The availability of high-quality models is considered a critical success factor for Alterra. To answer the complex questions of policy makers it is often necessary to link models that have been developed initially to study more limited questions. Whenmodels are linked, error propagation may enlarge the uncertainty of the model results. However, the quantification of uncertainty propagation may become more complex. This problem of uncertainty propagation in model chains is explored using a chain of the models SMART2/SUMO, P2E and NTM that predicts the potential nature conservation value of natural areas. Two methods have been explored to study the uncertainty propagation in the model chain: a regression-free method that estimates the uncertainty contributions of groups of sources of uncertainty, and an analysis by means of linear regression approximations of the sub-models of the model chain. The final analysis was done with a regression-free method. The results are presented as the contributions ofthe various sources of uncertainty to the uncertainty of the potential conservation value. From the results of this study, lessons are learned for the analyses of error propagation in model chains.
|Place of Publication||Wageningen|
|Number of pages||90|
|Publication status||Published - 2000|
- nature conservation