Consensus ranking as a method to identify non-conservative and dissenting tracers in fingerprinting studies

27 Pags.- 9 Figs. The definitive version is available at: https://www.sciencedirect.com/science/journal/00489697

Detalles Bibliográficos
Autores: Lizaga Villuendas, Iván, Latorre Garcés, Borja, Gaspar Ferrer, Leticia, Navas Izquierdo, Ana
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2020
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/210171
Acceso en línea:http://hdl.handle.net/10261/210171
Access Level:acceso abierto
Palabra clave:sediment fingerprinting
tracer selection
Consensus ranking
Artificial mixture
conservativeness index
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spelling Consensus ranking as a method to identify non-conservative and dissenting tracers in fingerprinting studiesLizaga Villuendas, IvánLatorre Garcés, BorjaGaspar Ferrer, LeticiaNavas Izquierdo, Anasediment fingerprintingtracer selectionConsensus rankingArtificial mixtureconservativeness index27 Pags.- 9 Figs. The definitive version is available at: https://www.sciencedirect.com/science/journal/00489697Soil erosion and fine particle transport are two of the major challenges in food security and water quality for the growing global population. Information of the areas prone to erosion is needed to prevent the release of pollutants and the loss of nutrients. Sediment fingerprinting is becoming a widely used tool to tackle this problem, allowing to identify the sources of sediments in a catchment. Methods in fingerprinting techniques are still under discussion with tracer selection at the centre of the debate. We propose a novel method, termed as consensus ranking (CR), that combines the predictions of single-tracer models to identify non-conservative tracers. In this context, a numerical procedure to quantify the predictions of individual tracers is first delivered. The scoring function to rank the tracers is based on several random debates between tracers in which the tracer that prevents consensus is discarded. Based on these results, a conservativeness index (CI) is presented along with a clustering method to identify groups of similar tracers. To illustrate the CI and CR procedures, an artificial mixture created with real soil to independently test the method is analysed. The results demonstrate the capability of our method to identify non-conservative tracers beyond the capability of currently used selection methods. Further, a real sediment sample from a Mediterranean mountain catchment is evaluated to emphasise its utility in complex natural environments. To test the utility of our method, it was decided to include the conservative and consensus-enforcing tracers extracted by this new approach with two different unmixing models. Furthermore, CR and CI procedures are displayed together with the most widespread statistical tests and the within-a-polygon approach used for tracer selection in fingerprinting studies. The new proposed method will enable the research community to homogenise results for replicability as well as allowing comparisons among study areas.This research was financially supported by the project TRAZESCAR (CGL2014-52986-R) and the aid of a predoctoral contract (BES-2015-071780), funded by the Spanish Ministry of Science and Innovation.Peer reviewedElsevierMinisterio de Ciencia e Innovación (España)Latorre Garcés, Borja [0000-0002-6720-3326]Gaspar Ferrer, Leticia [0000-0002-3473-7110]Navas Izquierdo, Ana [0000-0002-4724-7532]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202020202020info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/210171reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/CGL2014-52986-Rhttps://doi.org/10.1016/j.scitotenv.2020.137537Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2101712026-05-22T06:33:51Z
dc.title.none.fl_str_mv Consensus ranking as a method to identify non-conservative and dissenting tracers in fingerprinting studies
title Consensus ranking as a method to identify non-conservative and dissenting tracers in fingerprinting studies
spellingShingle Consensus ranking as a method to identify non-conservative and dissenting tracers in fingerprinting studies
Lizaga Villuendas, Iván
sediment fingerprinting
tracer selection
Consensus ranking
Artificial mixture
conservativeness index
title_short Consensus ranking as a method to identify non-conservative and dissenting tracers in fingerprinting studies
title_full Consensus ranking as a method to identify non-conservative and dissenting tracers in fingerprinting studies
title_fullStr Consensus ranking as a method to identify non-conservative and dissenting tracers in fingerprinting studies
title_full_unstemmed Consensus ranking as a method to identify non-conservative and dissenting tracers in fingerprinting studies
title_sort Consensus ranking as a method to identify non-conservative and dissenting tracers in fingerprinting studies
dc.creator.none.fl_str_mv Lizaga Villuendas, Iván
Latorre Garcés, Borja
Gaspar Ferrer, Leticia
Navas Izquierdo, Ana
author Lizaga Villuendas, Iván
author_facet Lizaga Villuendas, Iván
Latorre Garcés, Borja
Gaspar Ferrer, Leticia
Navas Izquierdo, Ana
author_role author
author2 Latorre Garcés, Borja
Gaspar Ferrer, Leticia
Navas Izquierdo, Ana
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia e Innovación (España)
Latorre Garcés, Borja [0000-0002-6720-3326]
Gaspar Ferrer, Leticia [0000-0002-3473-7110]
Navas Izquierdo, Ana [0000-0002-4724-7532]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv sediment fingerprinting
tracer selection
Consensus ranking
Artificial mixture
conservativeness index
topic sediment fingerprinting
tracer selection
Consensus ranking
Artificial mixture
conservativeness index
description 27 Pags.- 9 Figs. The definitive version is available at: https://www.sciencedirect.com/science/journal/00489697
publishDate 2020
dc.date.none.fl_str_mv 2020
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/210171
url http://hdl.handle.net/10261/210171
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/CGL2014-52986-R
https://doi.org/10.1016/j.scitotenv.2020.137537
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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