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Title: Using multiple composite fingerprints to quantify fine sediment source contributions: A new direction

Author
item Zhang, Xunchang
item LIU, BENLI - National Engineering Research Center For Information Technology In Agriculture

Submitted to: Geoderma
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/20/2016
Publication Date: 2/20/2016
Citation: Zhang, X.J., Liu, B. 2016. Using multiple composite fingerprints to quantify fine sediment source contributions: A new direction. Geoderma. 268:108-118.

Interpretive Summary: Sediment source fingerprinting provides a useful means for estimating sediment source contributions, which are needed not only for soil conservation planning but also for erosion model evaluation. 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, and (2) present a new direction of using multiple composite fingerprints to improve the accuracy and reliability of source contribution estimation. This study shows that tracer selection directly 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. To overcome this shortcoming, the use of multiple composite fingerprints becomes essential. The new approach uses a maximum number of composite fingerprints with non-contradictory tracers in each to take the advantage of all fingerprint information available. It is extremely likely that source proportional contributions averaged over multiple composite fingerprints are closer to the true values 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. This work will be useful to erosion scientists and soil conservationists for estimating sediment source contribution areas or erosion types so that most effective erosion control measures can be placed where the most erosion occurs.

Technical Abstract: Sediment source fingerprinting provides a vital means for estimating sediment source contributions, which are needed not only for soil conservation planning but also for erosion model evaluation. 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 the optimization model formulation and the validity of the tracer discriminatory weighting. This study shows that tracer selection directly impacts the estimated source contributions. The optimum composite fingerprint may not exist, or at least it 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) tracer measurement errors, and (3) degree of the conservativeness of each tracer or lack of. To overcome this shortcoming, the use of multiple composite fingerprints becomes essential. The new approach uses a maximum number of composite fingerprints with non-contradictory tracers in each to take the advantage of all fingerprint information available. It is extremely likely that source proportions averaged over multiple composite fingerprints are 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. The model of the absolute relative difference performed slightly better than the model of the squared relative difference in estimating source contributions, suggesting the former be preferred. The results also indicated that the tracer discriminatory weighting should be avoided as it tends to bias contribution estimates.