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1. Phenology of Photosynthesis in Winter-Dormant Temperate and Boreal Forests:Long-Term Observations From Flux Towers and Quantitative Evaluation of Phenology Models

Author: Bowling, David R., Schädel, Christina, Smith, Kenneth R., Richardson, Andrew D., Bahn, Michael, Arain, M. Altaf, Varlagin, Andrej, Ouimette, Andrew P., Frank, John M., Barr, Alan G., Mammarella, Ivan, Šigut, Ladislav, Foord, Vanessa, Burns, Sean P., Montagnani, Leonardo, Litvak, Marcy E., Munger, J. William, Ikawa, Hiroki, Hollinger, David Y., Blanken, Peter D., Ueyama, Masahito, Matteucci, Giorgio, Bernhofer, Christian, Bohrer, Gil, Iwata, Hiroki, Ibrom, Andreas, Pilegaard, Kim, Spittlehouse, David L., Kobayashi, Hideki, Desai, Ankur R., Staebler, Ralf M., Black, T. Andrew, Bowling, David R., Schädel, Christina, Smith, Kenneth R., Richardson, Andrew D., Bahn, Michael, Arain, M. Altaf, Varlagin, Andrej, Ouimette, Andrew P., Frank, John M., Barr, Alan G., Mammarella, Ivan, Šigut, Ladislav, Foord, Vanessa, Burns, Sean P., Montagnani, Leonardo, Litvak, Marcy E., Munger, J. William, Ikawa, Hiroki, Hollinger, David Y., Blanken, Peter D., Ueyama, Masahito, Matteucci, Giorgio, Bernhofer, Christian, Bohrer, Gil, Iwata, Hiroki, Ibrom, Andreas, Pilegaard, Kim, Spittlehouse, David L., Kobayashi, Hideki, Desai, Ankur R., Staebler, Ralf M., and Black, T. Andrew
Abstract: We examined the seasonality of photosynthesis in 46 evergreen needleleaf (evergreen needleleaf forests (ENF)) and deciduous broadleaf (deciduous broadleaf forests (DBF)) forests across North America and Eurasia. We quantified the onset and end (StartGPP and EndGPP) of photosynthesis in spring and autumn based on the response of net ecosystem exchange of CO2 to sunlight. To test the hypothesis that snowmelt is required for photosynthesis to begin, these were compared with end of snowmelt derived from soil temperature. ENF forests achieved 10% of summer photosynthetic capacity ∼3 weeks before end of snowmelt, while DBF forests achieved that capacity ∼4 weeks afterward. DBF forests increased photosynthetic capacity in spring faster (1.95% d−1) than ENF (1.10% d−1), and their active season length (EndGPP–StartGPP) was ∼50 days shorter. We hypothesized that warming has influenced timing of the photosynthesis season. We found minimal evidence for long-term change in StartGPP, EndGPP, or air temperature, but their interannual anomalies were significantly correlated. Warmer weather was associated with earlier StartGPP (1.3–2.5 days °C−1) or later EndGPP (1.5–1.8 days °C−1, depending on forest type and month). Finally, we tested whether existing phenological models could predict StartGPP and EndGPP. For ENF forests, air temperature- and daylength-based models provided best predictions for StartGPP, while a chilling-degree-day model was best for EndGPP. The root mean square errors (RMSE) between predicted and observed StartGPP and EndGPP were 11.7 and 11.3 days, respectively. For DBF forests, temperature- and daylength-based models yielded the best results (RMSE 6.3 and 10.5 days).
Published: 2024

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

Author: Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A., Blanken, Peter D, Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, Desai, Ankur R., Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A., Blanken, Peter D, Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, and Desai, Ankur R.
Abstract: Accounting for temporal changes in carbon dioxide (CO2) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO2 based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO2 flux at all sites. By accounting for diel variation, only 11% of site-months were net daily sinks of CO2. Annual CO2 emissions had an average of 25% (range 3%–58%) interannual variation. Similar to studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO2 flux variability were delineated through mutual information analysis. Sample analysis of CO2 fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions., Correction in: Environmental Research Letters, vol. 18, issue 10, article number 109501DOI: 10.1088/1748-9326/acfb97
Published: 2023
Full Text: View/download PDF

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

Author: Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A., Blanken, Peter D, Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, Desai, Ankur R., Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A., Blanken, Peter D, Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, and Desai, Ankur R.
Abstract: Accounting for temporal changes in carbon dioxide (CO2) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO2 based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO2 flux at all sites. By accounting for diel variation, only 11% of site-months were net daily sinks of CO2. Annual CO2 emissions had an average of 25% (range 3%–58%) interannual variation. Similar to studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO2 flux variability were delineated through mutual information analysis. Sample analysis of CO2 fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions., Correction in: Environmental Research Letters, vol. 18, issue 10, article number 109501DOI: 10.1088/1748-9326/acfb97
Published: 2023
Full Text: View/download PDF

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

Author: Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A, Blanken, Peter D., Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, Desai, Ankur R., Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A, Blanken, Peter D., Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, and Desai, Ankur R.
Abstract: Accounting for temporal changes in carbon dioxide (CO2) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO2 based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO2 flux at all sites. By accounting for diel variation, only 11% of site-months were net daily sinks of CO2. Annual CO2 emissions had an average of 25% (range 3%-58%) interannual variation. Similar to studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO2 flux variability were delineated through mutual information analysis. Sample analysis of CO2 fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions.
Published: 2023
Full Text: View/download PDF

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

Author: Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A., Blanken, Peter D, Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, Desai, Ankur R., Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A., Blanken, Peter D, Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, and Desai, Ankur R.
Abstract: Accounting for temporal changes in carbon dioxide (CO2) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO2 based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO2 flux at all sites. By accounting for diel variation, only 11% of site-months were net daily sinks of CO2. Annual CO2 emissions had an average of 25% (range 3%–58%) interannual variation. Similar to studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO2 flux variability were delineated through mutual information analysis. Sample analysis of CO2 fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions., Correction in: Environmental Research Letters, vol. 18, issue 10, article number 109501DOI: 10.1088/1748-9326/acfb97
Published: 2023
Full Text: View/download PDF

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

Author: Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A, Blanken, Peter D., Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, Desai, Ankur R., Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A, Blanken, Peter D., Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, and Desai, Ankur R.
Abstract: Accounting for temporal changes in carbon dioxide (CO2) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO2 based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO2 flux at all sites. By accounting for diel variation, only 11% of site-months were net daily sinks of CO2. Annual CO2 emissions had an average of 25% (range 3%-58%) interannual variation. Similar to studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO2 flux variability were delineated through mutual information analysis. Sample analysis of CO2 fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions.
Published: 2023
Full Text: View/download PDF

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

Author: Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A, Blanken, Peter D., Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, Desai, Ankur R., Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A, Blanken, Peter D., Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, and Desai, Ankur R.
Abstract: Accounting for temporal changes in carbon dioxide (CO2) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO2 based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO2 flux at all sites. By accounting for diel variation, only 11% of site-months were net daily sinks of CO2. Annual CO2 emissions had an average of 25% (range 3%-58%) interannual variation. Similar to studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO2 flux variability were delineated through mutual information analysis. Sample analysis of CO2 fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions.
Published: 2023
Full Text: View/download PDF

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

Author: Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A., Blanken, Peter D, Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, Desai, Ankur R., Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A., Blanken, Peter D, Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, and Desai, Ankur R.
Abstract: Accounting for temporal changes in carbon dioxide (CO2) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO2 based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO2 flux at all sites. By accounting for diel variation, only 11% of site-months were net daily sinks of CO2. Annual CO2 emissions had an average of 25% (range 3%–58%) interannual variation. Similar to studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO2 flux variability were delineated through mutual information analysis. Sample analysis of CO2 fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions., Correction in: Environmental Research Letters, vol. 18, issue 10, article number 109501DOI: 10.1088/1748-9326/acfb97
Published: 2023
Full Text: View/download PDF

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

Author: Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A, Blanken, Peter D., Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, Desai, Ankur R., Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A, Blanken, Peter D., Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, and Desai, Ankur R.
Abstract: Accounting for temporal changes in carbon dioxide (CO2) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO2 based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO2 flux at all sites. By accounting for diel variation, only 11% of site-months were net daily sinks of CO2. Annual CO2 emissions had an average of 25% (range 3%-58%) interannual variation. Similar to studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO2 flux variability were delineated through mutual information analysis. Sample analysis of CO2 fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions.
Published: 2023
Full Text: View/download PDF

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

Author: Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A, Blanken, Peter D., Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, Desai, Ankur R., Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A, Blanken, Peter D., Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, and Desai, Ankur R.
Abstract: Accounting for temporal changes in carbon dioxide (CO2) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO2 based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO2 flux at all sites. By accounting for diel variation, only 11% of site-months were net daily sinks of CO2. Annual CO2 emissions had an average of 25% (range 3%-58%) interannual variation. Similar to studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO2 flux variability were delineated through mutual information analysis. Sample analysis of CO2 fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions.
Published: 2023
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11. Diel, seasonal, and inter-annual variation in carbon dioxide effluxes from lakes and reservoirs

Author: Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A., Blanken, Peter D, Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, Desai, Ankur R., Golub, Malgorzata, Koupaei-Abyazani, Nikaan, Vesala, Timo, Mammarella, Ivan, Ojala, Anne, Bohrer, Gil, Weyhenmeyer, Gesa A., Blanken, Peter D, Eugster, Werner, Koebsch, Franziska, Chen, Jiquan, Czajkowski, Kevin, Deshmukh, Chandrashekhar, Guérin, Frederic, Heiskanen, Jouni, Humphreys, Elyn, Jonsson, Anders, Karlsson, Jan, Kling, George, Lee, Xuhui, Liu, Heping, Lohila, Annalea, Lundin, Erik, Morin, Tim, Podgrajsek, Eva, Provenzale, Maria, Rutgersson, Anna, Sachs, Torsten, Sahlée, Erik, Serça, Dominique, Shao, Changliang, Spence, Christopher, Strachan, Ian B., Xiao, Wei, and Desai, Ankur R.
Abstract: Accounting for temporal changes in carbon dioxide (CO2) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO2 based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO2 flux at all sites. By accounting for diel variation, only 11% of site-months were net daily sinks of CO2. Annual CO2 emissions had an average of 25% (range 3%–58%) interannual variation. Similar to studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO2 flux variability were delineated through mutual information analysis. Sample analysis of CO2 fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions., Correction in: Environmental Research Letters, vol. 18, issue 10, article number 109501DOI: 10.1088/1748-9326/acfb97
Published: 2023
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12. Coupling plant litter quantity to a novel metric for litter quality explains C storage changes in a thawing permafrost peatland.

Author: Hough, Moira, Hough, Moira, McCabe, Samantha, Vining, S Rose, Pickering Pedersen, Emily, Wilson, Rachel M, Lawrence, Ryan, Chang, Kuang-Yu, Bohrer, Gil, IsoGenie Coordinators, Riley, William J, Crill, Patrick M, Varner, Ruth K, Blazewicz, Steven J, Dorrepaal, Ellen, Tfaily, Malak M, Saleska, Scott R, Rich, Virginia I, Hough, Moira, Hough, Moira, McCabe, Samantha, Vining, S Rose, Pickering Pedersen, Emily, Wilson, Rachel M, Lawrence, Ryan, Chang, Kuang-Yu, Bohrer, Gil, IsoGenie Coordinators, Riley, William J, Crill, Patrick M, Varner, Ruth K, Blazewicz, Steven J, Dorrepaal, Ellen, Tfaily, Malak M, Saleska, Scott R, and Rich, Virginia I
Abstract: Permafrost thaw is a major potential feedback source to climate change as it can drive the increased release of greenhouse gases carbon dioxide (CO2 ) and methane (CH4 ). This carbon release from the decomposition of thawing soil organic material can be mitigated by increased net primary productivity (NPP) caused by warming, increasing atmospheric CO2 , and plant community transition. However, the net effect on C storage also depends on how these plant community changes alter plant litter quantity, quality, and decomposition rates. Predicting decomposition rates based on litter quality remains challenging, but a promising new way forward is to incorporate measures of the energetic favorability to soil microbes of plant biomass decomposition. We asked how the variation in one such measure, the nominal oxidation state of carbon (NOSC), interacts with changing quantities of plant material inputs to influence the net C balance of a thawing permafrost peatland. We found: (1) Plant productivity (NPP) increased post-thaw, but instead of contributing to increased standing biomass, it increased plant biomass turnover via increased litter inputs to soil; (2) Plant litter thermodynamic favorability (NOSC) and decomposition rate both increased post-thaw, despite limited changes in bulk C:N ratios; (3) these increases caused the higher NPP to cycle more rapidly through both plants and soil, contributing to higher CO2 and CH4 fluxes from decomposition. Thus, the increased C-storage expected from higher productivity was limited and the high global warming potential of CH4 contributed a net positive warming effect. Although post-thaw peatlands are currently C sinks due to high NPP offsetting high CO2 release, this status is very sensitive to the plant community's litter input rate and quality. Integration of novel bioavailability metrics based on litter chemistry, including NOSC, into studies of ecosystem dynamics, is needed to improve the understanding of controls on ar
Published: 2022

13. 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, Jackson, Robert, 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
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 +1C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH(4 )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
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14. Coupling plant litter quantity to a novel metric for litter quality explains C storage changes in a thawing permafrost peatland

