The Demonstration Flow Assessment (DFA) method evaluates instream flow benefits using expert judgment and direct observation of habitat during several flows. Early DFA applications were low‐effort, qualitative, and vulnerable to well‐known biases. We describe a higher‐effort, more quantitative DFA (or expert habitat mapping) approach that uses techniques from the judgment‐based decision analysis literature to increase objectivity and reproducibility. Specific metrics—habitat types to be quantified visually during flow observations—are designed from appropriate conceptual models of how flow affects target resources. During field observations, patches of each habitat type are delineated by consensus and marked on maps for digital analysis. A case study illustrates these procedures applied to instream flows for salmon spawning and rearing.

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The quantification of uncertainty through probability is central to definitions of risk used in environmental poficy analysis. This essay explores the translation of unquantified uncertainty into probability and the expression of allied philosophical problems in the practice of environmental risk and decision analysis. First we look at means used in science for handling uncertainty associated with some maior risks and which are not well represented through probability. 'Saving the science' without quantified probability is addressed through the role of probabilistic events in risk analysis, suggesting the need to expand the scope of risk analysis to include some types of unquantified reasoning about adverse events. Next we look at uses of subjective probability and decision analysis to overcome problems of unquantified uncertainty in science, where we argue that a constructive conception of probability judgments, based in the foundations of decision analysis, provides the most useful approach for such methods. A theme throughout is the role of intellectual control implicit in our efforts 'to tame change' through the representation of uncertainty through probability.

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