13C dicarboxylic acid signatures indicate temporal shifts in catchment sediment sources in response to extreme winter rainfall.

Rainfall and land-use interactions drive temporal shifts in suspended sediment sources, yet the magnitude of such changes remains poorly understood due to the lack of land-use specific source tracers. We investigated α,ω-dicarboxylic fatty acid root-specific biomarkers, as diagnostic tracers for apportioning sources of time-integrated suspended sediment samples collected from a grassland dominated agricultural catchment in the southwest of England during the wet winter period. Applying fatty acids-specific stable carbon isotope analysis and a Bayesian isotope mixing model, we show that stream banks contributed most of the sediment in the early winter, i.e. October-December, while winter cereal-dominated arable land contributed more than half of the sediment during the late winter, i.e. January-March. The dominant sediment source shifted in conjunction with a period of prolonged consecutive rainfall days in the later period suggesting that intervention required to mitigate soil erosion and sediment delivery should adapt to changing rainfall patterns. Our novel findings demonstrate that isotopic signatures of α,ω-dicarboxylic fatty acids are promising tracers for understanding the resistance of agricultural soils to water erosion and quantifying the interactive effects of extreme rainfall and land use on catchment sediment source dynamics. Supplementary Information: The online version contains supplementary material available at 10.1007/s10311-023-01684-1.

Comparison of sediment biomarker signatures generated using time-integrated and discrete suspended sediment samples.

Sediment source fingerprinting using biomarker properties has led to new insights in our understanding of land use contributions to time-integrated suspended sediment samples at catchment scale. A time-integrated mass-flux sampler (TIMS; also known as the 'Phillips' sampler), a cost-effective approach for suspended sediment collection in situ. Such samplers are widely being used to collect sediment samples for source fingerprinting purposes, including studies using biomarkers as opposed to more conventional tracer properties. Here, we assessed the performance of TIMS for collecting representative sediment samples for biomarkers during high discharge events in a small lowland grassland-dominated catchment. Concentrations of long odd-chain n-alkanes (> C23) and both saturated free and bound fatty acids (C14-C32), as well as compound-specific 13C were compared between sediment collected by both TIMS and autosamplers (ISCO). The results showed that concentrations of alkanes, free fatty acids, and bound fatty acids are consistently comparable between TIMS and ISCO suspended sediment samples. Similarly, compound-specific 13C signals were not found to be significantly different in the suspended sediment samples collected using the different samplers. However, different magnitudes of resemblance in biomarker concentrations and compositions between the samples collected using the two sediment collection methods were confirmed by overlapping index and symmetric coordinates-based correlation analysis. Here, the difference is attributed to the contrasting temporal basis of TIMS (time-integrated) vs. ISCO (discrete) samples, as well as potential differences in the particle sizes collected by these different sediment sampling methods. Nevertheless, our findings suggest that TIMS can be used to generate representative biomarker data for suspended sediment samples collected during high discharge events.

Prolonged heavy rainfall and land use drive catchment sediment source dynamics: Appraisal using multiple biotracers.

Excessive sediment loss degrades freshwater quality and is prone to further elevation and variable source contributions due to the combined effect of extreme rainfall and differing land uses. To quantify erosion and sediment source responses across scales, this study integrated work at both field and catchment scale for two hydrologically contrasting winters (2018-19 and 2019-20). Sediment load was estimated at the field scale (grassland-arable conversion system). Sediment source apportionment work was undertaken at the catchment scale (4.5 km2) and used alkanes, and both free and bound fatty acid carbon isotope signatures as diagnostic fingerprints to distinguish sediment sources: arable, pasture, woodland and stream banks. Sediment source apportionment based on bound fatty acids revealed a substantial shift in contributions, from stream banks dominating (70 ± 5%) in winter 2018-19, to arable land dominating (52 ± 7%) in the extreme wet winter 2019-20. Increases in sediment contributions from arable (∼3.9 times) and pasture (∼2.4 times) land at the catchment outlet during the winter 2019-20 were consistent with elevated sediment losses monitored at the field scale which indicated that low-magnitude high frequency rainfall alone increased sediment loss even from pasture by 350%. In contrast, carbon isotope signatures of alkanes and free fatty acids consistently estimated stream banks as a dominant source (i.e., ∼36% and ∼70% respectively) for both winters regardless of prolonged rainfall in winter 2019-20. Beyond quantifying the shifts in field scale sediment load and catchment scale sediment sources due to the changes in rainfall patterns, our results demonstrate valuable insight into how the fate of biotracers in soil and sediment manifests in the δ13C values of homologues and, in turn, their role in information gain for estimating sediment source contributions. Discrepancies in the estimated sediment source contributions using different biotracers indicate that without a careful appreciation of their biogeochemical limitations, erroneous interpretation of sediment source contributions can undermine management strategies for delivering more sustainable and resilient agriculture.

