Parallel Processes in Hydrology and Water Quality: A Unified Time‐Series Approach

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Abstract
Most well‐known time‐series methods treat the system as a univariate, bivariate or multivariate ‘black box’whose parameters provide a convenient and concise description of the data. This is in contrast to physically based, mechanistic models, whose parameters normally have an identifiable physical interpretation. The present paper describes a unified ‘data‐based mechanistic’approach to the modelling of dynamic systems from time‐series data using continuous or discrete‐time transfer function models in the time derivative, backward shift or delta operator. This approach, which exploits recursive methods of parameter estimation, represents a useful compromise between the physically based methods of mechanistic modelling and the ‘black box’methods of time‐series analysis. It provides a powerful tool for the objective investigation of environmental dynamic systems when time‐series data are available for analysis. Its practical potential is illustrated by several real examples concerned with the objective investigation of parallel processes in hydrology and water quality.