Predicting Biomass and Species Composition in the Siberian Boreal Forest Using a New Spatially-Explicit Vegetation Dynamics Model: Model Development, Calibration, and Climate Sensitivity Analysis.

Abstract:

Circumpolar boreal forests contain one third of the terrestrial carbon stores, and it has been shown that they are already affected by climate change. As temperature and precipitation regimes shift, the total biomass and species composition may change in ways that promote further warming on the regional level through atmosphere-vegetation feedbacks. Changes in vegetation cover and the resulting atmosphere-vegetation feedbacks may be the determining factors in how regional terrestrial carbon stores change with climate change. This project reports on the development of a new spatially-explicit individual-based gap model SibBorK that can be utilized to investigate the potential changes in biomass and species composition in the Siberian boreal forest over the coming decades and centuries.
SibBorK tracks the establishment, growth, and mortality of individual trees on 0.01-ha plots within a 9-ha simulation area. The new model is based on the principles of the ZELIG vegetation model, implemented in Python to facilitate interface with geographic information systems for explicit modeling of vegetation across artificial and real terrain. SibBorK was trained on modal (actual) regional forestry yield tables for southern taiga region of central Siberia. The model was calibrated and tested against the regional forestry yield tables, and further tested against an independent dataset from a forest inventory. Model comparisons were made on monospecies and mixed species stands, and included the evaluation of total stand biomass, species-specific biomass, species composition, and stem density based on site index and terrain elevation. Additionally, species distribution along altitudinal gradients and total biomass for specific locations was independently tested against other published forest inventory values. SibBorK is particularly good at predicting biomass and species composition on poor soils, with Orlov site indices III-V, which dominate the Siberian landscape. Herein, SibBorK is used to explore species distribution along soil quality and altitudinal gradients, as well as slope aspects within a complex terrain. SibBorK is further employed in a climate sensitivity analysis.