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Tool Catalogue for Uncertainty Assessment
Introduction
1. Sensitivity analysis
2. Error propagation equation ("TIER 1")
Description
Goals and use
Sorts and locations of uncertainty addressed
Required resources
Strengths and limitations
Guidance on application
Pitfalls
References
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

Strengths and limitations

Typical strengths are:

  • Requires very little resources and skills (but the choice of the aggregation level for the analysis is an important issue that does require skills)
  • Quick (but can be dirty)
  • Typical weaknesses are:
  • Has a limited domain of applicability (e.g. near-linearity assumption)
  • The basic error propagation equations cannot cope well with distributions with other shapes than normal (but the method can be extended to account for other distributions).
  • Leads to a tendency to assume that all distributions are normal, even in cases where knowledge of the shape is absent and hence a uniform distribution would be reflecting better the state of knowledge.
  • Can not easily be applied in complex calculations