Author: Hough, Moira, McCabe, Samantha, Vining, S. Rose, Pickering Pedersen, Emily, Wilson, Rachel M., Lawrence, Ryan, Chang, Kuang-Yu, Bohrer, Gil, Riley, William J., Crill, Patrick M., Varner, Ruth K., Blazewicz, Steven J., Dorrepaal, Ellen, Tfaily, Malak M., Saleska, Scott R., Rich, Virginia, I, Hough, Moira, McCabe, Samantha, Vining, S. Rose, Pickering Pedersen, Emily, Wilson, Rachel M., Lawrence, Ryan, Chang, Kuang-Yu, Bohrer, Gil, Riley, William J., Crill, Patrick M., Varner, Ruth K., Blazewicz, Steven J., Dorrepaal, Ellen, Tfaily, Malak M., Saleska, Scott R., and Rich, Virginia, I
Abstract: Permafrost thaw is a major potential feedback source to climate change as it can drive the increased release of greenhouse gases carbon dioxide (CO2) and methane (CH4). This carbon release from the decomposition of thawing soil organic material can be mitigated by increased net primary productivity (NPP) caused by warming, increasing atmospheric CO2, and plant community transition. However, the net effect on C storage also depends on how these plant community changes alter plant litter quantity, quality, and decomposition rates. Predicting decomposition rates based on litter quality remains challenging, but a promising new way forward is to incorporate measures of the energetic favorability to soil microbes of plant biomass decomposition. We asked how the variation in one such measure, the nominal oxidation state of carbon (NOSC), interacts with changing quantities of plant material inputs to influence the net C balance of a thawing permafrost peatland. We found: (1) Plant productivity (NPP) increased post-thaw, but instead of contributing to increased standing biomass, it increased plant biomass turnover via increased litter inputs to soil; (2) Plant litter thermodynamic favorability (NOSC) and decomposition rate both increased post-thaw, despite limited changes in bulk C:N ratios; (3) these increases caused the higher NPP to cycle more rapidly through both plants and soil, contributing to higher CO2 and CH4 fluxes from decomposition. Thus, the increased C-storage expected from higher productivity was limited and the high global warming potential of CH4 contributed a net positive warming effect. Although post-thaw peatlands are currently C sinks due to high NPP offsetting high CO2 release, this status is very sensitive to the plant community's litter input rate and quality. Integration of novel bioavailability metrics based on litter chemistry, including NOSC, into studies of ecosystem dynamics, is needed to improve the understanding of controls on arctic C stocks
Published: 2022
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15. Coupling plant litter quantity to a novel metric for litter quality explains C storage changes in a thawing permafrost peatland

Author: Hough, Moira, McCabe, Samantha, Vining, S. Rose, Pickering Pedersen, Emily, Wilson, Rachel M., Lawrence, Ryan, Chang, Kuang-Yu, Bohrer, Gil, Riley, William J., Crill, Patrick M., Varner, Ruth K., Blazewicz, Steven J., Dorrepaal, Ellen, Tfaily, Malak M., Saleska, Scott R., Rich, Virginia, I, Hough, Moira, McCabe, Samantha, Vining, S. Rose, Pickering Pedersen, Emily, Wilson, Rachel M., Lawrence, Ryan, Chang, Kuang-Yu, Bohrer, Gil, Riley, William J., Crill, Patrick M., Varner, Ruth K., Blazewicz, Steven J., Dorrepaal, Ellen, Tfaily, Malak M., Saleska, Scott R., and Rich, Virginia, I
Abstract: Permafrost thaw is a major potential feedback source to climate change as it can drive the increased release of greenhouse gases carbon dioxide (CO2) and methane (CH4). This carbon release from the decomposition of thawing soil organic material can be mitigated by increased net primary productivity (NPP) caused by warming, increasing atmospheric CO2, and plant community transition. However, the net effect on C storage also depends on how these plant community changes alter plant litter quantity, quality, and decomposition rates. Predicting decomposition rates based on litter quality remains challenging, but a promising new way forward is to incorporate measures of the energetic favorability to soil microbes of plant biomass decomposition. We asked how the variation in one such measure, the nominal oxidation state of carbon (NOSC), interacts with changing quantities of plant material inputs to influence the net C balance of a thawing permafrost peatland. We found: (1) Plant productivity (NPP) increased post-thaw, but instead of contributing to increased standing biomass, it increased plant biomass turnover via increased litter inputs to soil; (2) Plant litter thermodynamic favorability (NOSC) and decomposition rate both increased post-thaw, despite limited changes in bulk C:N ratios; (3) these increases caused the higher NPP to cycle more rapidly through both plants and soil, contributing to higher CO2 and CH4 fluxes from decomposition. Thus, the increased C-storage expected from higher productivity was limited and the high global warming potential of CH4 contributed a net positive warming effect. Although post-thaw peatlands are currently C sinks due to high NPP offsetting high CO2 release, this status is very sensitive to the plant community's litter input rate and quality. Integration of novel bioavailability metrics based on litter chemistry, including NOSC, into studies of ecosystem dynamics, is needed to improve the understanding of controls on arctic C stocks
Published: 2022
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16. Coupling plant litter quantity to a novel metric for litter quality explains C storage changes in a thawing permafrost peatland

Author: Hough, Moira, McCabe, Samantha, Vining, S. Rose, Pickering Pedersen, Emily, Wilson, Rachel M., Lawrence, Ryan, Chang, Kuang-Yu, Bohrer, Gil, Riley, William J., Crill, Patrick M., Varner, Ruth K., Blazewicz, Steven J., Dorrepaal, Ellen, Tfaily, Malak M., Saleska, Scott R., Rich, Virginia, I, Hough, Moira, McCabe, Samantha, Vining, S. Rose, Pickering Pedersen, Emily, Wilson, Rachel M., Lawrence, Ryan, Chang, Kuang-Yu, Bohrer, Gil, Riley, William J., Crill, Patrick M., Varner, Ruth K., Blazewicz, Steven J., Dorrepaal, Ellen, Tfaily, Malak M., Saleska, Scott R., and Rich, Virginia, I
Abstract: Permafrost thaw is a major potential feedback source to climate change as it can drive the increased release of greenhouse gases carbon dioxide (CO2) and methane (CH4). This carbon release from the decomposition of thawing soil organic material can be mitigated by increased net primary productivity (NPP) caused by warming, increasing atmospheric CO2, and plant community transition. However, the net effect on C storage also depends on how these plant community changes alter plant litter quantity, quality, and decomposition rates. Predicting decomposition rates based on litter quality remains challenging, but a promising new way forward is to incorporate measures of the energetic favorability to soil microbes of plant biomass decomposition. We asked how the variation in one such measure, the nominal oxidation state of carbon (NOSC), interacts with changing quantities of plant material inputs to influence the net C balance of a thawing permafrost peatland. We found: (1) Plant productivity (NPP) increased post-thaw, but instead of contributing to increased standing biomass, it increased plant biomass turnover via increased litter inputs to soil; (2) Plant litter thermodynamic favorability (NOSC) and decomposition rate both increased post-thaw, despite limited changes in bulk C:N ratios; (3) these increases caused the higher NPP to cycle more rapidly through both plants and soil, contributing to higher CO2 and CH4 fluxes from decomposition. Thus, the increased C-storage expected from higher productivity was limited and the high global warming potential of CH4 contributed a net positive warming effect. Although post-thaw peatlands are currently C sinks due to high NPP offsetting high CO2 release, this status is very sensitive to the plant community's litter input rate and quality. Integration of novel bioavailability metrics based on litter chemistry, including NOSC, into studies of ecosystem dynamics, is needed to improve the understanding of controls on arctic C stocks
Published: 2022
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17. Coupling plant litter quantity to a novel metric for litter quality explains C storage changes in a thawing permafrost peatland

Author: Hough, Moira, McCabe, Samantha, Vining, S. Rose, Pickering Pedersen, Emily, Wilson, Rachel M., Lawrence, Ryan, Chang, Kuang-Yu, Bohrer, Gil, The IsoGenie Coordinators, Riley, William J., Crill, Patrick M., Varner, Ruth K., Blazewicz, Steven J., Dorrepaal, Ellen, Tfaily, Malak M., Saleska, Scott R., Rich, Virginia I., Hough, Moira, McCabe, Samantha, Vining, S. Rose, Pickering Pedersen, Emily, Wilson, Rachel M., Lawrence, Ryan, Chang, Kuang-Yu, Bohrer, Gil, The IsoGenie Coordinators, Riley, William J., Crill, Patrick M., Varner, Ruth K., Blazewicz, Steven J., Dorrepaal, Ellen, Tfaily, Malak M., Saleska, Scott R., and Rich, Virginia I.
Abstract: Permafrost thaw is a major potential feedback source to climate change as it can drive the increased release of greenhouse gases carbon dioxide (CO2) and methane (CH4). This carbon release from the decomposition of thawing soil organic material can be mitigated by increased net primary productivity (NPP) caused by warming, increasing atmospheric CO2, and plant community transition. However, the net effect on C storage also depends on how these plant community changes alter plant litter quantity, quality, and decomposition rates. Predicting decomposition rates based on litter quality remains challenging, but a promising new way forward is to incorporate measures of the energetic favorability to soil microbes of plant biomass decomposition. We asked how the variation in one such measure, the nominal oxidation state of carbon (NOSC), interacts with changing quantities of plant material inputs to influence the net C balance of a thawing permafrost peatland. We found: (1) Plant productivity (NPP) increased post-thaw, but instead of contributing to increased standing biomass, it increased plant biomass turnover via increased litter inputs to soil; (2) Plant litter thermodynamic favorability (NOSC) and decomposition rate both increased post-thaw, despite limited changes in bulk C:N ratios; (3) these increases caused the higher NPP to cycle more rapidly through both plants and soil, contributing to higher CO2 and CH4 fluxes from decomposition. Thus, the increased C-storage expected from higher productivity was limited and the high global warming potential of CH4 contributed a net positive warming effect. Although post-thaw peatlands are currently C sinks due to high NPP offsetting high CO2 release, this status is very sensitive to the plant community's litter input rate and quality. Integration of novel bioavailability metrics based on litter chemistry, including NOSC, into studies of ecosystem dynamics, is needed to improve the understanding of controls on arctic C stocks
Published: 2022
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18. Coupling plant litter quantity to a novel metric for litter quality explains C storage changes in a thawing permafrost peatland.

Author: Hough, Moira, Hough, Moira, McCabe, Samantha, Vining, S Rose, Pickering Pedersen, Emily, Wilson, Rachel M, Lawrence, Ryan, Chang, Kuang-Yu, Bohrer, Gil, IsoGenie Coordinators, Riley, William J, Crill, Patrick M, Varner, Ruth K, Blazewicz, Steven J, Dorrepaal, Ellen, Tfaily, Malak M, Saleska, Scott R, Rich, Virginia I, Hough, Moira, Hough, Moira, McCabe, Samantha, Vining, S Rose, Pickering Pedersen, Emily, Wilson, Rachel M, Lawrence, Ryan, Chang, Kuang-Yu, Bohrer, Gil, IsoGenie Coordinators, Riley, William J, Crill, Patrick M, Varner, Ruth K, Blazewicz, Steven J, Dorrepaal, Ellen, Tfaily, Malak M, Saleska, Scott R, and Rich, Virginia I
Abstract: Permafrost thaw is a major potential feedback source to climate change as it can drive the increased release of greenhouse gases carbon dioxide (CO2 ) and methane (CH4 ). This carbon release from the decomposition of thawing soil organic material can be mitigated by increased net primary productivity (NPP) caused by warming, increasing atmospheric CO2 , and plant community transition. However, the net effect on C storage also depends on how these plant community changes alter plant litter quantity, quality, and decomposition rates. Predicting decomposition rates based on litter quality remains challenging, but a promising new way forward is to incorporate measures of the energetic favorability to soil microbes of plant biomass decomposition. We asked how the variation in one such measure, the nominal oxidation state of carbon (NOSC), interacts with changing quantities of plant material inputs to influence the net C balance of a thawing permafrost peatland. We found: (1) Plant productivity (NPP) increased post-thaw, but instead of contributing to increased standing biomass, it increased plant biomass turnover via increased litter inputs to soil; (2) Plant litter thermodynamic favorability (NOSC) and decomposition rate both increased post-thaw, despite limited changes in bulk C:N ratios; (3) these increases caused the higher NPP to cycle more rapidly through both plants and soil, contributing to higher CO2 and CH4 fluxes from decomposition. Thus, the increased C-storage expected from higher productivity was limited and the high global warming potential of CH4 contributed a net positive warming effect. Although post-thaw peatlands are currently C sinks due to high NPP offsetting high CO2 release, this status is very sensitive to the plant community's litter input rate and quality. Integration of novel bioavailability metrics based on litter chemistry, including NOSC, into studies of ecosystem dynamics, is needed to improve the understanding of controls on ar
Published: 2022

19. Water level changes in Lake Erie drive 21st century CO2 and CH4 fluxes from a coastal temperate wetland.

Author: Morin, Timothy H, Morin, Timothy H, Riley, William J, Grant, Robert F, Mekonnen, Zelalem, Stefanik, Kay C, Sanchez, A Camilo Rey, Mulhare, Molly A, Villa, Jorge, Wrighton, Kelly, Bohrer, Gil, Morin, Timothy H, Morin, Timothy H, Riley, William J, Grant, Robert F, Mekonnen, Zelalem, Stefanik, Kay C, Sanchez, A Camilo Rey, Mulhare, Molly A, Villa, Jorge, Wrighton, Kelly, and Bohrer, Gil
Abstract: Wetland water depth influences microbial and plant communities, which can alter the above- and below-ground carbon cycling of a wetland. Wetland water depths are likely to change due to shifting precipitation patterns, which will affect projections of greenhouse gas emissions; however, these effects are rarely incorporated into wetland greenhouse gas models. Seeking to address this gap, we used a mechanistic model, ecosys, to simulate a range of water depth scenarios in a temperate wetland, and analyzed simulated predictions of carbon dioxide (CO2) and methane (CH4) fluxes over the 21st century. We tested our model using eddy covariance measurements of CO2 and CH4 fluxes collected at the Old Woman Creek National Estuarine Research Reserve (OWC) during 2015 and 2016. OWC is a lacustrine, estuarine, freshwater, mineral-soil marsh. An empirical model found that the wetland water depth is highly dependent on the water depth of the nearby Lake Erie. Future wetland surface water depths were modeled based on projection of Lake Erie's water depth using four separate NOAA projections, resulting in four wetland water-depth scenarios. Two of the four 21st century projections for Lake Erie water depths used in this study indicated that the water depth of the wetland would remain nearly steady; however, the other two indicated decreases in the wetland water depth. In our scenario where the wetland dries out, we project the wetland's climatological warming effect will decrease due to smaller CH4 fluxes to the atmosphere and larger CO2 uptake by the wetland. We also found that increased water level can lower emissions by shifting the site towards more open water areas, which have lower CH4 emissions. We found that decreased water depths would cause more widespread colonization of the wetland by macrophyte vegetation. Using an empirical relationship, we also found that further drying could result in other, non-wetland vegetation to emerge, dramatically altering soil carbon cycling.
Published: 2022

