The representation of sediment source group tracer distributions in Monte Carlo uncertainty routines for fingerprinting: An analysis of accuracy and precision using data for four contrasting catchments

Previous studies comparing sediment fingerprinting un‐mixing models report large differences in their accuracy. The representation of tracer concentrations in source groups is perhaps the largest difference between published studies. However, the importance of decisions concerning the representation of tracer distributions has not been explored explicitly. Accordingly, potential sediment sources in four contrasting catchments were intensively sampled. Virtual sample mixtures were formed using between 10 and 100% of the retrieved samples to simulate sediment mobilization and delivery from subsections of each catchment. Source apportionment used models with a transformed multivariate normal distribution, normal distribution, 25th–75th percentile distribution and a distribution replicating the retrieved source samples. The accuracy and precision of model results were quantified and the reasons for differences were investigated. The 25th–75th percentile distribution produced the lowest mean inaccuracy (8.8%) and imprecision (8.5%), with the Sample Based distribution being next best (11.5%; 9.3%). The transformed multivariate (16.9%; 17.3%) and untransformed normal distributions (16.3%; 20.8%) performed poorly. When only a small proportion of the source samples formed the virtual mixtures, accuracy decreased with the 25th–75th percentile and Sample Based distributions so that when <20% of source samples were used, the actual mixture composition infrequently fell outside of the range of uncertainty shown in un‐mixing model outputs. Poor performance was due to combined random Monte Carlo numbers generated for all tracers not being viable for the retrieved source samples. Trialling the use of a 25th–75th percentile distribution alongside alternatives may result in significant improvements in both accuracy and precision of fingerprinting estimates, evaluated using virtual mixtures. Caution should be exercised when using a normal type distribution, without exploration of alternatives, as un‐mixing model performance may be unacceptably poor. The representation of source group tracer concentrations is perhaps the largest difference between sediment fingerprinting un‐mixing models. Despite this, the effects of different distributions on model accuracy have not been explored explicitly. ‘This study compared a transformed multivariate normal, a normal and a 25th–75th percentile distribution as well as a distribution replicating the retrieved source samples. The 25th–75th percentile distribution produced the lowest mean inaccuracy (8.8%), with the Sample Based being next best (11.5%). The transformed multivariate (16.9%) and untransformed normal distributions (16.3%) performed poorly

Apportioning sources of organic matter in streambed sediments: An integrated molecular and compound-specific stable isotope approach

We present a novel application for quantitatively apportioning sources of organic matter in streambed sediments via a coupled molecular and compound-specific isotope analysis (CSIA) of long-chain leaf wax n-alkane biomarkers using a Bayesian mixing model. Leaf wax extracts of 13 plant species were collected from across two environments (aquatic and terrestrial) and four plant functional types (trees, herbaceous perennials, and C3 and C4 graminoids) from the agricultural River Wensum catchment, UK. Seven isotopic (δ13C27, δ13C29, δ13C31, δ13C27–31, δ2H27, δ2H29, and δ2H27–29) and two n-alkane ratio (average chain length (ACL), carbon preference index (CPI)) fingerprints were derived, which successfully differentiated 93% of individual plant specimens by plant functional type. The δ2H values were the strongest discriminators of plants originating from different functional groups, with trees (δ2H27–29 = − 208‰ to − 164‰) and C3 graminoids (δ2H27–29 = − 259‰ to − 221‰) providing the largest contrasts. The δ13C values provided strong discrimination between C3 (δ13C27–31 = − 37.5‰ to − 33.8‰) and C4 (δ13C27–31 = − 23.5‰ to − 23.1‰) plants, but neither δ13C nor δ2H values could uniquely differentiate aquatic and terrestrial species, emphasizing a stronger plant physiological/biochemical rather than environmental control over isotopic differences. ACL and CPI complemented isotopic discrimination, with significantly longer chain lengths recorded for trees and terrestrial plants compared with herbaceous perennials and aquatic species, respectively. Application of a comprehensive Bayesian mixing model for 18 streambed sediments collected between September 2013 and March 2014 revealed considerable temporal variability in the apportionment of organic matter sources. Median organic matter contributions ranged from 22% to 52% for trees, 29% to 50% for herbaceous perennials, 17% to 34% for C3 graminoids and 3% to 7% for C4 graminoids. The results presented here clearly demonstrate the effectiveness of an integrated molecular and stable isotope analysis for quantitatively apportioning, with uncertainty, plant-specific organic matter contributions to streambed sediments via a Bayesian mixing model approach

