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How to select the appropriate methodology for uncertainty assessment? |
| Methodologies according to source and type of uncertainty |
Table 2 provides a list of applicable methodologies
for addressing uncertainty of different types and originating from
different sources. The reason for this is that this is a third dimension and
that each of the cells below may be divided into reducible
(epistemic) and irreducible (stochastic) uncertainty.
Table 2 Correspondence of the methodologies with the source and
types of uncertainty distinguished in the uncertainty taxonomy
(inspired by van der Sluijs et al., 2004).
|
Source of uncertainty
|
Taxonomy (types of uncertainty)
|
|
Statistical uncertainty
|
Scenario uncertainty
|
Qualitative uncertainty
|
Recognised ignorance
|
|
Context
|
Natural, technological, economic, social, political
|
EE
|
EE, SC, SI
|
EE, EPR, NUSAP, SI, UM
|
EE, EPR, NUSAP, SI, UM
|
|
Inputs
|
System data
|
DA, EPE, EE, MCA, SA
|
DA, EE, SC
|
DA, EE
|
DA, EE
|
|
Driving forces
|
DA, EPE, EE, MCA, SA
|
DA, EE, SC
|
DA, EE, EPR
|
DA, EE, EPR
|
|
Model
|
Model structure
|
EE, MMS, QA
|
EE, MMS, SC, QA
|
EE, NUSAP, QA
|
EA, NUSAP, QA
|
|
Technical
|
QA
|
QA
|
QA
|
QA
|
|
Parameters
|
IN-PA, SA, QA
|
IN-PA, SA, QA
|
QA
|
QA
|
|
Model outputs
|
EPE, EE, IN-UN, MCA, MMS, SA
|
EE, IN-UN, MMS, SA
|
EE, NUSAP
|
EE, NUSAP
|
Abbreviations of methodologies:
DA Data
Uncertainty
EPE Error Propagation
Equations
EE Expert
Elicitation
EPR Extended Peer Review
(review by stakeholders)
IN-PA Inverse modelling (parameter estimation)
IN-UN Inverse modelling (predictive uncertainty)
MCA Monte Carlo Analysis
MMS Multiple Model Simulation
NUSAP
NUSAP
QA Quality Assurance
SC Scenario
Analysis
SA Sensitivity
Analysis
SI
Stakeholder Involvement
UM Uncertainty Matrix
Overview
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Harmoni-CA is a research project supported by the European Commission under the 