20. 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, Jackson, Robert, 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
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

21. 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, Jackson, Robert, 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
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

22. Biological Earth observation with animal sensors

Author: Jetz, Walter, Tertitski, Grigori, Kays, Roland, Mueller, Uschi, Wikelski, Martin, Åkesson, Susanne, Anisimov, Yury, Antonov, Aleksey, Arnold, Walter, Bairlein, Franz, Baltà, Oriol, Baum, Diane, Beck, Mario, Belonovich, Olga, Belyaev, Mikhail, Berger, Matthias, Berthold, Peter, Bittner, Steffen, Blake, Stephen, Block, Barbara, Bloche, Daniel, Boehning-Gaese, Katrin, Bohrer, Gil, Bojarinova, Julia, Bommas, Gerhard, Bourski, Oleg, Bragin, Albert, Bragin, Alexandr, Bristol, Rachel, Brlík, Vojtěch, Bulyuk, Victor, Cagnacci, Francesca, Carlson, Ben, Chapple, Taylor K., Chefira, Kalkidan F., Cheng, Yachang, Chernetsov, Nikita, Cierlik, Grzegorz, Christiansen, Simon S., Clarabuch, Oriol, Cochran, William, Cornelius, Jamie Margaret, Couzin, Iain, Crofoot, Margret C., Cruz, Sebastian, Davydov, Alexander, Davidson, Sarah, Dech, Stefan, Snell, Katherine, Thorup, Kasper, Jetz, Walter, Tertitski, Grigori, Kays, Roland, Mueller, Uschi, Wikelski, Martin, Åkesson, Susanne, Anisimov, Yury, Antonov, Aleksey, Arnold, Walter, Bairlein, Franz, Baltà, Oriol, Baum, Diane, Beck, Mario, Belonovich, Olga, Belyaev, Mikhail, Berger, Matthias, Berthold, Peter, Bittner, Steffen, Blake, Stephen, Block, Barbara, Bloche, Daniel, Boehning-Gaese, Katrin, Bohrer, Gil, Bojarinova, Julia, Bommas, Gerhard, Bourski, Oleg, Bragin, Albert, Bragin, Alexandr, Bristol, Rachel, Brlík, Vojtěch, Bulyuk, Victor, Cagnacci, Francesca, Carlson, Ben, Chapple, Taylor K., Chefira, Kalkidan F., Cheng, Yachang, Chernetsov, Nikita, Cierlik, Grzegorz, Christiansen, Simon S., Clarabuch, Oriol, Cochran, William, Cornelius, Jamie Margaret, Couzin, Iain, Crofoot, Margret C., Cruz, Sebastian, Davydov, Alexander, Davidson, Sarah, Dech, Stefan, Snell, Katherine, and Thorup, Kasper
Abstract: Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmental change.
Published: 2022

23. Biological Earth observation with animal sensors

Author: Jetz, Walter, Tertitski, Grigori, Kays, Roland, Mueller, Uschi, Wikelski, Martin, Åkesson, Susanne, Anisimov, Yury, Antonov, Aleksey, Arnold, Walter, Bairlein, Franz, Baltà, Oriol, Baum, Diane, Beck, Mario, Belonovich, Olga, Belyaev, Mikhail, Berger, Matthias, Berthold, Peter, Bittner, Steffen, Blake, Stephen, Block, Barbara, Bloche, Daniel, Boehning-Gaese, Katrin, Bohrer, Gil, Bojarinova, Julia, Bommas, Gerhard, Bourski, Oleg, Bragin, Albert, Bragin, Alexandr, Bristol, Rachel, Brlík, Vojtěch, Bulyuk, Victor, Cagnacci, Francesca, Carlson, Ben, Chapple, Taylor K., Chefira, Kalkidan F., Cheng, Yachang, Chernetsov, Nikita, Cierlik, Grzegorz, Christiansen, Simon S., Clarabuch, Oriol, Cochran, William, Cornelius, Jamie Margaret, Couzin, Iain, Crofoot, Margret C., Cruz, Sebastian, Davydov, Alexander, Davidson, Sarah, Dech, Stefan, Snell, Katherine, Thorup, Kasper, Jetz, Walter, Tertitski, Grigori, Kays, Roland, Mueller, Uschi, Wikelski, Martin, Åkesson, Susanne, Anisimov, Yury, Antonov, Aleksey, Arnold, Walter, Bairlein, Franz, Baltà, Oriol, Baum, Diane, Beck, Mario, Belonovich, Olga, Belyaev, Mikhail, Berger, Matthias, Berthold, Peter, Bittner, Steffen, Blake, Stephen, Block, Barbara, Bloche, Daniel, Boehning-Gaese, Katrin, Bohrer, Gil, Bojarinova, Julia, Bommas, Gerhard, Bourski, Oleg, Bragin, Albert, Bragin, Alexandr, Bristol, Rachel, Brlík, Vojtěch, Bulyuk, Victor, Cagnacci, Francesca, Carlson, Ben, Chapple, Taylor K., Chefira, Kalkidan F., Cheng, Yachang, Chernetsov, Nikita, Cierlik, Grzegorz, Christiansen, Simon S., Clarabuch, Oriol, Cochran, William, Cornelius, Jamie Margaret, Couzin, Iain, Crofoot, Margret C., Cruz, Sebastian, Davydov, Alexander, Davidson, Sarah, Dech, Stefan, Snell, Katherine, and Thorup, Kasper
Abstract: Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmental change.
Published: 2022

24. Coupling plant litter quantity to a novel metric for litter quality explains C storage changes in a thawing permafrost peatland

Author: Hough, Moira, Mccabe, Samantha, Vining, S. Rose, Pickering Pedersen, Emily, Wilson, Rachel M., Lawrence, Ryan, Chang, Kuang‐Yu, Bohrer, Gil, Riley, William J., Crill, Patrick M., Varner, Ruth K., Blazewicz, Steven J., Dorrepaal, Ellen, Tfaily, Malak M., Saleska, Scott R., Rich, Virginia I., Frolking, Steve, Hodgkins, Suzanne B., Mccalley, Carmody K., Cooper, William T., Chanton, Jeffrey P., Sullivan, Matthew B., Tyson, Gene W., Brodie, Eoin L., Woodcroft, Ben B., Dominguez, Sky, Hough, Moira, Mccabe, Samantha, Vining, S. Rose, Pickering Pedersen, Emily, Wilson, Rachel M., Lawrence, Ryan, Chang, Kuang‐Yu, Bohrer, Gil, Riley, William J., Crill, Patrick M., Varner, Ruth K., Blazewicz, Steven J., Dorrepaal, Ellen, Tfaily, Malak M., Saleska, Scott R., Rich, Virginia I., Frolking, Steve, Hodgkins, Suzanne B., Mccalley, Carmody K., Cooper, William T., Chanton, Jeffrey P., Sullivan, Matthew B., Tyson, Gene W., Brodie, Eoin L., Woodcroft, Ben B., and Dominguez, Sky
Published: 2022

25. Coupling plant litter quantity to a novel metric for litter quality explains C storage changes in a thawing permafrost peatland

Author: Hough, Moira, McCabe, Samantha, Vining, S. Rose, Pickering Pedersen, Emily, Wilson, Rachel M., Lawrence, Ryan, Chang, Kuang-Yu, Bohrer, Gil, Riley, William J., Crill, Patrick M., Varner, Ruth K., Blazewicz, Steven J., Dorrepaal, Ellen, Tfaily, Malak M., Saleska, Scott R., Rich, Virginia, I, Hough, Moira, McCabe, Samantha, Vining, S. Rose, Pickering Pedersen, Emily, Wilson, Rachel M., Lawrence, Ryan, Chang, Kuang-Yu, Bohrer, Gil, Riley, William J., Crill, Patrick M., Varner, Ruth K., Blazewicz, Steven J., Dorrepaal, Ellen, Tfaily, Malak M., Saleska, Scott R., and Rich, Virginia, I
Abstract: Permafrost thaw is a major potential feedback source to climate change as it can drive the increased release of greenhouse gases carbon dioxide (CO2) and methane (CH4). This carbon release from the decomposition of thawing soil organic material can be mitigated by increased net primary productivity (NPP) caused by warming, increasing atmospheric CO2, and plant community transition. However, the net effect on C storage also depends on how these plant community changes alter plant litter quantity, quality, and decomposition rates. Predicting decomposition rates based on litter quality remains challenging, but a promising new way forward is to incorporate measures of the energetic favorability to soil microbes of plant biomass decomposition. We asked how the variation in one such measure, the nominal oxidation state of carbon (NOSC), interacts with changing quantities of plant material inputs to influence the net C balance of a thawing permafrost peatland. We found: (1) Plant productivity (NPP) increased post-thaw, but instead of contributing to increased standing biomass, it increased plant biomass turnover via increased litter inputs to soil; (2) Plant litter thermodynamic favorability (NOSC) and decomposition rate both increased post-thaw, despite limited changes in bulk C:N ratios; (3) these increases caused the higher NPP to cycle more rapidly through both plants and soil, contributing to higher CO2 and CH4 fluxes from decomposition. Thus, the increased C-storage expected from higher productivity was limited and the high global warming potential of CH4 contributed a net positive warming effect. Although post-thaw peatlands are currently C sinks due to high NPP offsetting high CO2 release, this status is very sensitive to the plant community's litter input rate and quality. Integration of novel bioavailability metrics based on litter chemistry, including NOSC, into studies of ecosystem dynamics, is needed to improve the understanding of controls on arctic C stocks
Published: 2022
Full Text: View/download PDF

26. Coupling plant litter quantity to a novel metric for litter quality explains C storage changes in a thawing permafrost peatland

Author: Hough, Moira, McCabe, Samantha, Vining, S. Rose, Pickering Pedersen, Emily, Wilson, Rachel M., Lawrence, Ryan, Chang, Kuang-Yu, Bohrer, Gil, Riley, William J., Crill, Patrick M., Varner, Ruth K., Blazewicz, Steven J., Dorrepaal, Ellen, Tfaily, Malak M., Saleska, Scott R., Rich, Virginia, I, Hough, Moira, McCabe, Samantha, Vining, S. Rose, Pickering Pedersen, Emily, Wilson, Rachel M., Lawrence, Ryan, Chang, Kuang-Yu, Bohrer, Gil, Riley, William J., Crill, Patrick M., Varner, Ruth K., Blazewicz, Steven J., Dorrepaal, Ellen, Tfaily, Malak M., Saleska, Scott R., and Rich, Virginia, I
Abstract: Permafrost thaw is a major potential feedback source to climate change as it can drive the increased release of greenhouse gases carbon dioxide (CO2) and methane (CH4). This carbon release from the decomposition of thawing soil organic material can be mitigated by increased net primary productivity (NPP) caused by warming, increasing atmospheric CO2, and plant community transition. However, the net effect on C storage also depends on how these plant community changes alter plant litter quantity, quality, and decomposition rates. Predicting decomposition rates based on litter quality remains challenging, but a promising new way forward is to incorporate measures of the energetic favorability to soil microbes of plant biomass decomposition. We asked how the variation in one such measure, the nominal oxidation state of carbon (NOSC), interacts with changing quantities of plant material inputs to influence the net C balance of a thawing permafrost peatland. We found: (1) Plant productivity (NPP) increased post-thaw, but instead of contributing to increased standing biomass, it increased plant biomass turnover via increased litter inputs to soil; (2) Plant litter thermodynamic favorability (NOSC) and decomposition rate both increased post-thaw, despite limited changes in bulk C:N ratios; (3) these increases caused the higher NPP to cycle more rapidly through both plants and soil, contributing to higher CO2 and CH4 fluxes from decomposition. Thus, the increased C-storage expected from higher productivity was limited and the high global warming potential of CH4 contributed a net positive warming effect. Although post-thaw peatlands are currently C sinks due to high NPP offsetting high CO2 release, this status is very sensitive to the plant community's litter input rate and quality. Integration of novel bioavailability metrics based on litter chemistry, including NOSC, into studies of ecosystem dynamics, is needed to improve the understanding of controls on arctic C stocks
Published: 2022
Full Text: View/download PDF

27. Coupling plant litter quantity to a novel metric for litter quality explains C storage changes in a thawing permafrost peatland

