r2 values of 0.93, 0.89, 0.27, and 0.71 were achieved, respectively. Water table (WT) variation was not well predicted (r2

Published
2018

30. A Comprehensive Assessment of Carbon Dioxide Removal Options for Germany

Author

Borchers, Malgorzata, primary, Förster, Johannes, additional, Thrän, Daniela, additional, Beck, Silke, additional, Thoni, Terese, additional, Korte, Klaas, additional, Gawel, Erik, additional, Markus, Till, additional, Schaller, Romina, additional, Rhoden, Imke, additional, Chi, Yaxuan, additional, Dahmen, Nicolaus, additional, Dittmeyer, Roland, additional, Dolch, Tobias, additional, Dold, Christian, additional, Herbst, Michael, additional, Heß, Dominik, additional, Kalhori, Aram, additional, Koop-Jakobsen, Ketil, additional, Li, Zhan, additional, Oschlies, Andreas, additional, Reusch, Thorsten B.H., additional, Sachs, Torsten, additional, Schmidt-Hattenberger, Cornelia, additional, Stevenson, Angela, additional, WU, Jiajun, additional, Yeates, Christopher, additional, and Mengis, Nadine, additional

Published
2023
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32. Temperature thresholds of ecosystem respiration at a global scale

Author

Johnston, Alice S. A., Meade, Andrew, Ardö, Jonas, Arriga, Nicola, Black, Andy, Blanken, Peter D., Bonal, Damien, Brümmer, Christian, Cescatti, Alessandro, Dušek, Jiří, Graf, Alexander, Gioli, Beniamino, Goded, Ignacio, Gough, Christopher M., Ikawa, Hiroki, Jassal, Rachhpal, Kobayashi, Hideki, Magliulo, Vincenzo, Manca, Giovanni, Montagnani, Leonardo, Moyano, Fernando E., Olesen, Jørgen E., Sachs, Torsten, Shao, Changliang, Tagesson, Torbern, Wohlfahrt, Georg, Wolf, Sebastian, Woodgate, William, Varlagin, Andrej, and Venditti, Chris

Published
2021
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33. 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, 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, 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

Published
2021
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34. Corrigendum: Diel, seasonal, and inter-annual variation in carbon dioxide effluxes from lakes and reservoirs (2023 Environ. Res. Lett. 18 034046)

Author

Golub, Malgorzata, primary, Koupaei-Abyazani, Nikaan, additional, Vesala, Timo, additional, Mammarella, Ivan, additional, Ojala, Anne, additional, Bohrer, Gil, additional, Weyhenmeyer, Gesa A, additional, Blanken, Peter D, additional, Eugster, Werner, additional, Koebsch, Franziska, additional, Chen, Jiquan, additional, Czajkowski, Kevin, additional, Deshmukh, Chandrashekhar, additional, Guérin, Frederic, additional, Heiskanen, Jouni, additional, Humphreys, Elyn, additional, Jonsson, Anders, additional, Karlsson, Jan, additional, Kling, George, additional, Lee, Xuhui, additional, Liu, Heping, additional, Lohila, Annalea, additional, Lundin, Erik, additional, Morin, Tim, additional, Podgrajsek, Eva, additional, Provenzale, Maria, additional, Rutgersson, Anna, additional, Sachs, Torsten, additional, Sahlée, Erik, additional, Serça, Dominique, additional, Shao, Changliang, additional, Spence, Christopher, additional, Strachan, Ian B, additional, Xiao, Wei, additional, and Desai, Ankur R, additional

Published
2023
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35. Super-high-resolution aerial imagery datasets of permafrost landscapes in Alaska and northwestern Canada

Author

Rettelbach, Tabea, primary, Nitze, Ingmar, additional, Grünberg, Inge, additional, Hammar, Jennika, additional, Schäffler, Simon, additional, Hein, Daniel, additional, Gessner, Matthias, additional, Bucher, Tilman, additional, Brauchle, Jörg, additional, Hartmann, Jörg, additional, Sachs, Torsten, additional, Boike, Julia, additional, and Grosse, Guido, additional

