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) to underpin sustainable management of soil and sediment. This new mixing model approach allows users to directly account for the 'structural hierarchy' of a river basin in terms of sub-watershed distribution. It works by deconvoluting apportionment data derived for multiple nodes along the stream-river network where sources are stratified by sub-watershed. Source and mixture samples were collected from two watersheds that represented (i) a longitudinal mixed agricultural watershed in the south west of Eng

SUBMITTER: Blake WH 

PROVIDER: S-EPMC6117284 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature