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Environmental Science and Pollution Research

, Volume 23, Issue 12, pp 11405–11429 | Cite as

Amazon River dissolved load: temporal dynamics and annual budget from the Andes to the ocean

  • Jean-Sébastien Moquet
  • Jean-Loup Guyot
  • Alain Crave
  • Jérôme Viers
  • Naziano Filizola
  • Jean-Michel Martinez
  • Tereza Cristina Oliveira
  • Liz Stefanny Hidalgo Sánchez
  • Christelle Lagane
  • Waldo Sven Lavado Casimiro
  • Luis Noriega
  • Rodrigo Pombosa
Pollution Issues of Large Rivers

Abstract

The aim of the present study is to estimate the export fluxes of major dissolved species at the scale of the Amazon basin, to identify the main parameters controlling their spatial distribution and to identify the role of discharge variability in the variability of the total dissolved solid (TDS) flux through the hydrological cycle. Data are compiled from the monthly hydrochemistry and daily discharge database of the “Programa Climatologico y Hidrologico de la Cuenca Amazonica de Bolivia” (PHICAB) and the HYBAM observatories from 34 stations distributed over the Amazon basin (for the 1983–1992 and 2000–2012 periods, respectively). This paper consists of a first global observation of the fluxes and temporal dynamics of each geomorphological domain of the Amazon basin. Based on mean interannual monthly flux calculations, we estimated that the Amazon basin delivered approximately 272 × 106 t year−1 (263–278) of TDS during the 2003–2012 period, which represents approximately 7 % of the continental inputs to the oceans. This flux is mainly made up by HCO3, Ca and SiO2, reflecting the preferential contributions of carbonate and silicate chemical weathering to the Amazon River Basin. The main tributaries contributing to the TDS flux are the Marañon and Ucayali Rivers (approximately 50 % of the TDS production over 14 % of the Amazon basin area) due to the weathering of carbonates and evaporites drained by their Andean tributaries. An Andes–sedimentary area–shield TDS flux (and specific flux) gradient is observed throughout the basin and is first explained by the TDS concentration contrast between these domains, rather than variability in runoff. This observation highlights that, under tropical context, the weathering flux repartition is primarily controlled by the geomorphological/geological setting and confirms that sedimentary areas are currently active in terms of the production of dissolved load. The log relationships of concentration vs discharge have been characterized over all the studied stations and for all elements. The analysis of the slope of the relationship within the selected contexts reveals that the variability in TDS flux is mainly controlled by the discharge variability throughout the hydrological year. At the outlet of the basin, a clockwise hysteresis is observed for TDS concentration and is mainly controlled by Ca and HCO3 hysteresis, highlighting the need for a sampling strategy with a monthly frequency to accurately determine the TDS fluxes of the basin. The evaporite dissolution flux tends to be constant, whereas dissolved load fluxes released from other sources (silicate weathering, carbonate weathering, biological and/or atmospheric inputs) are mainly driven by variability in discharge. These results suggest that past and further climate variability had or will have a direct impact on the variability of dissolved fluxes in the Amazon. Further studies need to be performed to better understand the processes controlling the dynamics of weathering fluxes and their applicability to present-day concentration–discharge relationships at longer timescales.

Keywords

Amazon basin Andes Sedimentary areas Large rivers Water chemistry Dissolved solid flux Weathering Hydrological variability 

Notes

Acknowledgments

We especially thank Daniel Ibarra (Stanford University) for his constructive recommendations under the review process. We also especially thank Dr. Julien Bouchez (CNRS-IPGP) for insightful discussions and his help to improve the manuscript. This work was funded by the French Institut de Recherche pour le Développement (IRD) and the French Institut des Sciences de l’Univers (INSU) through the SO-HYBAM Observatory. We especially thank Pascal Fraizy, Philippe Vauchel, William Santini, Elisa Armijos, Francis Sondag, Nore Arevalo, the Servicio Nacional de Meteorología e Hidrología—Lima, Peru and La Paz, Bolivia (SENAMHI), the Instituto Nacional de Meteorología e Hidrología—Quito, Ecuador (INAMHI), Agência Nacional de Águas—Brasília, Brazil (ANA), the Universidad Nacional Agraria de La Molina—Lima, Peru (UNALM), the Universidad Mayor de San Andres—La Paz, Bolivia (UMSA), Universidade de Brasília—Brazil (UNB), Universidade do Estado de Amazonas—Manaus, Brazil (UEA) and all members of the SO-HYBAM (Hydrogeodynamics of the Amazon basin), for providing hydrological and water chemistry data.

Supplementary material

11356_2015_5503_Fig10_ESM.jpg (276 kb)
Fig. S1

Monthly frequency solutes measurements (small symbols) and monthly averages (large symbols and lines) plotted against discharge and averages discharge, respectively, at Obidos gauging station (the discharge of the sampled date is considered here). Simple dilution curves (concentration variability of a constant flux) are added for reference. (JPEG 276 kb)

11356_2015_5503_MOESM1_ESM.docx (19 kb)
Table S1 Interannual mensual flux calculation since concentration (C) and discharge (Q) data. (Modified from Moatar et al. 2009) (DOCX 18 kb)
11356_2015_5503_MOESM2_ESM.docx (16 kb)
Table S2 Calculation of annual fluxes at OBI and ITA stations following the flux calculation methods reported in Table S1 for the period 2003–2012. The TDS flux at ALT has been calculated from the average of the TDS concentration of the 2 samples collected at ALT multiplied by the yearly discharge the TDS flux at this station is 5.8 × 106 t year−1. The Amazon flux corresponds to the sum of OBI, ITA and ALT TDS fluxes. Even considering a 100 % error at ALT gauging station, it would not significantly influence the Amazon budget because this river contributes only to around 2 % to the Amazon TDS production. (DOCX 16 kb)
11356_2015_5503_MOESM3_ESM.docx (38 kb)
Table S3 Linear regression parameters describing solutes concentration (mmol L−1) and conductivity (μS cm−1) as a function of the discharge (m3 s−1) (Eq. 1). Errors indicate ±1 standard error (SE). The MAE% corresponds to the mean absolute percentage error. (DOCX 38.0 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Jean-Sébastien Moquet
    • 1
  • Jean-Loup Guyot
    • 1
    • 2
  • Alain Crave
    • 3
  • Jérôme Viers
    • 1
  • Naziano Filizola
    • 4
  • Jean-Michel Martinez
    • 1
  • Tereza Cristina Oliveira
    • 4
  • Liz Stefanny Hidalgo Sánchez
    • 5
    • 6
  • Christelle Lagane
    • 1
  • Waldo Sven Lavado Casimiro
    • 6
  • Luis Noriega
    • 7
  • Rodrigo Pombosa
    • 8
  1. 1.Geosciences Environnement Toulouse / Observatoire Midi-Pyrénées, CNRS/IRD/Université Paul SabatierToulouseFrance
  2. 2.IRDLimaPeru
  3. 3.Géosciences Rennes (UMR CNRS 6118)/OSURUniversité de Rennes 1Rennes CedexFrance
  4. 4.LAPA (Laboratório de Potamologia da Amazônia)Universidade Federal do AmazonasManausBrazil
  5. 5.Universidade Federal do AmazonasManausBrazil
  6. 6.SENAMHILimaPeru
  7. 7.SENAMHILa PazBolivia
  8. 8.INAMHIQuitoEcuador

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