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Your search keyword '"Jurasinski, G."' showing total 12 results
Start Over Author "Jurasinski, G." Publication Type Electronic Resources
12 results on '"Jurasinski, G."'

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

Author

Yuan, K, Yuan, K, Zhu, Q, Li, F, Riley, WJ, Torn, M, Chu, H, McNicol, G, Chen, M, Knox, S, Delwiche, K, Wu, H, Baldocchi, D, Ma, H, Desai, AR, Chen, J, Sachs, T, Ueyama, M, Sonnentag, O, Helbig, M, Tuittila, ES, Jurasinski, G, Koebsch, F, Campbell, D, Schmid, HP, Lohila, A, Goeckede, M, Nilsson, MB, Friborg, T, Jansen, J, Zona, D, Euskirchen, E, Ward, EJ, Bohrer, G, Jin, Z, Liu, L, Iwata, H, Goodrich, J, Jackson, R, Yuan, K, Yuan, K, Zhu, Q, Li, F, Riley, WJ, Torn, M, Chu, H, McNicol, G, Chen, M, Knox, S, Delwiche, K, Wu, H, Baldocchi, D, Ma, H, Desai, AR, Chen, J, Sachs, T, Ueyama, M, Sonnentag, O, Helbig, M, Tuittila, ES, Jurasinski, G, Koebsch, F, Campbell, D, Schmid, HP, Lohila, A, Goeckede, M, Nilsson, MB, Friborg, T, Jansen, J, Zona, D, Euskirchen, E, Ward, EJ, Bohrer, G, Jin, Z, Liu, L, Iwata, H, Goodrich, J, and Jackson, R

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

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

Author

Delwiche, KB, Delwiche, KB, Knox, SH, Malhotra, A, Fluet-Chouinard, E, McNicol, G, Feron, S, Ouyang, Z, Papale, D, Trotta, C, Canfora, E, Cheah, YW, Christianson, D, Alberto, MCR, Alekseychik, P, Aurela, M, Baldocchi, D, Bansal, S, Billesbach, DP, Bohrer, G, Bracho, R, Buchmann, N, Campbell, DI, Celis, G, Chen, J, Chen, W, Chu, H, Dalmagro, HJ, Dengel, S, Desai, AR, Detto, M, Dolman, H, Eichelmann, E, Euskirchen, E, Famulari, D, Fuchs, K, Goeckede, M, Gogo, S, Gondwe, MJ, Goodrich, JP, Gottschalk, P, Graham, SL, Heimann, M, Helbig, M, Helfter, C, Hemes, KS, Hirano, T, Hollinger, D, Hörtnagl, L, Iwata, H, Jacotot, A, Jurasinski, G, Kang, M, Kasak, K, King, J, Klatt, J, Koebsch, F, Krauss, KW, Lai, DYF, Lohila, A, Mammarella, I, Belelli Marchesini, L, Manca, G, Matthes, JH, Maximov, T, Merbold, L, Mitra, B, Morin, TH, Nemitz, E, Nilsson, MB, Niu, S, Oechel, WC, Oikawa, PY, Ono, K, Peichl, M, Peltola, O, Reba, ML, Richardson, AD, Riley, W, Runkle, BRK, Ryu, Y, Sachs, T, Sakabe, A, Sanchez, CR, Schuur, EA, Schäfer, KVR, Sonnentag, O, Sparks, JP, Stuart-Haëntjens, E, Sturtevant, C, Sullivan, RC, Szutu, DJ, Thom, JE, Torn, MS, Tuittila, ES, Turner, J, Ueyama, M, Valach, AC, Vargas, R, Varlagin, A, Vazquez-Lule, A, Delwiche, KB, Delwiche, KB, Knox, SH, Malhotra, A, Fluet-Chouinard, E, McNicol, G, Feron, S, Ouyang, Z, Papale, D, Trotta, C, Canfora, E, Cheah, YW, Christianson, D, Alberto, MCR, Alekseychik, P, Aurela, M, Baldocchi, D, Bansal, S, Billesbach, DP, Bohrer, G, Bracho, R, Buchmann, N, Campbell, DI, Celis, G, Chen, J, Chen, W, Chu, H, Dalmagro, HJ, Dengel, S, Desai, AR, Detto, M, Dolman, H, Eichelmann, E, Euskirchen, E, Famulari, D, Fuchs, K, Goeckede, M, Gogo, S, Gondwe, MJ, Goodrich, JP, Gottschalk, P, Graham, SL, Heimann, M, Helbig, M, Helfter, C, Hemes, KS, Hirano, T, Hollinger, D, Hörtnagl, L, Iwata, H, Jacotot, A, Jurasinski, G, Kang, M, Kasak, K, King, J, Klatt, J, Koebsch, F, Krauss, KW, Lai, DYF, Lohila, A, Mammarella, I, Belelli Marchesini, L, Manca, G, Matthes, JH, Maximov, T, Merbold, L, Mitra, B, Morin, TH, Nemitz, E, Nilsson, MB, Niu, S, Oechel, WC, Oikawa, PY, Ono, K, Peichl, M, Peltola, O, Reba, ML, Richardson, AD, Riley, W, Runkle, BRK, Ryu, Y, Sachs, T, Sakabe, A, Sanchez, CR, Schuur, EA, Schäfer, KVR, Sonnentag, O, Sparks, JP, Stuart-Haëntjens, E, Sturtevant, C, Sullivan, RC, Szutu, DJ, Thom, JE, Torn, MS, Tuittila, ES, Turner, J, Ueyama, M, Valach, AC, Vargas, R, Varlagin, A, and Vazquez-Lule, A