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

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.

Sensitivity of source apportionment predicted by a Bayesian tracer mixing model to the inclusion of a sediment connectivity index as an informative prior: Illustration using the Kharka catchment (Nepal).

Long-chain saturated fatty acid (LCSFA) isotopic composition in tandem with Bayesian isotope mixing models (BIMM) can provide insight into land use-based sediment sources in catchment systems. Apportioning sediment sources robustly, however, requires careful consideration of how additional factors including topography, surface cover and land use practices interact to influence contributions from individual sources. Prior knowledge can be used in BIMM; however, the full capacity of this functionality has not been thoroughly exploited yet in conjunction with sediment fingerprinting. In response, we propose an approach for applying a state-of-the-art BIMM incorporating a sediment connectivity index (SCI) as an informative prior for sediment source apportionment in a highly hydrodynamic catchment in Nepal. A library of LCSFA carbon isotopic composition was constructed for surface soils collected from mixed forest, upland and lowland terraces in the Kharka micro-catchment. δ13C values of LCSFA of time-integrated suspended bulk (<2 mm) sediment were depleted by 4‰ compared to the fine (<0.063 mm) sediment fraction. Conventional source apportionment for fine sediment samples without the SCI informative prior suggested that 66% of the sediment is derived from forest soils followed by lowland (19%) and upland (15%) terraces. Incorporation of the SCI as an informative prior in BIMM, however, modified the original source apportionment estimates to 90%, 9% and 1% respectively. The lower contributions from agricultural terraces are explained by landscape complexity comprising small levelled terraces that reduce hillslope-to-channel sediment connectivity. This study demonstrates the sensitivity of BIMM posterior distributions to incorporation of an informative prior based on a SCI. Inclusion of SCI linked to land use and management can provide a more physically-grounded approach to estimating sediment source contributions from biogeochemical tracers, and critically one which generates results better reflecting what makes good environmental sense in the context of land management and visual evidence of sediment mobilisation and delivery.

Differentiating the geographical origin of Ethiopian coffee using XRF- and ICP-based multi-element and stable isotope profiling.

To test the potential of different analytical tools to determine the geographical origin of Ethiopian coffee, 103 green arabica coffee samples from four coffee regions in Ethiopia were subjected to multi-elements and δ13C, δ15N and δ18O determinations. Multi-elements were determined by using inductively coupled plasma (ICP)- and X-ray fluorescence spectrometry (XRF)-based techniques, and δ13C, δ15N and δ18O were determined by using elemental analyzer-isotope ratio mass spectrometry. Using linear discriminant analysis, XRF-based multi-elements with and without δ13C appeared to be most effective in discriminating the geographical origin of coffee, giving higher classification accuracy (89 and 86%, respectively) than ICP-based multi-elements with and without stable isotopes (80%, each). These results demonstrate the potential of XRF-based multi-element profiling as a relatively fast and low-cost tool to trace the geographical origin of Ethiopian coffee. All together this study offers the proof of concept for a promising method that, upon standardization, could be used for coffee provenance authentication and fraud detection.

Community managed forests dominate the catchment sediment cascade in the mid-hills of Nepal: A compound-specific stable isotope analysis.

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 (δ13C) 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 ±â€¯13%) contributor of sediment followed by broadleaf forest (15 ±â€¯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 ±â€¯8 and 9 ±â€¯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.