Interactive comment on “ Sediment source attribution from multiple land use systems with CSIA ”

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Introduction
The United States Environmental Protection Agency has identified sediments among the top ten causes of biological impairment in freshwater ecosystems (US EPA, 2009).On an European perspective, sediment pollution has been identified as one of the most relevant pressures to water bodies which will impede to achieve the aims of the water framework directive by the year 2015 (Borja et al., 2006).Restoration of rivers from sed-Figures

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Full iment impairment and adapted management strategies can only be efficient, if origin of sediment loads, contribution of sources and their connection to different land uses and management strategies are known.Geochemical fingerprinting has been used to discriminate between sources of sediments and was successful in discriminating between subsoil and surface soils (Collins et al., 1997;Walling, 2013) but the technique is limited in providing significant differences between sources of different land use types and vegetation cover in complex landscapes (Alewell et al., 2008;Mabit et al., 2013;Mabit et al., 2014;Hancock and Revill, 2013).If tracer signatures fail to be significantly different between sources, discriminant function analysis has been used to determine if the set of variables used would be effective in predicting category (source) membership (Collins et al., 1997;Cooper et al., 2015b;Smith and Blake, 2014).Tracer signatures being classified as suitable for fingerprinting were then used for sediment source attribution.
A new technique, using the compound specific stable isotope (CSSI) signatures of inherent soil organic biomarkers, can discriminate and apportion the source soil contribution from different land-uses in order to reinforce the effectiveness of soil conservation measures (Gibbs, 2008;Blake et al., 2012;Guzman et al., 2013;Hancock and Revill, 2013;Ponton et al., 2014).The compound specific stable isotope analysis (CSIA) measures the δ 13 C or δ 2 H isotope signature of specific organic compounds associated with the organic matter bound to the soil/sediment.In this study, we used δ 13 C of fatty acids to discriminate between soil sources of different land use types (forest, pastures, arable land).Because of their polar nature, FAs are easily leached from the plant or the decaying plant material and become tightly bound to soil particles.Although all plants produce the same FAs, the carbon stable isotopic signature (δ 13 C) of those biomarkers is different for each plant species (Tolosa et al., 2013;Pedentchouk et al., 2008;Chikaraishi and Naraoka, 2007) and assumed to be preserved during degradation and transport (Hughen et al., 2004;Gibbs, 2008).The CSIA method has already been successfully applied to link organic matter of sediments in estuarine or lake deposits to differentiate qualitatively between sources from algae, bacteria, zooplankton and higher Introduction

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Full plants and thus from terrestrial and aquatic sources (Galy et al., 2011;Tolosa et al., 2013;Fang et al., 2014;Ponton et al., 2014).In quantitative sediment attribution approaches, the precision of the method was impeded by the non-significant differences in the isotope signals between the different sources (Gibbs, 2008;Blake et al., 2012), especially if organic matter in sediment sources was dominated by C3 plant vegetation (Blake et al., 2012;Cooper et al., 2015b).The latter implied a restriction to (i) differ between sources with vegetation shifts from C3 plants to the warm-climate C4 grasses, which are considerably higher in δ 13 C values (Gibbs, 2008;Hancock and Revill, 2013;Cooper et al., 2015b), (ii) to achieve more effective discrimination by including information on δ 2 H of n-alkanes (Cooper et al., 2015b) or (iii) to include geochemical mineral tracers for the fingerprinting (Blake et al., 2012) which is useful with obvious shifts in geologic bedrock of the soils.The above approaches restrict the application of FAs as sediment tracers either to specific landscape settings (shift in geologic bedrock, shift from C3 to C4 plant cultivation) and/or complicate the analytical procedures (additional analysis of complex geochemical patterns or additional laborious analytical investigations on CSIA of biomarkers).In contrast to previous studies, we chose a relatively simple setting to evaluate whether or not sediment origin from soils with C3 plant cover only can be differentiated by CSSI signature.Furthermore, this constrained setting will allow evaluating the validity of the assumption that CSSI signature is preserved during degradation and transport.We were able to validate our results against the previous study of SchindlerWildhaber (2012) attributing sediment sources with bulk isotopic signatures (δ 13 C and δ 15 N) in a landscape setting with a shift from calcareous to siliceous bedrock.
Our aim was sediment source attribution from three different land use types within the Enziwigger catchment (Canton Lucerne, Switzerland) in (i) evaluating differences of CSSI signatures of soil samples from possible sediment source areas dominated by C3 vegetation land use types, (ii) comparing source signatures to tracer signals of suspended sediments in the river captured within a two year study (2009)(2010) and (iii) reducing method uncertainty in reducing the complexity of the unmixing procedure.Introduction

