PubAg

Main content area

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

Author:
Raigani, Zeinab Mohammadi, Nosrati, Kazem, Collins, Adrian L.
Source:
Journal of hydrology 2019 v.24 pp. 100613
ISSN:
2214-5818
Subject:
Bayesian theory, agricultural watersheds, arid lands, basins, farming systems, hydrology, models, mountains, prediction, rangelands, rivers, sediments, statistical analysis, tracer techniques, uncertainty, weathering
Abstract:
The Kamish River catchment (308 km2); a mountainous agricultural catchment under dry-land and rangeland farming located in Kermanshah province, in western Iran.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.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.
Agid:
6496164