Sensitivity of source sediment fingerprinting to tracer selection methods

In a context of accelerated soil erosion and sediment supply to water bodies, sediment fingerprinting techniques have received an increasing interest in the last 2 decades. The selection of tracers is a particularly critical step for the subsequent accurate prediction of sediment source contributions. To select tracers, the most conventional approach is the three-step method, although, more recently, the consensus method has also been proposed as an alternative. The outputs of these two approaches were compared in terms of identification of conservative properties, tracer selection, modelled contributions and performance on a single dataset. As for the three-step method, several range test criteria were compared, along with the impact of the discriminant function analysis (DFA). The dataset was composed of tracer properties analysed in soil (three potential sources; n = 56) and sediment core samples (n = 32). Soil and sediment samples were sieved to 63 µm and analysed for organic matter, elemental geochemistry and diffuse visible spectrometry. Virtual mixtures (n = 138) with known source proportions were generated to assess model accuracy of each tracer selection method. The Bayesian un-mixing model MixSIAR was then used to predict source contributions on both virtual mixtures and actual sediments. The different methods tested in the current research can be distributed into three groups according to their sensitivity to the conservative behaviour of properties, which was found to be associated with different predicted source contribution tendencies along the sediment core. The methods selecting the largest number of tracers were associated with a dominant and constant contribution of forests to sediment. In contrast, the methods selecting the lowest number of tracers were associated with a dominant and constant contribution of cropland to sediment. Furthermore, the intermediate selection of tracers led to more balanced contributions of both cropland and forest to sediments. The prediction of the virtual mixtures allowed us to compute several evaluation metrics, which are generally used to support the evaluation of model accuracy for each tracer selection method. However, strong differences or the absence of correspondence were observed between the range of predicted contributions obtained for virtual mixtures and those values obtained for actual sediments. These divergences highlight the fact that evaluation metrics obtained for virtual mixtures may not be directly transferable to models run for actual samples and must be interpreted with caution to avoid over-interpretation or misinterpretation. These divergences may likely be attributed to the occurrence of a not (fully) conservative behaviour of potential tracer properties during erosion, transport and deposition processes, which could not be fully reproduced when generating the virtual mixtures with currently available methods. Future research should develop novel metrics to quantify the conservative behaviour of tracer properties during erosion and transport processes. Furthermore, new methods should be designed to generate virtual mixtures closer to reality and to better evaluate model accuracy. These improvements would contribute to the development of more reliable sediment fingerprinting techniques, which are needed to better support the implementation of effective soil and water conservation measures at the catchment scale.</p

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

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

A 300-year record of sedimentation in a small tilled catena in Hungary based on δ13C, δ15N, and C/N distribution

Purpose Soil erosion is one of the most serious hazards that endanger sustainable food production. Moreover, it has marked effects on soil organic carbon (SOC) with direct links to global warming. At the same time, soil organic matter (SOM) changes in composition and space could influence these processes. The aim of this study was to predict soil erosion and sedimentation volume and dynamics on a typical hilly cropland area of Hungary due to forest clearance in the early eighteenth century. Materials and methods Horizontal soil samples were taken along two parallel intensively cultivated complex convex-concave slopes from the eroded upper parts at mid-slope positions and from sedimentation in toe-slopes. Samples were measured for SOC, total nitrogen (TN) content, and SOMcompounds (δ13C, δ15N, and photometric indexes). They were compared to the horizons of an in situ non-eroded profile under continuous forest. On the depositional profile cores, soil depth prior to sedimentation was calculated by the determination of sediment thickness. Results and discussion Peaks of SOC in the sedimentation profiles indicated thicker initial profiles, while peaks in C/N ratio and δ13C distribution showed the original surface to be ~ 20 cm lower. Peaks of SOC were presumed to be the results of deposition of SOC-enriched soil from the upper slope transported by selective erosion of finer particles (silts and clays). Therefore, changes in δ13C values due to tillage and delivery would fingerprint the original surface much better under the sedimentation scenario than SOC content. Distribution of δ13C also suggests that the main sedimentation phase occurred immediately after forest clearance and before the start of intense cultivation with maize. Conclusions This highlights the role of relief in sheet erosion intensity compared to intensive cultivation. Patterns of δ13C indicate the original soil surface, even in profiles deposited as sediment centuries ago. The δ13C and C/N decrease in buried in situ profiles had the same tendency as recent forest soil, indicating constant SOM quality distribution after burial. Accordingly, microbiological activity, root uptake, and metabolism have not been effective enough to modify initial soil properties