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Full 2 Materials and methods

Site description
The river Enziwigger is a small and canalized river located in the Canton Lucerne, Switzerland, near Willisau, with a watershed size of 31 km 2 .The flow regime at the sampling sites is not affected by any hydro-power or waste water treatment plants.The ecomorphology of the river has been strongly modified and currently only 5 % is close to natural.Terraces have been installed to prevent deep channel erosion and scouring of the bed during flood events.Three experimental sites A, B, and C (from up-to downstream, see Fig. 1) were installed at altitudes of 757, 625 and 583 m above sea level, respectively.For complete experimental setup and additional study site information please see Schindler Wildhaber et al. (2012b).

Suspended sediment sampling
Suspended sediments were sampled at three sites A, B and C along the river (Fig. 1), with site A being near the headwaters of the catchment under forested and pastured land covers, while river sections at site B and C are potentially influenced by pastures (C3 grasses only), forest (mainly coniferous) and arable land (mainly wheat production, some maize in single years but with no detectable effect on stable isotope signature of soils, Schindler Wildhaber et al., 2012a).We consider river bank not an original separate source to river sediments since we either have a continuum of forest or grassland soils down to the river banks or small grassland river banks act as intermediate deposits to sediments from source soils.Further, we did not include riverbed in our analysis, since riverbed sediments themselves (e.g., the underlying bedrock) should not influence the CSSI signal, assuming the fraction of petrogenic organic carbon to be low with no significant contribution in FAs to the sediments.The latter might be a source of error for storm flow events but most likely not for base flow conditions with low sediment contribution (Galy et al., 2015).If riverbed material contain biospheric FAs, Introduction

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Full these should be either originating from terrestrial sources which in our analysis will be attributed to the original source or should be of aquatic origin which means we cannot separate them from the riverine FA production not connected to sediment transport.Suspended sediments (SS) were collected at the three sites with time-integrated SSsamplers after Phillips et al. (2000).They were emptied in a weekly interval.For more detailed information see Schindler-Wildhaber et al. (2012b).
Water level at the three sites was measured in 15 s intervals with pressure transmitter probes (STS, Sensor Technik Sirnach, Switzerland).Average values were logged every 10 min.For detailed experimental setup see Schindler-Wildhaber et al. (2012b).

Soil sampling
Upstream of each of the three sites A, B and C, representative soil samples of each land use type forest, pasture and arable land were taken, each of them sampled in triplicates (see Fig. 1 for the location of the source area sampling sites).For forest sites, the humus layer was removed prior to sampling.The upper 5 cm of the topsoil were sampled with a cylindrical steel ring (98.2 cm 3 ) and then stored in plastic bags.
After collection, soil samples were stored in a fridge at 4 • C. For analysis of carbon and nitrogen contents in the soil and SS, the samples were oven-dried at 40 • C for at least 48 h, roughly ground in a mortar, and stones as well as root material were removed.The samples were ground with a ball mill (Retsch MM400, Retsch GmbH, 42781 Haan, Germany) for 90 s at a frequency of 24 s −1 .

Carbon and nitrogen analysis
The milled samples were analysed for organic and inorganic carbon as well as for nitrogen contents.Total nitrogen was measured with a LECO CN628.Total organic carbon (TOC) and total inorganic carbon (TIC) were analysed on a LECO RC612 (LECO, St. Joseph, Michigan 40985, USA).Introduction

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Lipid extraction and preparation
Soil samples (11-21 g) and SS (4.5-25 g) were extracted after the method of Elvert et al. (Elvert et al., 2003).For quality and quantification control an internal standard with known concentration and δ 13 C isotopic value, nonadecanoic acid, was added to the samples prior to extraction.To monitor the quality of lipid extraction batches and analysis performance, one sample (pasture at site C) was extracted in each extraction batch (n = 3) and further analysed.
Extraction was done by ultrasonication of the soil and sediment samples, which were put in PTFE centrifuge tubes, using solvent mixtures of declining polarity.First 25 mL of methanol(MeOH)/dichloromethane(DCM) (2 : 1, v/v), followed by MeOH/DCM (1 : 1, v/v) and two steps with pure DCM were used for the ultrasonic extraction.In between the ultrasonication steps, the PTFE tubes were centrifuged (5 min at 4000 rpm, 0 • C).The supernatant was pooled in a separation funnel and partitioned against preextracted 0.05 M KCl solution.The organic phase at the bottom of the funnel was collected and evaporated under a stream of nitrogen.This resulted in the total lipid extracts

