Browsing by Author "Bodé, Samuel"

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A deconvolutional Bayesian mixing model approach for river basin sediment source apportionment
(Scientific Reports, 2018-08-30) Blake, William; Boeckx, Pascal; Stock, Brian; Smith, Hugh; Bodé, Samuel; Upadhayay, Hari; Gaspar, Leticia; Goddard, Rupert; Lennard, Amy; Lizaga, Ivan; Lobb, David; Owens, Philip; Petticrew, Ellen; Kuzyk, Zou; Gari, Bayu; Munishi, Linus; Mtei, Kelvin; Nebiyu, Amsalu; Mabit, Lionel; Navas, Ana; Semmens, Brice
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.
Soil erosion and sediment transport in Tanzania: Part I – sediment source tracing in three neighbouring river catchments
(Earth Surface Processes and Landforms, 2021-12) Wynants, Maarten; Munishi, Linus; Mtei, Kelvin; Bodé, Samuel; Patrick, Aloyce; Taylor, Alex; Gilvear, David; Ndakidemi, Patrick; Blake, William; Boeckx, Pascal
Water bodies in Tanzania are experiencing increased siltation, which is threatening water quality, ecosystem health, and livelihood security in the region. This phenomenon is caused by increasing rates of upstream soil erosion and downstream sediment transport. However, a lack of knowledge on the contributions from different catchment zones, land-use types, and dominant erosion processes, to the transported sediment is undermining the mitigation of soil degradation at the source of the problem. In this context, complementary sediment source tracing techniques were applied in three Tanzanian river systems to further the understanding of the complex dynamics of soil erosion and sediment transport in the region. Analysis of the geochemical and biochemical fingerprints revealed a highly complex and variable soil system that could be grouped in distinct classes. These soil classes were unmixed against riverine sediment fingerprints using the Bayesian MixSIAR model, yielding proportionate source contributions for each catchment. This sediment source tracing indicated that hillslope erosion on the open rangelands and maize croplands in the mid-zone contributed over 75% of the transported sediment load in all three river systems during the sampling time-period. By integrating geochemical and biochemical fingerprints in sediment source tracing techniques, this study demonstrated links between land use, soil erosion and downstream sediment transport in Tanzania. This evidence can guide land managers in designing targeted interventions that safeguard both soil health and water quality