Abstract

Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20g g€¯S to 20g g€¯N) the spring onset of elevated CH4 emissions starts 3g€¯d earlier, and the CH4 emission season lasts 4

Published
2021

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

Author

Delwiche, KB, Delwiche, KB, Knox, SH, Malhotra, A, Fluet-Chouinard, E, McNicol, G, Feron, S, Ouyang, Z, Papale, D, Trotta, C, Canfora, E, Cheah, YW, Christianson, D, Alberto, MCR, Alekseychik, P, Aurela, M, Baldocchi, D, Bansal, S, Billesbach, DP, Bohrer, G, Bracho, R, Buchmann, N, Campbell, DI, Celis, G, Chen, J, Chen, W, Chu, H, Dalmagro, HJ, Dengel, S, Desai, AR, Detto, M, Dolman, H, Eichelmann, E, Euskirchen, E, Famulari, D, Fuchs, K, Goeckede, M, Gogo, S, Gondwe, MJ, Goodrich, JP, Gottschalk, P, Graham, SL, Heimann, M, Helbig, M, Helfter, C, Hemes, KS, Hirano, T, Hollinger, D, Hörtnagl, L, Iwata, H, Jacotot, A, Jurasinski, G, Kang, M, Kasak, K, King, J, Klatt, J, Koebsch, F, Krauss, KW, Lai, DYF, Lohila, A, Mammarella, I, Belelli Marchesini, L, Manca, G, Matthes, JH, Maximov, T, Merbold, L, Mitra, B, Morin, TH, Nemitz, E, Nilsson, MB, Niu, S, Oechel, WC, Oikawa, PY, Ono, K, Peichl, M, Peltola, O, Reba, ML, Richardson, AD, Riley, W, Runkle, BRK, Ryu, Y, Sachs, T, Sakabe, A, Sanchez, CR, Schuur, EA, Schäfer, KVR, Sonnentag, O, Sparks, JP, Stuart-Haëntjens, E, Sturtevant, C, Sullivan, RC, Szutu, DJ, Thom, JE, Torn, MS, Tuittila, ES, Turner, J, Ueyama, M, Valach, AC, Vargas, R, Varlagin, A, Vazquez-Lule, A, Delwiche, KB, Delwiche, KB, Knox, SH, Malhotra, A, Fluet-Chouinard, E, McNicol, G, Feron, S, Ouyang, Z, Papale, D, Trotta, C, Canfora, E, Cheah, YW, Christianson, D, Alberto, MCR, Alekseychik, P, Aurela, M, Baldocchi, D, Bansal, S, Billesbach, DP, Bohrer, G, Bracho, R, Buchmann, N, Campbell, DI, Celis, G, Chen, J, Chen, W, Chu, H, Dalmagro, HJ, Dengel, S, Desai, AR, Detto, M, Dolman, H, Eichelmann, E, Euskirchen, E, Famulari, D, Fuchs, K, Goeckede, M, Gogo, S, Gondwe, MJ, Goodrich, JP, Gottschalk, P, Graham, SL, Heimann, M, Helbig, M, Helfter, C, Hemes, KS, Hirano, T, Hollinger, D, Hörtnagl, L, Iwata, H, Jacotot, A, Jurasinski, G, Kang, M, Kasak, K, King, J, Klatt, J, Koebsch, F, Krauss, KW, Lai, DYF, Lohila, A, Mammarella, I, Belelli Marchesini, L, Manca, G, Matthes, JH, Maximov, T, Merbold, L, Mitra, B, Morin, TH, Nemitz, E, Nilsson, MB, Niu, S, Oechel, WC, Oikawa, PY, Ono, K, Peichl, M, Peltola, O, Reba, ML, Richardson, AD, Riley, W, Runkle, BRK, Ryu, Y, Sachs, T, Sakabe, A, Sanchez, CR, Schuur, EA, Schäfer, KVR, Sonnentag, O, Sparks, JP, Stuart-Haëntjens, E, Sturtevant, C, Sullivan, RC, Szutu, DJ, Thom, JE, Torn, MS, Tuittila, ES, Turner, J, Ueyama, M, Valach, AC, Vargas, R, Varlagin, A, and Vazquez-Lule, A

