As sediment loads impact freshwater systems and infrastructure, their origin
in complex landscape systems is of crucial importance for sustainable
management of agricultural catchments. We differentiated the sediment source
contribution to a lowland river in central Switzerland by using compound-specific
isotope analysis (CSIA). We found a clear distinction of sediment
sources originating from forest and agricultural land use. Our results
demonstrate that it is possible to reduce the uncertainty of sediment source
attribution in: (i) using compound content (in our case, long-chain fatty
acids; FAs) rather than soil organic matter content to transfer
Our findings are the first published results highlighting (i) significant differences in compound-specific stable isotope (CSSI) signature of sediment sources from land uses dominated by C3 plant cultivation and (ii) the use of these differences to quantify sediment contribution to a small river.
Sediment input to rivers causes clogging of river bed, eutrophication of waters, direct harmful effects of sediments on the biota and destruction of river infrastructure. The United States Environmental Protection Agency has identified sediments among the top 10 causes of biological impairment in freshwater ecosystems (US EPA, 2009), and at the European level, sediment pollution has been identified as one of the most relevant pressures to water bodies which impeded the aims of the water framework directive by the year 2015 (Borja et al., 2006). Restoration of rivers from sediment impact and associated management strategies can only be efficient if the origin of sediment loads, contribution of sources and their connection to different land uses and management strategies are identified. Geochemical (e.g., the use of elemental composition of source soils and sediments to track sediment origin) or isotopic fingerprinting has been used to discriminate between sources of sediments. However, successful discrimination between different sediment sources was often restricted to specific catchment settings having: (i) well-differentiated geological formation (at least two) and/or (ii) significant temporal shifts from C3 to C4 vegetation.
Using the compound-specific stable isotope (CSSI) signatures of inherent
soil organic biomarkers, allows to discriminate and apportion the source soil
contribution from different land uses, and the knowledge gained from CSSI
can 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; Cooper et al., 2015a). The compound-specific
isotope analysis (CSIA) measures the
In this study, we used the
In contrast to previous studies, we selected a relatively simple setting
with only three land-use types to evaluate whether or not sediment origin
from soils with C3 plant cover can solely be differentiated by CSSI
signature. The constrained setting will allow evaluation of the validity of the
assumption that CSSI signature is preserved during degradation and
transport. Further, results may be verified against Schindler Wildhaber et al. (2012a) who attributed sediment source origins using bulk isotopic
signatures (
Our aim was sediment source attribution from three different land-use types
within the Enziwigger catchment (Canton Lucerne, Switzerland) to: (i) evaluate differences of
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.
The river Enziwigger is a small and canalized river located in the Canton
Lucerne, Switzerland, near Willisau, with a watershed size of 31 km
Suspended sediments were sampled at three sites A, B and C along the river (Fig. 1); the site A being near the headwaters of the catchment is 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). The riverbanks have not been considered as original separate sources to river sediments since there is either a continuum of forest or grassland soils down to the riverbanks or small grassland riverbanks 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 as the fraction of petrogenic organic carbon is expected to be low with no significant contribution of FAs to the sediments. The latter might be a source of error during storm flow events but most likely not for base flow conditions with low sediment contribution (Galy et al., 2015). If riverbed material contains biospheric FAs, these should be either originating from terrestrial sources, which will be attributed in our analysis to the original source, or should be of aquatic origin which requires the identification of riverine FA production not connected to sediment transport (see below).
Suspended sediments (SS) were collected weekly at the three investigated sites with time-integrated SS samplers, according to Phillips et al. (2000). 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).
Upstream of each of the three sites A, B and C, representative soil samples
of each land-use type (i.e., forest, pasture and arable land) were taken.
Each soil sample represents a composite sample of three cores. In addition,
each site was sampled in triplicates (see Fig. 1 for the location of the
source area sampling sites). For the 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
After collection, soil samples were stored in a fridge at 4
The milled samples were analyzed 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 analyzed on a LECO RC612 (LECO, St. Joseph, Michigan 40985, USA).
