Parameter sensitivity and uncertainty of the forest carbon flux model FORUG: a Monte Carlo analysis

The Monte Carlo technique can be used to propagate input variable uncertainty and parameter uncertainty through a model to determine output uncertainty. However, to carry out Monte Carlo simulations, the uncertainty distributions or the probability density functions (PDFs) of the model parameters and input variables must be known. This remains one of the bottlenecks in current uncertainty research in forest carbon flux modeling. Because forest carbon flux models involve many parameters, we questioned whether it is necessary to take into account all parameters in the uncertainty analysis. A sensitivity analysis can determine the parameters contributing most to the overall model output uncertainty. This paper illustrates the usefulness of the Monte Carlo simulation technique for ranking parameters for sensitivity and uncertainty in process-based forest flux models. The uncertainty of the output (net ecosystem exchange, NEE) of the FORUG model was estimated for the Hesse beech forest (1997). Based on the arbitrary uncertainty of ten key parameters, a standard deviation of 0.88 Mg C ha(-1) year(-1) NEE was found which is equal to 24% of the mean value of NEE. Sensitivity analysis showed that the overall output uncertainty of the FORUG model can largely be determined by accounting for the uncertainty of only a few key parameters. The results led to the identification of the key FORUG parameters and to the recommendation for a process-based description of the soil respiration process in the FORUG model.