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Open AccessArticle

A Waterbody Typology Derived from Catchment Controls Using Self-Organising Maps

1
Department of Geography, King’s College London, London WC2B 4BG, UK
2
School of Social, Political and Geographical Sciences, Loughborough University, Leicestershire LE11 3TU, UK
*
Author to whom correspondence should be addressed.
Water 2020, 12(1), 78; https://doi.org/10.3390/w12010078
Received: 4 November 2019 / Revised: 13 December 2019 / Accepted: 18 December 2019 / Published: 24 December 2019
(This article belongs to the Special Issue Fluvial Geomorphology and River Management)
Multiple catchment controls contribute to the geomorphic functioning of river systems at the reach-level, yet only a limited number are usually considered by river scientists and managers. This study uses multiple morphometric, geological, climatic and anthropogenic catchment characteristics to produce a single national typology of catchment controls in England and Wales. Self-organising maps, a machine learning technique, are used to reduce the complexity of the GIS-derived characteristics to classify 4485 Water Framework Directive waterbodies into seven types. The waterbody typology is mapped across England and Wales, primarily reflecting an upland to lowland gradient in catchment controls and secondarily reflecting the heterogeneity of the catchment landscape. The seven waterbody types are evaluated using reach-level physical habitat indices (including measures of sediment size, flow, channel modification and diversity) extracted from River Habitat Survey data. Significant differences are found between each of the waterbody types for most habitat indices suggesting that the GIS-derived typology has functional application for reach-level habitats. This waterbody typology derived from catchment controls is a valuable tool for understanding catchment influences on physical habitats. It should prove useful for rapid assessment of catchment controls for river management, especially where regulatory compliance is based on reach-level monitoring. View Full-Text
Keywords: geomorphology; machine learning; River Habitat Survey; Water Framework Directive geomorphology; machine learning; River Habitat Survey; Water Framework Directive
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Heasley, E.L.; Millington, J.D.A.; Clifford, N.J.; Chadwick, M.A. A Waterbody Typology Derived from Catchment Controls Using Self-Organising Maps. Water 2020, 12, 78.

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