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<p>The conventional growth models used in forest management often rely on historic biometric relationships and do not consider climate’s impact on growth. Climate sensitive predictions of forest growth are essential to assess sustainable forest management and forest carbon, particularly under increasing climate change. In this study, we explored volume and stem biomass predictions from the climate sensitive, hybrid/process-based growth model 3-PG (Physiological Principles in Predicting Growth) for four tree species in British Columbia, Canada. Then, we used 3-PG to climate-adjust volume predictions from a conventional growth model without climate sensitivity. Yields from 3-PG and this model fusion were evaluated using repeated measurement plots. Stem biomass and volume predictions from 3-PG tracked the observed data, producing Relative Model Biases (RMBs) between 1 and -8% for lodgepole pine, subalpine fir, and interior spruce. Stem biomass and volume RMBs from 3-PG were approximately -15% for Douglas-fir. Climate-adjusted yields for the same projection period validated similarly to the conventional growth model. Long-term predictions of the model fusion were explored through year 2100 under three climate scenarios (low, medium, high). For plots on a moisture and temperature gradient, climate-adjusted yields increased volume predictions by 1-2% for lodgepole pine, 5-13% for Douglas-fir, 12-31% for subalpine fir, and 4-26% for interior spruce. For all species, climate-adjusted yields were moderated under drier conditions, and historically wet and cold plots experienced the greatest gains. This model fusion shows promise for supporting landscape-level timber supply and carbon accounting models that incorporate climate sensitive growth and decision-making based on site-level vulnerability.</p>

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Modifying Stand-level Growth under a Changing Climate using a Model Fusion Approach

  • 期刊论文

  • 发表日期：

    2026年1月31日

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  • Kirk M. Johnson,
  • Nicholas C. Coops,
  • Alexis Achim,
  • Verena C. Griess,
  • Ignacio Barbeito

摘要

The conventional growth models used in forest management often rely on historic biometric relationships and do not consider climate’s impact on growth. Climate sensitive predictions of forest growth are essential to assess sustainable forest management and forest carbon, particularly under increasing climate change. In this study, we explored volume and stem biomass predictions from the climate sensitive, hybrid/process-based growth model 3-PG (Physiological Principles in Predicting Growth) for four tree species in British Columbia, Canada. Then, we used 3-PG to climate-adjust volume predictions from a conventional growth model without climate sensitivity. Yields from 3-PG and this model fusion were evaluated using repeated measurement plots. Stem biomass and volume predictions from 3-PG tracked the observed data, producing Relative Model Biases (RMBs) between 1 and -8% for lodgepole pine, subalpine fir, and interior spruce. Stem biomass and volume RMBs from 3-PG were approximately -15% for Douglas-fir. Climate-adjusted yields for the same projection period validated similarly to the conventional growth model. Long-term predictions of the model fusion were explored through year 2100 under three climate scenarios (low, medium, high). For plots on a moisture and temperature gradient, climate-adjusted yields increased volume predictions by 1-2% for lodgepole pine, 5-13% for Douglas-fir, 12-31% for subalpine fir, and 4-26% for interior spruce. For all species, climate-adjusted yields were moderated under drier conditions, and historically wet and cold plots experienced the greatest gains. This model fusion shows promise for supporting landscape-level timber supply and carbon accounting models that incorporate climate sensitive growth and decision-making based on site-level vulnerability.