Soil samples (11–21 g) and suspended sediments (4.5–25 g) were extracted
using the method of Elvert et al. (2003). For quality and
quantification control purposes, an internal standard (i.e., nonadecanoic
acid) with known concentration and
Extraction was performed 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;
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
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
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 analyzing single samples of the source soils
repeatedly as procedural controls to estimate the reproducibility within the
analysis procedure (from taking the soil sample out of the sample bag, via
the lipid extraction, methylation, identification and quantification of FAs
up to the final determination of the CSSI) as well as the heterogeneity in
one sample bag. We analyzed three samples out of the same sample bag
(control soil), including lipid extraction (pasture, site C), which resulted in
an overall procedural standard deviation of 0.13, 0.84 and 0.26 ‰
For assessment of the source heterogeneity, we report the standard deviation of the different sampling spots within our source areas (see the Supplement; Table S1). To establish mixing lines for sediment source attribution, we calculated mean values of source areas (Figs. 2 and 3). Deviation of CSSI of suspended sediments from the mixing line should not be greater than the procedural error or the measurement precision otherwise contribution of additional sources and/or isotope fractionation during degradation cannot be excluded. For unmixing of the suspended sediment signature we decided to use the measurement uncertainty of 0.5 ‰ rather than the FA specific procedural error because the latter was even smaller for the C14 : 0 and the C28 : 0 FAs. In case of the C26 : 0 FA, a smaller value of the measurement uncertainty is tightening our requirements in respect to the sediment source attribution to the SS (e.g., the even larger error of 0.84 ‰ would allow a larger correction to the mixing line than we actually needed to do).
Deducing from mathematical constraints, it is possible to find unique
algebraic solutions for the sediment source attribution with
In this study, we have a limited number of sources (two for site A and three for sites B and C). For site A, the forest as well as the pasture value was calculated as average from three sample areas. Since site B includes subcatchment A and B, and catchment C includes A, B and C, these values include three forest and/or pasture areas from each site A and B and C, respectively. Accordingly, the arable land value consists of three areas for site B and six for site C. The averaged agricultural land value at site B consists of six pasture areas (A, B) and three arable land areas (B), and at site C, nine pasture areas (A, B, C) and six arable land areas (B, C). Standard deviations of the averaged values are given in Table S1. Due to the linear arrangement of the problem, we prefer the calculation of a unique algebraic solution that includes the uncertainty ranges resulting from the measurement uncertainty.
In case deviations from the mixing line occur that lie within the measurement uncertainty of 0.5 ‰, we consider it valid to correct the measured isotope signals to the mixing line. The corrected value corresponds to the intersect of the mixing line and a normal through the measured value. We applied IsoSource with a tolerance value equivalent to the measurement uncertainty, only if a unique algebraic solution was not possible due to the nonsignificant differences between the sources.
The CSIA rather traces the FAs which bind to the soil particles as part of
the organic matter than the mineral soil sediment itself. Therefore,
results need to be adjusted to account for the different amounts of the FAs
in each of the soil sources and to transfer signature contribution into soil
contribution to suspended sediments:
From all FAs analyzed (even numbered from C14 : 0 to C30 : 0), the C18 : 0, C22 : 0,
C26 : 0 and C28 : 0 FAs showed significant differences between the sources
forest and pasture soil as well as forest and arable soil (see Tables S1 and S2). The C26 : 0 and C28 : 0 FAs resulted in greatest
differences with highest significances between forest and agricultural land
use (see Tables S1 and S2). For the difference between
pasture and arable land, only the CSSI of the C14 : 0 FA was significantly
different (
Contribution of the different sediment source areas to the suspended sediment, calculated with the different methods and using two or three sources and two FAs as tracers (i.e., C26 : 0 and C28 : 0). Values in brackets represent the uncertainty ranges of the estimates.