Gas chromatography and isotope ratio mass spectrometry
Concentrations of FAMEs were determined by using a Trace Ultra gas chromatograph (GC) with a flame ionization detector (FID) (Thermo Scientific, Walthalm, MA 02451, USA).GC oven temperature started at 50 • C and was increased to 150 • C at a speed of 10 • C min −1 , hold for 1 min, increased to 300 • C at a speed of 4 • C min −1 and hold for 63 min.The carrier gas helium was set to a constant flow of 1 mL min −1 .Injector temperature was set to 300 • C and the detector temperature to 320 • C. Concentrations of FAMEs were calculated relative to the internal standard nonadecanoic acid, which was added prior extraction.The FAMEs were identified using the same Trace Ultra GC as above, coupled to a DSQ mass spectrometer (Thermo Scientific).The GC-MS is equipped with the same injector and capillary column and uses the same method as described above.Transfer line temperature to MS was set to 260 • C. Carbon isotopical compositions of the FAMEs were analysed using a Trace Ultra GC coupled via combustion interface GCIsolink and ConfloIV with a DeltaV Advantage isotope ratio mass spectrometer (Thermo Scientific).The system is equipped with a split/splitless injector, operated in splitless mode.The combustion oven was set to 1000 • C. GC oven temperature started at 50

Procedural error and measurement precision
Measurement precision of the GC-IRMS is 0.5 ‰.However, considering the analytical uncertainty only (e.g., checking an externally added standard) might neglect uncertainties, which bias the interpretation of isotope data.We recommend analysing single samples in multiplicities as procedural controls to estimate the reproducibility within the analysis procedure and the heterogeneity in one sample bag.We analysed three times a sample including lipid extraction (pasture, Site C) and resulted in an overall procedural standard deviation of 0.13, 0.84 and 0.26 ‰ δ 13 C for C14:0, C26:0 and C28:0 FAs, respectively.
For assessment of the source heterogeneity, we present the standard deviation of the different sampling spots within our source areas (Table S1 in Supplement).To establish mixing lines for sediment source attribution we calculated mean values of source areas (Figs.2-3).Deviation of SS from the mixing line should not be greater than procedural error or measurement precision otherwise contribution of additional sources cannot be excluded.

Unmixing of suspended sediment signatures
Deducing from mathematical constraints, it is possible to find unique algebraic solutions for the sediment source attribution with n tracers for n + 1 sources resulting in an equation system with n + 1 equations and n + 1 unknown variables.Mixing models like IsoSource (Phillips and Gregg, 2003)  tions, rather simply the range of values that might be plausible given the geometry of the system.In this study, we have a very limited number of sources (2 for site A and three for site B and C).Due to the linear arrangement of the problem we prefer the calculation of a unique algebraic solution, however, including the uncertainty ranges resulting of the procedural error.Only if an unique algebraic solution was not possible, due to the non-significant differences between the sources we applied IsoSource with a tolerance value equivalent to the procedural error.

Weighting sediment source attribution according to FA content
The CSIA method rather traces the FAs which bind to the soil particles as part of the organic matter than the mineral soil sediments itself.Therefore, results need to be adjusted to account for the different amounts of each FA in each of the soil sources and to convert signatures contribution into soil contribution to suspended sediments: where P n is the proportion for soil n resulting from the unmixing of FA signatures, and FA n is the sum of concentrations of fatty acids used for discrimination in the soil.

