Development, Calibration and Application of a 3-D Individual-Based Gap Model for Improved Characterization of Eurasian Boreal Forests in Response to Historical and Future Changes in Climate
Brazhnik, Ksenia, Environmental Sciences - Graduate School of Arts and Sciences, University of Virginia
Shugart, Herman, Department of Environmental Sciences, University of Virginia
Climate change is altering forests globally, some in ways that may promote further warming at the regional and even continental scales. The dynamics of complex systems that occupy large spatial domains and change on the order of decades to centuries, such as forests, do not lend themselves easily to direct observation. A simulation model is often an appropriate and attainable approach toward understanding the inner workings of forest ecosystems, and how they may change with imposed perturbations. A new spatially-explicit model SIBBORK has been developed to understand how the Siberian boreal forests may respond to near-future climate change. The predictive capabilities of SIBBORK are enhanced with 3-dimensional representation of terrain and associated environmental gradients, and a 3-D light ray-tracing subroutine. SIBBORK’s spatially-explicit outputs are easily converted to georeferenced maps of forest structure and species composition.
SIBBORK has been calibrated using forestry yield tables and validated against multiple independent multidimensional timeseries datasets from southern, middle, and northern taiga ecotones in central Siberia, including the southern and northern boundaries of the boreal forest. Model applications simulating the vegetation response to climate change revealed significant and irreversible changes in forest structure and composition, which are likely to be reached by mid-21st century. These changes in land cover will inevitably result in changes in the biodiversity, carbon storage, and the ecosystem services provided by the Siberian boreal forest.
PHD (Doctor of Philosophy)
SIBBORK, gap model, individual-based model, boreal forest, spatially-explicit, climate change
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