River mixing in the Amazon as a driver of concentration‐discharge relationships

Descripción del Articulo

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‐...

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Detalles Bibliográficos
Autores: Bouchez, Julien, Moquet, Jean Sébastien, Espinoza, Jhan Carlo, Martinez, Jean‐Michel, Guyot, Jean‐Loup, Lagane, Christelle, Filizola, Naziano, Noriega, Luis, Hidalgo Sánchez, Liz, Pombosa, Rodrigo
Formato: artículo
Fecha de Publicación:2017
Institución:Instituto Geofísico del Perú
Repositorio:IGP-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.igp.gob.pe:20.500.12816/2281
Enlace del recurso:http://hdl.handle.net/20.500.12816/2281
https://doi.org/10.1002/2017WR020591
Nivel de acceso:acceso abierto
Materia:Concentration‐discharge (C‐Q) relationships
Amazon River
C‐Q hysteresis loops
Tributary mixing
Spectral analysis
http://purl.org/pe-repo/ocde/ford#1.05.00
http://purl.org/pe-repo/ocde/ford#1.05.09
http://purl.org/pe-repo/ocde/ford#1.05.11
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dc.title.none.fl_str_mv River mixing in the Amazon as a driver of concentration‐discharge relationships
title River mixing in the Amazon as a driver of concentration‐discharge relationships
spellingShingle River mixing in the Amazon as a driver of concentration‐discharge relationships
Bouchez, Julien
Concentration‐discharge (C‐Q) relationships
Amazon River
C‐Q hysteresis loops
Tributary mixing
Spectral analysis
http://purl.org/pe-repo/ocde/ford#1.05.00
http://purl.org/pe-repo/ocde/ford#1.05.09
http://purl.org/pe-repo/ocde/ford#1.05.11
title_short River mixing in the Amazon as a driver of concentration‐discharge relationships
title_full River mixing in the Amazon as a driver of concentration‐discharge relationships
title_fullStr River mixing in the Amazon as a driver of concentration‐discharge relationships
title_full_unstemmed River mixing in the Amazon as a driver of concentration‐discharge relationships
title_sort River mixing in the Amazon as a driver of concentration‐discharge relationships
author Bouchez, Julien
author_facet Bouchez, Julien
Moquet, Jean Sébastien
Espinoza, Jhan Carlo
Martinez, Jean‐Michel
Guyot, Jean‐Loup
Lagane, Christelle
Filizola, Naziano
Noriega, Luis
Hidalgo Sánchez, Liz
Pombosa, Rodrigo
author_role author
author2 Moquet, Jean Sébastien
Espinoza, Jhan Carlo
Martinez, Jean‐Michel
Guyot, Jean‐Loup
Lagane, Christelle
Filizola, Naziano
Noriega, Luis
Hidalgo Sánchez, Liz
Pombosa, Rodrigo
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Bouchez, Julien
Moquet, Jean Sébastien
Espinoza, Jhan Carlo
Martinez, Jean‐Michel
Guyot, Jean‐Loup
Lagane, Christelle
Filizola, Naziano
Noriega, Luis
Hidalgo Sánchez, Liz
Pombosa, Rodrigo
dc.subject.none.fl_str_mv Concentration‐discharge (C‐Q) relationships
Amazon River
C‐Q hysteresis loops
Tributary mixing
Spectral analysis
topic Concentration‐discharge (C‐Q) relationships
Amazon River
C‐Q hysteresis loops
Tributary mixing
Spectral analysis
http://purl.org/pe-repo/ocde/ford#1.05.00
http://purl.org/pe-repo/ocde/ford#1.05.09
http://purl.org/pe-repo/ocde/ford#1.05.11
dc.subject.ocde.none.fl_str_mv http://purl.org/pe-repo/ocde/ford#1.05.00
http://purl.org/pe-repo/ocde/ford#1.05.09
http://purl.org/pe-repo/ocde/ford#1.05.11
description 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, Ca²⁺, Mg²⁺, K⁺, Na⁺, Cl‐, urn:x-wiley:00431397:media:wrcr22891:wrcr22891-math-0001) 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 “chemostatic” 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.
publishDate 2017
dc.date.accessioned.none.fl_str_mv 2018-08-07T11:36:31Z
dc.date.available.none.fl_str_mv 2018-08-07T11:36:31Z
dc.date.issued.fl_str_mv 2017-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.citation.none.fl_str_mv Bouchez, J., Moquet, J. -S., Espinoza, J. C., Martinez, J.‐M., Guyot, J. ‐L., Lagane, C., ... Pombosa, R. (2017). River mixing in the Amazon as a driver of concentration‐discharge relationships.==$Water Resources Research, 53$==(11), 8660-8685. https://doi.org/10.1002/2017WR020591
