Uncertainty analysis in carbon cycle models of forest ecosystems: Research needs and development of a theoretical framework to estimate error propagation

• Published in: Ecological modelling 2008-12, Vol.219 (3), p.400-412
• Main Authors: Larocque, Guy R., Bhatti, Jagtar S., Boutin, Robert, Chertov, Oleg
• Format: Article
• Language: English
• Subjects: Carbon cycle; Error propagation; Monte Carlo analysis; Process-based models; Uncertainty analysis
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2008.07.024

Few process-based models of the carbon (C) cycle of forest ecosystems integrate uncertainty analysis into their predictions. There are two explanations as to why uncertainty estimates in the predictions of these models have seldom been provided. First, as the development of forest ecosystem process-based models has begun only recently, research efforts have focused on theoretical development to improve realism rather than reducing the amplitude of variation of the predictions. Second, there is still little information on uncertainty estimates in parameters and key variables for forest ecosystem models. As process-based models usually contain several complex nonlinear relationships, the Monte Carlo method is most commonly used to facilitate uncertainty analysis. However, its full potential for error propagation analysis in process-based models of the C cycle of forest ecosystems remains to be developed. In this paper, commonly used methods to address uncertainty in C cycle forest ecosystem models are discussed and directions for further research are presented. Realizing the full potential of uncertainty analysis for these model types will require obtaining better estimates of the errors and distributions of key parameters for complex relationships in ecophysiological processes by increasing sampling intensity and testing different sampling designs. As the level of complexity of the type of relationships used in forest ecosystem models varies substantially, the application of uncertainty analysis methods can be further facilitated by developing a model-driven decision support system based on different analytical applications to derive optimum and efficient uncertainty analysis pathways.