FROM DATA AND THEORY TO ENVIRONMENTAL-MODEL - THE CASE OF RAINFALL-RUNOFF

Models of environmental systems, constructed for the purpose of predicting or understanding the effects of input changes, fall predominantly into two classes: those based on idealized equations of mathematical physics, and those based on compartmentalized conceptual descriptions of processes. Overparameterization is often a common feature of both these approaches. System identification offers the opportunity to begin model construction with simple structures and assumptions, and to build up the level of model detail by testing refinements for their consistency with system observations. We use rainfall runoff modelling to demonstrate this approach. We also use it to argue that model regionalization is a powerful technique to help generate better understanding and prediction of environmental systems. The construction of identifiable models which are transferable within a 'region' of similarity makes the exercise more tenable, and facilitates the jump to more generic models applicable in a wider (e.g. global) context.