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Discussion papers
https://doi.org/10.5194/esurf-2018-93
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-2018-93
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 21 Jan 2019

Research article | 21 Jan 2019

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This discussion paper is a preprint. A revision of the manuscript is under review for the journal Earth Surface Dynamics (ESurf).

An index concentration method for suspended load monitoring in large rivers of the Amazonian foreland

William Santini1,2, Benoît Camenen3, Jérôme Le Coz3, Philippe Vauchel1,2, Jean-Loup Guyot1,2, Waldo Lavado4, Jorge Carranza4, Marco A. Paredes5, Jhonatan J. Pérez Arévalo5, Nore Arévalo6, Raul Espinoza Villar6,7, Frédéric Julien8, and Jean-Michel Martinez1,2 William Santini et al.
  • 1IRD, Toulouse, 31400, France
  • 2Laboratoire GET, CNRS, IRD, UPS, OMP, Toulouse, 31400, France
  • 3IRSTEA, UR RIVERLY, Lyon-Villeurbanne, 69625 Villeurbanne, France
  • 4SENAMHI, Lima, Lima 11, Peru
  • 5SENAMHI, Iquitos, Peru
  • 6Facultad de Ingeniería Agrícola, UNALM, La Molina, Lima 12, Peru
  • 7IGP, Ate, Lima 15012, Peru
  • 8Laboratoire ECOLAB, CNRS, INPT, UPS, Toulouse, 31400, France

Abstract. Because increasing climatic variability and anthropic pressures have affected the sediment dynamics of large tropical rivers, long-term sediment concentration series have become crucial for understanding the related socio-economic and environmental impacts. For operational and cost rationalization purposes, index concentrations are often sampled in the flow and used as a surrogate of the cross-sectional average concentration. However, in large rivers where suspended sands are responsible for vertical concentration gradients, this index method can induce large uncertainties in the matter fluxes.

Assuming that physical laws describing the suspension of grains in turbulent flow are valid for large rivers, a simple formulation is derived to model the ratio (α) between index and average concentrations. The model is validated using an exceptional dataset (1330 water samples, 249 concentration profiles, 88 particle size distributions (PSDs) and 494 discharge measurements) that was collected between 2010 and 2017 in the Amazonian foreland. The α prediction requires the estimation of the Rouse number (P), which summarizes the balance between the suspended particle settling and the turbulent lift, weighted by the ratio of sediment to eddy diffusivity (β). Two particle size groups, washload and sand, were considered to evaluate P. Discrepancies were observed between the evaluated and measured P, that were attributed to biases related to the settling and shear velocities estimations, but also to diffusivity ratios β ≠ 1. An empirical expression taking into account these biases was then formulated to predict accurate estimates of β, then P (∆P = ±0.03) and finally α.

The proposed model is a powerful tool for optimizing the concentration sampling. It allows for detailed uncertainty analysis on the average concentration derived from an index method. Finally, this model can be coupled with remote sensing and hydrological modeling to serve as a step toward the development of an integrated approach for assessing sediment fluxes in poorly monitored basins.

William Santini et al.
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Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
William Santini et al.
Data sets

Hydro-sedimentary data collected between 2010 and 2017 within the framework of the Critical Zone Observatory HYBAM in the Amazonian foreland of Peru W. Santini, J.-M. Martinez, P. Vauchel, J.-L. Guyot, G. Cochonneau, W. Lavado, J. Carranza, M. A. Paredes, J. Pérez Arévalo, R. Espinoza-Villar, N. Arévalo, and K. Arévalo https://doi.org/10.6096/DV/CBUWTR

William Santini et al.
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Short summary
A simple model is proposed to improve the sediment concentration monitoring in the large rivers of the Peruvian Amazon from an index concentration sampled in the flow. This powerful tool for optimizing the concentration sampling allows for detailed uncertainty analysis on the sediment fluxes. It can be coupled with remote sensing and hydrological modeling to serve as a step toward the development of an integrated approach for assessing sediment fluxes in poorly monitored basins.
A simple model is proposed to improve the sediment concentration monitoring in the large rivers...
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