The dark art of interpretation in geomorphology

Gary Brierley*, Kirstie Fryirs, Helen Reid, Richard Williams

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    3 Citations (Scopus)
    12 Downloads (Pure)

    Abstract

    The process of interpretation, and the ways in which knowledge builds upon interpretations, has profound implications in scientific and managerial terms. Despite the significance of these issues, geomorphologists typically give scant regard to such deliberations. Geomorphology is not a linear, cause-and-effect science. Inherent complexities and uncertainties prompt perceptions of the process of interpretation in geomorphology as a frustrating form of witchcraft or wizardry — a dark art. Alternatively, acknowledging such challenges recognises the fun to be had in puzzle-solving encounters that apply abductive reasoning to make sense of physical landscapes, seeking to generate knowledge with a reliable evidence base. Carefully crafted approaches to interpretation relate generalised understandings derived from analysis of remotely sensed data with field observations/measurements and local knowledge to support appropriately contextualised place-based applications. In this paper we develop a cognitive approach (Describe-Explain-Predict) to interpret landscapes. Explanation builds upon meaningful description, thereby supporting reliable predictions, in a multiple lines of evidence approach. Interpretation transforms data into knowledge to provide evidence that supports a particular argument. Examples from fluvial geomorphology demonstrate the data-interpretation-knowledge sequence used to analyse river character, behaviour and evolution. Although Big Data and machine learning applications present enormous potential to transform geomorphology into a data-rich, increasingly predictive science, we outline inherent dangers in allowing prescriptive and synthetic tools to do the thinking, as interpreting local differences is an important element of geomorphic enquiry.

    Original languageEnglish
    Article number107870
    Pages (from-to)1-13
    Number of pages13
    JournalGeomorphology
    Volume390
    DOIs
    Publication statusPublished - 1 Oct 2021

    Bibliographical note

    Copyright the Crown 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

    Keywords

    • Landform
    • Landscape
    • Explanation
    • Prediction
    • Big Data
    • Fieldwork
    • Modelling

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