Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A Waterbody Typology Derived from Catchment Controls Using Self-Organising Maps
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.
A Waterbody Typology Derived from Catchment Controls Using Self-Organising Maps
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.
A Waterbody Typology Derived from Catchment Controls Using Self-Organising Maps
Eleanore L. Heasley (Autor:in) / James D. A. Millington (Autor:in) / Nicholas J. Clifford (Autor:in) / Michael A. Chadwick (Autor:in)
2019
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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