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

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dc.creatorBlake, William
dc.creatorBoeckx, Pascal
dc.creatorStock, Brian
dc.creatorSmith, Hugh
dc.creatorBodé, Samuel
dc.creatorUpadhayay, Hari
dc.creatorGaspar, Leticia
dc.creatorGoddard, Rupert
dc.creatorLennard, Amy
dc.creatorLizaga, Ivan
dc.creatorLobb, David
dc.creatorOwens, Philip
dc.creatorPetticrew, Ellen
dc.creatorKuzyk, Zou
dc.creatorGari, Bayu
dc.creatorMunishi, Linus
dc.creatorMtei, Kelvin
dc.creatorNebiyu, Amsalu
dc.creatorMabit, Lionel
dc.creatorNavas, Ana
dc.creatorSemmens, Brice
dc.date2019-05-21T12:16:23Z
dc.date2019-05-21T12:16:23Z
dc.date2018-08-30
dc.date.accessioned2022-10-25T09:20:35Z
dc.date.available2022-10-25T09:20:35Z
dc.descriptionResearch Article published by Scientific Reports
dc.descriptionIncreasing 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.
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dc.identifier| DOI:10.1038/s41598-018-30905-9
dc.identifierhttp://dspace.nm-aist.ac.tz/handle/123456789/123
dc.identifier.urihttp://hdl.handle.net/123456789/95070
dc.languageen_US
dc.publisherScientific Reports
dc.subjectResearch Subject Categories::FORESTRY, AGRICULTURAL SCIENCES and LANDSCAPE PLANNING
dc.titleA deconvolutional Bayesian mixing model approach for river basin sediment source apportionment
dc.typeArticle

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