dc.identifier.govdoc.none.fl_str_mv index-oti2018
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12816/2281
dc.identifier.journal.none.fl_str_mv Water Resources Research
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1002/2017WR020591
identifier_str_mv Bouchez, J., Moquet, J. -S., Espinoza, J. C., Martinez, J.‐M., Guyot, J. ‐L., Lagane, C., ... Pombosa, R. (2017). River mixing in the Amazon as a driver of concentration‐discharge relationships.==$Water Resources Research, 53$==(11), 8660-8685. https://doi.org/10.1002/2017WR020591
index-oti2018
Water Resources Research
url http://hdl.handle.net/20.500.12816/2281
https://doi.org/10.1002/2017WR020591
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv urn:issn:0043-1397
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.uri.none.fl_str_mv https://creativecommons.org/licences/by/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licences/by/4.0/
dc.format.none.fl_str_mv application/pdf
dc.coverage.spatial.none.fl_str_mv Cuenca del río Amazonas
Perú
dc.publisher.none.fl_str_mv American Geophysical Union (AGU)
publisher.none.fl_str_mv American Geophysical Union (AGU)
dc.source.none.fl_str_mv reponame:IGP-Institucional
instname:Instituto Geofísico del Perú
instacron:IGP
instname_str Instituto Geofísico del Perú
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collection IGP-Institucional
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spelling Bouchez, JulienMoquet, Jean SébastienEspinoza, Jhan CarloMartinez, Jean‐MichelGuyot, Jean‐LoupLagane, ChristelleFilizola, NazianoNoriega, LuisHidalgo Sánchez, LizPombosa, RodrigoCuenca del río AmazonasPerú2018-08-07T11:36:31Z2018-08-07T11:36:31Z2017-11Bouchez, J., Moquet, J. -S., Espinoza, J. C., Martinez, J.‐M., Guyot, J. ‐L., Lagane, C., ... Pombosa, R. (2017). River mixing in the Amazon as a driver of concentration‐discharge relationships.==$Water Resources Research, 53$==(11), 8660-8685. https://doi.org/10.1002/2017WR020591index-oti2018http://hdl.handle.net/20.500.12816/2281Water Resources Researchhttps://doi.org/10.1002/2017WR020591Large 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, Ca²⁺, Mg²⁺, K⁺, Na⁺, Cl‐, urn:x-wiley:00431397:media:wrcr22891:wrcr22891-math-0001) 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 “chemostatic” 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.Por paresapplication/pdfengAmerican Geophysical Union (AGU)urn:issn:0043-1397info:eu-repo/semantics/openAccesshttps://creativecommons.org/licences/by/4.0/Concentration‐discharge (C‐Q) relationshipsAmazon RiverC‐Q hysteresis loopsTributary mixingSpectral analysishttp://purl.org/pe-repo/ocde/ford#1.05.00http://purl.org/pe-repo/ocde/ford#1.05.09http://purl.org/pe-repo/ocde/ford#1.05.11River mixing in the Amazon as a driver of concentration‐discharge relationshipsinfo:eu-repo/semantics/articlereponame:IGP-Institucionalinstname:Instituto Geofísico del Perúinstacron:IGPORIGINALbouchez2017.pdfbouchez2017.pdfapplication/pdf3181865https://repositorio.igp.gob.pe/bitstreams/59561bdf-08de-4f95-a91b-adba0dc633f2/download8c05af1230d9f0a2f1b87373de1b36baMD53LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.igp.gob.pe/bitstreams/57558c08-1b81-4ca2-bde6-adb3e0968746/download8a4605be74aa9ea9d79846c1fba20a33MD52THUMBNAILbouchez2017.pdf.jpgbouchez2017.pdf.jpgIM Thumbnailimage/jpeg99394https://repositorio.igp.gob.pe/bitstreams/46959afb-2fec-4602-ae46-7dcb269ca52d/download46160c800490369692f51814478a6adeMD54TEXTbouchez2017.pdf.txtbouchez2017.pdf.txtExtracted texttext/plain91579https://repositorio.igp.gob.pe/bitstreams/d426bd1a-feab-4244-a4d5-e5c6cc1f5a02/download6c0908a7aa3e046aa90965c6746cdad8MD5520.500.12816/2281oai:repositorio.igp.gob.pe:20.500.12816/22812024-10-01 16:35:49.15https://creativecommons.org/licences/by/4.0/info:eu-repo/semantics/openAccessrestrictedhttps://repositorio.igp.gob.peRepositorio Geofísico Nacionalbiblio@igp.gob.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