xmlui.dri2xhtml.structural.head-subtitle

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

Show simple item record

dc.contributor.author Blake, William H.
dc.contributor.author Boeckx, Pascal
dc.contributor.author Stock, Brian C.
dc.contributor.author Smith, Hugh G.
dc.contributor.author Bodé, Samuel
dc.contributor.author Upadhayay, Hari R.
dc.contributor.author Gaspar, Leticia
dc.contributor.author Goddard, Rupert
dc.contributor.author Lennard, Amy T.
dc.contributor.author Lizaga, Ivan
dc.contributor.author Lobb, David A.
dc.contributor.author Owens, Philip N.
dc.contributor.author Petticrew, Ellen L.
dc.contributor.author Kuzyk, Zou Zou A.
dc.contributor.author Gari, Bayu D.
dc.contributor.author Munishi, Linus K.
dc.contributor.author Mtei, Kelvin M.
dc.contributor.author Nebiyu, Amsalu
dc.contributor.author Mabit, Lionel
dc.contributor.author Navas, Ana
dc.contributor.author Semmens, Brice X.
dc.date.accessioned 2019-05-21T12:16:23Z
dc.date.available 2019-05-21T12:16:23Z
dc.date.issued 2018-08-30
dc.identifier.uri | DOI:10.1038/s41598-018-30905-9
dc.identifier.uri http://dspace.nm-aist.ac.tz/handle/123456789/123
dc.description Research Article published by Scientific Reports en_US
dc.description.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 England which had a distinct upper and lower zone related to topography and (ii) a distributed mixed agricultural and forested watershed in the mid-hills of Nepal with two distinct sub-watersheds. In the former, geochemical fingerprints were based upon weathering profiles and anthropogenic soil amendments. In the latter compound-specific stable isotope markers based on soil vegetation cover were applied. Mixing model posterior distributions of proportional sediment source contributions differed when sources were pooled across the watersheds (pooled-MixSIAR) compared to those where source terms were stratified by sub-watershed and the outputs deconvoluted (D-MixSIAR). In the first example, the stratified source data and the deconvolutional approach provided greater distinction between pasture and cultivated topsoil source signatures resulting in a different posterior distribution to non-deconvolutional model (conventional approaches over-estimated the contribution of cultivated land to downstream sediment by 2 to 5 times). In the second example, the deconvolutional model elucidated a large input of sediment delivered from a small tributary resulting in differences in the reported contribution of a discrete mixed forest source. Overall D-MixSIAR model posterior distributions had lower (by ca 25–50%) uncertainty and quicker model run times. In both cases, the structured, deconvoluted output cohered more closely with field observations and local knowledge underpinning the need for closer attention to hierarchy in source and mixture terms in river basin source apportionment. Soil erosion and siltation challenge the energy-food-water-environment nexus. This new tool for source apportionment offers wider application across complex environmental systems affected by natural and human-induced change and the lessons learned are relevant to source apportionment applications in other disciplines. en_US
dc.language.iso en_US en_US
dc.publisher Scientific Reports en_US
dc.subject Research Subject Categories::FORESTRY, AGRICULTURAL SCIENCES and LANDSCAPE PLANNING en_US
dc.title A deconvolutional Bayesian mixing model approach for river basin sediment source apportionment en_US
dc.type Article en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search Our NM-AIST IR


Browse NM-AIST IR

My NM-AIST IR Account

Statistics