Feasibility of enhancing carbon sequestration and stock capacity in temperate and boreal European forests via changes to management regimes

Abstract

Forest management interventions can act as value-based agents to remove CO2 from the atmosphere and slow anthropogenic climate change and thus might play a strategic role in the framework of the EU forestry-based mitigation strategy. To what extent diversified management actions could lead to quantitatively important changes in carbon sequestration potential and stocking capacity at the tree level remains to be thoroughly assessed. To that end, we used a state-of-the-science bio-geochemically based forest growth model to assess effects of multiple alternative forest management scenarios on plant net primary productivity (NPP) and potential carbon woody stocks (pCWS) under differing scenarios of climate change. The experiments indicated that the capacity of trees to assimilate and store atmospheric CO2 in recalcitrant standing woody tissue is already being attained as its optimum under business-as-usual forest management conditions regardless of the different climate change scenarios investigated. Nevertheless, on the long-term and under increasing atmospheric CO2 concentration and warming, managed forests show both higher productivity and a larger pool of stored carbon than unmanaged ones as long as forest thinning and tree harvesting are of moderate intensity.

Simulating alternative forest management in a changing climate on aPinus nigrasubsp.laricioplantation in Southern Italy

“…Furthermore, the model was able to reproduce the mean seasonal cycle of daily GPP as obtained by the eddy covariance tower with sufficient accuracy, supporting previous assessments of model performance (Collalti et al, 2014, 2016, 2018; Alessio Collalti et al, 2020; Dalmonech et al, 2022; Engel et al, 2021; Marconi et al, 2017). The R 2 of 0.69 is in line with previous evaluations of simulated daily GPP across northern European forest sites (average R 2 across three sites = 0.73; Collalti et al, 2018), while the ME of 0.61 is within the range found for daily GPP simulated with other process-based models (0.42 - 0.84 in Bagnara et al, 2015; 0.61 - 0.98 in Minunno et al, 2016).…”

“…Furthermore, the model allows the simulation of different management scenarios by defining the intensity and the interval of removals, as well as the length of rotation periods and artificial replanting schemes, which can be varied through the simulation time. For a full description of key model principles and theoretical framework see also Collalti et al (2020, 2019, 2018, 2016, 2014), Dalmonech et al (2022), Engel et al (2021), and Marconi et al (2017).…”

Simulating diverse forest management options in a changing climate on a Pinus nigra subsp. laricio plantation in Southern Italy

“…Furthermore, the model was able to reproduce the mean seasonal cycle of daily GPP as obtained by the eddy covariance tower with sufficient accuracy, supporting previous assessments of model performance (Collalti et al, 2014(Collalti et al, , 2016(Collalti et al, , 2018Alessio Collalti et al, 2020;Dalmonech et al, 2022;Engel et al, 2021;Marconi et al, 2017). The R 2 of 0.69 is in line with previous evaluations of simulated daily GPP across northern European forest sites (average R 2 across three sites = 0.73; Collalti et al, 2018), while the ME of 0.61 is within the range found for daily GPP simulated with other process-based models (0.42 -0.84 in Bagnara et al, 2015;0.61 -0.98 in Minunno et al, 2016).…”

“…The 3D-CMCC-FEM forest model (v.5.6 BGC) is a biogeochemical, biophysical, and physiological process-based forest model developed to predict C, energy, and water fluxes coupled with stand development processes that determine relative stock changes in forest ecosystems (Collalti et al, 2019;Dalmonech et al, 2022). The model is designed to simulate the main physiological and hydrological processes at daily, monthly, and annual scales and at the species-specific level.…”

“…For a full description of key model principles and theoretical framework see also Collalti et al ( , 2019Collalti et al ( , 2018Collalti et al ( , 2016Collalti et al ( , 2014, Dalmonech et al (2022), Engel et al (2021), andMarconi et al (2017).…”