Published
2023
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37. The uncertain climate footprint of wetlands under human pressure

Author

Petrescu, Ana Maria Roxana, Lohila, Annalea, Tuovinen, Juha-Pekka, Baldocchi, Dennis D, Desai, Ankur R, Roulet, Nigel T, Vesala, Timo, Dolman, Albertus Johannes, Oechel, Walter C, Marcolla, Barbara, Friborg, Thomas, Rinne, Janne, Matthes, Jaclyn Hatala, Merbold, Lutz, Meijide, Ana, Kiely, Gerard, Sottocornola, Matteo, Sachs, Torsten, Zona, Donatella, Varlagin, Andrej, Lai, Derrick YF, Veenendaal, Elmar, Parmentier, Frans-Jan W, Skiba, Ute, Lund, Magnus, Hensen, Arjan, van Huissteden, Jacobus, Flanagan, Lawrence B, Shurpali, Narasinha J, Grünwald, Thomas, Humphreys, Elyn R, Jackowicz-Korczyński, Marcin, Aurela, Mika A, Laurila, Tuomas, Grüning, Carsten, Corradi, Chiara AR, Schrier-Uijl, Arina P, Christensen, Torben R, Tamstorf, Mikkel P, Mastepanov, Mikhail, Martikainen, Pertti J, Verma, Shashi B, Bernhofer, Christian, and Cescatti, Alessandro

Subjects
Climate Action, Life on Land, Carbon Dioxide, Climate, Climate Change, Ecology, Ecosystem, Geography, Human Activities, Humans, Methane, Models, Theoretical, Nitrous Oxide, Plants, Temperature, Uncertainty, Wetlands, wetland conversion, methane, radiative forcing, carbon dioxide
Abstract

Significant climate risks are associated with a positive carbon-temperature feedback in northern latitude carbon-rich ecosystems, making an accurate analysis of human impacts on the net greenhouse gas balance of wetlands a priority. Here, we provide a coherent assessment of the climate footprint of a network of wetland sites based on simultaneous and quasi-continuous ecosystem observations of CO2 and CH4 fluxes. Experimental areas are located both in natural and in managed wetlands and cover a wide range of climatic regions, ecosystem types, and management practices. Based on direct observations we predict that sustained CH4 emissions in natural ecosystems are in the long term (i.e., several centuries) typically offset by CO2 uptake, although with large spatiotemporal variability. Using a space-for-time analogy across ecological and climatic gradients, we represent the chronosequence from natural to managed conditions to quantify the "cost" of CH4 emissions for the benefit of net carbon sequestration. With a sustained pulse-response radiative forcing model, we found a significant increase in atmospheric forcing due to land management, in particular for wetland converted to cropland. Our results quantify the role of human activities on the climate footprint of northern wetlands and call for development of active mitigation strategies for managed wetlands and new guidelines of the Intergovernmental Panel on Climate Change (IPCC) accounting for both sustained CH4 emissions and cumulative CO2 exchange.

Published
2015

38. Monitoring cropland daily carbon dioxide exchange at field scales with Sentinel-2 satellite imagery.

Author

Gottschalk, Pia, Kalhori, Aram, Li, Zhan, Wille, Christian, and Sachs, Torsten

Subjects
REMOTE-sensing images, CARBON dioxide, FARMS, AGRICULTURE, LEAF area, ESTIMATES
Abstract

