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It is reasonable to consider that sequences of hydrologic data corresponding to daily, weekly, or monthly measurements occur in well‐defined groups. These groups possess collective properties of the data forming them. Such a collection of properties can be called a hydrologic pattern. A pattern is a description of an object, and the objects of concern in this paper are groups of data on hydrologic phenomena observed at regular time intervals. Hydrologic patterns describing each of these groups are expressed by n appropriate properties. Further, the dimensionality, n in number, can be reduced by considering only those characteristic properties, m in number (m≤n), that are common in all hydrologic patterns of the same category. These m characteristic properties are called features. A procedure is presented to extract information present within patterns and among patterns of the pertinent hydrologic data. In addition, on the basis of the above information, a zero‐order Markov feature prediction model is postulated. The basic assumptions of the model and their implications are presented. The model is applied to South Saskatchewan River flow data in an effort to demonstrate its usefulness in real situations.

The suitability of catastrophe theory as a source of models and theories for the behavioral and life sciences has been the subject of considerable controversy. Substantive articles in this special issue represent a selection of the first fruits of a major interdisciplinary and international effort to bring about an orderly and painstaking development of meaningful applications.

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