HF
Following the theoretical concept of
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 (see Tables S1 and S2). 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) were 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 not only produced by higher plants but also by microorganisms and algae, mainly by aquatic algae (Lichtfouse et al., 1995; Huang et al., 1996; Hughen et al., 2004; Eglinton and Eglinton, 2008; Freeman and Pancost, 2014). As such, the C14 : 0 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 SS compared to source soils, as well as in base flow SS compared to high flow SS (Table S1), which indicated the potential 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 (C24 : 0 to C30 : 0).
Because of the nonsignificant differences between the CSSI signatures of long-chain FAs of pasture and arable land (Fig. 3), we can solve the sediment contribution at sites B and C only for two different sources: forest vs. agricultural land (the latter averaging the signals from pasture and arable land). The algebraic solution was also used for site A, correcting suspended sediment isotope signals of both FAs to 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 nonsignificant difference between the sources but also because the combined source group has a functional significance (agricultural vs. forest land use). However, a separation between pasture and arable soil sources might seem desirable from catchment management perspectives. If we want to distinguish between pasture and arable land using the nonsignificant 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 to 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 statistically sound.
Because we trace with CSIA the FAs rather than the soil itself, the results
given by the unmixing of the
FA concentration compared to %
Following the above sediment source attribution approach, 30 and 70 % of sediments at site A originated from pastures and forests, respectively, during base flow (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 findings are consistent with the outcome of Schindler Wildhaber 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).
The results of our study 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 understory vegetation). 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. The larger forest contribution is likely conditioned by the extremely steep slopes and loosely structured calcareous soils under forests compared to the flat arable land on siliceous bedrock in the Enziwigger catchment.
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 is between 3 and 15 % at sites B and C. Sediment source attributions according to the IsoSource modeling 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 considered 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, 2001; Palmer et al., 2010; Layer et al., 2011), the expected increase of sediment input to rivers in the future is an unsolved problem (Scheurer et al., 2009; Matthaei et al., 2010). 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 study areas. Furthermore, with the rapid improvement of analytical tools in recent years, CSIA has all the potential to become a key decisional tool for investigating highly selective point measurements, where sediment origin and thus catchment management options are unclear. As such, research development targets should be directed towards biomarker tracer approaches with the least possible analytical effort, using low numbers of tracers set up for straightforward iso-space evaluations.
Our aim was a rigorous, quantitative sediment source attribution with CSIA
of FAs from three different land-use types (forest, pasture and arable land)
dominated by C3 vegetation only. We found significant differences between
forest and agricultural soil sources for four of the investigated FAs (i.e.,
C18 : 0, C22 : 0, C26 : 0 and C28 : 0). Only one FA (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 short-chain FA.
We recommend focusing on long-chain FAs (C24 : 0 to C30 : 0) only for sediment
source attribution from terrestrial sources. We further would like to
suggest using compound content – in our case long-chain FA content –
rather than soil organic matter content when converting the
Sediment source attribution resulted in high sediment contribution from forests during high flow conditions. In contrast, during base flow sediment input mostly originated from agricultural sources. Thus, connectivity of sediment source areas with the river changed with flow regime changes.
Catchment managers are often requested to take 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 develops further to a routine analysis in the future, it might become a valuable decision support tool as a sound and scientifically accepted “fingerprint” to track down sediment origin. Small-scale studies with well-defined sediment sources and significant differences in CSSI signature may help to verify the suitability of the CSIA as a sediment fingerprint technique in fluvial systems.
Christine Alewell: project idea, concept and initiative, data interpretation, manuscript writing; Katrin Meusburger: data evaluation, IsoSource modeling, manuscript writing; Axel Birkholz: method development, CSIA, data evaluation, manuscript writing; Yael Schindler Wildhaber: field study concept, sampling of suspended; sediments, interpretation; Lionel Mabit: interpretation, manuscript writing.
Suspended sediment samples were collected during a study which was funded by the Swiss National Foundation (SNFK-32K1-32K1-120486/1). Figure 1 was reproduced with permission of Swisstopo (BA15012). CSIA was based on analytical equipment and methods established in our laboratory by Helge Niemann. The authors wish to thank Elena Frenkel for the mathematical support provided to resolve the regression correction. Edited by: S. Bouillon