Improving the accuracy of monitoring cropland CO2 exchange at heterogeneous spatial scales is of high importance to limit spatial and temporal uncertainty of terrestrial carbon (C) dynamic estimates. A combination of field scale eddy covariance (EC) CO2 flux data and spatially matched Sentinel-2 derived vegetation indices (VIs) was tested as an approach to upscale agricultural C flux. The ability of different VIs to estimate daily net ecosystem exchange (NEE) and gross primary productivity (GPP) based on linear regression models was assessed. Most VIs showed high (>0.9) and statistically significant (p<0.001) correlations with GPP and NEE although some VIs deviated from the seasonal pattern of CO2 exchange. In contrast, correlations between ecosystem respiration (Reco) and VIs were weak and statistically not significant, and no attempt was made to estimate Reco from VIs. Linear regression models explained generally more than 80 % and 70 % of the variability of NEE and GPP, respectively, with high variability amongst the individual VIs. The performance in estimating daily C fluxes varied amongst VIs depending on C flux component (NEE or GPP) and observation period. RMSE values ranged from 1.35 g C m-2 d-1 using the Green Normilized Difference Vegetation Index (GNDVI) for NEE to 5 g C m-2 d-1 using the Simple Ratio (SR) for GPP. This equated to an underestimated net C uptake of only 41 g C m-2 (18 %) and an overestimation of gross C uptake of 854 g C m-2 (73 %). Differences between measured and estimated C fluxes were mainly explained by the diversion of the C flux and VI signal during winter, when C uptake stayed low while VI values indicated an increased C uptake due to relatively high crop leaf area. Overall, results exhibited similar error margins as mechanistic crop models. Thus, they indicated suitability and developability of the proposed approach to monitor cropland C exchange with satellite derived VIs. [ABSTRACT FROM AUTHOR]

Published
2024
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39. Characterization of atmospheric methane release in the outer Mackenzie River delta from biogenic and thermogenic sources.

Author

Wesley, Daniel, Dallimore, Scott, MacLeod, Roger, Sachs, Torsten, and Risk, David

Subjects
STABLE isotope analysis, NATURAL gas reserves, WETLAND soils, ATMOSPHERIC methane, CARBON isotopes, STABLE isotopes, CARBON dioxide
Abstract

The Mackenzie River delta is the second largest Arctic river delta in the world. Thin and destabilizing permafrost coupled with vast natural gas reserves at depth, high organic-content soils, and a high proportion of wetlands create a unique ecosystem conducive to high rates of methane (CH 4) emission from biogenic and thermogenic sources. Hotspots are known to have a significant contribution to summertime CH 4 emissions in the region. Still, little research has been done to determine how often geologic or biogenic CH 4 contributes to hotspots in the Mackenzie River delta. In the present study, stable carbon isotope analysis was used to identify the source of CH 4 at several aquatic and terrestrial sites thought to be hotspots of CH 4 flux to the atmosphere. Walking transects and point samples of atmospheric CH 4 and CO 2 concentrations were measured. Source stable carbon isotope (δ13 C-CH 4) signatures were derived from keeling plots of point samples and ranged from -42‰ to -88‰ δ13 C-CH 4 , identifying both biogenic and thermogenic sources. A CH 4 source was determined for eight hotspots, two of which were thermogenic in origin (-42.5‰ , -44.7‰), four of which were biogenic in origin (-71.9‰ to -88.3‰), and two of which may have been produced by the oxidation of biogenic CH 4 (-53.0‰ , -63.6‰), as evidenced by δ13 C-CH 4 signatures. This indicates that the largest hotspots of CH 4 production in the Mackenzie River delta are caused by a variety of sources. [ABSTRACT FROM AUTHOR]

Published
2023
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40. 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, 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, Gabriela, Powell, Thomas, Prasse, Heiko, Prober, Suzanne M., Rambal, Serge, Rannik, Üllar, Raz-Yaseef, Naama, Rebmann, Corinna, Reed, David, Dios, Victor Resco de, 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

Published
2020
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41. High methane production in drained lake basin wetlands in northern Alaska

Author

Strauss, Jens, Wolter, Juliane, Jones, Benjamin M, Fuchs, Matthias, Bußmann, Ingeborg Katharina, Lenz, Josefine, Myers-Smith, Isla H, Sachs, Torsten, and Grosse, Guido