Abstract

Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20g g€¯S to 20g g€¯N) the spring onset of elevated CH4 emissions starts 3g€¯d earlier, and the CH4 emission season lasts 4

Published
2021

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

Author

Delwiche, KB, Delwiche, KB, Knox, SH, Malhotra, A, Fluet-Chouinard, E, McNicol, G, Feron, S, Ouyang, Z, Papale, D, Trotta, C, Canfora, E, Cheah, YW, Christianson, D, Alberto, MCR, Alekseychik, P, Aurela, M, Baldocchi, D, Bansal, S, Billesbach, DP, Bohrer, G, Bracho, R, Buchmann, N, Campbell, DI, Celis, G, Chen, J, Chen, W, Chu, H, Dalmagro, HJ, Dengel, S, Desai, AR, Detto, M, Dolman, H, Eichelmann, E, Euskirchen, E, Famulari, D, Fuchs, K, Goeckede, M, Gogo, S, Gondwe, MJ, Goodrich, JP, Gottschalk, P, Graham, SL, Heimann, M, Helbig, M, Helfter, C, Hemes, KS, Hirano, T, Hollinger, D, Hörtnagl, L, Iwata, H, Jacotot, A, Jurasinski, G, Kang, M, Kasak, K, King, J, Klatt, J, Koebsch, F, Krauss, KW, Lai, DYF, Lohila, A, Mammarella, I, Belelli Marchesini, L, Manca, G, Matthes, JH, Maximov, T, Merbold, L, Mitra, B, Morin, TH, Nemitz, E, Nilsson, MB, Niu, S, Oechel, WC, Oikawa, PY, Ono, K, Peichl, M, Peltola, O, Reba, ML, Richardson, AD, Riley, W, Runkle, BRK, Ryu, Y, Sachs, T, Sakabe, A, Sanchez, CR, Schuur, EA, Schäfer, KVR, Sonnentag, O, Sparks, JP, Stuart-Haëntjens, E, Sturtevant, C, Sullivan, RC, Szutu, DJ, Thom, JE, Torn, MS, Tuittila, ES, Turner, J, Ueyama, M, Valach, AC, Vargas, R, Varlagin, A, Vazquez-Lule, A, Delwiche, KB, Delwiche, KB, Knox, SH, Malhotra, A, Fluet-Chouinard, E, McNicol, G, Feron, S, Ouyang, Z, Papale, D, Trotta, C, Canfora, E, Cheah, YW, Christianson, D, Alberto, MCR, Alekseychik, P, Aurela, M, Baldocchi, D, Bansal, S, Billesbach, DP, Bohrer, G, Bracho, R, Buchmann, N, Campbell, DI, Celis, G, Chen, J, Chen, W, Chu, H, Dalmagro, HJ, Dengel, S, Desai, AR, Detto, M, Dolman, H, Eichelmann, E, Euskirchen, E, Famulari, D, Fuchs, K, Goeckede, M, Gogo, S, Gondwe, MJ, Goodrich, JP, Gottschalk, P, Graham, SL, Heimann, M, Helbig, M, Helfter, C, Hemes, KS, Hirano, T, Hollinger, D, Hörtnagl, L, Iwata, H, Jacotot, A, Jurasinski, G, Kang, M, Kasak, K, King, J, Klatt, J, Koebsch, F, Krauss, KW, Lai, DYF, Lohila, A, Mammarella, I, Belelli Marchesini, L, Manca, G, Matthes, JH, Maximov, T, Merbold, L, Mitra, B, Morin, TH, Nemitz, E, Nilsson, MB, Niu, S, Oechel, WC, Oikawa, PY, Ono, K, Peichl, M, Peltola, O, Reba, ML, Richardson, AD, Riley, W, Runkle, BRK, Ryu, Y, Sachs, T, Sakabe, A, Sanchez, CR, Schuur, EA, Schäfer, KVR, Sonnentag, O, Sparks, JP, Stuart-Haëntjens, E, Sturtevant, C, Sullivan, RC, Szutu, DJ, Thom, JE, Torn, MS, Tuittila, ES, Turner, J, Ueyama, M, Valach, AC, Vargas, R, Varlagin, A, and Vazquez-Lule, A