CSSI signatures of terrestrial soil sources
From all FAs analysed (even numbered from C14:0 to C30:0), the C18:0, C22:0, C26:0 and C28:0 FAs showed significant differences (T test) between the sources forest and pasture soil as well as forest and arable soil (supporting information, Tables S1 and  Introduction Conclusions References Tables Figures

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Full ).The C26:0 and C28:0 FAs resulted in greatest differences with highest significances between forest and agricultural land use (Tables S1 and S2).For the difference between pasture and arable land, only the CSSI of the C14:0 FA was significantly different (p < 0.043).Thus, theoretically we might use five tracers to differentiate between sediment sources from forest and agricultural land use (pasture and arable land) but only one tracer (C14:0) to distinguish pasture and arable land sediment contribution.
In our study, with a maximum of three different land use types (forest, grassland and arable land), we should be able to separate the source attribution at all our sites with two tracers without the use of mixing models.

Unmixing of suspended sediment signatures
Following the theoretical concept of n tracers with n + 1 sources, we only need one tracer for site A where sediments might origin from only two different land use types.
However, using only one tracer no mixing line can be established and deviations from mixing lines, either due to the influence of an additional source or due to degradation during transport, will not be recognized.The latter can be overcome due to the fact that several significantly different tracer signals should result in the same source attribution.This is the case if the suspended sediments plot exactly on the mixing line.In general, whether or not using a mixing model, the isotopic values of the sediment mixture being evaluated must be within the isotopic values of the source endmembers (Phillips and Gregg, 2003).In our case suspended sediments are not exactly on the mixing line between the two source soils (Fig. 2), which resulted in differences of up to 15 % for source attribution using either the C26:0 or the C28:0 FA.Since the deviation from the mixing line is within the uncertainty associated with the measurement precision or the procedural error of 0.5 ‰, we consider it valid to correct the measured isotope signals in forcing them on to the mixing line with a linear regression (Fig. 2).When using the stable isotope signals which were corrected by regression to the intersect value of the mixing line, sediment source attribution results in the same source attribution for both tracer applications (Table 1).Nevertheless, we Introduction

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Full cannot exclude a small contribution from an additional source and/or slight degradation of the signal during transport.
Because of the non-significant difference between the CSSI signatures of pasture and arable land (Fig. 3, left), we can solve for the sediment contribution at sites B and C also only from two different sources if we want to remain statistically firm: forest versus agricultural land (the latter bulking the signals from pasture and arable land).The same algebraic solution was used as for site A, correcting suspended sediment isotope signals of both FAs on the mixing line of sediment sources.
Aggregating the data from the land use types pasture and arable land is useful, not only because of the non-significant difference between the sources but also because the combined source group has a functional significance (agricultural versus forest land use).However, a separation between pasture and arable soil sources might seem desirable from catchment management perspectives.If we want to separate between pasture and arable land using the non-significant source signal differences of C26:0 and C28:0 as tracers, the mixing model IsoSource is useful.IsoSource constrains the relative proportions of the various sources in the mixture by evaluating all possible combinations of each source contribution (from 0-100 %).Even though we used the model to calculate sediment source contribution from all three sources (Table 1), we are fully aware that the separation between pasture and arable land cannot be considered as statistically firm.The latter also implies that the application of a more complex Bayesian mixing model seems meaningless.
The only FA resulting in significant differences between tracer signals of soils from the two land use types pasture and arable land was the C14:0 FA (Table S1).However, using this FA as a tracer did not lead to meaningful solutions (e.g.negative sediment source contributions), because the isotopic values of the sediment mixture (suspended sediments) are not within the isotopic values of the source endmembers (Fig. 3

right).
No set of source proportions is possible if the isotope mixture of the suspended sediments is outside the convex polygon bounded by the sources (Phillips and Gregg, 2003).Short-chain and medium-chain FAs (C12:0 to C16:0) are mainly not produced

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Full by higher plants but by microorganisms and algae, mainly by aquatic algae (Hughen et al., 2004;Freeman and Pancost, 2014).As such, the FA signals we determined in the suspended sediments were most likely influenced by aquatic contribution as an additional source.The latter is confirmed by the generally higher concentrations of C14:0 FAs in our suspended sediments compared to source soils as well as in base flow SS compared to high flow SS (Table S1), which indicated the riverine origin.Thus, even though short-chain and medium-chain FAs have been used to track terrestrial sediment contribution to rivers (Gibbs, 2008;Blake et al., 2012;Hancock and Revill, 2013) we would highly suggest constraining the concept of tracking terrestrial sediments to the long-chain FAs (C22:0 to C30:0).
Because the CSIA method traces carbon rather than the soil itself, the results given by the unmixing of the δ 13 C signals need to be adjusted to account for the different amounts of each of the soil sources.Following solutions in recent literature the percent carbon content of each source was used to weight sediment source attribution (Gibbs, 2008;Hancock and Revill, 2013;Blake et al., 2012).However, the relative carbon distribution in each source might be very different than the relative distribution of the specific tracer FA (Fig. 4).Since we used specific FAs as tracers and not the total soil organic carbon, we corrected with the concentration sum of the respective FAs (see methods).The difference between these two correction approaches might be considerable.In our study, a correction with soil organic carbon content would overestimate forest contribution and underestimates arable land up to 13 %.However, depending on the site-specific differences in the relation of soil organic carbon to specific FA content, the uncertainty introduced might be even higher at other study locations.Further, if quality and characteristics of bulk SOC is different between sources, degradability during detachment and transport might also be very different which will increase uncertainty if correction is carried out with bulk SOC.Thus, we highly recommend for future CSIA studies to correct with the sum of FA content and not with the soil organic matter content.

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Apportionment of suspended sediment during high and base flow
Following the above sediment source attribution approach at site A during base flow, 30 and 70 % of sediments were contributed from pastures and forests, respectively (Table 1).Downstream, at sites B and C, sediments from agricultural sources increase considerably during base flow (65 % from agricultural sources and 35 % from forests) reflecting the contribution from more intensively used arable land and pasture.At the two investigated high flow events, sediment sources varied considerably at site A (between 15 and 40 % from pastures and between 60 and 85 % from forests) and site B and C (contribution between 6 to 45 % from agricultural land and 55 to 93 % from forests), with sediment contribution from forests clearly being dominant during high flow events.
Our results are consistent with SchindlerWildhaber et al. (2012a) where sediment source attribution was achieved with bulk isotope signals (the latter was feasible due to the change in geology from calcareous bedrock under forest soils and siliceous bedrock under agricultural soils).
Results indicate that connectivity of sediment source areas with the river change from base to high flow regime.Management options to decrease sediment peaks during storm events should thus aim at adapted forest management (e.g.increasing soil and understorey vegetation) even though the latter will be difficult due to extremely steep slopes and loosely structured calcareous soils under forests in the Enziwigger catchment.The dominance of forest soil sources to sediment contribution during high flow is an important and surprising result since typically agricultural areas are in the focus of soil conservation management.
Separation between the agricultural land use types pasture and arable soil with IsoSource pointed to the same direction as the unique algebraic solution regarding the high forest contributions during high flow (Table 1).The difference between the IsoSource results and our unique solutions regarding the forest contribution are be- IsoSource modelling at sites B and C from pasture are 20-30 % during base flow and 5-20 % during high flow and from arable land 45 % during base flow and 10-30 % during high flow.However, these separations within the agricultural land uses should be regarded with caution, as tracer signals of sources are not significantly different.
As rivers are slowly but progressively recovering from the effects of acidification, eutrophication and pollutant contamination (Alewell et al., 2000(Alewell et al., , 2001;;Palmer et al., 2010;Layer et al., 2011), the expected increase of sediment input to rivers in the future is an unsolved problem (Matthaei et al., 2010;Scheurer et al., 2009).Without assessing sediment sources and their connection to different land use types, catchment management will be impeded to make progress in sediment load reduction.Because of the work and cost intensive analytical procedures, CSIA might be far from being used as a regular management tool.Nevertheless, it might give insight into sources of sediments in some selected studies.Furthermore, as we have seen with the rapid improvement of analytical tools in recent years, CSIA has the potential to develop as an important tool for highly selective point measurements, where sediment origin and thus catchment management options are unclear.As such, focus of research development should be directed towards biomarker tracer approaches with least possible analytical effort using low numbers of tracers set up for straight forward iso-space evaluations.

