Understanding Uncertain Environmental Systems

Abstract

Developments over the past two decades in the identification of models of environmental systems are reviewed, with special reference to the quality and pollution of surface freshwaters. As in so many fields, the early 1970s were a time of great expectations: it would not be long, we believed, before the admittedly less well defined problems of environmental systems analysis would nevertheless yield to the already vast array of methods available from applied mathematics and control theory (which had been so successful in their application, for example, to the analysis of aerospace systems). Such a yielding has still to come to pass, at least for multivariable models of more than, say, five or six state variables. In the past decade, because of the seemingly insuperable difficulties of model identifiability, we have promoted the pragmatic view that what really matters is the ability to generate “robust” predictions that are maximally insensitive to a lack of identifiability. Such pragmatism, coupled with a continuing dearth of successful techniques of system identification, does not bode well. The digital computing technology on which we are able to realise our “set of concepts” (our models) continues to expand rapidly. A similar expansion, although less dramatically so, is apparent in the technology of instrumentation and remote sensing, through which our “given data” are acquired in ever greater volumes. No such expansion is evident in the capacity of the brain to juggle with disparate facts and figures until the ever more comprehensive, given data can be reconciled with the increasingly massive sets of concepts. Whither, then, is environmental system identification bound in the next decade? A modest attempt to answer this question will be made, by way of conclusion.