|Forest Investment Account (FIA) - Forest Science Program|
|FIA Project G106109|
|GYMP: Representation of climate change impacts on forest growth in FORECAST|
|Project lead: Brad Seely (University of British Columbia)|
|Contributing Authors: Seely, Brad A.; Welham, Clive; Blanco, Juan A.|
|Subject: Forest Investment Account (FIA), British Columbia|
|Series: Forest Investment Account (FIA) - Forest Science Program|
|The impact of anthropogenic climate change on forest health and growth has been identified as a key issue with respect to the sustainability of forest management in British Columbia (MoFR 2006). A recent analysis of the potential effects of climate change on tree distribution suggests that important timber species including white spruce and lodgepole pine may lose suitable habitat and suffer adversely from a combination of warming trends and reduced growing season precipitation. In contrast, species such as Douglas fir and Ponderosa pine may actually expand their range and potentially show improved growth rates in parts of their range (Hamann and Wang 2006). Recent dendroclimatological studies along elevation gradients in the North Cascade Range found that both lodgepole pine and Douglas-fir responded differently to climate factors depending on elevation (Case and Peterson 2007; Case and Peterson 2005). At high elevation, trees responded positively to increased temperatures, while at low elevations trees showed a negative response to growing season max temperature and a positive correlation with growing season precipitation. White spruce has shown variable responses to temperature variables but generally positive responses precipitation particularly in drier parts of its range (Andalo et al. 2005; Wilmking et al. 2004; Johnson and Williamson). |
While tree growth has been shown to be correlated to climate variables, the direct or indirect causal factors are often less clear. Climate can influence nutrient dynamics and subsequently productivity through its impact on organic matter decomposition rates. Recent litter decomposition studies have shown that temperature and soil moisture influence mass loss and mineralization rates (Trofymow et al 2002; Prescott et al. 2004)
Modelling tools are required to help forest planners navigate the potential implications of climate change on timber supply through the use of scenario analysis and case studies. Although detailed physiological models have been useful in exploring climate impacts on tree growth and ecosystem processes, they are often data intensive and difficult to apply for management related applications (e.g. Grant et al. 2005; Grant et al. 2006). To be effective for guiding management, such tools must be able to capture the current understanding of the effect of specific climate variables on ecosystem processes governing forest growth, but still be practical for estimating impacts on tangible projections of forest growth and yield and other ecosystem values (Landsberg 2003; MoFR 2006).
Here we propose the further development of the FORECAST model to give it the capability to explicitly represent the potential impacts of climate change on forest growth and development. FORECAST is an ecosystem-based, stand-level, forest management model designed using a hybrid approach drawing upon both mechanistic and empirical modelling techniques (see below). The development of the FORECAST family of models has been supported by various provincial and federal funding sources for more than 20 years. Most recently funding has focused on model testing and validation in a range of forest types (see Blanco et al. 2007; Seely et al., in press; Bi et al. 2007; Welham et al. 2007), the development of spatially explicit versions of the model for applications in complex silviculture systems (see Seely 2005), and the development of a companion forest hydrology model (ForWaDy) to evaluate effects of alternative climate scenarios on tree water stress.
Tree growth in the FORECAST model is presently limited by light and nutrient availability. The proposed linkage with ForWaDy will provide a third feedback on tree growth rates based on a climate-driven quantification of tree water stress. Moreover, the simulation of soil and litter moisture content in ForWaDy will facilitate a climate-based representation of organic matter decomposition and associated nutrient mineralization rates. These developments in combination with a simulation of temperature effects on length of growing season and forest growth rates will provide the foundation for the representation of climate impacts on forest growth in FORECAST. The completed model will allow users to explore the potential impacts on varying climate scenarios on indicators of multiple forest values.
Andalo et al. (2005)The impact of climate change on growth of local white spruce populations in Québec, Canada. For. Ecol. Manage. 205: 169-182.
Arp and Yin (1992). Predicting water fluxes through forests from monthly precipitation and mean monthly air temperature records. Can. J. For. Res. 22: 864-877.
Bi et al. (2007). Yield decline in Chinese-fir plantations: A simulation investigation with implications for model complexity. Can. J. For. Res. 37: 1615-1630.
Blanco et al. (2007). Testing the performance of FORECAST, a forest ecosystem model, against 29 years of field data in a Pseudotsuga menziesii plantation at Shawnigan Lake (B.C., Canada). Can. J. For. Res. 37: 1808-1820.
Case and Peterson (2005). Fine-scale variability in growth–climate relationships of Douglas-fir, North Cascade Range, Washington. Can. J. For. Res. 35: 2258-2267.
Case and Peterson (2007). Growth-climate relations of lodgepole pine in the North Cascades National Park, Washington. Northwest Sceince 81: 62-75.
Coughlan and Running (1997). Regional ecosystem simulation: A general model for simulating snow accumulation and melt in mountainous terrain. Landscape Ecology 12: 119-136.
Hamann and Wang (2006). Potential effects of climate change on ecosystem and tree species distribution in British Columbia. Ecology 87:2773-2786.
Grant et al. (2005). Intercomparison of techniques to model high temperature effects
on CO2 and energy exchange in temperate and boreal coniferous forests. Ecological Modelling 188: 217–252.
Grant et al. (2006). Intercomparison of techniques to model water stress effects
on CO2 and energy exchange in temperate and boreal deciduous forests. Ecol. Model. 196: 289–312.
Johnson and Williamson (2005). Climate change implication for stand yields and soil expectation values: a northern Saskatchewan case study. For. Chron. 81: 683-690.
Landsberg, J.J. (2003). Modelling forest ecosystems: state of the art, challenges, and future directions. Can. J. For. Res. 33: 385-397.
MoFR (2006). Preparing for Climate Change:Adapting to Impacts on British Columbia’s Forest and Range Resources. BC Ministry of Forests and Range, May 18, 2006, Victoria, BC.
Prescott et al. (2004). Litter decomposition in B.C.forests: controlling factors and influences of forestry activities. Journal of Ecosystems and Management 5: 30-43.
Seely et al. (1997). A forest hydrology submodel for simulating the effect of management and climate change on stand water stress. Pp. 463-477 In: A. Amaro and M. Tomé (eds.) Proceedings of Empirical and Process-based models for forest, tree and stand growth simulation. Oeiras, Portugal, 21-27, September 1997. Edições Salamandra, Lisboa.
Seely et al. (2004). The application of a hierarchical, decision-support system to evaluate multi-objective forest management strategies: A case study in northeastern British Columbia, Canada. For. Ecol. Manage. 199: 283-305.
Seely (2005). South Coast LLEMS Project Phase 3: Application and Evaluation. BC Forest Investment Account Project Report. Prepared for International Forest Products.
Seely et al. (In Press). Evaluation of a mechanistic approach to mixedwood modelling. Accepted in Forestry. Chronicle.
Trofymow et al. 2002. Rates of litter decomposition over six years in Canadian forests: influence of litter quality and climate. Can. J. For. Res. 32: 789–804.
Welham et al. (2007). Projected long-term productivity in Saskatchewan hybrid poplar plantations: weed competition and fertilizer effects. Can. J. For. Res. 37:356-370.
Wilmking et al. (2004). Recent climate warming forces contrasting growth responses of white spruce at treeline in Alaska through temperature thresholds. Global Change Biology (2004) 10, 1724–1736.
|Year 2 Executive Summary (80Kb)|
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Updated August 16, 2010
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