Hard or soft environmental systems?

Recent trends in lake and stream water quality modeling indicate a conflict between the search for improved accuracy through increasing model size and complexity, and the search for applicability through simplification of already existing models. Much of this conflict turns on the fact that that which can be simulated in principle issimply not matched by that which can be observed and verified in practice. This paper is concerned with that conflict. Its aim is to introduce and clarify some of the arguments surrounding two issues of key importance in resolving the conflict: uncertainty in the mathematical relationships hypothesized for a particular model (calibration and model structure identification); and uncertainty associated with the predictions obtained from the model (prediction error analysis). These are issues concerning the reliability of models and model-based forecasts. The paper argues, in particular, that there is an intimate relationship between prediction and model calibration. This relationship is especially important in accounting for uncertainty in the development and use of models. Using this argument it is possible to state a dilemma which captures some limiting features of both large and small models.