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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Agroclimate and Natural Resources Research » Research » Publications at this Location » Publication #329207


Location: Agroclimate and Natural Resources Research

Title: A new approach of using multiple composite fingerprints to apportion sediment sources

item Zhang, Xunchang
item Benli, Liu - Chinese Academy Of Sciences

Submitted to: Agro-Environment Symposium
Publication Type: Abstract Only
Publication Acceptance Date: 5/25/2016
Publication Date: 8/16/2016
Citation: Zhang, X.J., Benli, L. 2016. A new approach of using multiple composite fingerprints to apportion sediment sources [abstract]. In: Agro-Environ 2016. 10th International Symposium on Agriculture and the Environment, May 23-27, 2016, West Lafayette, Indiana. Available:

Interpretive Summary: Abstract only.

Technical Abstract: Sediment source fingerprinting provides an essential means for estimating sediment source contributions, which are needed not only for soil conservation planning but also for erosion model evaluation and refinement. A single optimum composite fingerprint has been widely used in the literature to estimate sediment provenance. The objectives of this work are to (1) verify whether an optimum composite fingerprint exists, (2) present a new direction of using multiple composite fingerprints to improve the accuracy and reliability of source contribution estimation, and (3) evaluate and compare the new approach and the optimum composite fingerprint approach using synthetic sediment mixture. We collected 50 surface samples from overland in a watershed (15.6 km^2), 28 subsoil samples from gully bank, and 8 sediment samples at the outlet. We analyzed 31 elements using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) and a Macro Analyzer. The results show that tracer selection greatly impacts the estimated source contributions. The optimum composite fingerprint may not exist, or at least cannot be identified simply based on tracer’s ability to discriminate sources because of the lack of correlation between the tracer’s ability to discriminate and its rigor in estimating source contributions. The weak link is likely caused by (1) tracer conflicts, (2) different levels of tracer measurement errors, and (3) varying degree of the conservativeness of each tracer or lack of it. To overcome this shortcoming, we propose a new approach of using multiple composite fingerprints. Contrary to the popular tracer reduction strategies, this new approach uses a maximum number of composite fingerprints, which contain non-contradictory tracers in each composite, to maximize the use of all tracer information. The new approach assumes that source proportions averaged over multiple composite fingerprints are more likely to be closer to the population means than any estimates using a single fingerprint alone. Such a ‘mean of the means’ approach has been shown to not only improve the accuracy but also reduce the uncertainty of the proportion estimates.