The application of sediment fingerprinting to floodplain and lake sediment cores: assumptions and uncertainties evaluated through case studies in the Nene Basin, UK

Purpose: Fine sediment has been shown to be a major cause of the degradation of lakes and rivers and, as a result, research has been directed towards the understanding of fine sediment dynamics and the minimisation of sediment inputs. The use of tracers within a sediment fingerprinting framework has become a heavily used technique to investigate the sources of fine sediment pressures. When combined with the use of historically deposited sediment, the technique provides the opportunity to reconstruct past changes to the environment. However, alterations to tracer signatures during sediment transport and storage are a major potential source of uncertainty associated with tracer use. At present, few studies have quantified the uncertainties associated with tracer use. Materials and methods: This paper investigated uncertainty by determining the differences between sediment provenance predictions obtained using lithogenic radionuclide, geochemical and mineral magnetic signatures when fingerprinting lake and floodplain sedimentary deposits. It also investigated the potential causes of the observed differences. Results and discussion: A reservoir core was fingerprinted with the least uncertainty, with tracer group predictions ∼28 % apart and a consistent down-core trend in changing sediment provenance produced. When fingerprinting an on-line lake core and four floodplain cores, differences between tracer group predictions were as large as 100 %; the down-core trends in changing sediment provenance were also different. The differences between tracer group predictions could be attributed to the organic matter content and particle size of the sediment. There was also evidence of the in-growth of bacterially derived magnetite and chemical dissolution affecting the preservation of tracer signatures. Simple data corrections for sediment organic matter content and particle size did not result in significantly greater agreement between the predictions of the different tracer groups. Likewise, the inclusions of weightings for tracer discriminatory efficiency and within-source variability had minimal effects on the fingerprinting results. Conclusions: This paper highlights the importance of tracer selection and the consideration of recognising tracer non-conservatism when using lake and floodplain sediment deposits to reconstruct anthropogenic changes to the environment and changing sediment dynamics. It was recommended that future research focus on the assessment of uncertainty using the artificial mixing of sediment source samples, the limitation of the fingerprinting to narrow particle size fractions and the development of specific particle size and organic matter correction factors for each tracer

Sediment source fingerprinting: benchmarking recent outputs, remaining challenges and emerging themes

Abstract: Purpose: This review of sediment source fingerprinting assesses the current state-of-the-art, remaining challenges and emerging themes. It combines inputs from international scientists either with track records in the approach or with expertise relevant to progressing the science. Methods: Web of Science and Google Scholar were used to review published papers spanning the period 2013–2019, inclusive, to confirm publication trends in quantities of papers by study area country and the types of tracers used. The most recent (2018–2019, inclusive) papers were also benchmarked using a methodological decision-tree published in 2017. Scope: Areas requiring further research and international consensus on methodological detail are reviewed, and these comprise spatial variability in tracers and corresponding sampling implications for end-members, temporal variability in tracers and sampling implications for end-members and target sediment, tracer conservation and knowledge-based pre-selection, the physico-chemical basis for source discrimination and dissemination of fingerprinting results to stakeholders. Emerging themes are also discussed: novel tracers, concentration-dependence for biomarkers, combining sediment fingerprinting and age-dating, applications to sediment-bound pollutants, incorporation of supportive spatial information to augment discrimination and modelling, aeolian sediment source fingerprinting, integration with process-based models and development of open-access software tools for data processing. Conclusions: The popularity of sediment source fingerprinting continues on an upward trend globally, but with this growth comes issues surrounding lack of standardisation and procedural diversity. Nonetheless, the last 2 years have also evidenced growing uptake of critical requirements for robust applications and this review is intended to signpost investigators, both old and new, towards these benchmarks and remaining research challenges for, and emerging options for different applications of, the fingerprinting approach

The challenges and opportunities of addressing particle size effects in sediment source fingerprinting: A review

publisher: Elsevier articletitle: The challenges and opportunities of addressing particle size effects in sediment source fingerprinting: A review journaltitle: Earth-Science Reviews articlelink: http://dx.doi.org/10.1016/j.earscirev.2017.04.009 content_type: article copyright: © 2017 Elsevier B.V. All rights reserved

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