Conclusions
Our aim was a rigorous sediment source attribution with CSIA of fatty acids from three different land use types (forest, pasture and arable land) dominated by C3 vegetation only.We achieved significant differences between forest and agricultural soil sources for four of the investigated fatty acids (C18:0, C22:0, C26:0 and C28:0 FAs).Only one fatty acid (C14:0) resulted in significant differences between pastures and arable land, but a discrimination within these two agricultural sources was not possible, because results indicated a likely influence of aquatic contribution to the CSSI of this low chain fatty acid.We recommend using long chain fatty acids (C22:0 to C30:0) only for sedi-Introduction

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Full ment source attribution from terrestrial sources.We further would like to suggest using compound content (in our case long chain fatty acid content) rather than soil organic matter content when converting isotopic signature to soil contribution.Sediment source attribution resulted in high sediment contribution from forests during high flow conditions but domination of sediment input from agricultural sources during base flow.Thus, connectivity of sediment source areas with the river changed with changes in flow regime.
Catchment managers are often called to make soil conservation decisions on the basis of land use, as different land use types are connected to differences in soil erosion severity.Assuming the CSIA to develop further to a routine analysis in the future, it might become a valuable decision tool as a sound and scientifically accepted proof to track down sediment origin.We would like to recommend setting the research focus in the near future on developing sediment source attribution biomarker approaches with low tracer numbers aiming at unique mathematical solutions, thus optimizing analytical efforts and reducing uncertainty.
The Supplement related to this article is available online at doi:10.5194/bgd-12-14245-2015-supplement.
Author contributions.Christine Alewell provided the project idea and the concept of the study and took the initiative to start the project.She contributed to the data interpretation and had the leading role in manuscript writing.Katrin Meusburger contributed most to data evaluation, was responsible for the IsoSource modelling and contributed to the manuscript writing.Axel Birkholz took the lead in method development and CSIA, but was also part of data evaluation and manuscript writing.Yael SchindlerWildhaber developed and implemented the field study and sampled the suspended sediments.Lionel Mabit was involved in data interpretation, discussion and manuscript writing.Introduction

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TLE).Half of the TLE was removed and stored as backup in the freezer at −20 • C. The other half was transferred to a 5 mL reaction vial and 1 mL of 12 % KOH in MeOH for saponification was added.Saponification was maintained at 80 • C for 3 h.After cooling down 1 mL of 0.1 M KCl was added.The neutral lipid fraction was then extracted from the basic solution by agitating 4 times with ca. 2 mL hexane, dried under a stream of nitrogen and stored in the freezer at −20 • C. The remaining solution was set to pH 1 with concentrated HCl.Free FAs were extracted by again agitating 4 times with ca 2 mL hexane.The extract was also dried under a stream of nitrogen and then 1 mL of 12-14 % BF 3 in MeOH was added.Methylation reaction of free FAs to fatty acid methyl esters (FAMEs) took then place at 60 • C for 1 h.A last hexane extraction step as above in presence of 1 mL 0.1 M KCl was performed.The final extract was evaporated under a stream of nitrogen and stored in the freezer at −20 • C. Samples were extracted in three different extraction batches.To monitor the quality of lipid extraction and analy-Discussion Paper | Discussion Paper | Discussion Paper | sis performance, one sample (Pasture source site C) was extracted in each extraction batch and further analysed.
• C and was increased to 140 • C at a speed of 10 • C min −1 .Then it was hold for 2 min and increased to 300 • C at a speed of 4 • C min −1 and hold for 35 min.The carrier gas helium was set to a constant flow of 1.2 mL min −1 .Injector temperature was set to 300 • C. Carbon isotopes were reported in delta notation, per mil deviation from Vienna Pee Dee Belemnite (VPDB).The system was externally calibrated with Schimmelmann Std B3.Performance has been controlled with a C19:0 FA internal standard.The reported δ 13 C values have been corrected for the additional carbon atom introduced during methylation and had an analytical uncertainty lower than ±0.5 ‰. Discussion Paper | Discussion Paper | Discussion Paper | or, more recently, Bayesian mixing modelling (e.g.,Smith and Blake, 2014;Cooper et al., 2015a) have been employed to establish confidence intervals around the estimates.Mixing models like IsoSource(Phillips and Gregg, 2003) relax the strictly linear system and allow for multiple solutions but without explicit incorporation of source and suspended sediment variability.The multiple valid solutions to the linear system produced by IsoSource can be plotted in a histogram-like fashion, although unlike Bayesian models they do not represent probability distribu-Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | S2 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | tween 3 and 15 % at sites B and C. Sediment source attributions according to the Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Figure 1 .Figure 3 .
Figure 1.The Enziwigger catchment (Canton Lucerne, Switzerland) with the three suspended sediment sampling sites A, B, C and location of the source soil sampling spots forest, pasture and arable land.