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

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

Capabilities and Choices: Do They Make Sen'Se for Understanding Objective and Subjective Well-Being?: An Empirical Test of Sen's Capability Framework on German and British Panel Data

In Sen's Capability Approach (CA) well-being can be defined as the freedom of choice to achieve the things in life which one has reason to value most for his or her personal life. Capabilities are in Sen's vocabulary therefore the real freedoms people have or the opportunities available to them. In this paper we examine the impact of capabilities alongside choices on subjective and objective well-being. There is a lot of theoretical work on Sen's capability framework but still a lack of empirical research in measuring and testing his capability model especially in a dynamic perspective. The aim of the paper is to elaborate and test a "stock-flow" model measuring capabilities and choices to explain longer-term changes in well-being using 25 years of German and 18 years of British data. Three measures of well-being are constructed: life satisfaction for subjective well-being (SWB) and relative income and employment security for objective well-being (OWB). We ran random and fixed effects GLS models. The findings strongly support Sen's capabilities framework and provide new evidence on the way capabilities and choices matter for well-being. Capabilities indicated by human capital, trust, altruism and risk taking, and family, work-leisure, lifestyle and social choices show to strongly affect the three well-being indicators but their effect sizes differ largely dependent on the type of indicator used

Late Pleistocene Upwelling Variability on the Pacific Shelf of Baja California and in the Santa Barbara Basin

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