Modeling long-term effects of fuel treatments on fuel loads and fire regimes in the Great Basin

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The primary objective of this study was to explore the application of a dynamic global vegetation model (DGVM), the Ecosystem Demography (EDv2.2), to understand vegetation dynamics and ecosystem productivity in varying climate and fire scenarios. Most vegetation models do not represent sagebrush’s physical and physiological functions. Thus, we developed a sagebrush plant functional type (PFT) to use in modeling. Associated with this, the researchers performed a series of analyses and evaluations of the sagebrush and in the context of scenarios under natural (undisturbed) and disturbed (fire) environments.

• Results indicate that a number of sagebrush parameters are most sensitive to how productive the plant is (in our model). These include specific leaf area (SLA), stomatal slope, fine root turnover rate, cuticular conductance, and maximum carboxylation rate. These findings allow future sagebrush modeling efforts to further refine these parameters in different environments.
• The researchers comparisons between model runs and field data from Reynold Creek Experimental Watershed (RCEW), show good agreement. Improvements are needed to refine the model with additional PFTs representative of a range of elevations in the Great Basin.
• The researchers fire scenario modeling suggested that fire substantially reduced shrub gross primary production (GPP) and it took several decades before it was restored to pre-fire conditions. Grass GPP, however, responded more quickly in post-fire conditions. While these processes are representative of field observations and other studies, additional PFTs and improvement in fire routines in the model will provide for a better prognosis of future ecosystem dynamics of the sagebrush-steppe.