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

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  • Kunxiaojia Yuan
  • Qing Zhu
  • Fa Li
  • William J. Riley
  • Margaret Torn
  • Housen Chu
  • Gavin McNicol
  • Min Chen
  • Sara Knox
  • Kyle 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 Campbell
  • Hans Peter Schmid
  • Annalea Lohila
  • Mathias Goeckede
  • Mats B. Nilsson
  • Joachim Jansen
  • Donatella Zona
  • Eugenie Euskirchen
  • Eric J. Ward
  • Gil Bohrer
  • Zhenong Jin
  • Licheng Liu
  • Hiroki Iwata
  • Jordan Goodrich
  • Robert Jackson

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.

Original languageEnglish
Article number109115
JournalAgricultural and Forest Meteorology
Number of pages10
Publication statusPublished - 2022

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Publisher Copyright:
© 2022

    Research areas

  • Causal inference, Eddy covariance CH emission, Machine learning, Wetlands

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