Abstract

Wetlands in drained lake basins are important elements of the Arctic carbon budget. They may store large amounts of carbon while also producing substantial amounts of greenhouse gasses. After lake drainage the former lake bottom is colonized by pioneer graminoids, succeeded by mosssedge-dwarf shrub vegetation, producing a typical peat sequence. However, post-drainage organic matter dynamics are not well studied. We hypothesize that vegetation composition reflects both succession and surface wetness, which in turn determine soil organic matter content and methane production. We propose that vegetation types detected by remote sensing-based landcover classification may be used to extrapolate methane production and organic matter composition across drained lake basin landscapes. We investigated (i) plots along a temporal drainage gradient, surveying vegetation, surface sediment, and pond water. We then used (ii) landcover classification of main eco-hydrological classes to (iii) upscale from plot to basin scale. We found that vegetation and organic matter changed markedly between recently drained basins and older age classes. Overall, vegetation composition differed more between eco-hydrological classes than between age classes. Surface sediments had very high water contents (>80 %), suggesting largely anaerobic conditions favouring methane production. Methane concentrations were indeed relatively constant throughout, and particularly high in sediments beneath few centimetres of water (“wet patches”, up to 200 μmol/L) and in pond water (up to 22 μmol/L). Landcover classification yielded seven classes including five classes we also identified using statistical clustering of vegetation data plus a water class and a bare ground class. We found that 67 % of basin areas were occupied by wet patches with especially high methane production. Our study shows that remote sensing-based landcover classifications are useful for quantifying wet-vs-moist patches and high-vs-moderate methane production in Arctic drained lake basins. The study highlights the potential for future upscaling of methane emissions from these abundant wetland environments.

Published
2023

42. Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method

Author

Yao, Yunjun, Liang, Shunlin, Li, Xianglan, Zhang, Yuhu, Chen, Jiquan, Jia, Kun, Zhang, Xiaotong, Fisher, Joshua B., Wang, Xuanyu, Zhang, Lilin, Xu, Jia, Shao, Changliang, Posse, Gabriela, Li, Yingnian, Magliulo, Vincenzo, Varlagin, Andrej, Moors, Eddy J., Boike, Julia, Macfarlane, Craig, Kato, Tomomichi, Buchmann, Nina, Billesbach, D.P., Beringer, Jason, Wolf, Sebastian, Papuga, Shirley A., Wohlfahrt, Georg, Montagnani, Leonardo, Ardö, Jonas, Paul-Limoges, Eugénie, Emmel, Carmen, Hörtnagl, Lukas, Sachs, Torsten, Gruening, Carsten, Gioli, Beniamino, López-Ballesteros, Ana, Steinbrecher, Rainer, and Gielen, Bert

Published
2017
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45. Diel, seasonal, and inter-annual variation in carbon dioxide effluxes from lakes and reservoirs

Author

Golub, Malgorzata, primary, Koupaei-Abyazani, Nikaan, additional, Vesala, Timo, additional, Mammarella, Ivan, additional, Ojala, Anne, additional, Bohrer, Gil, additional, Weyhenmeyer, Gesa A, additional, Blanken, Peter D, additional, Eugster, Werner, additional, Koebsch, Franziska, additional, Chen, Jiquan, additional, Czajkowski, Kevin, additional, Deshmukh, Chandrashekhar, additional, Guérin, Frederic, additional, Heiskanen, Jouni, additional, Humphreys, Elyn, additional, Jonsson, Anders, additional, Karlsson, Jan, additional, Kling, George, additional, Lee, Xuhui, additional, Liu, Heping, additional, Lohila, Annalea, additional, Lundin, Erik, additional, Morin, Tim, additional, Podgrajsek, Eva, additional, Provenzale, Maria, additional, Rutgersson, Anna, additional, Sachs, Torsten, additional, Sahlée, Erik, additional, Serça, Dominique, additional, Shao, Changliang, additional, Spence, Christopher, additional, Strachan, Ian B, additional, Xiao, Wei, additional, and Desai, Ankur R, additional

Published
2023
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