Abstract

Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20g g€¯S to 20g g€¯N) the spring onset of elevated CH4 emissions starts 3g€¯d earlier, and the CH4 emission season lasts 4

Published
2021

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

Author

Irvin, J, Irvin, J, Zhou, S, McNicol, G, Lu, F, Liu, V, Fluet-Chouinard, E, Ouyang, Z, Knox, SH, Lucas-Moffat, A, Trotta, C, Papale, D, Vitale, D, Mammarella, I, Alekseychik, P, Aurela, M, Avati, A, Baldocchi, D, Bansal, S, Bohrer, G, Campbell, DI, Chen, J, Chu, H, Dalmagro, HJ, Delwiche, KB, Desai, AR, Euskirchen, E, Feron, S, Goeckede, M, Heimann, M, Helbig, M, Helfter, C, Hemes, KS, Hirano, T, Iwata, H, Jurasinski, G, Kalhori, A, Kondrich, A, Lai, DY, Lohila, A, Malhotra, A, Merbold, L, Mitra, B, Ng, A, Nilsson, MB, Noormets, A, Peichl, M, Rey-Sanchez, AC, Richardson, AD, Runkle, BR, Schäfer, KV, Sonnentag, O, Stuart-Haëntjens, E, Sturtevant, C, Ueyama, M, Valach, AC, Vargas, R, Vourlitis, GL, Ward, EJ, Wong, GX, Zona, D, Alberto, MCR, Billesbach, DP, Celis, G, Dolman, H, Friborg, T, Fuchs, K, Gogo, S, Gondwe, MJ, Goodrich, JP, Gottschalk, P, Hörtnagl, L, Jacotot, A, Koebsch, F, Kasak, K, Maier, R, Morin, TH, Nemitz, E, Oechel, WC, Oikawa, PY, Ono, K, Sachs, T, Sakabe, A, Schuur, EA, Shortt, R, Sullivan, RC, Szutu, DJ, Tuittila, ES, Varlagin, A, Verfaillie, JG, Wille, C, Windham-Myers, L, Poulter, B, Jackson, RB, Irvin, J, Irvin, J, Zhou, S, McNicol, G, Lu, F, Liu, V, Fluet-Chouinard, E, Ouyang, Z, Knox, SH, Lucas-Moffat, A, Trotta, C, Papale, D, Vitale, D, Mammarella, I, Alekseychik, P, Aurela, M, Avati, A, Baldocchi, D, Bansal, S, Bohrer, G, Campbell, DI, Chen, J, Chu, H, Dalmagro, HJ, Delwiche, KB, Desai, AR, Euskirchen, E, Feron, S, Goeckede, M, Heimann, M, Helbig, M, Helfter, C, Hemes, KS, Hirano, T, Iwata, H, Jurasinski, G, Kalhori, A, Kondrich, A, Lai, DY, Lohila, A, Malhotra, A, Merbold, L, Mitra, B, Ng, A, Nilsson, MB, Noormets, A, Peichl, M, Rey-Sanchez, AC, Richardson, AD, Runkle, BR, Schäfer, KV, Sonnentag, O, Stuart-Haëntjens, E, Sturtevant, C, Ueyama, M, Valach, AC, Vargas, R, Vourlitis, GL, Ward, EJ, Wong, GX, Zona, D, Alberto, MCR, Billesbach, DP, Celis, G, Dolman, H, Friborg, T, Fuchs, K, Gogo, S, Gondwe, MJ, Goodrich, JP, Gottschalk, P, Hörtnagl, L, Jacotot, A, Koebsch, F, Kasak, K, Maier, R, Morin, TH, Nemitz, E, Oechel, WC, Oikawa, PY, Ono, K, Sachs, T, Sakabe, A, Schuur, EA, Shortt, R, Sullivan, RC, Szutu, DJ, Tuittila, ES, Varlagin, A, Verfaillie, JG, Wille, C, Windham-Myers, L, Poulter, B, and Jackson, RB

