a one-time approval step. Evaluation emerged from this debate as the most appropriate descriptor and is characteristic of a life-cycle process.

Two decades ago, model “validation” (as it was referred to then) was defined as the assessment of a model’s predictive performance against a second set of (independent) field data given model parameter (coefficient) values identified or calibrated from a first set of data. In this restricted sense, “validation” is still a part of the common vocabulary of model builders.

The difficulty in finding a label for the process of judging whether a model is adequate and reliable for its task is described as follows. The terms “validation” and “assurance” prejudice expectations of the outcome of the procedure toward only the positive—the model is valid or its quality is assured—whereas evaluation is neutral in what might be expected of the outcome. Because awareness of environmental regulatory models has become so widespread in a more scientifically aware audience of stakeholders and the public, words used within the scientific enterprise can have meanings that are misleading in contexts outside the confines of the laboratory world. The public knows well that supposedly authoritative scientists can have diametrically opposed views on the benefits of proposed measures to protect the environment.

When there is great uncertainty surrounding the science base of an issue, groups of stakeholders within society can take this issue as a license to assert utter confidence in their respective versions of the science, each of which contradicts those of the other groups. Great uncertainty can lead paradoxically to a situation of “contradictory certainties” (Thompson et al. 1986), or at least to a plurality of legitimate perspectives on the given issue, with each such perspective buttressed by a model proclaimed to be valid. Those developing models have found this situation disquieting (Bredehoeft and Konikow 1993) because, even though science thrives on the competition of ideas, when two different models yield clearly contradictory results, as a matter of logic, they cannot both be true. It matters greatly how science and society communicate with each other (Nowotny et al. 2001); hence, in part, scientists shunned the word “validation” in judging model performance.

Today, evaluation comprises more than merely a test of whether history has been matched. Evaluation should not be something of an afterthought but, indeed, a process encompassing the entire life cycle of the task. Furthermore, for models used in environmental regulatory activities, the model builder is not the only archetypal interested party holding a stake in the process but is also one among several key players,

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