2021 ESA Annual Meeting (August 2 - 6)

Machine learning to predict peatland greenhouse gas emissions

On Demand
Yuanyuan Huang, CSIRO Oceans and Atmosphere;
Background/Question/Methods

Water table drawdown across peatlands increases carbon dioxide (CO2) and reduces methane (CH4) emissions. The net climatic effect remains unclear.

Results/Conclusions

Based on observations from 130 sites around the globe, we found a positive (warming) net climate effect of water table drawdown. Using a machine-learning based upscaling approach, we predict that peatland water table drawdown driven by climate drying and human activities will increase CO2 emissions by 1.13 Gt yr-1 and reduce CH4 by 0.27 Gt CO2-eq yr-1, resulting in a net greenhouse gas (GHG) source of 0.86 Gt CO2-eq yr-1 by the end of the 21st century under the RCP8.5 climate scenario. This net source drops to 0.73 Gt CO2-eq yr-1 under RCP2.6. Our results point to an urgent need to preserve pristine and rehabilitate drained peatlands to decelerate the positive (warming) feedback among water table drawdown, GHG emissions and climate change.