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

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

Author

Irvin, J., Zhou, S., McNicol, G., Lu, F., Liu, V., Fluet-Chouinard, E., Ouyang, Z., Knox, S.H., Lucas-Moffat, A., Trotta, C., Papale, D., Vitale, D., Mammarella, I., Alekseychik, P., Aurela, M., Avati, A., Baldocchi, D., Bansal, S., Bohrer, G., Campbell, D.I., Chen, J., Chu, H., Dalmagro, H.J., Delwiche, K.B., Desai, A.R., Euskirchen, E., Feron, S., Goeckede, M., Heimann, M., Helbig, M., Helfter, C., Hemes, K.S., Hirano, T., Iwata, H., Jurasinski, G., Kalhori, A., Kondrich, A., Lai, D.Y., Lohila, A., Malhotra, A., Merbold, L., Mitra, B., Ng, A., Nilsson, M.B., Noormets, A., Peichl, M., Rey-Sanchez, A.C., Richardson, A.D., Runkle, B.R., Schäfer, K.V., Sonnentag, O., Stuart-Haëntjens, E., Sturtevant, C., Ueyama, M., Valach, A.C., Vargas, R., Vourlitis, G.L., Ward, E.J., Wong, G.X., Zona, D., Alberto, M.C.R., Billesbach, D.P., Celis, G., Dolman, H., Friborg, T., Fuchs, K., Gogo, S., Gondwe, M.J., Goodrich, J.P., Gottschalk, P., Hörtnagl, L., Jacotot, A., Koebsch, F., Kasak, K., Maier, R., Morin, T.H., Nemitz, E., Oechel, W.C., Oikawa, P.Y., Ono, K., Sachs, T., Sakabe, A., Schuur, E.A., Shortt, R., Sullivan, R.C., Szutu, D.J., Tuittila, E.-S., Varlagin, A., Verfaillie, J.G., Wille, C., Windham-Myers, L., Poulter, B., Jackson, R.B., Irvin, J., Zhou, S., McNicol, G., Lu, F., Liu, V., Fluet-Chouinard, E., Ouyang, Z., Knox, S.H., Lucas-Moffat, A., Trotta, C., Papale, D., Vitale, D., Mammarella, I., Alekseychik, P., Aurela, M., Avati, A., Baldocchi, D., Bansal, S., Bohrer, G., Campbell, D.I., Chen, J., Chu, H., Dalmagro, H.J., Delwiche, K.B., Desai, A.R., Euskirchen, E., Feron, S., Goeckede, M., Heimann, M., Helbig, M., Helfter, C., Hemes, K.S., Hirano, T., Iwata, H., Jurasinski, G., Kalhori, A., Kondrich, A., Lai, D.Y., Lohila, A., Malhotra, A., Merbold, L., Mitra, B., Ng, A., Nilsson, M.B., Noormets, A., Peichl, M., Rey-Sanchez, A.C., Richardson, A.D., Runkle, B.R., Schäfer, K.V., Sonnentag, O., Stuart-Haëntjens, E., Sturtevant, C., Ueyama, M., Valach, A.C., Vargas, R., Vourlitis, G.L., Ward, E.J., Wong, G.X., Zona, D., Alberto, M.C.R., Billesbach, D.P., Celis, G., Dolman, H., Friborg, T., Fuchs, K., Gogo, S., Gondwe, M.J., Goodrich, J.P., Gottschalk, P., Hörtnagl, L., Jacotot, A., Koebsch, F., Kasak, K., Maier, R., Morin, T.H., Nemitz, E., Oechel, W.C., Oikawa, P.Y., Ono, K., Sachs, T., Sakabe, A., Schuur, E.A., Shortt, R., Sullivan, R.C., Szutu, D.J., Tuittila, E.-S., Varlagin, A., Verfaillie, J.G., Wille, C., Windham-Myers, L., Poulter, B., and Jackson, R.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
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7. Altered energy partitioning across terrestrial ecosystems in the European drought year 2018

