A unified approach to environmental systems modeling

Abstract

The paper considers the differences between hypothetico-deductive and inductive modeling: between modelers who put their primary trust in their scientific intuition about the nature of an environmental model and tend to produce quite complex computer simulation models; and those who prefer to rely on the analysis of observational data to identify the simplest form of model that can represent these data. The tension that sometimes arises because of the different philosophical outlooks of these two modeling groups can be harmful because it tends to fractionate the effort that goes into the investigation of important environmental problems, such as global warming. In an attempt to improve this situation, the paper will outline a new Data-Based Mechanistic (DBM) approach to modeling that tries to meld together the best aspects of these two modeling philosophies in order to develop a unified approach that combines the hypothetico-deductive virtues of good scientific intuition and simulation modeling with the pragmatism of inductive data-based modeling, where more objective inference from data is the primary driving force. In particular, it demonstrates the feasibility of a new method for complex simulation model emulation, in which the methodological tools of DBM modeling are used to develop a reduced dynamic order model that represents the 'dominant modes' of the complex simulation model. In this form, the 'dynamic emulation' model can be compared with the DBM model obtained directly from the analysis of real data and any tensions between the two modeling approaches may be relaxed to produce models that suit multiple modeling objectives.