Browsing by Author "Nebiyu, Amsalu"

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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.
Tackling soil degradation and environmental changes in Lake Manyara Basin, Tanzania to support sustainable landscape/ecosystem management.
(EGU General Assembly Conference Abstracts, 2017-04) Munishi, Linus; Mtei, Kelvin; Bode, Samuel; Dume, Bayu; Navas, Ana; Nebiyu, Amsalu; Semmens, Brice; Smith, Hugh; Stock, Brian; Boeckx, Pascal; Blake, Will
The Lake Manyara Basin (LMB), which encompasses Lake Manyara National Park a world ranking World Biosphere Reserve, is of great ecological and socio-economic value because it hosts a small-holder rain fed and extensive irrigation agriculture, grazing grounds for pastoralists, terrestrial and aquatic habitat for wildlife and tourism business contributing to poverty alleviation. Despite these multiple ecosystem services that support the local communities, the LMB is threatened by; (a) siltation from eroded soil fed from the wider catchment and rift escarpment of the basin and (b) declining water levels due to water capture by agriculture and possibly climate change. These threats to the ecosystem and its services are augmented by increasing human population, pollution by agricultural pesticides, poaching, human encroachment and infrastructure development, and illegal fisheries. Despite these challenges, here is a dearth of information on erosion hotspots and to date soil erosion and siltation problems in LMB have been interpreted largely in qualitative terms, and no coherent interpretative framework of these records exists. Despite concerns that modern sediment fluxes to the Lake may exceed long-term fluxes, little is known about erosion sources, how erosion rates and processes vary across the landscape and how erosion rates are influenced by the strong climate gradients in the basin. This contribution describes a soil erosion and sediment management project that aims to deliver a demonstration dataset generated from inter-disciplinary sediment-source tracing technologies and approaches to assess erosion hotspots, processes and spatial patterns of erosion in the area. The work focuses on a sub basin, the Monduli Sub catchment, located within the greater LMB. This is part of efforts to establish an understanding of soil erosion and landscape degradation in the basin as a pathway for generating and developing knowledge, building capacity to assist conservationists, farmers and pastoralists, agro-entrepreneurs, and their support agents to address the problems while feeding the information into the national development policies in Tanzania and the entire East African region.