Author

Graf, A., Klosterhalfen, A., Arriga, N., Bernhofer, C., Bogena, H., Bornet, F., Brüggemann, N., Brümmer, C., Buchmann, N., Chi, J., Chipeaux, C., Cremonese, E., Cuntz, M., Dušek, J., El-Madany, T.S., Fares, S., Fischer, M., Foltýnová, L., Gharun, M., Ghiasi, S., Gielen, B., Gottschalk, P., Grünwald, T., Heinemann, G., Heinesch, B., Heliasz, M., Holst, J., Hörtnagl, L., Ibrom, A., Ingwersen, J., Jurasinski, G., Klatt, J., Knohl, A., Koebsch, F., Konopka, J., Korkiakoski, M., Kowalska, N., Kremer, P., Kruijt, B., Lafont, S., Léonard, J., de Ligne, A., Longdoz, B., Loustau, D., Magliulo, V., Mammarella, I., Manca, G., Mauder, M., Migliavacca, M., Mölder, M., Neirynck, J., Ney, P., Nilsson, M., Paul-Limoges, E., Peichl, M., Pitacco, A., Poyda, A., Rebmann, Corinna, Roland, M., Sachs, T., Schmidt, M., Schrader, F., Siebicke, L., Šigut, L., Tuittila, E.-S., Varlagin, A., Vendrame, N., Vincke, C., Völksch, I., Weber, S., Wille, C., Wizemann, H.-D., Zeeman, M., Vereecken, H., Graf, A., Klosterhalfen, A., Arriga, N., Bernhofer, C., Bogena, H., Bornet, F., Brüggemann, N., Brümmer, C., Buchmann, N., Chi, J., Chipeaux, C., Cremonese, E., Cuntz, M., Dušek, J., El-Madany, T.S., Fares, S., Fischer, M., Foltýnová, L., Gharun, M., Ghiasi, S., Gielen, B., Gottschalk, P., Grünwald, T., Heinemann, G., Heinesch, B., Heliasz, M., Holst, J., Hörtnagl, L., Ibrom, A., Ingwersen, J., Jurasinski, G., Klatt, J., Knohl, A., Koebsch, F., Konopka, J., Korkiakoski, M., Kowalska, N., Kremer, P., Kruijt, B., Lafont, S., Léonard, J., de Ligne, A., Longdoz, B., Loustau, D., Magliulo, V., Mammarella, I., Manca, G., Mauder, M., Migliavacca, M., Mölder, M., Neirynck, J., Ney, P., Nilsson, M., Paul-Limoges, E., Peichl, M., Pitacco, A., Poyda, A., Rebmann, Corinna, Roland, M., Sachs, T., Schmidt, M., Schrader, F., Siebicke, L., Šigut, L., Tuittila, E.-S., Varlagin, A., Vendrame, N., Vincke, C., Völksch, I., Weber, S., Wille, C., Wizemann, H.-D., Zeeman, M., and Vereecken, H.

Abstract

Drought and heat events, such as the 2018 European drought, interact with the exchange of energy between the land surface and the atmosphere, potentially affecting albedo, sensible and latent heat fluxes, as well as CO2 exchange. Each of these quantities may aggravate or mitigate the drought, heat, their side effects on productivity, water scarcity and global warming. We used measurements of 56 eddy covariance sites across Europe to examine the response of fluxes to extreme drought prevailing most of the year 2018 and how the response differed across various ecosystem types (forests, grasslands, croplands and peatlands). Each component of the surface radiation and energy balance observed in 2018 was compared to available data per site during a reference period 2004–2017. Based on anomalies in precipitation and reference evapotranspiration, we classified 46 sites as drought affected. These received on average 9% more solar radiation and released 32% more sensible heat to the atmosphere compared to the mean of the reference period. In general, drought decreased net CO2 uptake by 17.8%, but did not significantly change net evapotranspiration. The response of these fluxes differed characteristically between ecosystems; in particular, the general increase in the evaporative index was strongest in peatlands and weakest in croplands.

