Determining Suitable Fingerprinting Properties for Discrimination of Sediment Sources (Case study: Amrovan and Atary Catchments)

Document Type: Research Paper

Authors

1 MSc.Graduate, University of Tehran and Member of Young Researcher Club of Islamic Azad University, Shiraz Branch, Shiraz, Iran

2 Professor, Faculty of Natural Resources, University of Tehran, Karaj, Iran

3 Professor, Science and Research Branch, Tehran Islamic Azad University, Iran

4 Researcher, Agriculture and Natural Resource Research Center, Semnan, Iran

5 Graduate Student, Faculty of Natural Resources, University of Tehran, Karaj, Iran

Abstract

This contribution determines suitable fingerprinting properties for sediment source discrimination within the
Amrovan and Atary catchments in Semnan Province, Iran. These catchments are representative of a range of geology formations and should therefore provide a meaningful basis for a general assessment of the degree of sediment source discrimination afforded by a range of fingerprint properties. By field investigation, 10 representative samples were collected from each sediment sources per catchments. Geological formation map was selected as the base of grouping samples. For the case of Amrovan catchment Hezar Dareh, Upper Red and Quaterrnary formations as well as gully walls were selected as the origin of sediments whereas in Atary Catchment karaj, Qum, Upper Red, Hezar Dareh and Quaternary formations were selected as the origin of sediments. The 15 properties selected as a tracer, comprised five groups of fingerprinting properties, including Organic constituents (C, N, P), base cations (Na, K, Ca, Mg), acidextractable metals (Cr, Co), clay minerals (Smectite, Colorite, Illite, Kaolinite) and magnetic properties consisting of Low Frequency Magnetic Susceptibility (XLF) and Frequency Dependent Magnetic Susceptibility (XFD). Several statistical methods were applied to the data including the Kruskal-Wallis, discrimination function analysis (DFA) and
multivariate stepwise selection algorithm. Results indicate that the most powerful individual fingerprint property is organic constituent C, which successfully classifies 70% and 66% of samples in Amrovan and Atary catchmentsrespectively. Composite fingerprints incorporating constituents selected from several groups of properties using astepwise statistical selection procedure consistently provide the most robust discrimination of potential sediment sources. Results show also that organic constituents group of properties is extremely useful for sediment source discrimination in this catchments.

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