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

Testing for effects of growth rate on isotope trophic discrimination factors and evaluating the performance of Bayesian stable isotope mixing models experimentally: A moment of truth?

Discerning assimilated diets of wild animals using stable isotopes is well established where potential dietary items in food webs are isotopically distinct. With the advent of mixing models, and Bayesian extensions of such models (Bayesian Stable Isotope Mixing Models, BSIMMs), statistical techniques available for these efforts have been rapidly increasing. The accuracy with which BSIMMs quantify diet, however, depends on several factors including uncertainty in tissue discrimination factors (TDFs; Δ) and identification of appropriate error structures. Whereas performance of BSIMMs has mostly been evaluated with simulations, here we test the efficacy of BSIMMs by raising domestic broiler chicks (Gallus gallus domesticus) on four isotopically distinct diets under controlled environmental conditions, ideal for evaluating factors that affect TDFs and testing how BSIMMs allocate individual birds to diets that vary in isotopic similarity. For both liver and feather tissues, δ13C and δ 15N values differed among dietary groups. Δ13C of liver, but not feather, was negatively related to the rate at which individuals gained body mass. For Δ15N, we identified effects of dietary group, sex, and tissue type, as well as an interaction between sex and tissue type, with females having higher liver Δ15N relative to males. For both tissues, BSIMMs allocated most chicks to correct dietary groups, especially for models using combined TDFs rather than diet-specific TDFs, and those applying a multiplicative error structure. These findings provide new information on how biological processes affect TDFs and confirm that adequately accounting for variability in consumer isotopes is necessary to optimize performance of BSIMMs. Moreover, results demonstrate experimentally that these models reliably characterize consumed diets when appropriately parameterized.

Enrichment, bioaccumulation and human health assessment of organochlorine pesticides in sediments and edible fish of a plateau lake

The organochlorine pesticides (OCPs) are with features of persistence, high toxicity, bioaccumulation and adverse impact on ecosystems and human beings. Although OCPs pollutions have been observed in the plateau lakes, comprehensive understandings in the distribution characteristics and human health risks of OCPs in these valuable but fragile ecosystems are limited. We here investigated the distribution, bioaccumulation process and health risks of OCPs in the Jianhu lake, a representative plateau lake in China. The endrin ketone, endrin aldehyde and heptachlor were the most dominant species in surface and columnar sediments. Their total contents ranged between 0 ~ 1.92 × 103 ng·g-1. The distribution of OCPs in sediment cores combined with chronology information indicated that the fast accumulation of OCPs happened during the last decades. Combining the distribution features of OCPs in different sources with mixing model results of carbon isotope (δ13C), farming area was identified as the main source (46%), and the OCPs were transported to lake by inflow-rivers (37%). The enrichment of OCPs in sediments caused considerable bioaccumulation of OCPs in local fish (∑OCPs 0-3199.93 ng·g-1, dw) with the bio-sediment accumulation factor (BSAF) ranging from ND to 9.41. Moreover, growing time was another key factor governing the accumulation in specific species (Carassius auratus and Cyprinus carpio). Eventually, the carcinogenic risk index (CRI) and exposure risk index (ERI) of the endrin category and aldrin exceeded the reference value, indicating relatively high health risks through consumption of fish. Overall, this study systematically illustrated the bioaccumulation process and health risks of OCPs in the typical plateau lake, providing theoretical support for the better protection of this kind of lakes.

Impacts of tracer type, tracer selection, and source dominance on source apportionment with sediment fingerprinting

Sediment fingerprinting estimates the proportional contribution of fine sediment from distinct catchment sources delivered to downstream receiving environments. Increased attention has focused on assessing the accuracy of source contribution estimates, particularly in relation to tracer selection and statistical un-mixing procedures. However, no studies have systematically tested the impact of source combination or dominance on the accuracy of source estimates. Here, we assess sensitivity to tracer type, selection, and number of sources, and examine how variations in the dominant sediment source affect the accuracy of source apportionments using numerical mixtures. Sources were sampled according to erosion process and land cover from a New Zealand catchment. Topsoil and subsoil (landslide) samples were collected from pasture, harvested pine, kānuka scrub, and native forest, while banks were sampled along the main channel. Samples were analysed for bulk geochemistry, fallout radionuclides, and compound specific stable isotopes (CSSIs). Source apportionment accuracy tended to decrease as source number increased, which reflected decreasing source discrimination. Tracer selection showed variations in accuracy but exhibited no clear pattern overall. Source combination and particularly the dominant source had the largest impact on accuracy, reflecting the level of discrimination for each source. Notably, channel bank was frequently identified as the dominant source when using CSSI tracers. While this partly reflected lower levels of discrimination, the CSSI apportionment was particularly sensitive to the use of post-unmixing corrections routinely applied to derive soil proportional contributions from isotopic proportions. This sensitivity likely related to the low organic carbon content in bank material and the assumption that source apportionments based on isotopic proportions can be corrected using a linear relationship with organic carbon content. These results indicate that the use of CSSI tracers in catchments where erosion sources exhibit large differences in soil organic carbon content may introduce significant unquantified error in source apportionments.

