Advances in Earth Science ›› 2019, Vol. 34 ›› Issue (10): 1092-1098. doi: 10.11867/j.issn.1001-8166.2019.10.1092

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A Multiple Composite Fingerprinting Method and Its Application

Benli Liu 1( ),Xunchang John Zhang 2,Baicheng Niu 1, 3,Jianjun Qu 1   

  1. 1. Dunhuang Gobi Desert Research Station, Key Laboratory of Desert and Desertification/Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
    2. Grazinglands Research Laboratory, Agricultural Research Service, United States Department of Agriculture, El Reno, OK 73036, USA
    3. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2019-04-15 Revised:2019-08-16 Online:2019-10-10 Published:2019-12-09
  • About author:Liu Benli (1986-), male, Luohe City, He'nan Province, Associate professor. Research areas include aeolian disaster and aeolian engineering. E-mail:
  • Supported by:
    the Strategic Priority Research Program of the Chinese Academy of Sciences “Technologies and demonstration on the stabilization of expanding desert margin belt”(XDA23060201);The Youth Innovation Promotion Association of the Chinese Academy of Sciences(2016373)

Benli Liu,Xunchang John Zhang,Baicheng Niu,Jianjun Qu. A Multiple Composite Fingerprinting Method and Its Application[J]. Advances in Earth Science, 2019, 34(10): 1092-1098.

Fingerprinting technique provides an essential means for estimating source contributions of watershed sediments, in which a single group of “optimum composite fingerprints” has been widely used in the literature to estimate sediment provenance. This type of methods is not restricted by the scale or process of sediment transportation so that similar procedures can be applied in sediment provenance research for aeolian depositions. However, recent studies found no direct link (positive relationship) between the ability of the tracer group to discriminate sources and its rigor in estimating source contributions after optimization. Here, we introduced a recently developed multiple composite fingerprinting method with additional screening based on analytical solutions, and further reviewed its verification in watersheds at different scales. It turned out that compared to Monte Carlo optimization method, a reasonable estimation can be achieved using the mean of the maximum number of composite fingerprints that given analytical solution to the mixing model, but the computational cost can be reduced significantly. The reliability of this new method was also tested in source contribution estimating of aeolian sediments, and the provenance quantification of reservoir sediment in an arid region experiencing both wind and water sediments.

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