Author: Hough, Moira, Mccabe, Samantha, Vining, S. Rose, Pickering Pedersen, Emily, Wilson, Rachel M., Lawrence, Ryan, Chang, Kuang‐Yu, Bohrer, Gil, Riley, William J., Crill, Patrick M., Varner, Ruth K., Blazewicz, Steven J., Dorrepaal, Ellen, Tfaily, Malak M., Saleska, Scott R., Rich, Virginia I., Frolking, Steve, Hodgkins, Suzanne B., Mccalley, Carmody K., Cooper, William T., Chanton, Jeffrey P., Sullivan, Matthew B., Tyson, Gene W., Brodie, Eoin L., Woodcroft, Ben B., Dominguez, Sky, Hough, Moira, Mccabe, Samantha, Vining, S. Rose, Pickering Pedersen, Emily, Wilson, Rachel M., Lawrence, Ryan, Chang, Kuang‐Yu, Bohrer, Gil, Riley, William J., Crill, Patrick M., Varner, Ruth K., Blazewicz, Steven J., Dorrepaal, Ellen, Tfaily, Malak M., Saleska, Scott R., Rich, Virginia I., Frolking, Steve, Hodgkins, Suzanne B., Mccalley, Carmody K., Cooper, William T., Chanton, Jeffrey P., Sullivan, Matthew B., Tyson, Gene W., Brodie, Eoin L., Woodcroft, Ben B., and Dominguez, Sky
Published: 2022

28. Biological Earth observation with animal sensors

Author: Jetz, Walter, Tertitski, Grigori, Kays, Roland, Mueller, Uschi, Wikelski, Martin, Akesson, Susanne, Anisimov, Yury, Antonov, Aleksey, Arnold, Walter, Bairlein, Franz, Balta, Oriol, Baum, Diane, Beck, Mario, Belonovich, Olga, Belyaev, Mikhail, Berger, Matthias, Berthold, Peter, Bittner, Steffen, Blake, Stephen, Block, Barbara, Bloche, Daniel, Boehning-Gaese, Katrin, Bohrer, Gil, Bojarinova, Julia, Bommas, Gerhard, Bourski, Oleg, Bragin, Albert, Bragin, Alexandr, Bristol, Rachel, Brlik, Vojtech, Bulyuk, Victor, Cagnacci, Francesca, Carlson, Ben, Chapple, Taylor K., Chefira, Kalkidan F., Cheng, Yachang, Chernetsov, Nikita, Cierlik, Grzegorz, Christiansen, Simon S., Clarabuch, Oriol, Cochran, William, Cornelius, Jamie Margaret, Couzin, Iain, Crofoot, Margret C., Cruz, Sebastian, Davydov, Alexander, Davidson, Sarah, Dech, Stefan, Dechmann, Dina, Demidova, Ekaterina, Dettmann, Jan, Dittmar, Sven, Dorofeev, Dmitry, Drenckhahn, Detlev, Dubyanskiy, Vladimir, Egorov, Nikolay, Ehnbom, Sophie, Ellis-Soto, Diego, Ewald, Ralf, Feare, Chris, Fefelov, Igor, Fehervari, Peter, Fiedler, Wolfgang, Flack, Andrea, Froboese, Magnus, Fufachev, Ivan, Futoran, Pavel, Gabyshev, Vyachaslav, Gagliardo, Anna, Garthe, Stefan, Gashkov, Sergey, Gibson, Luke, Goymann, Wolfgang, Gruppe, Gerd, Guglielmo, Chris, Hartl, Phil, Hedenstrom, Anders, Hegemann, Arne, Heine, Georg, Ruiz, Maggi Hieber, Hofer, Heribert, Huber, Felix, Hurme, Edward, Iannarilli, Fabiola, Illa, Marc, Isaev, Arkadiy, Jakobsen, Bent, Jenni, Lukas, Jenni-Eiermann, Susi, Jesmer, Brett R., Jiguet, Frederic, Karimova, Tatiana, Kasdin, N. Jeremy, Kazansky, Fedor, Kirillin, Ruslan, Klinner, Thomas, Knopp, Andreas, Koelzsch, Andrea, Kondratyev, Alexander, Krondorf, Marco, Ktitorov, Pavel, Kulikova, Olga, Kumar, R. Suresh, Kuenzer, Claudia, Larionov, Anatoliy, Larose, Christine, Liechti, Felix, Linek, Nils, Lohr, Ashley, Lushchekina, Anna, Mansfield, Kate, Matantseva, Maria, Markovets, Mikhail, Marra, Peter, Masello, Juan F., Melzheimer, Joerg, Menz, Myles HM M., Menzie, Stephen, Meshcheryagina, Swetlana, Miquelle, Dale, Morozov, Vladimir, Mukhin, Andrey, Mueller, Inge, Mueller, Thomas, Navedo, Juan G., Nathan, Ran, Nelson, Luke, Nemeth, Zoltan, Newman, Scott, Norris, Ryan, Nsengimana, Olivier, Okhlopkov, Innokentiy, Oles, Wioleta, Oliver, Ruth, O'Mara, Teague, Palatitz, Peter, Partecke, Jesko, Pavlick, Ryan, Pedenko, Anastasia, Perry, Alys, Pham, Julie, Piechowski, Daniel, Pierce, Allison, Piersma, Theunis, Pitz, Wolfgang, Plettemeier, Dirk, Pokrovskaya, Irina, Pokrovskaya, Liya, Pokrovsky, Ivan, Pot, Morrison, Prochazka, Petr, Quillfeldt, Petra, Rakhimberdiev, Eldar, Ramenofsky, Marilyn, Ranipeta, Ajay, Rapczynski, Jan, Remisiewicz, Magdalena, Rozhnov, Viatcheslav, Rienks, Froukje, Rozhnov, Vyacheslav, Rutz, Christian, Sakhvon, Vital, Sapir, Nir, Safi, Kamran, Schaeuffelhut, Friedrich, Schimel, David, Schmidt, Andreas, Shamoun-Baranes, Judy, Sharikov, Alexander, Shearer, Laura, Shemyakin, Evgeny, Sherub, Sherub, Shipley, Ryan, Sica, Yanina, Smith, Thomas B., Simonov, Sergey, Snell, Katherine, Sokolov, Aleksandr, Sokolov, Vasiliy, Solomina, Olga, Spina, Fernando, Spoelstra, Kamiel, Storhas, Martin, Sviridova, Tatiana, Swenson, George, Taylor, Phil, Thorup, Kasper, Tsvey, Arseny, Tucker, Marlee, Tuppen, Sophie, Turner, Woody, Twizeyimana, Innocent, van der Jeugd, Henk, van Schalkwyk, Louis, van Toor, Marielle, Viljoen, Pauli, Visser, Marcel E., Volkmer, Tamara, Volkov, Andrey, Volkov, Sergey, Volkov, Oleg, von Ronn, Jan AC C., Vorneweg, Bernd, Wachter, Bettina, Waldenstrom, Jonas, Weber, Natalie, Wegmann, Martin, Wehr, Aloysius, Weinzierl, Rolf, Weppler, Johannes, Wilcove, David, Wild, Timm, Williams, Hannah J., Wilshire, John H., Wingfield, John, Wunder, Michael, Yachmennikova, Anna, Yanco, Scott, Yohannes, Elisabeth, Zeller, Amelie, Ziegler, Christian, Ziecik, Anna, Zook, Cheryl, Jetz, Walter, Tertitski, Grigori, Kays, Roland, Mueller, Uschi, Wikelski, Martin, Akesson, Susanne, Anisimov, Yury, Antonov, Aleksey, Arnold, Walter, Bairlein, Franz, Balta, Oriol, Baum, Diane, Beck, Mario, Belonovich, Olga, Belyaev, Mikhail, Berger, Matthias, Berthold, Peter, Bittner, Steffen, Blake, Stephen, Block, Barbara, Bloche, Daniel, Boehning-Gaese, Katrin, Bohrer, Gil, Bojarinova, Julia, Bommas, Gerhard, Bourski, Oleg, Bragin, Albert, Bragin, Alexandr, Bristol, Rachel, Brlik, Vojtech, Bulyuk, Victor, Cagnacci, Francesca, Carlson, Ben, Chapple, Taylor K., Chefira, Kalkidan F., Cheng, Yachang, Chernetsov, Nikita, Cierlik, Grzegorz, Christiansen, Simon S., Clarabuch, Oriol, Cochran, William, Cornelius, Jamie Margaret, Couzin, Iain, Crofoot, Margret C., Cruz, Sebastian, Davydov, Alexander, Davidson, Sarah, Dech, Stefan, Dechmann, Dina, Demidova, Ekaterina, Dettmann, Jan, Dittmar, Sven, Dorofeev, Dmitry, Drenckhahn, Detlev, Dubyanskiy, Vladimir, Egorov, Nikolay, Ehnbom, Sophie, Ellis-Soto, Diego, Ewald, Ralf, Feare, Chris, Fefelov, Igor, Fehervari, Peter, Fiedler, Wolfgang, Flack, Andrea, Froboese, Magnus, Fufachev, Ivan, Futoran, Pavel, Gabyshev, Vyachaslav, Gagliardo, Anna, Garthe, Stefan, Gashkov, Sergey, Gibson, Luke, Goymann, Wolfgang, Gruppe, Gerd, Guglielmo, Chris, Hartl, Phil, Hedenstrom, Anders, Hegemann, Arne, Heine, Georg, Ruiz, Maggi Hieber, Hofer, Heribert, Huber, Felix, Hurme, Edward, Iannarilli, Fabiola, Illa, Marc, Isaev, Arkadiy, Jakobsen, Bent, Jenni, Lukas, Jenni-Eiermann, Susi, Jesmer, Brett R., Jiguet, Frederic, Karimova, Tatiana, Kasdin, N. Jeremy, Kazansky, Fedor, Kirillin, Ruslan, Klinner, Thomas, Knopp, Andreas, Koelzsch, Andrea, Kondratyev, Alexander, Krondorf, Marco, Ktitorov, Pavel, Kulikova, Olga, Kumar, R. Suresh, Kuenzer, Claudia, Larionov, Anatoliy, Larose, Christine, Liechti, Felix, Linek, Nils, Lohr, Ashley, Lushchekina, Anna, Mansfield, Kate, Matantseva, Maria, Markovets, Mikhail, Marra, Peter, Masello, Juan F., Melzheimer, Joerg, Menz, Myles HM M., Menzie, Stephen, Meshcheryagina, Swetlana, Miquelle, Dale, Morozov, Vladimir, Mukhin, Andrey, Mueller, Inge, Mueller, Thomas, Navedo, Juan G., Nathan, Ran, Nelson, Luke, Nemeth, Zoltan, Newman, Scott, Norris, Ryan, Nsengimana, Olivier, Okhlopkov, Innokentiy, Oles, Wioleta, Oliver, Ruth, O'Mara, Teague, Palatitz, Peter, Partecke, Jesko, Pavlick, Ryan, Pedenko, Anastasia, Perry, Alys, Pham, Julie, Piechowski, Daniel, Pierce, Allison, Piersma, Theunis, Pitz, Wolfgang, Plettemeier, Dirk, Pokrovskaya, Irina, Pokrovskaya, Liya, Pokrovsky, Ivan, Pot, Morrison, Prochazka, Petr, Quillfeldt, Petra, Rakhimberdiev, Eldar, Ramenofsky, Marilyn, Ranipeta, Ajay, Rapczynski, Jan, Remisiewicz, Magdalena, Rozhnov, Viatcheslav, Rienks, Froukje, Rozhnov, Vyacheslav, Rutz, Christian, Sakhvon, Vital, Sapir, Nir, Safi, Kamran, Schaeuffelhut, Friedrich, Schimel, David, Schmidt, Andreas, Shamoun-Baranes, Judy, Sharikov, Alexander, Shearer, Laura, Shemyakin, Evgeny, Sherub, Sherub, Shipley, Ryan, Sica, Yanina, Smith, Thomas B., Simonov, Sergey, Snell, Katherine, Sokolov, Aleksandr, Sokolov, Vasiliy, Solomina, Olga, Spina, Fernando, Spoelstra, Kamiel, Storhas, Martin, Sviridova, Tatiana, Swenson, George, Taylor, Phil, Thorup, Kasper, Tsvey, Arseny, Tucker, Marlee, Tuppen, Sophie, Turner, Woody, Twizeyimana, Innocent, van der Jeugd, Henk, van Schalkwyk, Louis, van Toor, Marielle, Viljoen, Pauli, Visser, Marcel E., Volkmer, Tamara, Volkov, Andrey, Volkov, Sergey, Volkov, Oleg, von Ronn, Jan AC C., Vorneweg, Bernd, Wachter, Bettina, Waldenstrom, Jonas, Weber, Natalie, Wegmann, Martin, Wehr, Aloysius, Weinzierl, Rolf, Weppler, Johannes, Wilcove, David, Wild, Timm, Williams, Hannah J., Wilshire, John H., Wingfield, John, Wunder, Michael, Yachmennikova, Anna, Yanco, Scott, Yohannes, Elisabeth, Zeller, Amelie, Ziegler, Christian, Ziecik, Anna, and Zook, Cheryl
Abstract: Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmental change.
Published: 2022

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

Author: Chang, Kuang-Yu, 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, Zona, Donatella, Chang, Kuang-Yu, 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

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

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

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

Author: Knox, Sara H, 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, Jackson, Robert B, Knox, Sara H, 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
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
Published: 2021

