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.

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.

Compound-specific stable isotope (CSSI) fingerprinting of sediment sources is a recently introduced tool to overcome some limitations of conventional approaches for sediment source apportionment. The technique uses the C-13 CSSI signature of plant-derived fatty acids (delta C-13-fatty acids) associated with soil minerals as a tracer. This paper provides methodological perspectives to advance the use of CSSI fingerprinting in combination with stable isotope mixing models (SIMMs) to apportion the relative contributions of different sediment sources (i.e. land uses) to sediments. CSSI fingerprinting allows quantitative estimation of the relative contribution of sediment sources within a catchment at a spatio-temporal resolution, taking into account the following approaches. First, application of CSSI fingerprinting techniques to complex catchments presents particular challenges and calls for well-designed sampling strategies and data handling. Hereby, it is essential to balance the effort required for representative sample collection and analyses against the need to accurately quantify the variability within the system. Second, robustness of the CSSI approach depends on the specificity and conservativeness of the delta C-13-FA fingerprint. Therefore, saturated long-chain (> 20 carbon atoms) FAs, which are biosynthesised exclusively by higher plants and are more stable than the more commonly used short-chain FAs, should be used. Third, given that FA concentrations can vary largely between sources, concentration-dependent SIMMs that are also able to incorporate delta C-13-FA variability should be standard operation procedures to correctly assess the contribution of sediment sources via SIMMs. This paper reflects on the use of delta C-13-FAs in erosion studies and provides recommendations for its application. We strongly advise the use of saturated long-chain (> 20 carbon atoms) FAs as tracers and concentration-dependent Bayesian SIMMs. We anticipate progress in CSSI sediment fingerprinting from two current developments: (i) development of hierarchical Bayesian SIMMs to better address catchment complexity and (ii) incorporation of dual isotope approaches (delta C-13- and delta H-2-FA) to improve estimates of sediment sources

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Quantifying biologically fixed nitrogen (BNF) by legumes through the N-15 natural abundance techniques requires correct determination of a so-called B value. We hypothesized that significant variations in B values exist between faba bean (Vicia faba L.) varieties having consequences for BNF and N balance calculations. We experimentally determined B values for a range of faba bean varieties and quantified to what extent variety has an effect on B values and hence BNF quantification. Seeds of six faba bean varieties released in Ethiopia were inoculated with Rhizobium fabae strain LMG 23997-19 and grown in vermiculite with an N-free nutrient solution in a growth room until full flowering. Total N and N-15 content of nodules, roots, and shoot components was analyzed separately to determine the weighted whole plant N-15 fractionation during N-2 fixation, i.e., the B value. Owing to its large seed size and high N content, a correction for seed N was carried out. We then calculated the percentage of N derived from air (%Ndfa), BNF, and N balance for faba beans grown in the field using three B value scenarios (variety specific B value corrected for seed N, variety specific B value without seed N correction, and a literature derived B value). Whole plant seed N corrected B values were significantly different (P < 0.05) between varieties and varied between +0.5 +/- 0.4 and -1.9 +/- 1.4aEuro degrees suggesting a variable isotope fractionation during N-2 fixation. The %Ndfa was significantly (P < 0.05) different between varieties (59 +/- 4.2-84 +/- 4.5 %) using seed N corrected B values. BNF (218 +/- 26.2-362 +/- 34.7 kg N ha(-1)) was significantly (P < 0.05) different between varieties for corrected and uncorrected B values. Soil N balance did not result in statistically significant (P > 0.05) difference between varieties for all three B value scenarios. Use of inappropriate B values masked the difference between varieties and affected their ranking in terms of BNF, resulting from an over- to underestimation of 15 and 19 %, respectively. When applying the N-15 natural abundance technique to compare BNF of legume accessions, we recommend determining a B value for each accession. For legumes with large seeds such as faba beans, it is moreover essential to account for seed N when determining the B value

Soil erosion by water is critical for soil, lake and reservoir degradation in the mid-hills of Nepal. Identification of the nature and relative contribution of sediment sources in rivers is important to mitigate water erosion within catchments and siltation problems in lakes and reservoirs. We estimated the relative contribution of land uses (i.e. sources) to suspended and streambed sediments in the Chitlang catchment using stable carbon isotope signature (δC) of long-chain fatty acids as a tracer input for MixSIAR, a Bayesian mixing model used to apportion sediment sources. Our findings reveal that the relative contribution of land uses varied between suspended and streambed sediment, but did not change over the monsoon period. Significant over- or under-prediction of source contributions could occur due to overlapping source tracer values, if source groups are classified on a catchment-wide basis. Therefore, we applied a novel deconvolutional framework of MixSIAR (D-MixSIAR) to improve source apportionment of suspended sediment collected at tributary confluences (i.e. sub-catchment level) and at the outlet of the entire catchment. The results indicated that the mixed forest was the dominant (41 ± 13%) contributor of sediment followed by broadleaf forest (15 ± 8%) at the catchment outlet during the pre-wet season, suggesting that forest disturbance as well as high rainfall and steep slopes interact for high sediment generation within the study catchment. Unpaved rural road tracks located on flat and steep slopes (11 ± 8 and 9 ± 7% respectively) almost equally contributed to the sediment. Importantly, agricultural terraces (upland and lowland) had minimal contribution (each <7%) confirming that proper terrace management and traditional irrigation systems played an important role in mitigating sediment generation and delivery. Source contributions had a small temporal, but large spatial, variation in the sediment cascade of Chitlang stream. D-MixSIAR provided significant improvement regarding spatially explicit sediment source apportionment within the entire catchment system. This information is essential to prioritize implementation measures to control erosion in community managed forests to reduce sediment loadings to Kulekhani hydropower reservoir. In conclusion, using compound-specific stable isotope (CSSI) tracers for sediment fingerprinting in combination with a deconvolutional Bayesian mixing model offers a versatile approach to deal with the large tracer variability within catchment land uses and thus to successfully apportion multiple sediment sources.

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