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Abstract
Validation studies of the Forest Vegetation Simulator (FVS), a stand projection model widely used to predict carbon outcomes of alternative forest management strategies, have found departures from observed forest growth for several FVS variants in the western USA. We evaluated the accuracy of stand-level biomass accumulation predicted by FVS across an environmental gradient represented by three > 2 million ha landscapes that overlap seven FVS variants in Oregon, USA and the extent to which commonly deployed calibrations could correct for any departures. Calibrations included: growth multipliers, maximum stand density parameters, and limits to large tree growth. FVS “out-of-the-box” (uncalibrated) simulations overpredicted net stand growth by 12–35 percent when evaluated against 10-yr stand remeasurements in Forest Inventory and Analysis (FIA) data. Applying all calibrations reduced departures to the −5 to + 9 % range for these landscapes, but variation in their efficacy among landscapes, stand ages, and forest types warrant thoughtful choices of calibrations that are specific to area of interest. Even with all calibrations applied, longer term (80-yr) FVS projections remained departed from a reference dataset constructed from yield curves fit to FIA data by substituting space for time, with percent error on net stand growth ranging from + 21 to + 49 percent (reduced from +109 to +161 % with uncalibrated projections). Bias reduction via calibration is an important, tractable, and short-term solution for obtaining meaningful short-term predictions to support policy. Accurate long-term predictions likely require additional calibrations to account for increased mortality and stress due to changes in climate and disturbance regimes.
Citation
Swann, Daniel E.B.; Fried, Jeremy S.; Gray, Andrew N. 2025. Evaluating Forest Vegetation Simulator (FVS) calibration options for predicting biomass accumulation across diverse Oregon landscapes. Forest Ecology and Management. 594(3): 122937. https://doi.org/10.1016/j.foreco.2025.122937.