Fingerprinting the spatial sources of fine-grained sediment deposited in the bed of the Mehran River, southern Iran

Accurate information on the sources of suspended sediment in riverine systems is essential to target mitigation. Accordingly, we applied a generalized likelihood uncertainty estimation (GLUE) framework for quantifying contributions from three sub-basin spatial sediment sources in the Mehran River catchment draining into the Persian Gulf, Hormozgan province, southern Iran. A total of 28 sediment samples were collected from the three sub-basin sources and six from the overall outlet. 43 geochemical elements (e.g., major, trace and rare earth elements) were measured in the samples. Four different combinations of statistical tests comprising: (1) traditional range test (TRT), Kruskal–Wallis (KW) H-test and stepwise discriminant function analysis (DFA) (TRT + KW + DFA); (2) traditional range test using mean values (RTM) and two additional tests (RTM + KW + DFA); (3) TRT + KW + PCA (principle component analysis), and; 4) RTM + KW + PCA, were used to the spatial sediment source discrimination. Tracer bi-plots were used as an additional step to assess the tracers selected in the different final composite signatures for source discrimination. The predictions of spatial source contributions generated by GLUE were assessed using statistical tests and virtual sample mixtures. On this basis, TRT + KW + DFA and RTM + KW + DFA yielded the best source discrimination and the tracers in these composite signatures were shown by the biplots to be broadly conservative during transportation from source to sink. Using these final two composite signatures, the estimated mean contributions for the western, central and eastern sub-basins, respectively, ranged between 10–60% (overall mean contribution 36%), 0.3–16% (overall mean contribution 6%) and 38–77% (overall mean contribution 58%). In comparison, the final tracers selected using TRT + KW + PCA generated respective corresponding contributions of 1–42% (overall mean 20%), 0.5–30% (overall mean 12%) and 55–84% (overall mean 68%) compared with 17–69% (overall mean 41%), 0.2–12% (overall mean 5%) and 29–76% (overall mean 54%) using the final tracers selected by RTM + KW + PCA. Based on the mean absolute fit (MAF; ≥ 95% for all target sediment samples) and goodness-of-fit (GOF; ≥ 99% for all samples), GLUE with the final tracers selected using TRT + KW + PCA performed slightly better than GLUE with the final signatures selected by the three other combinations of statistical tests. Based on the virtual mixture tests, however, predictions provided by GLUE with the final tracers selected using TRT + KW + DFA and RTM + KW + DFA (mean MAE = 11% and mean RMSE = 13%) performed marginally better than GLUE with RTM + KW + PCA (mean MAE = 14% and mean RMSE = 16%) and GLUE with TRT + KW + PCA (mean MAE = 17% and mean RMSE = 19%). The estimated source proportions can help watershed engineers plan the targeting of conservation programmes for soil and water resources.

Source identification of particulate organic carbon using stable isotopes and n-alkanes: modeling and application

