Sediment fingerprinting in fluvial systems: review of tracers, sediment sources and mixing models

被引:170
|
作者
Haddadchi, Arman [1 ]
Ryder, Darren S. [2 ]
Evrard, Olivier [3 ]
Olley, Jon [1 ]
机构
[1] Griffith Univ, Australian Rivers Inst, Nathan, Qld 4111, Australia
[2] Univ New England, Sch Environm & Rural Sci, Armidale, NSW 2351, Australia
[3] UVSQ, CNRS, CEA, LSCE,IPSL,Unite Mixte Rech 8212, F-91198 Gif Sur Yvette, France
关键词
watershed; fluvial sampling; source tracing; modeling; local optimization; genetic algorithm; TRACING SUSPENDED SEDIMENT; FINE SEDIMENT; SOIL-EROSION; COMPOSITE FINGERPRINTS; AGRICULTURAL CATCHMENT; SOUTHEASTERN AUSTRALIA; SOURCE IDENTIFICATION; MAGNETIC-PROPERTIES; FORESTED CATCHMENT; GULLIED CATCHMENT;
D O I
10.1016/S1001-6279(14)60013-5
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Suspended sediments in fluvial systems originate from a myriad of diffuse and point sources, with the relative contribution from each source varying over time and space. The process of sediment fingerprinting focuses on developing methods that enable discrete sediment sources to be identified from a composite sample of suspended material. This review identifies existing methodological steps for sediment fingerprinting including fluvial and source sampling, and critically compares biogeochemical and physical tracers used in fingerprinting studies. Implications of applying different mixing models to the same source data are explored using data from 41 catchments across Europe, Africa, Australia, Asia, and North and South America. The application of seven commonly used mixing models to two case studies from the US (North Fork Broad River watershed) and France (Bleone watershed) with local and global (genetic algorithm) optimization methods identified all outputs remained in the acceptable range of error defined by the original authors. We propose future sediment fingerprinting studies use models that combine the best explanatory parameters provided by the modified Collins (using correction factors) and Hughes (relying on iterations involving all data, and not only their mean values) models with optimization using genetic algorithms to best predict the relative contribution of sediment sources to fluvial systems.
引用
收藏
页码:560 / 578
页数:19
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