Published
2020

8. Sulfate deprivation triggers high methane production in a disturbed and rewetted coastal peatland

Author

Koebsch, F., Winkel, M., Liebner, S., Liu, Bo, Westphal, J., Schmiedinger, I., Spitzy, A., Gehre, M., Jurasinski, G., Köhler, S., Unger, V., Koch, M., Sachs, T., Böttcher, M. E., Koebsch, F., Winkel, M., Liebner, S., Liu, Bo, Westphal, J., Schmiedinger, I., Spitzy, A., Gehre, M., Jurasinski, G., Köhler, S., Unger, V., Koch, M., Sachs, T., and Böttcher, M. E.

Published
2019

9. Sulfate deprivation triggers high methane production in a disturbed and rewetted coastal peatland

Author

Koebsch, F., Winkel, M., Liebner, S., Liu, Bo, Westphal, J., Schmiedinger, I., Spitzy, A., Gehre, M., Jurasinski, G., Köhler, S., Unger, V., Koch, M., Sachs, T., Böttcher, M. E., Koebsch, F., Winkel, M., Liebner, S., Liu, Bo, Westphal, J., Schmiedinger, I., Spitzy, A., Gehre, M., Jurasinski, G., Köhler, S., Unger, V., Koch, M., Sachs, T., and Böttcher, M. E.

Published
2019

10. Sulfate deprivation triggers high methane production in a disturbed and rewetted coastal peatland

Author

Koebsch, F., Winkel, M., Liebner, S., Liu, B., Westphal, J., Schmiedinger, I., Spitzy, A., Gehre, Matthias, Jurasinski, G., Köhler, S., Unger, V., Koch, M., Sachs, T., Böttcher, M.E., Koebsch, F., Winkel, M., Liebner, S., Liu, B., Westphal, J., Schmiedinger, I., Spitzy, A., Gehre, Matthias, Jurasinski, G., Köhler, S., Unger, V., Koch, M., Sachs, T., and Böttcher, M.E.

Abstract

In natural coastal wetlands, high supplies of marine sulfate suppress methanogenesis. Coastal wetlands are, however, often subject to disturbance by diking and drainage for agricultural use and can turn to potent methane sources when rewetted for remediation. This suggests that preceding land use measures can suspend the sulfate-related methane suppressing mechanisms. Here, we unravel the hydrological relocation and biogeochemical S and C transformation processes that induced high methane emissions in a disturbed and rewetted peatland despite former brackish impact. The underlying processes were investigated along a transect of increasing distance to the coastline using a combination of concentration patterns, stable isotope partitioning, and analysis of the microbial community structure. We found that diking and freshwater rewetting caused a distinct freshening and an efficient depletion of the brackish sulfate reservoir by dissimilatory sulfate reduction (DSR). Despite some legacy effects of brackish impact expressed as high amounts of sedimentary S and elevated electrical conductivities, contemporary metabolic processes operated mainly under sulfate-limited conditions. This opened up favorable conditions for the establishment of a prospering methanogenic community in the top 30–40 cm of peat, the structure and physiology of which resemble those of terrestrial organic-rich environments. Locally, high amounts of sulfate persisted in deeper peat layers through the inhibition of DSR, probably by competitive electron acceptors of terrestrial origin, for example Fe(III). However, as sulfate occurred only in peat layers below 30–40 cm, it did not interfere with high methane emissions on an ecosystem scale. Our results indicate that the climate effect of disturbed and remediated coastal wetlands cannot simply be derived by analogy with their natural counterparts. From a greenhouse gas perspective, the re-exposure of diked wetlands to natural coastal dynamics wou