32. Global transpiration data from sap flow measurements: the SAPFLUXNET database

Author: Poyatos, Rafael, Granda, Victor, Flo, Victor, Adams, Mark A., Adorjan, Balazs, Aguade, David, Aidar, Marcos P. M., Allen, Scott, Susana Alvarado-Barrientos, M., Anderson-Teixeira, Kristina J., Aparecido, Luiza Maria, Arain, M. Altaf, Aranda, Ismael, Asbjornsen, Heidi, Baxter, Robert, Beamesderfer, Eric, Berry, Z. Carter, Berveiller, Daniel, Blakely, Bethany, Boggs, Johnny, Bohrer, Gil, Bolstad, Paul, V, Bonal, Damien, Bracho, Rosvel, Brito, Patricia, Brodeur, Jason, Casanoves, Fernando, Chave, Jerome, Chen, Hui, Cisneros, Cesar, Clark, Kenneth, Cremonese, Edoardo, Dang, Hongzhong, David, Jorge S., David, Teresa S., Delpierre, Nicolas, Desai, Ankur R., Do, Frederic C., Dohnal, Michal, Domec, Jean-Christophe, Dzikiti, Sebinasi, Edgar, Colin, Eichstaedt, Rebekka, El-Madany, Tarek S., Elbers, Jan, Eller, Cleiton B., Euskirchen, Eugenie S., Ewers, Brent, Fonti, Patrick, Forner, Alicia, Forrester, David, I, Freitas, Helber C., Galvagno, Marta, Garcia-Tejera, Omar, Ghimire, Chandra Prasad, Gimeno, Teresa E., Grace, John, Granier, Andre, Griebel, Anne, Guangyu, Yan, Gush, Mark B., Hanson, Paul J., Hasselquist, Niles J., Heinrich, Ingo, Hernandez-Santana, Virginia, Herrmann, Valentine, Holtta, Teemu, Holwerda, Friso, Irvine, James, Ayutthaya, Supat Isarangkool Na, Jarvis, Paul G., Jochheim, Hubert, Joly, Carlos A., Kaplick, Julia, Kim, Hyun Seok, Klemedtsson, Leif, Kropp, Heather, Lagergren, Fredrik, Lane, Patrick, Lang, Petra, Lapenas, Andrei, Lechuga, Victor, Lee, Minsu, Leuschner, Christoph, Limousin, Jean-Marc, Linares, Juan Carlos, Linderson, Maj-Lena, Lindroth, Anders, Llorens, Pilar, Lopez-Bernal, Alvaro, Loranty, Michael M., Luttschwager, Dietmar, Macinnis-Ng, Cate, Marechaux, Isabelle, Martin, Timothy A., Matheny, Ashley, McDowell, Nate, McMahon, Sean, Meir, Patrick, Meszaros, Ilona, Migliavacca, Mirco, Mitchell, Patrick, Molder, Meelis, Montagnani, Leonardo, Moore, Georgianne W., Nakada, Ryogo, Niu, Furong, Nolan, Rachael H., Norby, Richard, Novick, Kimberly, Oberhuber, Walter, Obojes, Nikolaus, Oishi, A. Christopher, Oliveira, Rafael S., Oren, Ram, Ourcival, Jean-Marc, Paljakka, Teemu, Perez-Priego, Oscar, Peri, Pablo L., Peters, Richard L., Pfautsch, Sebastian, Pockman, William T., Preisler, Yakir, Rascher, Katherine, Robinson, George, Rocha, Humberto, Rocheteau, Alain, Roll, Alexander, Rosado, Bruno H. P., Rowland, Lucy, Rubtsov, Alexey, V, Sabate, Santiago, Salmon, Yann, Salomon, Roberto L., Sanchez-Costa, Elisenda, Schafer, Karina V. R., Schuldt, Bernhard, Shashkin, Alexandr, Stahl, Clement, Stojanovic, Marko, Suarez, Juan Carlos, Sun, Ge, Szatniewska, Justyna, Tatarinov, Fyodor, Tesar, Miroslav, Thomas, Frank M., Tor-ngern, Pantana, Urban, Josef, Valladares, Fernando, van der Tol, Christiaan, van Meerveld, Ilja, Varlagin, Andrej, Voigt, Holm, Warren, Jeffrey, Werner, Christiane, Werner, Willy, Wieser, Gerhard, Wingate, Lisa, Wullschleger, Stan, Yi, Koong, Zweifel, Roman, Steppe, Kathy, Mencuccini, Maurizio, Martinez-Vilalta, Jordi, Poyatos, Rafael, Granda, Victor, Flo, Victor, Adams, Mark A., Adorjan, Balazs, Aguade, David, Aidar, Marcos P. M., Allen, Scott, Susana Alvarado-Barrientos, M., Anderson-Teixeira, Kristina J., Aparecido, Luiza Maria, Arain, M. Altaf, Aranda, Ismael, Asbjornsen, Heidi, Baxter, Robert, Beamesderfer, Eric, Berry, Z. Carter, Berveiller, Daniel, Blakely, Bethany, Boggs, Johnny, Bohrer, Gil, Bolstad, Paul, V, Bonal, Damien, Bracho, Rosvel, Brito, Patricia, Brodeur, Jason, Casanoves, Fernando, Chave, Jerome, Chen, Hui, Cisneros, Cesar, Clark, Kenneth, Cremonese, Edoardo, Dang, Hongzhong, David, Jorge S., David, Teresa S., Delpierre, Nicolas, Desai, Ankur R., Do, Frederic C., Dohnal, Michal, Domec, Jean-Christophe, Dzikiti, Sebinasi, Edgar, Colin, Eichstaedt, Rebekka, El-Madany, Tarek S., Elbers, Jan, Eller, Cleiton B., Euskirchen, Eugenie S., Ewers, Brent, Fonti, Patrick, Forner, Alicia, Forrester, David, I, Freitas, Helber C., Galvagno, Marta, Garcia-Tejera, Omar, Ghimire, Chandra Prasad, Gimeno, Teresa E., Grace, John, Granier, Andre, Griebel, Anne, Guangyu, Yan, Gush, Mark B., Hanson, Paul J., Hasselquist, Niles J., Heinrich, Ingo, Hernandez-Santana, Virginia, Herrmann, Valentine, Holtta, Teemu, Holwerda, Friso, Irvine, James, Ayutthaya, Supat Isarangkool Na, Jarvis, Paul G., Jochheim, Hubert, Joly, Carlos A., Kaplick, Julia, Kim, Hyun Seok, Klemedtsson, Leif, Kropp, Heather, Lagergren, Fredrik, Lane, Patrick, Lang, Petra, Lapenas, Andrei, Lechuga, Victor, Lee, Minsu, Leuschner, Christoph, Limousin, Jean-Marc, Linares, Juan Carlos, Linderson, Maj-Lena, Lindroth, Anders, Llorens, Pilar, Lopez-Bernal, Alvaro, Loranty, Michael M., Luttschwager, Dietmar, Macinnis-Ng, Cate, Marechaux, Isabelle, Martin, Timothy A., Matheny, Ashley, McDowell, Nate, McMahon, Sean, Meir, Patrick, Meszaros, Ilona, Migliavacca, Mirco, Mitchell, Patrick, Molder, Meelis, Montagnani, Leonardo, Moore, Georgianne W., Nakada, Ryogo, Niu, Furong, Nolan, Rachael H., Norby, Richard, Novick, Kimberly, Oberhuber, Walter, Obojes, Nikolaus, Oishi, A. Christopher, Oliveira, Rafael S., Oren, Ram, Ourcival, Jean-Marc, Paljakka, Teemu, Perez-Priego, Oscar, Peri, Pablo L., Peters, Richard L., Pfautsch, Sebastian, Pockman, William T., Preisler, Yakir, Rascher, Katherine, Robinson, George, Rocha, Humberto, Rocheteau, Alain, Roll, Alexander, Rosado, Bruno H. P., Rowland, Lucy, Rubtsov, Alexey, V, Sabate, Santiago, Salmon, Yann, Salomon, Roberto L., Sanchez-Costa, Elisenda, Schafer, Karina V. R., Schuldt, Bernhard, Shashkin, Alexandr, Stahl, Clement, Stojanovic, Marko, Suarez, Juan Carlos, Sun, Ge, Szatniewska, Justyna, Tatarinov, Fyodor, Tesar, Miroslav, Thomas, Frank M., Tor-ngern, Pantana, Urban, Josef, Valladares, Fernando, van der Tol, Christiaan, van Meerveld, Ilja, Varlagin, Andrej, Voigt, Holm, Warren, Jeffrey, Werner, Christiane, Werner, Willy, Wieser, Gerhard, Wingate, Lisa, Wullschleger, Stan, Yi, Koong, Zweifel, Roman, Steppe, Kathy, Mencuccini, Maurizio, and Martinez-Vilalta, Jordi
Abstract: Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land-atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is free
Published: 2021

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

Author: Delwiche, Kyle B., Knox, Sara Helen, Malhotra, Avni, Fluet-Chouinard, Etienne, McNicol, Gavin, Feron, Sarah, Ouyang, Zutao, Papale, Dario, Trotta, Carlo, Canfora, Eleonora, Cheah, You-Wei, Christianson, Danielle, Alberto, Ma. Carmelita R., Alekseychik, Pavel, Aurela, Mika, Baldocchi, Dennis, Bansal, Sheel, Billesbach, David P., Bohrer, Gil, Bracho, Rosvel, Buchmann, Nina, Campbell, David I., Celis, Gerardo, Chen, Jiquan, Chen, Weinan, Chu, Housen, Dalmagro, Higo J., Dengel, Sigrid, Desai, Ankur R., Detto, Matteo, Dolman, Han, Eichelmann, Elke, Euskirchen, Eugenie, Famulari, Daniela, Fuchs, Kathrin, Goeckede, Mathias, Gogo, Sébastien, Gondwe, Mangaliso J., Goodrich, Jordan P., Gottschalk, Pia, Graham, Scott L., Heimann, Martin, Helbig, Manuel, Helfter, Carole, Hemes, Kyle S., Hirano, Takashi, Hollinger, David, Hörtnagl, Lukas, Iwata, Hiroki, Jacotot, Adrien, Jurasinski, Gerald, Kang, Minseok, Kasak, Kuno, King, John, Klatt, Janina, Koebsch, Franziska, Krauss, Ken W., Lai, Derrick Y.F., Lohila, Annalea, Mammarella, Ivan, Belelli Marchesini, Luca, Manca, Giovanni, Matthes, Jaclyn Hatala, Maximov, Trofim, Merbold, Lutz, Mitra, Bhaskar, Morin, Timothy H., Nemitz, Eiko, Nilsson, Mats B., Niu, Shuli, Oechel, Walter C., Oikawa, Patricia Y., Ono, Keisuke, Peichl, Matthias, Peltola, Olli, Reba, Michele L., Richardson, Andrew D., Riley, William, Runkle, Benjamin R.K., Ryu, Youngryel, Sachs, Torsten, Sakabe, Ayaka, Sanchez, Camilo Rey, Schuur, Edward A., Schäfer, Karina V.R., Sonnentag, Oliver, Sparks, Jed P., Stuart-Haëntjens, Ellen, Sturtevant, Cove, Sullivan, Ryan C., Szutu, Daphne J., Thom, Jonathan E., Torn, Margaret S., Tuittila, Eeva-Stiina, Turner, Jessica, Ueyama, Masahito, Valach, Alex C., Vargas, Rodrigo, Varlagin, Andrej, Vazquez-Lule, Alma, Verfaillie, Joseph G., Vesala, Timo, Vourlitis, George L., Ward, Eric J., Wille, Christian, Wohlfahrt, Georg, Wong, Guan Xhuan, Zhang, Zhen, Zona, Donatella, Windham-Myers, Lisamarie, Poulter, Benjamin, Jackson, Robert B., Delwiche, Kyle B., Knox, Sara Helen, Malhotra, Avni, Fluet-Chouinard, Etienne, McNicol, Gavin, Feron, Sarah, Ouyang, Zutao, Papale, Dario, Trotta, Carlo, Canfora, Eleonora, Cheah, You-Wei, Christianson, Danielle, Alberto, Ma. Carmelita R., Alekseychik, Pavel, Aurela, Mika, Baldocchi, Dennis, Bansal, Sheel, Billesbach, David P., Bohrer, Gil, Bracho, Rosvel, Buchmann, Nina, Campbell, David I., Celis, Gerardo, Chen, Jiquan, Chen, Weinan, Chu, Housen, Dalmagro, Higo J., Dengel, Sigrid, Desai, Ankur R., Detto, Matteo, Dolman, Han, Eichelmann, Elke, Euskirchen, Eugenie, Famulari, Daniela, Fuchs, Kathrin, Goeckede, Mathias, Gogo, Sébastien, Gondwe, Mangaliso J., Goodrich, Jordan P., Gottschalk, Pia, Graham, Scott L., Heimann, Martin, Helbig, Manuel, Helfter, Carole, Hemes, Kyle S., Hirano, Takashi, Hollinger, David, Hörtnagl, Lukas, Iwata, Hiroki, Jacotot, Adrien, Jurasinski, Gerald, Kang, Minseok, Kasak, Kuno, King, John, Klatt, Janina, Koebsch, Franziska, Krauss, Ken W., Lai, Derrick Y.F., Lohila, Annalea, Mammarella, Ivan, Belelli Marchesini, Luca, Manca, Giovanni, Matthes, Jaclyn Hatala, Maximov, Trofim, Merbold, Lutz, Mitra, Bhaskar, Morin, Timothy H., Nemitz, Eiko, Nilsson, Mats B., Niu, Shuli, Oechel, Walter C., Oikawa, Patricia Y., Ono, Keisuke, Peichl, Matthias, Peltola, Olli, Reba, Michele L., Richardson, Andrew D., Riley, William, Runkle, Benjamin R.K., Ryu, Youngryel, Sachs, Torsten, Sakabe, Ayaka, Sanchez, Camilo Rey, Schuur, Edward A., Schäfer, Karina V.R., Sonnentag, Oliver, Sparks, Jed P., Stuart-Haëntjens, Ellen, Sturtevant, Cove, Sullivan, Ryan C., Szutu, Daphne J., Thom, Jonathan E., Torn, Margaret S., Tuittila, Eeva-Stiina, Turner, Jessica, Ueyama, Masahito, Valach, Alex C., Vargas, Rodrigo, Varlagin, Andrej, Vazquez-Lule, Alma, Verfaillie, Joseph G., Vesala, Timo, Vourlitis, George L., Ward, Eric J., Wille, Christian, Wohlfahrt, Georg, Wong, Guan Xhuan, Zhang, Zhen, Zona, Donatella, Windham-Myers, Lisamarie, Poulter, Benjamin, and Jackson, Robert B.
Abstract: Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20∘ S to 20∘ N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longe
Published: 2021

