A Bayesian strategy for combining predictions from empirical and process-based models |
| |
Authors: | Philip J Radtke Andrew P Robinson |
| |
Institution: | aVirginia Tech, Department of Forestry, Blacksburg, VA 24061, USA;bDepartment of Forest Resources, University of Idaho, Moscow, ID 83843, USA |
| |
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. |
| |
Keywords: | Bayesian melding Bayesian synthesis Ecosystem modeling Forest growth and yield modeling Likelihood Sampling importance resampling |
本文献已被 ScienceDirect 等数据库收录! |
|