A deconvolutional Bayesian mixing model approach for river basin sediment source apportionment
A deconvolutional Bayesian mixing model approach for river basin sediment source apportionment
About this item
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Author / Creator
Blake, William H. , Boeckx, Pascal , Stock, Brian C. , Smith, Hugh G. , Bodé, Samuel , Upadhayay, Hari R. , Gaspar, Leticia , Goddard, Rupert , Lennard, Amy T. , Lizaga, Ivan , Lobb, David A. , Owens, Philip N. , Petticrew, Ellen L. , Kuzyk, Zou Zou A. , Gari, Bayu D. , Munishi, Linus , Mtei, Kelvin , Nebiyu, Amsalu , Mabit, Lionel , Navas, Ana and Semmens, Brice X.
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
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Publication information
Publisher
London: Nature Publishing Group UK
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More information
Scope and Contents
Contents
Increasing complexity in human-environment interactions at multiple watershed scales presents major challenges to sediment source apportionment data acquisition and analysis. Herein, we present a step-change in the application of Bayesian mixing models: Deconvolutional-MixSIAR (D-MIXSIAR) to underpin sustainable management of soil and sediment. Thi...
Alternative Titles
Full title
A deconvolutional Bayesian mixing model approach for river basin sediment source apportionment
Authors, Artists and Contributors
Author / Creator
Boeckx, Pascal
Stock, Brian C.
Smith, Hugh G.
Bodé, Samuel
Upadhayay, Hari R.
Gaspar, Leticia
Goddard, Rupert
Lennard, Amy T.
Lizaga, Ivan
Lobb, David A.
Owens, Philip N.
Petticrew, Ellen L.
Kuzyk, Zou Zou A.
Gari, Bayu D.
Munishi, Linus
Mtei, Kelvin
Nebiyu, Amsalu
Mabit, Lionel
Navas, Ana
Semmens, Brice X.
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_bd359d5009f242559baa150c94ec9127
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_bd359d5009f242559baa150c94ec9127
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-018-30905-9
