A deconvolutional Bayesian mixing model approach for river basin sediment source apportionment

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 underpi...

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Detalles Bibliográficos
Autores principales: 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, Semmens, Brice X.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6117284/
https://www.ncbi.nlm.nih.gov/pubmed/30166587
http://dx.doi.org/10.1038/s41598-018-30905-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6117284/
https://www.ncbi.nlm.nih.gov/pubmed/30166587
http://dx.doi.org/10.1038/s41598-018-30905-9

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