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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 #365497

Research Project: Uncertainty of Future Water Availability Due to Climate Change and Impacts on the Long Term Sustainability and Resilience of Agricultural Lands in the Southern Great Plains

Location: Agroclimate and Natural Resources Research

Title: The performance of different fingerprinting methods in estimating sediment source contributions in an arid region

Author
item NIU, BAICHENG - Chinese Academy Of Sciences
item Zhang, Xunchang

Submitted to: Soil Science Society of America Annual Meeting
Publication Type: Abstract Only
Publication Acceptance Date: 6/15/2019
Publication Date: 11/11/2019
Citation: Niu, B., Zhang, X.J. 2019. The performance of different fingerprinting methods in estimating sediment source contributions in an arid region [abstract]. Soil Science Society of America Annual Meeting. Available at: https://scisoc.confex.com/scisoc/2019am/meetingapp.cgi/Paper/118909.

Interpretive Summary: Abstract only.

Technical Abstract: Fingerprinting methods are widely used to quantify the provenance of sediment at a watershed scale. However, the different fingerprinting methods often produce different results in a particular watershed. Thus, the selection of an appropriate fingerprinting method is of great importance to obtain reliable estimates of sediment source contributions. In this study, we tested the performance of six fingerprinting methods to quantitatively identify sediment sources in a large arid watershed that experienced both wind and water erosion. The source samples were taken from three geomorphic source areas of dune, desert floor, and mountains, and the sediment samples were collected from the watercourses of the Danghe river near a reservoir. Results showed that the multiple composite fingerprints (MCF) method using an analytical solution performed the best in the study area, followed by MCF using Monte Carlo simulation and optimization after filtering with Discriminant Function Analysis (DFA), MCF by optimization without DFA filtering, single (optimal) composite fingerprint (SCF) method using a DFA distance weighting method, and conventional SCF by DFA or PCA (principal component analysis) selection. The MCF using analytical solutions is also the simplest method among all methods studied. Overall results showed that MCF methods were superior to SCF methods to estimate proportional contributions of the three sediment sources. Result also indicated that increasing the number of tracers in a composite fingerprint might not improve the estimation accuracy, probably due to conflicts among tracers. This conclusion may need to be further examined in different watersheds under different erosive forces.