Particulate organic carbon (POC) sources, which regulate dissolved organic carbon, sediment organic carbon, and inorganic carbon via deposition, degradation, and mineralization, play an important role in lake ecosystems. Linear or Bayesian algorithms on isotope and n-alkanes have been widely used to identify the source proportion of organic carbon. However, the applicability of these methods is ambiguous because of the unilateral advantages of each model and trace factors. To test the applicability of the various methods for identifying POC sources, we analyzed dual isotopes and n-alkanes in surface water samples of Lake Taihu, and Multi-source mixing model and Bayesian mixing model were used to distinguish between endogenous and exogenous contributions. Carbon isotope presented a clear advantage in West Taihu (-21.85 ± 0.78‰) and Southwest Taih (-22.61 ± 1.35‰); nitrogen isotope also showed high values in Meiliang Bay (9.76 ± 0.92‰). The majority of the lake was dominated by short-chain n-alkanes, except for East Taihu Lake (dominated by medium-chain n-alkanes) and areas with riverine input (dominated by long-chain n-alkanes). Different principles between the Bayesian mixing model (based on the Markov Chain Monte Carlo algorithm) and the Multi-source mixing model (based on linear estimation) caused discrepancies in the estimations of source contributions. But the fraction of chemical compounds during the migration process, and the overlap of potential sources play important role in the inconsistency of results. The estimations from the different models were consistent in indicating the dominance of endogenous organic carbon in Lake Taihu (mean of 60.18 ± 20.26%), particularly in the north and western regions (West Taihu, Meiliang Bay, and Southwest Taihu). This was likely due to algal aggregation influenced by human activities and climatic factors.

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

In many parts of Africa, soil erosion is an important problem, which is evident from high sediment yields in tropical montane streams. Previous studies in Kenya pointed to a large contribution from catchments cultivated by smallholder farmers. This led to the hypothesis that unpaved tracks and gullies are the main sediment sources in smallholder agriculture catchments of the highlands of Kenya. The aim of this study was to investigate the sediment sources with sediment fingerprinting to generate the knowledge base to improve land management and to reduce sediment yields. Four main sediment sources (agricultural land, unpaved tracks, gullies and channel banks) and suspended sediments were analysed for biogeochemical elements as potential tracers. To apportion the catchments target sediment to different sources, we applied the MixSIAR un-mixing modelling under a Bayesian framework. Surprisingly, the fingerprinting analysis showed that agricultural land accounted for 75% (95% confidence interval 63-86%) of the total sediment. Channel banks contributed 21% (8-32%), while the smallest contributions to sediment were generated by the unpaved tracks and gullies with 3% (0-12%) and 1% (0-4%), respectively. Erosion management strategies should target agricultural lands with an emphasis on disconnecting unpaved tracks form hillslope source areas to reduce sediment yields to Lake Victoria.

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.

Benchmarking inference methods for water quality monitoring and status classification

River water quality monitoring at limited temporal resolution can lead to imprecise and inaccurate classification of physicochemical status due to sampling error. Bayesian inference allows for the quantification of this uncertainty, which can assist decision-making. However, implicit assumptions of Bayesian methods can cause further uncertainty in the uncertainty quantification, so-called second-order uncertainty. In this study, and for the first time, we rigorously assessed this second-order uncertainty for inference of common water quality statistics (mean and 95th percentile) based on sub-sampling high-frequency (hourly) total reactive phosphorus (TRP) concentration data from three watersheds. The statistics were inferred with the low-resolution sub-samples using the Bayesian lognormal distribution and bootstrap, frequentist t test, and face-value approach and were compared with those of the high-frequency data as benchmarks. The t test exhibited a high risk of bias in estimating the water quality statistics of interest and corresponding physicochemical status (up to 99% of sub-samples). The Bayesian lognormal model provided a good fit to the high-frequency TRP concentration data and the least biased classification of physicochemical status (< 5% of sub-samples). Our results suggest wide applicability of Bayesian inference for water quality status classification, a new approach for regulatory practice that provides uncertainty information about water quality monitoring and regulatory classification with reduced bias compared to frequentist approaches. Furthermore, the study elucidates sizeable second-order uncertainty due to the choice of statistical model, which could be quantified based on the high-frequency data.

Quantification of tributaries contributions using a confluence-based sediment fingerprinting approach in the Canche river watershed (France)