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

35. Representativeness of eddy-covariance flux footprints for areas surrounding AmeriFlux sites

Author: Chu, Housen, Luo, Xiangzhong, Ouyang, Zutao, Chan, W. Stephen, Dengel, Sigrid, Biraud, Sebastien, Torn, Margaret S., Metzger, Stefan, Kumar, Jitendra, Arain, M. Altaf, Arkebauer, Tim J., Baldocchi, Dennis D., Bernacchi, Carl, Billesbach, Dave, Black, T. Andrew, Blanken, Peter D., Bohrer, Gil, Bracho, Rosvel, Brown, Shannon, Brunsell, Nathaniel A., Chen, Jiquan, Chen, Xingyuan, Clark, Kenneth, Desai, Ankur R., Duman, Tomer, Durden, David J., Fares, Silvano, Forbrich, Inke, Gamon, John, Gough, Christopher M., Griffis, Timothy, Helbig, Manuel, Hollinger, David, Humphreys, Elyn, Ikawa, Hiroki, Iwata, Hiroki, Ju, Yang, Knowles, John F., Knox, Sara H., Kobayashi, Hideki, Kolb, Thomas, Law, Beverly, Lee, Xuhui, Litvak, Marcy, Liu, Heping, Munger, J. William, Noormets, Asko, Novick, Kim, Oberbauer, Steven F., Oechel, Walter, Oikawa, Patty, Papuga, Shirley A., Pendall, Elise, Prajapati, Prajaya, Prueger, John, Quinton, William L., Richardson, Andrew D., Russell, Eric S., Scott, Russell L., Starr, Gregory, Staebler, Ralf, Stoy, Paul C., Stuart-Haëntjens, Ellen, Sonnentag, Oliver, Sullivan, Ryan C., Suyker, Andy, Ueyama, Masahito, Vargas, Rodrigo, Wood, Jeffrey D., Zona, Donatella, Chu, Housen, Luo, Xiangzhong, Ouyang, Zutao, Chan, W. Stephen, Dengel, Sigrid, Biraud, Sebastien, Torn, Margaret S., Metzger, Stefan, Kumar, Jitendra, Arain, M. Altaf, Arkebauer, Tim J., Baldocchi, Dennis D., Bernacchi, Carl, Billesbach, Dave, Black, T. Andrew, Blanken, Peter D., Bohrer, Gil, Bracho, Rosvel, Brown, Shannon, Brunsell, Nathaniel A., Chen, Jiquan, Chen, Xingyuan, Clark, Kenneth, Desai, Ankur R., Duman, Tomer, Durden, David J., Fares, Silvano, Forbrich, Inke, Gamon, John, Gough, Christopher M., Griffis, Timothy, Helbig, Manuel, Hollinger, David, Humphreys, Elyn, Ikawa, Hiroki, Iwata, Hiroki, Ju, Yang, Knowles, John F., Knox, Sara H., Kobayashi, Hideki, Kolb, Thomas, Law, Beverly, Lee, Xuhui, Litvak, Marcy, Liu, Heping, Munger, J. William, Noormets, Asko, Novick, Kim, Oberbauer, Steven F., Oechel, Walter, Oikawa, Patty, Papuga, Shirley A., Pendall, Elise, Prajapati, Prajaya, Prueger, John, Quinton, William L., Richardson, Andrew D., Russell, Eric S., Scott, Russell L., Starr, Gregory, Staebler, Ralf, Stoy, Paul C., Stuart-Haëntjens, Ellen, Sonnentag, Oliver, Sullivan, Ryan C., Suyker, Andy, Ueyama, Masahito, Vargas, Rodrigo, Wood, Jeffrey D., and Zona, Donatella
Abstract: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Chu, H., Luo, X., Ouyang, Z., Chan, W. S., Dengel, S., Biraud, S. C., Torn, M. S., Metzger, S., Kumar, J., Arain, M. A., Arkebauer, T. J., Baldocchi, D., Bernacchi, C., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Bracho, R., Brown, S., Brunsell, N. A., Chen, J., Chen, X., Clark, K., Desai, A. R., Duman, T., Durden, D., Fares, S., Forbrich, I., Gamon, J. A., Gough, C. M., Griffis, T., Helbig, M., Hollinger, D., Humphreys, E., Ikawa, H., Iwata, H., Ju, Y., Knowles, J. F., Knox, S. H., Kobayashi, H., Kolb, T., Law, B., Lee, X., Litvak, M., Liu, H., Munger, J. W., Noormets, A., Novick, K., Oberbauer, S. F., Oechel, W., Oikawa, P., Papuga, S. A., Pendall, E., Prajapati, P., Prueger, J., Quinton, W. L., Richardson, A. D., Russell, E. S., Scott, R. L., Starr, G., Staebler, R., Stoy, P. C., Stuart-Haentjens, E., Sonnentag, O., Sullivan, R. C., Suyker, A., Ueyama, M., Vargas, R., Wood, J. D., & Zona, D. Representativeness of eddy-covariance flux footprints for areas surrounding AmeriFlux sites. Agricultural and Forest Meteorology, 301, (2021): 108350, https://doi.org/10.1016/j.agrformet.2021.108350., Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applicat, We thank the AmeriFlux site teams for sharing their data and metadata with the network. Funding for these flux sites is acknowledged in the site data DOI, shown in Table S1. This analysis was supported in part by funding provided to the AmeriFlux Management Project by the U.S. Department of Energy's Office of Science under Contract No. DE-AC02-05CH11231. All footprint climatologies, site-level representativeness indices, and monthly EVI and sensor location biases can be accessed via the Zenodo Data Repository (Datasets S1–S6, http://doi.org/10.5281/zenodo.4015350).
Published: 2021

36. Representativeness of eddy-covariance flux footprints for areas surrounding AmeriFlux sites

Author: Chu, Housen, Luo, Xiangzhong, Ouyang, Zutao, Chan, W. Stephen, Dengel, Sigrid, Biraud, Sebastien, Torn, Margaret S., Metzger, Stefan, Kumar, Jitendra, Arain, M. Altaf, Arkebauer, Tim J., Baldocchi, Dennis D., Bernacchi, Carl, Billesbach, Dave, Black, T. Andrew, Blanken, Peter D., Bohrer, Gil, Bracho, Rosvel, Brown, Shannon, Brunsell, Nathaniel A., Chen, Jiquan, Chen, Xingyuan, Clark, Kenneth, Desai, Ankur R., Duman, Tomer, Durden, David J., Fares, Silvano, Forbrich, Inke, Gamon, John, Gough, Christopher M., Griffis, Timothy, Helbig, Manuel, Hollinger, David, Humphreys, Elyn, Ikawa, Hiroki, Iwata, Hiroki, Ju, Yang, Knowles, John F., Knox, Sara H., Kobayashi, Hideki, Kolb, Thomas, Law, Beverly, Lee, Xuhui, Litvak, Marcy, Liu, Heping, Munger, J. William, Noormets, Asko, Novick, Kim, Oberbauer, Steven F., Oechel, Walter, Oikawa, Patty, Papuga, Shirley A., Pendall, Elise, Prajapati, Prajaya, Prueger, John, Quinton, William L., Richardson, Andrew D., Russell, Eric S., Scott, Russell L., Starr, Gregory, Staebler, Ralf, Stoy, Paul C., Stuart-Haëntjens, Ellen, Sonnentag, Oliver, Sullivan, Ryan C., Suyker, Andy, Ueyama, Masahito, Vargas, Rodrigo, Wood, Jeffrey D., Zona, Donatella, Chu, Housen, Luo, Xiangzhong, Ouyang, Zutao, Chan, W. Stephen, Dengel, Sigrid, Biraud, Sebastien, Torn, Margaret S., Metzger, Stefan, Kumar, Jitendra, Arain, M. Altaf, Arkebauer, Tim J., Baldocchi, Dennis D., Bernacchi, Carl, Billesbach, Dave, Black, T. Andrew, Blanken, Peter D., Bohrer, Gil, Bracho, Rosvel, Brown, Shannon, Brunsell, Nathaniel A., Chen, Jiquan, Chen, Xingyuan, Clark, Kenneth, Desai, Ankur R., Duman, Tomer, Durden, David J., Fares, Silvano, Forbrich, Inke, Gamon, John, Gough, Christopher M., Griffis, Timothy, Helbig, Manuel, Hollinger, David, Humphreys, Elyn, Ikawa, Hiroki, Iwata, Hiroki, Ju, Yang, Knowles, John F., Knox, Sara H., Kobayashi, Hideki, Kolb, Thomas, Law, Beverly, Lee, Xuhui, Litvak, Marcy, Liu, Heping, Munger, J. William, Noormets, Asko, Novick, Kim, Oberbauer, Steven F., Oechel, Walter, Oikawa, Patty, Papuga, Shirley A., Pendall, Elise, Prajapati, Prajaya, Prueger, John, Quinton, William L., Richardson, Andrew D., Russell, Eric S., Scott, Russell L., Starr, Gregory, Staebler, Ralf, Stoy, Paul C., Stuart-Haëntjens, Ellen, Sonnentag, Oliver, Sullivan, Ryan C., Suyker, Andy, Ueyama, Masahito, Vargas, Rodrigo, Wood, Jeffrey D., and Zona, Donatella
Abstract: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Chu, H., Luo, X., Ouyang, Z., Chan, W. S., Dengel, S., Biraud, S. C., Torn, M. S., Metzger, S., Kumar, J., Arain, M. A., Arkebauer, T. J., Baldocchi, D., Bernacchi, C., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Bracho, R., Brown, S., Brunsell, N. A., Chen, J., Chen, X., Clark, K., Desai, A. R., Duman, T., Durden, D., Fares, S., Forbrich, I., Gamon, J. A., Gough, C. M., Griffis, T., Helbig, M., Hollinger, D., Humphreys, E., Ikawa, H., Iwata, H., Ju, Y., Knowles, J. F., Knox, S. H., Kobayashi, H., Kolb, T., Law, B., Lee, X., Litvak, M., Liu, H., Munger, J. W., Noormets, A., Novick, K., Oberbauer, S. F., Oechel, W., Oikawa, P., Papuga, S. A., Pendall, E., Prajapati, P., Prueger, J., Quinton, W. L., Richardson, A. D., Russell, E. S., Scott, R. L., Starr, G., Staebler, R., Stoy, P. C., Stuart-Haentjens, E., Sonnentag, O., Sullivan, R. C., Suyker, A., Ueyama, M., Vargas, R., Wood, J. D., & Zona, D. Representativeness of eddy-covariance flux footprints for areas surrounding AmeriFlux sites. Agricultural and Forest Meteorology, 301, (2021): 108350, https://doi.org/10.1016/j.agrformet.2021.108350., Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applicat, We thank the AmeriFlux site teams for sharing their data and metadata with the network. Funding for these flux sites is acknowledged in the site data DOI, shown in Table S1. This analysis was supported in part by funding provided to the AmeriFlux Management Project by the U.S. Department of Energy's Office of Science under Contract No. DE-AC02-05CH11231. All footprint climatologies, site-level representativeness indices, and monthly EVI and sensor location biases can be accessed via the Zenodo Data Repository (Datasets S1–S6, http://doi.org/10.5281/zenodo.4015350).
Published: 2021

37. Root lateral interactions drive water uptake patterns under water limitation

Author: UCL - SST/ELI/ELIA - Agronomy, Agee, Elizabeth, He, Lingli, Bisht, Gautam, Couvreur, Valentin, Shahbaz, Parisa, Meunier, Félicien, Gough, Christopher M., Matheny, Ashley M., Bohrer, Gil, Ivanov, Valeriy, UCL - SST/ELI/ELIA - Agronomy, Agee, Elizabeth, He, Lingli, Bisht, Gautam, Couvreur, Valentin, Shahbaz, Parisa, Meunier, Félicien, Gough, Christopher M., Matheny, Ashley M., Bohrer, Gil, and Ivanov, Valeriy
Abstract: The plasticity of root water uptake determines the maintenance of transpiration during periods of water limitation and drought. However, the mechanistic basis of plant water uptake, as well as the implications of water uptake strategies at the individual and ecosystem scale remain elusive. We model three-dimensional root water uptake under variably saturated conditions for a one-hectare temperate forest plot for a growing season with a pronounced mid-season dry period. Variations in root architecture, hydraulic properties, and degree of lateral interaction between root systems produce divergent local responses to water limitation and provide insights on individual and community response to meteorological conditions. Results demonstrate the plasticity of ecosystem-scale responses to surface drying, where interacting roots shift regions of active uptake to deeper soil layers with less abundant root biomass. These shifts, a product of both root system and soil hydraulic properties, illustrate intimate links between root and soil hydraulics in determining plant water sourcing. We further demonstrate that root lateral interactions are beneficial at the ecosystem-scale, even when trees compete for water. Specifically, a more spatially extensive root system facilitates access to a larger soil water reservoir, often ameliorating water limitation and reducing sharp water potential gradients. While the reduction of water stress is a benefit, it can be offset by increased root construction and maintenance costs associated with the larger rooting system. A plausible “viability” region of root communal co-existence is therefore implied where competitive pressures and root production costs are balanced by a potential water benefit.
Published: 2021

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

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

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

Author: Knox, Sara H, 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, Jackson, Robert B, Knox, Sara H, 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
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
Published: 2021

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

Author: Chang, Kuang-Yu, 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, Zona, Donatella, Chang, Kuang-Yu, 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

41. Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites

Author: Chu, Housen, Luo, Xiangzhong, Ouyang, Zutao, Chan, W. Stephen, Dengel, Sigrid, Biraud, Sébastien C., Torn, Margaret S., Metzger, Stefan, Kumar, Jitendra, Arain, M. Altaf, Arkebauer, Tim J., Baldocchi, Dennis, Bernacchi, Carl, Billesbach, Dave, Black, T. Andrew, Blanken, Peter D., Bohrer, Gil, Bracho, Rosvel, Brown, Shannon, Brunsell, Nathaniel A., Chen, Jiquan, Chen, Xingyuan, Clark, Kenneth, Desai, Ankur R., Duman, Tomer, Durden, David, Fares, Silvano, Forbrich, Inke, Gamon, John A., Gough, Christopher M., Griffis, Timothy, Helbig, Manuel, Hollinger, David, Humphreys, Elyn, Ikawa, Hiroki, Iwata, Hiroki, Ju, Yang, Knowles, John F., Knox, Sara H., Kobayashi, Hideki, Chu, Housen, Luo, Xiangzhong, Ouyang, Zutao, Chan, W. Stephen, Dengel, Sigrid, Biraud, Sébastien C., Torn, Margaret S., Metzger, Stefan, Kumar, Jitendra, Arain, M. Altaf, Arkebauer, Tim J., Baldocchi, Dennis, Bernacchi, Carl, Billesbach, Dave, Black, T. Andrew, Blanken, Peter D., Bohrer, Gil, Bracho, Rosvel, Brown, Shannon, Brunsell, Nathaniel A., Chen, Jiquan, Chen, Xingyuan, Clark, Kenneth, Desai, Ankur R., Duman, Tomer, Durden, David, Fares, Silvano, Forbrich, Inke, Gamon, John A., Gough, Christopher M., Griffis, Timothy, Helbig, Manuel, Hollinger, David, Humphreys, Elyn, Ikawa, Hiroki, Iwata, Hiroki, Ju, Yang, Knowles, John F., Knox, Sara H., and Kobayashi, Hideki
Abstract: Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applicat
Published: 2021

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

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

43. Global transpiration data from sap flow measurements: the SAPFLUXNET database

Author: Poyatos, Rafael, Granda, Victor, Flo, Victor, Adams, Mark A., Adorjan, Balazs, Aguade, David, Aidar, Marcos P. M., Allen, Scott, Susana Alvarado-Barrientos, M., Anderson-Teixeira, Kristina J., Aparecido, Luiza Maria, Arain, M. Altaf, Aranda, Ismael, Asbjornsen, Heidi, Baxter, Robert, Beamesderfer, Eric, Berry, Z. Carter, Berveiller, Daniel, Blakely, Bethany, Boggs, Johnny, Bohrer, Gil, Bolstad, Paul, V, Bonal, Damien, Bracho, Rosvel, Brito, Patricia, Brodeur, Jason, Casanoves, Fernando, Chave, Jerome, Chen, Hui, Cisneros, Cesar, Clark, Kenneth, Cremonese, Edoardo, Dang, Hongzhong, David, Jorge S., David, Teresa S., Delpierre, Nicolas, Desai, Ankur R., Do, Frederic C., Dohnal, Michal, Domec, Jean-Christophe, Dzikiti, Sebinasi, Edgar, Colin, Eichstaedt, Rebekka, El-Madany, Tarek S., Elbers, Jan, Eller, Cleiton B., Euskirchen, Eugenie S., Ewers, Brent, Fonti, Patrick, Forner, Alicia, Forrester, David, I, Freitas, Helber C., Galvagno, Marta, Garcia-Tejera, Omar, Ghimire, Chandra Prasad, Gimeno, Teresa E., Grace, John, Granier, Andre, Griebel, Anne, Guangyu, Yan, Gush, Mark B., Hanson, Paul J., Hasselquist, Niles J., Heinrich, Ingo, Hernandez-Santana, Virginia, Herrmann, Valentine, Holtta, Teemu, Holwerda, Friso, Irvine, James, Ayutthaya, Supat Isarangkool Na, Jarvis, Paul G., Jochheim, Hubert, Joly, Carlos A., Kaplick, Julia, Kim, Hyun Seok, Klemedtsson, Leif, Kropp, Heather, Lagergren, Fredrik, Lane, Patrick, Lang, Petra, Lapenas, Andrei, Lechuga, Victor, Lee, Minsu, Leuschner, Christoph, Limousin, Jean-Marc, Linares, Juan Carlos, Linderson, Maj-Lena, Lindroth, Anders, Llorens, Pilar, Lopez-Bernal, Alvaro, Loranty, Michael M., Luttschwager, Dietmar, Macinnis-Ng, Cate, Marechaux, Isabelle, Martin, Timothy A., Matheny, Ashley, McDowell, Nate, McMahon, Sean, Meir, Patrick, Meszaros, Ilona, Migliavacca, Mirco, Mitchell, Patrick, Molder, Meelis, Montagnani, Leonardo, Moore, Georgianne W., Nakada, Ryogo, Niu, Furong, Nolan, Rachael H., Norby, Richard, Novick, Kimberly, Oberhuber, Walter, Obojes, Nikolaus, Oishi, A. Christopher, Oliveira, Rafael S., Oren, Ram, Ourcival, Jean-Marc, Paljakka, Teemu, Perez-Priego, Oscar, Peri, Pablo L., Peters, Richard L., Pfautsch, Sebastian, Pockman, William T., Preisler, Yakir, Rascher, Katherine, Robinson, George, Rocha, Humberto, Rocheteau, Alain, Roll, Alexander, Rosado, Bruno H. P., Rowland, Lucy, Rubtsov, Alexey, V, Sabate, Santiago, Salmon, Yann, Salomon, Roberto L., Sanchez-Costa, Elisenda, Schafer, Karina V. R., Schuldt, Bernhard, Shashkin, Alexandr, Stahl, Clement, Stojanovic, Marko, Suarez, Juan Carlos, Sun, Ge, Szatniewska, Justyna, Tatarinov, Fyodor, Tesar, Miroslav, Thomas, Frank M., Tor-ngern, Pantana, Urban, Josef, Valladares, Fernando, van der Tol, Christiaan, van Meerveld, Ilja, Varlagin, Andrej, Voigt, Holm, Warren, Jeffrey, Werner, Christiane, Werner, Willy, Wieser, Gerhard, Wingate, Lisa, Wullschleger, Stan, Yi, Koong, Zweifel, Roman, Steppe, Kathy, Mencuccini, Maurizio, Martinez-Vilalta, Jordi, Poyatos, Rafael, Granda, Victor, Flo, Victor, Adams, Mark A., Adorjan, Balazs, Aguade, David, Aidar, Marcos P. M., Allen, Scott, Susana Alvarado-Barrientos, M., Anderson-Teixeira, Kristina J., Aparecido, Luiza Maria, Arain, M. Altaf, Aranda, Ismael, Asbjornsen, Heidi, Baxter, Robert, Beamesderfer, Eric, Berry, Z. Carter, Berveiller, Daniel, Blakely, Bethany, Boggs, Johnny, Bohrer, Gil, Bolstad, Paul, V, Bonal, Damien, Bracho, Rosvel, Brito, Patricia, Brodeur, Jason, Casanoves, Fernando, Chave, Jerome, Chen, Hui, Cisneros, Cesar, Clark, Kenneth, Cremonese, Edoardo, Dang, Hongzhong, David, Jorge S., David, Teresa S., Delpierre, Nicolas, Desai, Ankur R., Do, Frederic C., Dohnal, Michal, Domec, Jean-Christophe, Dzikiti, Sebinasi, Edgar, Colin, Eichstaedt, Rebekka, El-Madany, Tarek S., Elbers, Jan, Eller, Cleiton B., Euskirchen, Eugenie S., Ewers, Brent, Fonti, Patrick, Forner, Alicia, Forrester, David, I, Freitas, Helber C., Galvagno, Marta, Garcia-Tejera, Omar, Ghimire, Chandra Prasad, Gimeno, Teresa E., Grace, John, Granier, Andre, Griebel, Anne, Guangyu, Yan, Gush, Mark B., Hanson, Paul J., Hasselquist, Niles J., Heinrich, Ingo, Hernandez-Santana, Virginia, Herrmann, Valentine, Holtta, Teemu, Holwerda, Friso, Irvine, James, Ayutthaya, Supat Isarangkool Na, Jarvis, Paul G., Jochheim, Hubert, Joly, Carlos A., Kaplick, Julia, Kim, Hyun Seok, Klemedtsson, Leif, Kropp, Heather, Lagergren, Fredrik, Lane, Patrick, Lang, Petra, Lapenas, Andrei, Lechuga, Victor, Lee, Minsu, Leuschner, Christoph, Limousin, Jean-Marc, Linares, Juan Carlos, Linderson, Maj-Lena, Lindroth, Anders, Llorens, Pilar, Lopez-Bernal, Alvaro, Loranty, Michael M., Luttschwager, Dietmar, Macinnis-Ng, Cate, Marechaux, Isabelle, Martin, Timothy A., Matheny, Ashley, McDowell, Nate, McMahon, Sean, Meir, Patrick, Meszaros, Ilona, Migliavacca, Mirco, Mitchell, Patrick, Molder, Meelis, Montagnani, Leonardo, Moore, Georgianne W., Nakada, Ryogo, Niu, Furong, Nolan, Rachael H., Norby, Richard, Novick, Kimberly, Oberhuber, Walter, Obojes, Nikolaus, Oishi, A. Christopher, Oliveira, Rafael S., Oren, Ram, Ourcival, Jean-Marc, Paljakka, Teemu, Perez-Priego, Oscar, Peri, Pablo L., Peters, Richard L., Pfautsch, Sebastian, Pockman, William T., Preisler, Yakir, Rascher, Katherine, Robinson, George, Rocha, Humberto, Rocheteau, Alain, Roll, Alexander, Rosado, Bruno H. P., Rowland, Lucy, Rubtsov, Alexey, V, Sabate, Santiago, Salmon, Yann, Salomon, Roberto L., Sanchez-Costa, Elisenda, Schafer, Karina V. R., Schuldt, Bernhard, Shashkin, Alexandr, Stahl, Clement, Stojanovic, Marko, Suarez, Juan Carlos, Sun, Ge, Szatniewska, Justyna, Tatarinov, Fyodor, Tesar, Miroslav, Thomas, Frank M., Tor-ngern, Pantana, Urban, Josef, Valladares, Fernando, van der Tol, Christiaan, van Meerveld, Ilja, Varlagin, Andrej, Voigt, Holm, Warren, Jeffrey, Werner, Christiane, Werner, Willy, Wieser, Gerhard, Wingate, Lisa, Wullschleger, Stan, Yi, Koong, Zweifel, Roman, Steppe, Kathy, Mencuccini, Maurizio, and Martinez-Vilalta, Jordi
Abstract: Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land-atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is free
Published: 2021

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

Author: Delwiche, Kyle B., Knox, Sara Helen, Malhotra, Avni, Fluet-Chouinard, Etienne, McNicol, Gavin, Feron, Sarah, Ouyang, Zutao, Papale, Dario, Trotta, Carlo, Canfora, Eleonora, Cheah, You-Wei, Christianson, Danielle, Alberto, Ma. Carmelita R., Alekseychik, Pavel, Aurela, Mika, Baldocchi, Dennis, Bansal, Sheel, Billesbach, David P., Bohrer, Gil, Bracho, Rosvel, Buchmann, Nina, Campbell, David I., Celis, Gerardo, Chen, Jiquan, Chen, Weinan, Chu, Housen, Dalmagro, Higo J., Dengel, Sigrid, Desai, Ankur R., Detto, Matteo, Dolman, Han, Eichelmann, Elke, Euskirchen, Eugenie, Famulari, Daniela, Fuchs, Kathrin, Goeckede, Mathias, Gogo, Sébastien, Gondwe, Mangaliso J., Goodrich, Jordan P., Gottschalk, Pia, Graham, Scott L., Heimann, Martin, Helbig, Manuel, Helfter, Carole, Hemes, Kyle S., Hirano, Takashi, Hollinger, David, Hörtnagl, Lukas, Iwata, Hiroki, Jacotot, Adrien, Jurasinski, Gerald, Kang, Minseok, Kasak, Kuno, King, John, Klatt, Janina, Koebsch, Franziska, Krauss, Ken W., Lai, Derrick Y.F., Lohila, Annalea, Mammarella, Ivan, Belelli Marchesini, Luca, Manca, Giovanni, Matthes, Jaclyn Hatala, Maximov, Trofim, Merbold, Lutz, Mitra, Bhaskar, Morin, Timothy H., Nemitz, Eiko, Nilsson, Mats B., Niu, Shuli, Oechel, Walter C., Oikawa, Patricia Y., Ono, Keisuke, Peichl, Matthias, Peltola, Olli, Reba, Michele L., Richardson, Andrew D., Riley, William, Runkle, Benjamin R.K., Ryu, Youngryel, Sachs, Torsten, Sakabe, Ayaka, Sanchez, Camilo Rey, Schuur, Edward A., Schäfer, Karina V.R., Sonnentag, Oliver, Sparks, Jed P., Stuart-Haëntjens, Ellen, Sturtevant, Cove, Sullivan, Ryan C., Szutu, Daphne J., Thom, Jonathan E., Torn, Margaret S., Tuittila, Eeva-Stiina, Turner, Jessica, Ueyama, Masahito, Valach, Alex C., Vargas, Rodrigo, Varlagin, Andrej, Vazquez-Lule, Alma, Verfaillie, Joseph G., Vesala, Timo, Vourlitis, George L., Ward, Eric J., Wille, Christian, Wohlfahrt, Georg, Wong, Guan Xhuan, Zhang, Zhen, Zona, Donatella, Windham-Myers, Lisamarie, Poulter, Benjamin, Jackson, Robert B., Delwiche, Kyle B., Knox, Sara Helen, Malhotra, Avni, Fluet-Chouinard, Etienne, McNicol, Gavin, Feron, Sarah, Ouyang, Zutao, Papale, Dario, Trotta, Carlo, Canfora, Eleonora, Cheah, You-Wei, Christianson, Danielle, Alberto, Ma. Carmelita R., Alekseychik, Pavel, Aurela, Mika, Baldocchi, Dennis, Bansal, Sheel, Billesbach, David P., Bohrer, Gil, Bracho, Rosvel, Buchmann, Nina, Campbell, David I., Celis, Gerardo, Chen, Jiquan, Chen, Weinan, Chu, Housen, Dalmagro, Higo J., Dengel, Sigrid, Desai, Ankur R., Detto, Matteo, Dolman, Han, Eichelmann, Elke, Euskirchen, Eugenie, Famulari, Daniela, Fuchs, Kathrin, Goeckede, Mathias, Gogo, Sébastien, Gondwe, Mangaliso J., Goodrich, Jordan P., Gottschalk, Pia, Graham, Scott L., Heimann, Martin, Helbig, Manuel, Helfter, Carole, Hemes, Kyle S., Hirano, Takashi, Hollinger, David, Hörtnagl, Lukas, Iwata, Hiroki, Jacotot, Adrien, Jurasinski, Gerald, Kang, Minseok, Kasak, Kuno, King, John, Klatt, Janina, Koebsch, Franziska, Krauss, Ken W., Lai, Derrick Y.F., Lohila, Annalea, Mammarella, Ivan, Belelli Marchesini, Luca, Manca, Giovanni, Matthes, Jaclyn Hatala, Maximov, Trofim, Merbold, Lutz, Mitra, Bhaskar, Morin, Timothy H., Nemitz, Eiko, Nilsson, Mats B., Niu, Shuli, Oechel, Walter C., Oikawa, Patricia Y., Ono, Keisuke, Peichl, Matthias, Peltola, Olli, Reba, Michele L., Richardson, Andrew D., Riley, William, Runkle, Benjamin R.K., Ryu, Youngryel, Sachs, Torsten, Sakabe, Ayaka, Sanchez, Camilo Rey, Schuur, Edward A., Schäfer, Karina V.R., Sonnentag, Oliver, Sparks, Jed P., Stuart-Haëntjens, Ellen, Sturtevant, Cove, Sullivan, Ryan C., Szutu, Daphne J., Thom, Jonathan E., Torn, Margaret S., Tuittila, Eeva-Stiina, Turner, Jessica, Ueyama, Masahito, Valach, Alex C., Vargas, Rodrigo, Varlagin, Andrej, Vazquez-Lule, Alma, Verfaillie, Joseph G., Vesala, Timo, Vourlitis, George L., Ward, Eric J., Wille, Christian, Wohlfahrt, Georg, Wong, Guan Xhuan, Zhang, Zhen, Zona, Donatella, Windham-Myers, Lisamarie, Poulter, Benjamin, and Jackson, Robert B.
Abstract: Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20∘ S to 20∘ N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longe
Published: 2021

