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Article Dans Une Revue Water Resources Research Année : 2017

River Mixing in the Amazon as a Driver of Concentration-Discharge Relationships

Résumé

Large hydrological systems aggregate compositionally different waters derived from a variety of pathways. In the case of continental-scale rivers, such aggregation occurs noticeably at confluences between tributaries. Here we explore how such aggregation can affect solute concentration-discharge (C-Q) relationships and thus obscure the message carried by these relationships in terms of weathering properties of the Critical Zone. We build up a simple model for tributary mixing to predict the behavior of C-Q relationships during aggregation. We test a set of predictions made in the context of the largest world's river, the Amazon. In particular, we predict that the C-Q relationships of the rivers draining heterogeneous catchments should be the most ''dilutional'' and should display the widest hysteresis loops. To check these predictions, we compute 10 day-periodicity time series of Q and major solute (Si, Ca 21 , Mg 21 , K 1 , Na 1 , Cl-, SO 22 4) C and fluxes (F) for 13 gauging stations located throughout the Amazon basin. In agreement with the model predictions, C-Q relationships of most solutes shift from a fairly ''chemo-static'' behavior (nearly constant C) at the Andean mountain front and in pure lowland areas, to more ''dilutional'' patterns (negative C-Q relationship) toward the system mouth. More prominent C-Q hysteresis loops are also observed at the most downstream stations. Altogether, this study suggests that mixing of water and solutes between different flowpaths exerts a strong control on C-Q relationships of large-scale hydrological systems.
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Dates et versions

hal-02133275 , version 1 (18-05-2019)

Identifiants

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Julien Bouchez, Jean-Sébastien Moquet, Jhan Carlo Espinoza, Jean-Michel Martinez, Jean-Loup Guyot, et al.. River Mixing in the Amazon as a Driver of Concentration-Discharge Relationships. Water Resources Research, 2017, 53 (11), pp.8660-8685. ⟨10.1002/2017WR020591⟩. ⟨hal-02133275⟩
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