Since a few years, land use management aims to reduce and control water erosion processes in watersheds but there is a lack of quantitative information on the contribution of the sources of transported sediment. This is most important in agricultural areas where soils are sensitive to erosion. The geology of these areas is often characterized by large expanses of relatively homogeneous quaternary silts. The possibility of distinguishing the sources of erosion according to their geological substratum is thus very delicate. This information is important because its lack can lead to the mis-implementation of erosion control measures. To address this request, a confluence-based sediment fingerprinting approach was developed on the Canche river watershed (1274 km²; northern France), located in the European loess belt, an area that is affected by diffuse and concentrate erosion processes. Suspended particulate matter was collected during five seasonal sampling campaigns using sediment traps at the outlet of each tributary and confluence with the main stream of the Canche river. The final composite fingerprint was defined using physico-chemical and statistical analyses. The best tracer parameters for each tributary were selected using stepwise discriminant function analyses. These parameters were introduced into a mass balance mixing model incorporating Monte-Carlo simulations to represent the uncertainty. Estimates of the overall mean contributions from each tributary were quantified at different temporal scales. The annual sediment flux tributaries contributions range from 3 to 22% at the outlet of the Canche river, and annual sediment flux range from 0.87 to 40.7 kt yr^-1. The Planquette and the Créquoise tributaries appear to be those producing the largest sediment flux. In contrast, tributaries with the highest number of erosion control on their area exhibit the lowest values of sediment flux. Our results indicate a positive impact of recent land management policies in the Canche river watershed.

Monte Carlo fingerprinting of the terrestrial sources of different particle size fractions of coastal sediment deposits using geochemical tracers: some lessons for the user community

A sediment source fingerprinting method, including a Monte Carlo simulation framework, was used to quantify the contributions of terrestrial sources of fine- (< 63 μm) and coarse-grained (63-500 μm) sediments sampled from three categories of coastal sediment deposits in the Jagin catchment, south-east of Jask, Hormozgan province, southern Iran: coastal dunes (CD), terrestrial sand dunes or onshore sediments (TSD), and marine or offshore sediments (MD). Forty-nine geochemical properties were measured in the two size fractions and a three-stage statistical process consisting of a conservation test, the Kruskal-Wallis H test, and stepwise discriminant function analysis (DFA) was applied to select final composite fingerprints for terrestrial source discrimination. Based on the statistical tests, four final fingerprints comprising Be, Ni, K and Cu and seven final fingerprints consisting Cu, Th, Be, Al, La, Mg and Fe were selected for discriminating terrestrial sources of the coastal fine- and coarse-grained sediments, respectively. Two geological spatial sources, including Quaternary (clay flat, high and low level fans and valley terraces) and Palaeocene age deposits, were identified as the main terrestrial sources of the fine-grained sediment sampled from the coastal deposits. A geological spatial source consisting of sandstone with siltstone, mudstone and minor conglomerate (Palaeocene age deposits) was identified as the main terrestrial source for coarse-grained sediment sampled from the coastal deposits.

Fingerprinting sources of reservoir sediment via two modelling approaches

Reliable quantitative information about sediment sources is a key requirement for river catchment management, especially in settings with high sediment loads. This study explores the potential for using source fingerprinting techniques to establish the relative contribution of three sub-basins to the sediment deposited in a reservoir impounded by an earth dam located at the outlet of the Lavar watershed, in Hormozgan Province, southern Iran. The three sub-basins feeding the reservoir are characterized by complex topography and underlying geology. The source material and target sediment samples were analyzed for 53 potential geochemical tracers, including trace elements and rare earth elements (REEs) and their ratios. Stepwise discriminant function analysis (DFA) was applied to select optimum composite fingerprints from those fingerprint properties passing the range test and we compared two different modelling procedures to estimate the relative contribution of the three sub-basins to the sediment deposited in the reservoir. The first involves a Bayesian mixing model within a Markov Chain Monte Carlo framework (BM) and, the second, an un-mixing model within a Monte Carlo simulation framework (UM). The latter model permits the use of ratio properties, which represents a novel aspect of our study. Particular attention was directed to the uncertainty associated with the source contribution estimates provided by the two models. A goodness of fit estimator was employed to evaluate the results of the UM. Both modelling procedures demonstrated that the southern sub-basin was the main source of the majority of samples we collected from the reservoir. The BM model indicated that the central sub-basin was the dominant source of two samples (S6 and S8). Overall, the results provided by the BM model for the source of seven sediment samples (S1, S2, S3, S4, S5, S7 and S9) are compatible with those provided by the UM model and the central sub-basin was recognized as the most important source supplying sediment in the study area. Both approaches offer potential for using geochemical fingerprinting to quantify spatial sediment source contributions and the uncertainty associated with those estimates.

