EGU21-3348, updated on 03 Mar 2021
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Predicting Non-Growing Season Net Ecosystem Exchanges of CO2 from a Canadian Peatland 

Arash Rafat1,2, Fereidoun Rezanezhad2, William Quinton3, Elyn Humphreys4, Kara Webster5, and Philippe Van Cappellen2
Arash Rafat et al.
  • 1Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Canada
  • 2Ecohydrology Research Group, Department of Earth and Environmental Sciences and Water Institute, University of Waterloo, Waterloo, Canada
  • 3Cold Regions Research Centre, Wilfrid Laurier University, Waterloo, Canada
  • 4Department of Geography and Environmental Studies, Carleton University, Ottawa, Canada
  • 5Canadian Forest Service Great Lakes Forestry Centre - Natural Resources Canada, Sault Ste Marie, Canada

The world’s cold regions are experiencing some of the fastest warming, especially during the winter and shoulder seasons. Recent studies have further highlighted the significance of carbon dioxide (CO2) emissions during the non-growing season (NGS) to the annual carbon (C) budgets of northern peatlands. Because of the positive feedback of soil microbial respiration to warming, even at sub-zero temperatures, a warmer NGS may be expected to alter the C balance of peatlands, which are estimated to store about one-third of global terrestrial organic C stocks. However, estimates of NGS net ecosystem CO2 exchange (NEE) of peatlands remain highly uncertain. In this study, we use a variable selection methodology and a global sensitivity analysis (GSA) to determine the most influential environmental variables affecting the NGS-NEE of CO2 in a temperate Canadian peatland (Mer Bleue Bog; Ottawa, Canada). A data-driven machine learning model is trained on a 13-year (1998-2010) continuous record of eddy covariance flux measurements at the site. The model successfully reproduces the observed NGS-NEE CO2 fluxes using only 7 variables: soil temperature, soil moisture, air temperature, wind direction and speed, net radiation, and upwelling photosynthetic photon flux density. Of these 7 input variables, NGS-NEE is most sensitive to changes in net radiation, likely through the latter’s strong linkages to variations in plant phenology and snow cover. We further predict how the future NGS-NEE of the Mer Bleue Bog will change under three climate scenarios (RCP2.6, RCP4.5, and RCP8.5). According to the projections, mean NEE during the NGS could increase by up to 103% by the end of the 21st century. Our results thus reinforce the urgent need for a comprehensive understanding of peatlands as evolving sources of atmospheric CO2 in a warming world.

How to cite: Rafat, A., Rezanezhad, F., Quinton, W., Humphreys, E., Webster, K., and Van Cappellen, P.: Predicting Non-Growing Season Net Ecosystem Exchanges of CO2 from a Canadian Peatland , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3348,, 2021.