Published
2019

11. Accelerated increase in plant species richness on mountain summits is linked to warming

Author

Steinbauer, M.J., Grytnes, J.-A., Jurasinski, G., Kulonen, A., Lenoir, J., Pauli, H., Rixen, C., Winkler, M., Bardy-Durchhalter, M., Barni, E., Bjorkman, A.D., Breiner, F.T., Burg, S., Czortek, P., Dawes, M.A., Delimat, A., Dullinger, S., Erschbamer, B., Felde, V.A., Fernández-Arberas, O., Fossheim, K.F., Gómez-García, D., Georges, D., Grindrud, E.T., Haider, S., Haugum, S.V., Henriksen, H., Herreros, M.J., Jaroszewicz, B., Jaroszynska, F., Kanka, R., Kapfer, J., Klanderud, K., Kühn, Ingolf, Lamprecht, A., Matteodo, M., Morra di Cella, U., Normand, S., Odland, A., Olsen, S.L., Palacio, S., Petey, M., Piscová, V., Sedlakova, B., Steinbauer, K., Stöckli, V., Svenning, J.-C., Teppa, G., Theurillat, J.-P., Vittoz, P., Woodin, S.J., Zimmermann, N.E., Wipf, S., Steinbauer, M.J., Grytnes, J.-A., Jurasinski, G., Kulonen, A., Lenoir, J., Pauli, H., Rixen, C., Winkler, M., Bardy-Durchhalter, M., Barni, E., Bjorkman, A.D., Breiner, F.T., Burg, S., Czortek, P., Dawes, M.A., Delimat, A., Dullinger, S., Erschbamer, B., Felde, V.A., Fernández-Arberas, O., Fossheim, K.F., Gómez-García, D., Georges, D., Grindrud, E.T., Haider, S., Haugum, S.V., Henriksen, H., Herreros, M.J., Jaroszewicz, B., Jaroszynska, F., Kanka, R., Kapfer, J., Klanderud, K., Kühn, Ingolf, Lamprecht, A., Matteodo, M., Morra di Cella, U., Normand, S., Odland, A., Olsen, S.L., Palacio, S., Petey, M., Piscová, V., Sedlakova, B., Steinbauer, K., Stöckli, V., Svenning, J.-C., Teppa, G., Theurillat, J.-P., Vittoz, P., Woodin, S.J., Zimmermann, N.E., and Wipf, S.

Abstract

Globally accelerating trends in societal development and human environmental impacts since the mid-twentieth century1,2,3,4,5,6,7 are known as the Great Acceleration and have been discussed as a key indicator of the onset of the Anthropocene epoch6. While reports on ecological responses (for example, changes in species range or local extinctions) to the Great Acceleration are multiplying8, 9, it is unknown whether such biotic responses are undergoing a similar acceleration over time. This knowledge gap stems from the limited availability of time series data on biodiversity changes across large temporal and geographical extents. Here we use a dataset of repeated plant surveys from 302 mountain summits across Europe, spanning 145 years of observation, to assess the temporal trajectory of mountain biodiversity changes as a globally coherent imprint of the Anthropocene. We find a continent-wide acceleration in the rate of increase in plant species richness, with five times as much species enrichment between 2007 and 2016 as fifty years ago, between 1957 and 1966. This acceleration is strikingly synchronized with accelerated global warming and is not linked to alternative global change drivers. The accelerating increases in species richness on mountain summits across this broad spatial extent demonstrate that acceleration in climate-induced biotic change is occurring even in remote places on Earth, with potentially far-ranging consequences not only for biodiversity, but also for ecosystem functioning and services.

Published
2018

12. Upward shift of alpine plants increases floristic similarity of mountain summits

Author

Jurasinski, G., Kreyling, Jürgen, Jurasinski, G., and Kreyling, Jürgen

Abstract

Question: Does the upward shift of species and accompanied increase in species richness, induced by climate change, lead to homogenization of Alpine summit vegetation? Location: Bernina region of the Swiss Alps. Methods: Based on a data set from previous literature we expand the analysis from species richness to beta-diversity and spatial heterogeneity. Species compositions of mountain summits are compared using a two-component heterogeneity concept including the mean and the variance of Sorensen similarities calculated between the summits. Non-metric multidimensional scaling is applied to explore developments of single summits in detail. Results: Both heterogeneity components (mean dissimilarity and variance) decrease over time, indicating a trend towards more homogeneous vegetation among Alpine summits. However, the development on single summits is not strictly unidirectional. Conclusions: The upward shift of plant species leads to homogenization of alpine summit regions. Thus, increasing alpha-diversity is accompanied by decreasing beta-diversity. Beta-diversity demands higher recognition by scientists as well as nature conservationists as it detects changes which cannot be described using species richness alone

Published
2007

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