Greenhouse gas and energy fluxes in a boreal peatland forest after clear-cutting

Abstract

Abstract. The most common forest management method in Fennoscandia is rotation forestry, including clear-cutting and forest regeneration. In clear-cutting, stem wood is removed and the logging residues are either removed or left on site. Clear-cutting changes the microclimate and vegetation structure at the site, both of which affect the site's carbon balance. Peat soils with poor aeration and high carbon densities are especially prone to such changes, and significant changes in greenhouse gas exchange can be expected. We measured carbon dioxide (CO2) and energy fluxes with the eddy covariance method for 2 years (April 2016–March 2018) after clear-cutting a drained peatland forest. We observed a significant rise (23 cm) in the water table level and a large CO2 source (first year: 3086±148 g CO2 m−2 yr−1; second year: 2072±124 g CO2 m−2 yr−1). These large CO2 emissions resulted from the very low gross primary production (GPP) following the removal of photosynthesizing trees and the decline of ground vegetation, unable to compensate for the decomposition of logging residues and peat. During the second summer (June–August) after the clear-cutting, GPP had already increased by 96 % and total ecosystem respiration decreased by 14 % from the previous summer. The mean daytime ratio of sensible to latent heat flux decreased after harvesting from 2.6 in May 2016 to 1.0 in August 2016, and in 2017 it varied mostly within 0.6–1.0. In April–September, the mean daytime sensible heat flux was 33 % lower and latent heat flux 40 % higher in 2017, probably due to the recovery of ground vegetation that increased evapotranspiration and albedo of the site. In addition to CO2 and energy fluxes, we measured methane (CH4) and nitrous oxide (N2O) fluxes with manual chambers. After the clear-cutting, the site turned from a small CH4 sink into a small source and from N2O neutral to a significant N2O source. Compared to the large CO2 emissions, the 100-year global warming potential (GWP100) of the CH4 emissions was negligible. Also, the GWP100 due to increased N2O emissions was less than 10 % of that of the CO2 emission change.

Selection Cuttings as a Tool to Control Water Table Level in Boreal Drained Peatland Forests

“…After clear-cutting, the rise of WTL to near the soil surface results in anoxic redox reactions, enhancing the mobilization and outflow of redox-sensitive compounds, such as phosphate, iron, and dissolved organic nitrogen and carbon (Kaila et al, 2014;Kaila et al, 2015;Nieminen et al, 2015). High WTL after clear-cutting may also increase methane (CH 4 ) emissions, particularly when the WTL rises higher than 30 cm below the soil surface (Ojanen et al, 2010;Ojanen et al, 2013;Korkiakoski et al, 2019). Also, very low WTL, prevailing in densely stocked stands, may be environmentally unfavorable.…”

Sensitivity of 21st century simulated ecosystem indicators to model parameters, prescribed climate drivers, RCP scenarios and forest management actions for two Finnish boreal forest sites

“…Climate data for years 1980-2100 were obtained from five global climate models (GCMs; CanESM2, CNRM-CM5, GFDL-CM3, HadGEM2-ES and MIROC5). The climate variables were bias corrected and further downscaled to a 0.2 • ×0.1 • longitude-latitude grid, similarly to Lehtonen et al (2016) and Holmberg et al (2019). The bias correction methods are described in Räisänen and Räty (2013) and Räty et al (2014).…”

“…The bias correction methods are described in Räisänen and Räty (2013) and Räty et al (2014). The FMI meteorological observation data, harmonised by kriging with external drift (Aalto et al, 2013), were used as a reference climate for the period 1980-2010 (Lehtonen et al, 2016).…”

“…The subset of five climate models was selected because of their good performance in reproducing current climate in northern Europe and because they provided complete data sets for running impact models (Lehtonen et al, 2016). The future wintertime precipitation changes in Finland for the five models in RCP4.5 cover the range of variability depicted by 24 out of 28 CMIP5 models investigated by Ruosteenoja et al (2016).…”

“…Forest management in Finland is a strong modifier of ecosystem carbon budgets and usually an unaccounted source of uncertainty in future predictions. The harvesting intensity defines the impact of the management practices on the ecosystem carbon exchange (Korkiakoski et al, 2019). According to Kalliokoski et al (2018), the future forest productivity in Finland was predicted to increase towards the end of the century.…”