Abstract. Part 1 of this paper has discussed the uncertainties arising from gaps in
knowledge or limited understanding of the processes involved in different
natural hazard areas. Such deficits may include uncertainties about
frequencies, process representations, parameters, present and future boundary
conditions, consequences and impacts, and the meaning of observations in
evaluating simulation models. These are the epistemic uncertainties that can
be difficult to constrain, especially in terms of event or scenario
probabilities, even as elicited probabilities rationalized on the basis of
expert judgements. This paper reviews the issues raised by trying to quantify
the effects of epistemic uncertainties. Such scientific uncertainties might
have significant influence on decisions made, say, for risk management, so it
is important to examine the sensitivity of such decisions to different
feasible sets of assumptions, to communicate the meaning of associated
uncertainty estimates, and to provide an audit trail for the analysis. A
conceptual framework for good practice in dealing with epistemic
uncertainties is outlined and the implications of applying the principles to
natural hazard assessments are discussed. Six stages are recognized, with
recommendations at each stage as follows: (1) framing the analysis, preferably with
input from potential users; (2) evaluating the available data for epistemic uncertainties,
especially when they might lead to inconsistencies; (3) eliciting information on sources
of uncertainty from experts; (4) defining a workflow that will give reliable and accurate
results; (5) assessing robustness to uncertainty, including the impact on any
decisions that are dependent on the analysis; and (6) communicating the findings and meaning
of the analysis to potential users, stakeholders, and decision makers. Visualizations are
helpful in conveying the nature of the uncertainty outputs, while recognizing that the
deeper epistemic uncertainties might not be readily amenable to visualizations.