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

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

Abstract 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) t...

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Autores principales: William H. Blake, Pascal Boeckx, Brian C. Stock, Hugh G. Smith, Samuel Bodé, Hari R. Upadhayay, Leticia Gaspar, Rupert Goddard, Amy T. Lennard, Ivan Lizaga, David A. Lobb, Philip N. Owens, Ellen L. Petticrew, Zou Zou A. Kuzyk, Bayu D. Gari, Linus Munishi, Kelvin Mtei, Amsalu Nebiyu, Lionel Mabit, Ana Navas, Brice X. Semmens
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2018
Materias:
Medicine
R
Science
Q
Acceso en línea:https://doaj.org/article/bd359d5009f242559baa150c94ec9127
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https://doaj.org/article/bd359d5009f242559baa150c94ec9127

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