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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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: Nature Publishing Group 2018-08-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-018-30905-9

Internet

https://doi.org/10.1038/s41598-018-30905-9

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