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• W2037097457 abstract "We present a strategy for using an empirical forest growth model to reduce uncertainty in predictions made with a physiological process-based forest ecosystem model. The uncertainty reduction is carried out via Bayesian melding, in which information from prior knowledge and a deterministic computer model is conditioned on a likelihood function. We used predictions from an empirical forest growth model G-HAT in place of field observations of aboveground net primary productivity (ANPP) in a deciduous temperate forest ecosystem. Using Bayesian melding, priors for the inputs of the process-based forest ecosystem PnET-II were propagated through the model, and likelihoods for the PnET-II output ANPP were calculated using the G-HAT predictions. Posterior distributions for ANPP and many PnET-II inputs obtained using the G-HAT predictions largely matched posteriors obtained using field data. Since empirical growth models are often more readily available than extensive field data sets, the method represents a potential gain in efficiency for reducing the uncertainty of process-based model predictions when reliable empirical models are available but high-quality data are not." @default.
• W2037097457 created "2016-06-24" @default.
• W2037097457 creator A5019414747 @default.
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• W2037097457 date "2006-01-01" @default.
• W2037097457 modified "2023-09-26" @default.
• W2037097457 title "A Bayesian strategy for combining predictions from empirical and process-based models" @default.
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• W2037097457 doi "https://doi.org/10.1016/j.ecolmodel.2005.04.021" @default.
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