Skip to main content
U.S. flag

An official website of the United States government

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

August 31, 2022

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.

Publication Year 2022
Title Causality guided machine learning model on wetland CH4 emissions across global wetlands
DOI 10.1016/j.agrformet.2022.109115
Authors Kunxiaojia Yuan, Qing Zhu, Fa Li, William J. Riley, Margaret Torn, Housen Chu, Gavin McNicol, Mingshu Chen, Sara Knox, Kyle B. Delwiche, Huayi Wu, Dennis Baldocchi, Hongxu 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 B. Nilsson, Thomas Friborg, Joachim Jansen, Donatella Zona, Eugenie S. Euskirchen, Eric Ward, Gil Bohrer, Zhenong Jin, Licheng Liu, Hiroki Iwata, Jordan P. Goodrich, Robert B. Jackson
Publication Type Article
Publication Subtype Journal Article
Series Title Agricultural and Forest Meteorology
Index ID 70237577
Record Source USGS Publications Warehouse
USGS Organization Wetland and Aquatic Research Center