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Perceptual perplexity and parameter parsimony
Author(s) -
Beven Keith J.,
Chappell Nick A.
Publication year - 2021
Publication title -
wiley interdisciplinary reviews: water
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.413
H-Index - 24
ISSN - 2049-1948
DOI - 10.1002/wat2.1530
Subject(s) - perplexity , perception , predictability , computer science , cognitive psychology , artificial intelligence , machine learning , mathematics , statistics , psychology , neuroscience , language model
Abstract This article reconsiders the concept of a perceptual model of hydrological processes as the first stage to be considered in developing a procedural model for a particular catchment area. While various perceptual models for experimental catchments have been developed, the concept is not widely used in defining or evaluating catchment models. This is, at least in part, because of the evident complexity possible in a perceptual model and the approximate nature of procedural model structures and parameterizations, particularly where there is a requirement for parameter parsimony. A perceptual model for catchments in Cumbria, North‐West England, is developed as an exemplar and illustrated in terms of time varying distribution functions. Two critical questions are addressed: how can perceptual model hypotheses be tested at scales of interest, and how can constraints then be imposed on the basis of qualitative perceptual knowledge in conditioning predictive models? It is suggested that there is value in perceptual information, particularly in thinking about predicting the impacts of future change and that we still have much to learn about moving from observational and perceptual complexity to parsimonious predictability. This article is categorized under: Science of Water > Hydrological Processes Science of Water > Methods

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