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Tool Catalogue for Uncertainty Assessment
Introduction
1. Sensitivity analysis
Description
Goals and use
Sorts and locations of uncertainty addressed
Required resources
Strengths and limitations
Guidance on application
Pitfalls
References
2. Error propagation equation ("TIER 1")
3. Monte Carlo analysis ("TIER 2")
4. NUSAP
5. Expert elicitation for uncertainty quantification
6. Scenario analysis
7. PRIMA: A framework for perspective-based uncertainty management
8. Checklist for model quality assistance
9. A method for critical review of assumptions in model-based assessments

Pitfalls

Typical pitfalls of SA can be:

  • Forgetting that SA takes the model structure and boundaries for granted
  • Wasting time on finding out likely ranges for unimportant parameters
    This could be avoided by using a two-step approach applying a default range (e.g. a factor 2) on all parameters to find out which parameters appear to be sensitive at all. In such a case one should however also go over the list of variables identified as insensitive and include for the second step also those variables where one has doubts as to whether one is sure that the default range used in that calculation captures the full conceivable range for that parameter.
  • Ignoring dependencies between parameters
  • Exploring irrelevant or physically unrealistic parts of the parameter hyper space