Evaluating the Impact of High Source Variability and Extreme Contributing Sources on Sediment Fingerprinting Models

Borja Latorre (), Ivan Lizaga, Leticia Gaspar and Ana Navas
Additional contact information
Borja Latorre: Estación Experimental de Aula-Dei (EEAD-CSIC), Spanish National Research Council
Ivan Lizaga: Ghent University
Leticia Gaspar: Estación Experimental de Aula-Dei (EEAD-CSIC), Spanish National Research Council
Ana Navas: Estación Experimental de Aula-Dei (EEAD-CSIC), Spanish National Research Council

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 9, No 17, 4589-4603

Abstract: Abstract Sediment fingerprinting is a powerful tool used in drainage basin analysis to identify and quantify sediment sources, crucial for effective water management strategies. However, methodological debates persist regarding the influence of tracer type, tracer selection, and source dominance on fingerprinting model accuracy. This study introduces a novel linear variability propagation analysis (LVP method) to address and quantify potential bias in fingerprinting model outcomes, particularly when dealing with dominant or non-contributing sources and high source variability. We compare the results from two different models, Frequentist and Bayesian, to assess these effects using two datasets: the first one which was synthetically generated, and the other, obtained from a published laboratory study. Both datasets consisted of virtual mixtures. In such a way, uncertainties related to physical processes were eliminated, leaving only those which were introduced by mathematical or statistical methods. The comparison between theoretical and estimated apportionments from the synthetic dataset reveals systematic discrepancies in the results of both models when dominant or non-contributing sources coexist with high source variability. We analytically demonstrated that these deviations arise from the classical variability analysis used in both models. The proposed LVP method provides a means to quantify and mitigate these biases, offering a significant advancement for field fingerprinting studies where direct comparison with theoretical apportionments is not feasible. The laboratory dataset further validates these findings, revealing systematic deviations when non-contributing or dominant sources are present. Increasing the number of sources from 2 to 4 further enhanced the discrepancies that were observed.

Keywords: Sediment source estimation; Linear variability propagation (LVP) method; Bias and systematic errors; Non-contributing and dominant sources; Frequentist and Bayesian models; Virtual mixtures (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-025-04169-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:39:y:2025:i:9:d:10.1007_s11269-025-04169-8

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-025-04169-8

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().