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

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.

Agricultural land is the main source of stream sediments after conversion of an African montane forest

“…The range test was used to exclude elements that do not differentiate sediment sources 44 . A target element concentration should be in range with the source mixing polygon showing whether a tracer on the target sediment is enriched or depleted compared to the sediment sources 35,105 .…”

HydroMix v1.0: a new Bayesian mixing framework for attributing uncertain hydrological sources

“…(1) is solved by assigning an average tracer concentration to each source, estimated typically from time or space-averages of observed field data (Maule et al, 1994;Winograd et al, 1998). Alternatively, Bayesian mixing models can be used, which explicitly acknowledge the variability of source tracer concentrations estimated from observed samples (Barbeta and Peñuelas, 2017;Blake et al, 2018). Rather than a single estimate of source contributions, Bayesian approaches yield full probability density functions (pdfs) of the fraction of different sources in the target mixture (Parnell et al, 2010;Stock et al, 2018), hereafter referred to as 'mixing ratios'.…”

“…Bayesian mixing was first developed in ecology to estimate the proportion of different food sources to animal diets (Parnell et al, 2010;Stock et al, 2018). Hydrological applications of such models are still rare (Blake et al, 2018;Evaristo et al, 2016Oerter et al, 2019). In a Bayesian mixing model, a statistical distribution is fitted to both the measured source tracer concentrations, and to the measured tracer concentrations from the target (e.g.…”

Determining contemporary and historical sediment sources in a large drainage basin impacted by cumulative effects: the regulated Nechako River, British Columbia, Canada

“…MixSIAR is a Bayesian mixing model framework that was originally designed to estimate the proportion of prey sources ingested by a predator based on biological tracer data; for a detailed description, see Parnell et al (2013). The effectiveness of Bayesian sediment fingerprinting models to estimate the proportion of sediment sources based on geochemical signatures has been demonstrated in several previous studies (e.g., Koiter et al 2013a;Cooper and Krueger 2017;Blake et al 2018;Liu et al 2018) and they have been evaluated positively by Davies et al (2018). The probabilistic Bayesian hierarchical model with Markov Chain Monte Carlo (MCMC) sampling was run using the JAGS software (Just Another Gibbs Sampler; v. 4.3.0; Plummer 2003) interfaced with the R software.…”