Separating the wheat from the chaff: the effective use of mathematical models as decision tools.
The purpose of this paper is to discuss the effective use of quantitative modeling in environmental decision making, with a particular focus on problems of contaminated sediment and surface water. The intended audience includes both model developers and model users. Our goal is to facilitate more effective communication among model developers and those using the information produced by models to aid decision making. We provide a series of observations or conclusions we have reached in our experience that are as follows. A model is a tool for evaluating alternate hypotheses; a model itself is not a hypothesis. All decisions are actually based upon models, either explicitly or implicitly. Models are used to address diagnostic and prognostic questions. Models can provide value added when applied throughout the lifetime of a project. Uncertainty, and therefore the need for models, is greater in systems near background. Models can provide useful information even when based on relatively small data sets. The utility of a model depends on the strength of the constraints placed upon it. The calibration process can be only partially specified a priori. Model calibration and evaluation require multiple lines of evidence. Uncertainty analysis is both qualitative and quantitative. Validation is provided by the application of the model under a wide range of conditions. Communication of the strength of model constraints is critical to model acceptance. We conclude that while models are often used in the evaluation of contaminated sediment problems, distrust in the use of models remains strong. The assessment of uncertainty is the factor most limiting acceptability.
Glaser
David
D
Quantitative Environmental Analysis, 305 West Grand Avenue, Montvale, New Jersey 07645, USA. dglaser@qeallc.com
Bridges
Todd S
TS
D003657:Decision Making; D003661:Decision Support Techniques; D004777:Environment; D008962:Models, Theoretical; D015203:Reproducibility of Results; D035501:Uncertainty