Mitigating river sediment enrichment through the construction of roadside wetlands

Metalled roads have been shown to act as a major pathway for land-to-river sediment transfer, but there currently exists limited research into mitigation solutions to tackle this pollution source. The aim of this study was to assess the effectiveness of three roadside constructed wetlands, installed in September 2016, at reducing sediment enrichment in a tributary of the River Wensum, UK. Two wetland designs were trialled (linear and 'U-shaped'), both of which act as settling ponds to encourage entrained sediment to fall out of suspension and allow cleaner water to discharge into the river. Wetland efficiency was monitored through automated, high-resolution (30 min) turbidity probes installed upstream and downstream of the wetlands, providing a near-continuous record of river turbidity before (October 2011-August 2016) and after (November 2016-February 2018) installation. This was supplemented by lower resolution monitoring of the wetland inflows and outflows, as well as an assessment of sediment and nutrient accumulation rates within the linear wetland. Results revealed median river sediment concentrations decreased up to 14% after wetland construction and sediment load decreased by up to 82%, although this was largely driven by low river discharge post-installation. Median sediment concentrations discharging from the linear wetland (7.2 mg L^-1) were higher than the U-shaped wetland (3.9 mg L^-1), confirming that a longer flow pathway through wetlands can improve sediment retention efficiency. After 12 months of operation, the linear wetland had retained 7253 kg (305 kg ha^-1 y^-1) of sediment, 11.6 kg (0.5 kg ha^-1 y^-1) of total phosphorus, 29.7 kg (1.3 kg ha^-1 y^-1) of total nitrogen and 400 kg (17 kg ha^-1 y^-1) of organic carbon. This translates into mitigated pollutant damage costs of £392 for sediment, £148 for phosphorus and £13 for nitrogen, thus giving a combined total mitigated damage cost of £553 y^-1. With the linear wetland costing £3411 to install and £145-182 y^-1 to maintain, this roadside constructed wetland has an estimated payback time of 8 years, making it a cost-effective pollution mitigation measure for tackling sediment-enriched road runoff that could be widely adopted at the catchment-scale.

Tracing sediment sources in a mountainous forest catchment under road construction in northern Iran: comparison of Bayesian and frequentist approaches

Development and land use change lead to accelerated soil erosion as a serious environmental problem in river catchments in Iran. Reliable information about the sources of sediment in catchments is therefore necessary to design effective control strategies. This study used a composite sediment source tracing procedure to determine the importance of forest road cuttings as a sediment source in a mountainous catchment located in northern Iran. A fallout radionuclide (¹³⁷Cs) and 12 geochemical tracers (Ca, Cu, Fe, K, Mg, Mn, Na, Ni, OC, Pb, Sr and TN) were used to determine the relative contributions of three sediment source types (hillslopes, road cuttings and channel banks) to both suspended and bed sediment samples. Two mixing models based on different mathematical concepts were used to apportion the sediment sources: the mixture sampling importance resampling Bayesian model which incorporates the mass-balance matrix and a distribution model using normal and summed probability of normal distributions. The results of both mixing models indicated that sub-soil erosion from road cuttings and channel banks dominated the sources of river bed and suspended sediment samples, respectively. These results therefore highlight that conservation that works in the study area to remedy the sediment problem should initially focus on stabilisation and rehabilitation of road cuttings and channel banks. This successful application of a composite (radionuclide and geochemical) tracing technique for discriminating source end members characterised by different erosion processes underscores the importance of sub-soil erosion in this case study.

Developing an EFDC and Numerical Source-Apportionment Model for Nitrogen and Phosphorus Contribution Analysis in a Lake Basin

The numerical source-apportionment model is an efficient and useful method for analyzing water-quality responses to nutrient loading in rivers and lakes. In this study, the Environmental Fluid Dynamic Code (EFDC) and numerical source-apportionment model were applied to Lake Bali in Jiujiang City, China to predict the contributions of various pollution sources to the lake at any time and position. We calibrated and validated the model by comparing its predictions with observed hydrodynamic and water-quality parameters from 2014 to 2015. Application of the calibrated model to simulate water-quality responses to a pollution source showed that the contribution of a pollution source to water quality in the lake has strong spatial heterogeneity. The results provide useful information for the optimization of pollution load reduction in Lake Bali and can be used to determine the most effective implementation of its pollution-control plan. The model built in this study can also be used for pollution source-apportionment in other urban lakes and is superior to other traditional source-apportionment models.

