Sensitivity of fluvial sediment source apportionment to mixing model assumptions: A Bayesian model comparison

Cooper, Richard J., Krueger, Tobias, Hiscock, Kevin M. and Rawlins, Barry G. (2014) Sensitivity of fluvial sediment source apportionment to mixing model assumptions: A Bayesian model comparison. Water Resources Research, 50 (11). pp. 9031-9047. ISSN 0043-1397

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Abstract

Mixing models have become increasingly common tools for apportioning fluvial sediment load to various sediment sources across catchments using a wide variety of Bayesian and frequentist modeling approaches. In this study, we demonstrate how different model setups can impact upon resulting source apportionment estimates in a Bayesian framework via a one-factor-at-a-time (OFAT) sensitivity analysis. We formulate 13 versions of a mixing model, each with different error assumptions and model structural choices, and apply them to sediment geochemistry data from the River Blackwater, Norfolk, UK, to apportion suspended particulate matter (SPM) contributions from three sources (arable topsoils, road verges, and subsurface material) under base flow conditions between August 2012 and August 2013. Whilst all 13 models estimate subsurface sources to be the largest contributor of SPM (median ∼76%), comparison of apportionment estimates reveal varying degrees of sensitivity to changing priors, inclusion of covariance terms, incorporation of time-variant distributions, and methods of proportion characterization. We also demonstrate differences in apportionment results between a full and an empirical Bayesian setup, and between a Bayesian and a frequentist optimization approach. This OFAT sensitivity analysis reveals that mixing model structural choices and error assumptions can significantly impact upon sediment source apportionment results, with estimated median contributions in this study varying by up to 21% between model versions. Users of mixing models are therefore strongly advised to carefully consider and justify their choice of model structure prior to conducting sediment source apportionment investigations.

Item Type: Article
Additional Information: © 2014. The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords: bayesian,mixing model,fingerprinting,optimization,sensitivity analysis,sediment
Faculty \ School: Faculty of Science
Faculty of Science > School of Environmental Sciences
Depositing User: Pure Connector
Date Deposited: 19 Jan 2015 16:54
Last Modified: 22 Jul 2020 00:07
URI: https://ueaeprints.uea.ac.uk/id/eprint/51892
DOI: 10.1002/2014WR016194

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