Causality guided machine learning model on wetland CH4 emissions across global wetlands
Kunxiaojia Yuan, Qing Zhu, Fa Li, William J. Riley, M. S. Torn, Housen Chu, Gavin McNicol, Min Chen, Sara Knox, Kyle Delwiche, Huayi Wu, Dennis Baldocchi, Hengbo Ma, Ankur R. Desai, Jiquan Chen, Torsten Sachs, Masahito Ueyama, Oliver Sonnentag, Manuel Helbig, Eeva‐Stiina Tuittila, Gerald Jurasinski, Franziska Koebsch, David I. Campbell, Hans Peter Schmid, Annalea Lohila, Mathias Goeckede, Mats Nilsson, Thomas Friborg, Joachim Jansen, Donatella Zona, Eugénie Euskirchen, Eric J. Ward, Gil Bohrer, Zhenong Jin, Licheng Liu, Hiroyasu Iwata, Jordan P. Goodrich, Robert B. Jackson
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.- Cite:
- Kunxiaojia Yuan, Qing Zhu, Fa Li, William J. Riley, M. S. Torn, Housen Chu, Gavin McNicol, Min Chen, Sara Knox, Kyle Delwiche, Huayi Wu, Dennis Baldocchi, Hengbo Ma, Ankur R. Desai, Jiquan Chen, Torsten Sachs, Masahito Ueyama, Oliver Sonnentag, Manuel Helbig, et al.. 2022. Causality guided machine learning model on wetland CH4 emissions across global wetlands. Agricultural and Forest Meteorology, Volume 324, 324:109115.
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@article{Yuan-2022-Causality, title = "Causality guided machine learning model on wetland CH4 emissions across global wetlands", author = "Yuan, Kunxiaojia and Zhu, Qing and Li, Fa and Riley, William J. and Torn, M. S. and Chu, Housen and McNicol, Gavin and Chen, Min and Knox, Sara and Delwiche, Kyle and Wu, Huayi and Baldocchi, Dennis and Ma, Hengbo and Desai, Ankur R. and Chen, Jiquan and Sachs, Torsten and Ueyama, Masahito and Sonnentag, Oliver and Helbig, Manuel and Tuittila, Eeva‐Stiina and Jurasinski, Gerald and Koebsch, Franziska and Campbell, David I. and Schmid, Hans Peter and Lohila, Annalea and Goeckede, Mathias and Nilsson, Mats and Friborg, Thomas and Jansen, Joachim and Zona, Donatella and Euskirchen, Eug{\'e}nie and Ward, Eric J. and Bohrer, Gil and Jin, Zhenong and Liu, Licheng and Iwata, Hiroyasu and Goodrich, Jordan P. and Jackson, Robert B.", journal = "Agricultural and Forest Meteorology, Volume 324", volume = "324", year = "2022", publisher = "Elsevier BV", url = "https://gwf-uwaterloo.github.io/gwf-publications/G22-28002", doi = "10.1016/j.agrformet.2022.109115", pages = "109115", 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{\mbox{$^\circ$}}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.", }
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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. 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%0 Journal Article %T Causality guided machine learning model on wetland CH4 emissions across global wetlands %A Yuan, Kunxiaojia %A Zhu, Qing %A Li, Fa %A Riley, William J. %A Torn, M. S. %A Chu, Housen %A McNicol, Gavin %A Chen, Min %A Knox, Sara %A Delwiche, Kyle %A Wu, Huayi %A Baldocchi, Dennis %A Ma, Hengbo %A Desai, Ankur R. %A Chen, Jiquan %A Sachs, Torsten %A Ueyama, Masahito %A Sonnentag, Oliver %A Helbig, Manuel %A Tuittila, Eeva‐Stiina %A Jurasinski, Gerald %A Koebsch, Franziska %A Campbell, David I. %A Schmid, Hans Peter %A Lohila, Annalea %A Goeckede, Mathias %A Nilsson, Mats %A Friborg, Thomas %A Jansen, Joachim %A Zona, Donatella %A Euskirchen, Eugénie %A Ward, Eric J. %A Bohrer, Gil %A Jin, Zhenong %A Liu, Licheng %A Iwata, Hiroyasu %A Goodrich, Jordan P. %A Jackson, Robert B. %J Agricultural and Forest Meteorology, Volume 324 %D 2022 %V 324 %I Elsevier BV %F Yuan-2022-Causality %X 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. %R 10.1016/j.agrformet.2022.109115 %U https://gwf-uwaterloo.github.io/gwf-publications/G22-28002 %U https://doi.org/10.1016/j.agrformet.2022.109115 %P 109115
Markdown (Informal)
[Causality guided machine learning model on wetland CH4 emissions across global wetlands](https://gwf-uwaterloo.github.io/gwf-publications/G22-28002) (Yuan et al., GWF 2022)
- Causality guided machine learning model on wetland CH4 emissions across global wetlands (Yuan et al., GWF 2022)
ACL
- Kunxiaojia Yuan, Qing Zhu, Fa Li, William J. Riley, M. S. Torn, Housen Chu, Gavin McNicol, Min Chen, Sara Knox, Kyle Delwiche, Huayi Wu, Dennis Baldocchi, Hengbo Ma, Ankur R. Desai, Jiquan Chen, Torsten Sachs, Masahito Ueyama, Oliver Sonnentag, Manuel Helbig, et al.. 2022. Causality guided machine learning model on wetland CH4 emissions across global wetlands. Agricultural and Forest Meteorology, Volume 324, 324:109115.