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Tributary contributions to sediment deposited in the Jacuí Delta, Southern Brazil

Abstract : The Jacuí Delta drains into Lake Guaíba which is the main source of water for more than two million people in South Brazil and has suffered from pollution with heavy metals and phosphorus. The objective of the current research is to demonstrate how the use of sediment source tracing techniques, in combination with sediment flux monitoring, can improve understanding of the sediment source contributions to one of the largest lakes in South America. The sediment flux monitoring results were based on data obtained from 12-years of records of water flow and suspended sediment concentrations. The sediment source fingerprinting approach was based on the use of geochemical tracers. Based on the results of the source tracing study, the respective contributions of the tributaries to the sediment in Lake Guaíba were estimated to be as follows: the Jacuí River (median of 54%-interquartile range (IQR) 34-71%), the Caí River (12%, IQR 7-16%), the Sinos River (5%, IQR 1-20%), and the Gravataí River (16%, IQR 10-30%). These results are similar to those derived from the sediment flux monitoring, namely: the Jacuí River (70%), the Caí River (19%), the Gravataí River (4%), and the Sinos River (7%). These results demonstrate that the sediment source fingerprinting approach combined with sediment flux monitoring can provide a useful means of estimating the respective sediment contributions from individual tributaries in large and complex delta systems and may provide a powerful tool to guide water resource management.
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Contributor : Olivier Evrard Connect in order to contact the contributor
Submitted on : Wednesday, February 23, 2022 - 11:38:12 AM
Last modification on : Friday, October 14, 2022 - 3:54:12 AM
Long-term archiving on: : Tuesday, May 24, 2022 - 6:32:26 PM


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Tales Tiecher, Rafael Ramon, Leonardo de Andrade, Flávio A.O. Camargo, O. Evrard, et al.. Tributary contributions to sediment deposited in the Jacuí Delta, Southern Brazil. Journal of Great Lakes Research, 2022, 48 (3), pp.669-685. ⟨10.1016/j.jglr.2022.02.006⟩. ⟨cea-03585605⟩



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