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

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

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

47. 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 R. K., Ryu, Youngryel, Sachs, Torsten, Schäfer, Karina V. R., Schmid, Hans Peter, Shurpali, Narasinha, Sonnentag, Oliver, Tang, Angela C. I., Torn, Margaret S., Trotta, Carlo, Tuittila, Eeva-stiina, Ueyama, Masahito, Vargas, Rodrigo, Vesala, Timo, Windham-myers, Lisamarie, Zhang, Zhen, Zona, Donatella, 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 R. K., Ryu, Youngryel, Sachs, Torsten, Schäfer, Karina V. R., Schmid, Hans Peter, Shurpali, Narasinha, Sonnentag, Oliver, Tang, Angela C. I., Torn, Margaret S., Trotta, Carlo, Tuittila, Eeva-stiina, Ueyama, Masahito, Vargas, Rodrigo, Vesala, Timo, Windham-myers, Lisamarie, Zhang, Zhen, and Zona, Donatella
Published: 2021

48. Global transpiration data from sap flow measurements: the SAPFLUXNET database

Author: Ministerio de Ciencia e Innovación (España), Llorens, Pilar [0000-0003-4591-5303], Poyatos, Rafael, Granda, Víctor, Flo, Víctor, Adams, Mark A., Adorján, Balázs, Aguadé, David, Aidar, Marcos P. M., Alle, Scott, Alvarado-Barrientos, M. Susana, Anderson-Teixeira, Kristina J., Aparecido, Luiza Maria, Mitchell, Patrick, Mölder, Meelis, Montagnani, Leonardo, Moore, Georgianne W., Nakada, Ryogo, Niu, Furong, Nolan, Rachael H., Norby, Richard, Novick, Kimberly, Oberhuber, Walter, Arain, M. Altaf, Obojes, Nikolaus, Oishi, A. Christopher, Oliveira, Rafael S., Oren, Ram, Ourcival, Jean-Marc, Paljakka, Teemu, Pérez-Priego, Óscar, Peri, Pablo L., Peters, Richard L., Pfautsch, Sebastian, Aranda García, Ismael, Pockman, William T., Preisler, Yakir, Rascher, Katherine, Robinson, George, Rocha, Humberto, Rocheteau, Alain, Röll, Alexander, Rosado, Bruno H. P., Rowland, Lucy, Rubtsov, Alexey V., Asbjornsen, Heidi, Sabaté, Santiago, Salmon, Yann, Roberto L. Salomón, Salomón, Roberto L., Sánchez-Costa, Elisenda, Schäfer, Karina V. R., Schuldt, Bernhard, Shashkin, Alexandr, Stahl, Clément, Stojanović, Marko, Baxter, Robert, Suárez, Juan Carlos, Sun, Ge, Szatniewska, Justyna, Tatarinov, Fyodor, Tesař, Miroslav, Thomas, Frank M., Tor-ngern, Pantana, Urban, Josef, Valladares Ros, Fernando, Van der Tol, Christiaan, Beamesderfer, Eric, Van Meerveld, Ilja, Varlagin, Andrej, Voigt, Hom, Warren, Jeffrey, Werner, Christiane, Werner, Willy, Wieser, Gerhard, Wingate, Lisa, Wullschleger, Stan, Zweifel, Roman, Berry, Z. Carter, Yi, Koon, Steppe, Kathy, Mencuccini, Maurizio, Martínez-Vilalta, Jordi, Berveiller, Daniel, Blakely, Bethany, Boggs, Johnny, Bohrer, Gil, Bolstad, Paul V., Bonal, Damien, Brito, Patricia, Brodeur, Jason, Casanoves, Fernando, Chave, Jérôme, Chen, Hui, Cisneros, Cesar, Clark, Kenneth, Cremonese, Edoardo, Dang, Hongzhong, David, Jorge S., David, Teresa S., Delpierre, Nicolas, Desai, Ankur R., Do, Frederic C., Dohnal, Michal, Domec, Jean-Christophe, Dzikiti, Sebinasi, Edgar, Colin, Eichstaedt, Rebekka, El-Madany, Tarek S., Eller, Cleiton B., Elbers, Jan, Euskirchen, Eugénie S., Ewers, Brent, Fonti, Patrick, Forner, Alicia, Forrester, David I., Freitas, Helber C., Galvagno, Marta, García-Tejera, Omar, Ghimire, Chandra Prasad, Gimeno, Teresa E., Grace, John, Granier, André, Griebel, Anne, Guangyu, Yan, Gush, Mark B., Hanson, Paul J., Hasselquist, Niles J., Heinrich, Ingo, Hernández-Santana, Virginia, Herrmann, Valentine, Hölttä, Teemu, Holwerda, Friso, Irvine, James, Na Ayutthaya, Supat Isarangkool, Jarvis, Paul G., Jochheim, Hubert, Kaplick, Julia, Joly, Carlos A., Kim, Hyun Seok, Klemedtsson, Leif, Kropp, Heather, Lagergren, Fredrik, Lane, Patrick, Lang, Petra, Lapenas, Andrei, Lechuga, Víctor, Lee, Minsu, Leuschner, Christoph, Limousin, Jean-Marc, Linares, Juan Carlos, Linderson, Maj-Lena, Lindroth, Anders, Llorens, Pilar, López-Bernal, Álvaro, Loranty, Michael M., Lüttschwager, Dietmar, Macinnis-Ng, Cate, Maréchaux, Isabelle, Martin, Timothy A., Matheny, Ashley, McDowell, Nate, McMahon, Sean, Meir, Patrick, Mészáros, Ilona, Migliavacca, Mirco, Ministerio de Ciencia e Innovación (España), Llorens, Pilar [0000-0003-4591-5303], Poyatos, Rafael, Granda, Víctor, Flo, Víctor, Adams, Mark A., Adorján, Balázs, Aguadé, David, Aidar, Marcos P. M., Alle, Scott, Alvarado-Barrientos, M. Susana, Anderson-Teixeira, Kristina J., Aparecido, Luiza Maria, Mitchell, Patrick, Mölder, Meelis, Montagnani, Leonardo, Moore, Georgianne W., Nakada, Ryogo, Niu, Furong, Nolan, Rachael H., Norby, Richard, Novick, Kimberly, Oberhuber, Walter, Arain, M. Altaf, Obojes, Nikolaus, Oishi, A. Christopher, Oliveira, Rafael S., Oren, Ram, Ourcival, Jean-Marc, Paljakka, Teemu, Pérez-Priego, Óscar, Peri, Pablo L., Peters, Richard L., Pfautsch, Sebastian, Aranda García, Ismael, Pockman, William T., Preisler, Yakir, Rascher, Katherine, Robinson, George, Rocha, Humberto, Rocheteau, Alain, Röll, Alexander, Rosado, Bruno H. P., Rowland, Lucy, Rubtsov, Alexey V., Asbjornsen, Heidi, Sabaté, Santiago, Salmon, Yann, Roberto L. Salomón, Salomón, Roberto L., Sánchez-Costa, Elisenda, Schäfer, Karina V. R., Schuldt, Bernhard, Shashkin, Alexandr, Stahl, Clément, Stojanović, Marko, Baxter, Robert, Suárez, Juan Carlos, Sun, Ge, Szatniewska, Justyna, Tatarinov, Fyodor, Tesař, Miroslav, Thomas, Frank M., Tor-ngern, Pantana, Urban, Josef, Valladares Ros, Fernando, Van der Tol, Christiaan, Beamesderfer, Eric, Van Meerveld, Ilja, Varlagin, Andrej, Voigt, Hom, Warren, Jeffrey, Werner, Christiane, Werner, Willy, Wieser, Gerhard, Wingate, Lisa, Wullschleger, Stan, Zweifel, Roman, Berry, Z. Carter, Yi, Koon, Steppe, Kathy, Mencuccini, Maurizio, Martínez-Vilalta, Jordi, Berveiller, Daniel, Blakely, Bethany, Boggs, Johnny, Bohrer, Gil, Bolstad, Paul V., Bonal, Damien, Brito, Patricia, Brodeur, Jason, Casanoves, Fernando, Chave, Jérôme, Chen, Hui, Cisneros, Cesar, Clark, Kenneth, Cremonese, Edoardo, Dang, Hongzhong, David, Jorge S., David, Teresa S., Delpierre, Nicolas, Desai, Ankur R., Do, Frederic C., Dohnal, Michal, Domec, Jean-Christophe, Dzikiti, Sebinasi, Edgar, Colin, Eichstaedt, Rebekka, El-Madany, Tarek S., Eller, Cleiton B., Elbers, Jan, Euskirchen, Eugénie S., Ewers, Brent, Fonti, Patrick, Forner, Alicia, Forrester, David I., Freitas, Helber C., Galvagno, Marta, García-Tejera, Omar, Ghimire, Chandra Prasad, Gimeno, Teresa E., Grace, John, Granier, André, Griebel, Anne, Guangyu, Yan, Gush, Mark B., Hanson, Paul J., Hasselquist, Niles J., Heinrich, Ingo, Hernández-Santana, Virginia, Herrmann, Valentine, Hölttä, Teemu, Holwerda, Friso, Irvine, James, Na Ayutthaya, Supat Isarangkool, Jarvis, Paul G., Jochheim, Hubert, Kaplick, Julia, Joly, Carlos A., Kim, Hyun Seok, Klemedtsson, Leif, Kropp, Heather, Lagergren, Fredrik, Lane, Patrick, Lang, Petra, Lapenas, Andrei, Lechuga, Víctor, Lee, Minsu, Leuschner, Christoph, Limousin, Jean-Marc, Linares, Juan Carlos, Linderson, Maj-Lena, Lindroth, Anders, Llorens, Pilar, López-Bernal, Álvaro, Loranty, Michael M., Lüttschwager, Dietmar, Macinnis-Ng, Cate, Maréchaux, Isabelle, Martin, Timothy A., Matheny, Ashley, McDowell, Nate, McMahon, Sean, Meir, Patrick, Mészáros, Ilona, and Migliavacca, Mirco
Abstract: Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land–atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is free
Published: 2021

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

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

50. 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 R. K., Ryu, Youngryel, Sachs, Torsten, Schäfer, Karina V. R., Schmid, Hans Peter, Shurpali, Narasinha, Sonnentag, Oliver, Tang, Angela C. I., Torn, Margaret S., Trotta, Carlo, Tuittila, Eeva-stiina, Ueyama, Masahito, Vargas, Rodrigo, Vesala, Timo, Windham-myers, Lisamarie, Zhang, Zhen, Zona, Donatella, 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 R. K., Ryu, Youngryel, Sachs, Torsten, Schäfer, Karina V. R., Schmid, Hans Peter, Shurpali, Narasinha, Sonnentag, Oliver, Tang, Angela C. I., Torn, Margaret S., Trotta, Carlo, Tuittila, Eeva-stiina, Ueyama, Masahito, Vargas, Rodrigo, Vesala, Timo, Windham-myers, Lisamarie, Zhang, Zhen, and Zona, Donatella
Published: 2021

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