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Fingerprinting sub-basin spatial sediment sources in a large Iranian catchment under dry-land cultivation and rangeland farming: Combining geochemical tracers and weathering indices

STUDY REGION: The Kamish River catchment (308 km(2)); a mountainous agricultural catchment under dry-land and rangeland farming located in Kermanshah province, in western Iran. STUDY FOCUS: The main objective of this study was to apportion sub-basin spatial source relative contributions to target ch...

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Autores principales: Raigani, Zeinab Mohammadi, Nosrati, Kazem, Collins, Adrian L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6686636/
https://www.ncbi.nlm.nih.gov/pubmed/31417849
http://dx.doi.org/10.1016/j.ejrh.2019.100613
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author Raigani, Zeinab Mohammadi
Nosrati, Kazem
Collins, Adrian L.
author_facet Raigani, Zeinab Mohammadi
Nosrati, Kazem
Collins, Adrian L.
author_sort Raigani, Zeinab Mohammadi
collection PubMed
description STUDY REGION: The Kamish River catchment (308 km(2)); a mountainous agricultural catchment under dry-land and rangeland farming located in Kermanshah province, in western Iran. STUDY FOCUS: The main objective of this study was to apportion sub-basin spatial source relative contributions to target channel bed sediment samples using a composite fingerprinting procedure including a Bayesian un-mixing model. In total, thirty-four geochemical tracers, eleven elemental ratios and different weathering indices were measured or estimated for 43 tributary sediment samples collected to characterise three sub-basin spatial sediment sources and eleven target bed sediment samples collected at the outlet of the main basin. Statistical analysis was used to select three different composite signatures. NEW HYDROLOGICAL INSIGHTS FOR THE REGION: Using a composite signature based on KW-H and DFA, the respective relative contributions (with uncertainty ranges) from tributary sub-basins 1, 2 and 3 were estimated as 54.3% (47.8–62.0), 11.4% (4.2–18.7) and 34.3% (27.6–39.9), compared to 72.0% (61.6–82.7), 13.6% (9.0–18.5) and 14.2% (3.1–25.4) using a combination of KW-H and data mining, and 50.8% (42.8–59.9), 28.7% (20.2–37.3) and 20.3% (12.7–27.2) using a fingerprint selected by KW-H and PCCA. The root mean square difference between these source estimates highlighted sensitivity to the composite signatures. Evaluation of the un-mixing model predictions using virtual mixture tests confirmed agreement between modelled and known source proportions.
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spelling pubmed-66866362019-08-13 Fingerprinting sub-basin spatial sediment sources in a large Iranian catchment under dry-land cultivation and rangeland farming: Combining geochemical tracers and weathering indices Raigani, Zeinab Mohammadi Nosrati, Kazem Collins, Adrian L. J Hydrol Reg Stud Article STUDY REGION: The Kamish River catchment (308 km(2)); a mountainous agricultural catchment under dry-land and rangeland farming located in Kermanshah province, in western Iran. STUDY FOCUS: The main objective of this study was to apportion sub-basin spatial source relative contributions to target channel bed sediment samples using a composite fingerprinting procedure including a Bayesian un-mixing model. In total, thirty-four geochemical tracers, eleven elemental ratios and different weathering indices were measured or estimated for 43 tributary sediment samples collected to characterise three sub-basin spatial sediment sources and eleven target bed sediment samples collected at the outlet of the main basin. Statistical analysis was used to select three different composite signatures. NEW HYDROLOGICAL INSIGHTS FOR THE REGION: Using a composite signature based on KW-H and DFA, the respective relative contributions (with uncertainty ranges) from tributary sub-basins 1, 2 and 3 were estimated as 54.3% (47.8–62.0), 11.4% (4.2–18.7) and 34.3% (27.6–39.9), compared to 72.0% (61.6–82.7), 13.6% (9.0–18.5) and 14.2% (3.1–25.4) using a combination of KW-H and data mining, and 50.8% (42.8–59.9), 28.7% (20.2–37.3) and 20.3% (12.7–27.2) using a fingerprint selected by KW-H and PCCA. The root mean square difference between these source estimates highlighted sensitivity to the composite signatures. Evaluation of the un-mixing model predictions using virtual mixture tests confirmed agreement between modelled and known source proportions. Elsevier B.V 2019-08 /pmc/articles/PMC6686636/ /pubmed/31417849 http://dx.doi.org/10.1016/j.ejrh.2019.100613 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Raigani, Zeinab Mohammadi
Nosrati, Kazem
Collins, Adrian L.
Fingerprinting sub-basin spatial sediment sources in a large Iranian catchment under dry-land cultivation and rangeland farming: Combining geochemical tracers and weathering indices
title Fingerprinting sub-basin spatial sediment sources in a large Iranian catchment under dry-land cultivation and rangeland farming: Combining geochemical tracers and weathering indices
title_full Fingerprinting sub-basin spatial sediment sources in a large Iranian catchment under dry-land cultivation and rangeland farming: Combining geochemical tracers and weathering indices
title_fullStr Fingerprinting sub-basin spatial sediment sources in a large Iranian catchment under dry-land cultivation and rangeland farming: Combining geochemical tracers and weathering indices
title_full_unstemmed Fingerprinting sub-basin spatial sediment sources in a large Iranian catchment under dry-land cultivation and rangeland farming: Combining geochemical tracers and weathering indices
title_short Fingerprinting sub-basin spatial sediment sources in a large Iranian catchment under dry-land cultivation and rangeland farming: Combining geochemical tracers and weathering indices
title_sort fingerprinting sub-basin spatial sediment sources in a large iranian catchment under dry-land cultivation and rangeland farming: combining geochemical tracers and weathering indices
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6686636/
https://www.ncbi.nlm.nih.gov/pubmed/31417849
http://dx.doi.org/10.1016/j.ejrh.2019.100613
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