EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

A novel mechanistic boreal forest model with dynamical carbon allocation to quantify climate mitigation potential of management scenarios

Holger Metzler1,2, Samuli Launiainen3, and Giulia Vico2
Holger Metzler et al.
  • 1Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden (
  • 2Department of Crop Production Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden
  • 3Natural Resources Institute Finland (Luke), Helsinki, Finland

Boreal forests have enormous potential to mitigate climate change by taking up and holding back carbon (C) from the atmosphere, but often they are managed to maximize wood productivity. To achieve regional and global climate goals, boreal forest management must consider trade-offs between wood productivity and potential climate change mitigation. Quantifying forests' climate change mitigation potentials requires knowledge of both the amount of C fixed from the atmosphere and how long trees and subsequently soil and wood products withhold it from the atmosphere (C transit time). Despite its importance for climate change mitigation, transit time is often overlooked when focusing on climate change mitigation potential.

We developed a novel mass-balanced and process-based compartmental forest management model comprising trees of different ages and species in a single stand. The model follows the C path from photosynthetical fixation to return to the atmosphere by autotrophic or heterotrophic respiration or by wood-product burning. The fixed C is allocated to different tree organs according to dynamically changing allometries derived from site- and species-specific forest inventory data and affected by the tree's physiological state (healthy or stressed). The compartmental model structure and its mathematical description as a system of ordinary differential equations enable computing stored nonstructural C age as well as age and provenance of C used for tissue growth and respiration. Furthermore, the dynamical implementation of nonstructural C provides a measure of the forest stands' resilience to stressors and a mechanism for tree mortality .

We apply the model to even-aged pure Scots pine and Norway spruce stands as well as to an even-aged mixed-species stand and to a mixed-aged pine stand, under conditions for southern Finland. We compute: 1) wood productivity as the amount of C in harvested wood products, 2) the net balance of C in trees, soil, and wood products, and 3) the amount of fixed C together with its transit time - a key metric to assess climate change mitigation potential. Different metrics entail different conclusions regarding the most beneficial stand structure and management strategy. Even-aged stand management strategies provide more long-lasting wood products than the mixed-aged stand, and the same amount of short-lasting and long-lasting wood products combined. Furthermore, they have substantially better net C balance afters an 80-years rotation. However, it takes them about 40 years to regain the C lost at initial clear cut. Considering also the transit time of C, the even-aged strategies need almost the entire rotation to offset early clear-cut related C losses. While C sequestration assessed by the net C balance evaluates even-aged strategies as beneficial, a trade-off emerges between considering long-lasting wood products and climate change mitigation potential when taking the C transit time into account.

These results show the importance of considering the transit time in the assessment of forest management strategies and highlight that clear-cut based, even-aged management strategies on stand level undermine climate goals on regional and global scale.

How to cite: Metzler, H., Launiainen, S., and Vico, G.: A novel mechanistic boreal forest model with dynamical carbon allocation to quantify climate mitigation potential of management scenarios, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-11113,, 2023.