A two-pronged approach to interpreting field data through the use of models is presented. This approach builds upon both data- and theory-based models and their associated methods of system identification. It seeks to overcome their respective limitations: that theory-based models are not unambiguously identifiable from the observations, while a well identified data-based model may not be capable of a satisfactory theoretical interpretation. The purpose of the approach is thereby to gain a deeper understanding of complex environmental systems. Recursive methods of time-series analysis are used to identify the data-based models and the modified recursive prediction error algorithm is employed for parameter estimation of the theory-based models. The results of these identification exercises for the two classes of models can be compared in terms of the macro-parameters of the studied system's time constant and steady-state gain. Two case studies are presented to illustrate the overall performance of the two-pronged approach. It is found that: (1) more is to be gained through the joint application of the two classes of models than the exclusive use of either; (2) to some extent, identifying the structure and estimating the parameters of one type of model can be improved by recourse to the corresponding results for the other; and (3) reconciliation of the results from identifying the two classes of model in the parameter space has significant advantages over the more familiar process of evaluating a model's performance in the terms of its (observed) state space features.

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