Many different process-based models of ecosystems are in use today. The majority of these models are parameter-rich, deterministic dynamic models, which require considerable input information and computation time. These characteristics, combined with the fact that the models tend to be parameterised at the point-support spatial scale, have made their use for larger regions problematic. Quantifying the uncertainties caused by incomplete knowledge of model inputs and structure, as well as uncertainty due to upscaling, is a difficult task. Various examples of model application and uncertainty quantification are presented here and the possibility to use a Bayesian approach to uncertainty quantification is discussed
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