Use of sediment source fingerprinting to assess the role of subsurface erosion in the supply of fine sediment in a degraded catchment in the Eastern Cape, South Africa

Sediment source fingerprinting has been successfully deployed to provide information on the surface and subsurface sources of sediment in many catchments around the world. However, there is still scope to re-examine some of the major assumptions of the technique with reference to the number of fingerprint properties used in the model, the number of model iterations and the potential uncertainties of using more than one sediment core collected from the same floodplain sink. We investigated the role of subsurface erosion in the supply of fine sediment to two sediment cores collected from a floodplain in a small degraded catchment in the Eastern Cape, South Africa. The results showed that increasing the number of individual fingerprint properties in the composite signature did not improve the model goodness-of-fit. This is still a much debated issue in sediment source fingerprinting. To test the goodness-of-fit further, the number of model repeat iterations was increased from 5000 to 30,000. However, this did not reduce uncertainty ranges in modelled source proportions nor improve the model goodness-of-fit. The estimated sediment source contributions were not consistent with the available published data on erosion processes in the study catchment. The temporal pattern of sediment source contributions predicted for the two sediment cores was very different despite the cores being collected in close proximity from the same floodplain. This highlights some of the potential limitations associated with using floodplain cores to reconstruct catchment erosion processes and associated sediment source contributions. For the source tracing approach in general, the findings here suggest the need for further investigations into uncertainties related to the number of fingerprint properties included in un-mixing models. The findings support the current widespread use of ≤5000 model repeat iterations for estimating the key sources of sediment samples.

Sediment source fingerprinting as an aid to catchment management: A review of the current state of knowledge and a methodological decision-tree for end-users

The growing awareness of the environmental significance of fine-grained sediment fluxes through catchment systems continues to underscore the need for reliable information on the principal sources of this material. Source estimates are difficult to obtain using traditional monitoring techniques, but sediment source fingerprinting or tracing procedures, have emerged as a potentially valuable alternative. Despite the rapidly increasing numbers of studies reporting the use of sediment source fingerprinting, several key challenges and uncertainties continue to hamper consensus among the international scientific community on key components of the existing methodological procedures. Accordingly, this contribution reviews and presents recent developments for several key aspects of fingerprinting, namely: sediment source classification, catchment source and target sediment sampling, tracer selection, grain size issues, tracer conservatism, source apportionment modelling, and assessment of source predictions using artificial mixtures. Finally, a decision-tree representing the current state of knowledge is presented, to guide end-users in applying the fingerprinting approach.

Characterization and quantification of suspended sediment sources to the Manawatu River, New Zealand

Knowledge of sediment movement throughout a catchment environment is essential due to its influence on the character and form of our landscape relating to agricultural productivity and ecological health. Sediment fingerprinting is a well-used tool for evaluating sediment sources within a fluvial catchment but still faces areas of uncertainty for applications to large catchments that have a complex arrangement of sources. Sediment fingerprinting was applied to the Manawatu River Catchment to differentiate 8 geological and geomorphological sources. The source categories were Mudstone, Hill Subsurface, Hill Surface, Channel Bank, Mountain Range, Gravel Terrace, Loess and Limestone. Geochemical analysis was conducted using XRF and LA-ICP-MS. Geochemical concentrations were analysed using Discriminant Function Analysis and sediment un-mixing models. Two mixing models were used in conjunction with GRG non-linear and Evolutionary optimization methods for comparison. Discriminant Function Analysis required 16 variables to correctly classify 92.6% of sediment sources. Geological explanations were achieved for some of the variables selected, although there is a need for mineralogical information to confirm causes for the geochemical signatures. Consistent source estimates were achieved between models with optimization techniques providing globally optimal solutions for sediment quantification. Sediment sources was attributed primarily to Mudstone, ≈38-46%; followed by the Mountain Range, ≈15-18%; Hill Surface, ≈12-16%; Hill Subsurface, ≈9-11%; Loess, ≈9-15%; Gravel Terrace, ≈0-4%; Channel Bank, ≈0-5%; and Limestone, ≈0%. Sediment source apportionment fits with the conceptual understanding of the catchment which has recognized soft sedimentary mudstone to be highly susceptible to erosion. Inference of the processes responsible for sediment generation can be made for processes where there is a clear relationship with the geomorphology, but is problematic for processes which occur within multiple terrains.

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.