A Bayesian approach for estimating the parameters of a forest process model based on long-term growth data

Process-based models are increasingly being used to model growth dynamics of forest ecosystems. Ideally, estimates of the parameters for these types of models should be obtained through physiological experiments. However, in many situations there is little or no experimental data available to parameterize such models. This paper presents some results of an ongoing study on alternative methods to estimate physiological parameters that are not readily available. The specific focus of this paper is the use of a Bayesian approach based on rejection sampling for estimating physiological parameters using observed state variables of process-based models for forest growth. The method is computationally intensive and can be used to estimate model parameters and their multi-dimensional distributions. The method is used to estimate some of the physiological parameters of a process-based growth model for Norway spruce [Picea abies (L.) Karst.] in Denmark. The estimated one-, two-, and three-dimensional distributions of the parameters of the process-based growth model are given.