In recent years, Compound Specific Stable Isotope (CSSI) techniques have enabled promising new tracers to track land-use-specific sediment sources. However, empirical data exploring the technique, particularly under controlled conditions, is still scarce. Hence, the main goal of this study is to explore the suitability of CSSI to identify sediment sources under different land use in the small agricultural site of Mistelbach (8.7 ha) located in Austria. In a previous study, the authors quantified, with a ¹³⁷Cs-based reconnaissance approach, a sedimentation magnitude of 4 mm year⁻¹ in the deposition zone at the outlet of that study site. To obtain detailed information on the sediment provenance, CSSI techniques based on the measurement of δ¹³C signatures of natural fatty acids (FAs), were used. A cost effective sampling approach involving composite sampling, identified potential sediment source materials from the four main agricultural fields. Two long-chain FAs (i.e. C22:0 = behenic acid; C24:0 = lignoceric acid) as well as bulk δ¹³C allowed the best statistical discrimination for apportioning the origin of the sediments. Four mixing models (i.e. IsoSource, SIAR, MixSIAR and SIMMR) applied to the data generated similar results. IsoSource performed as well as the other Bayesian models tested.
The main grazed waterway of the basin, identified as one of the four sources of the sediment, was evaluated to have contributed 55.1 ± 5% (IsoSource), 53.9 ± 2.7% (SIAR), 53.9 ± 2.7% (MixSIAR) and 54.0 ± 2.7% (SIMMR) to the sediment. The estimated contributions of the sources to the sediment are consistent with the land use information and the distance of the sources to the outlet. More than 80% of the sediment deposited at the basin exit originates from the two sources which had maize cultivation, one of the more erosive crops, in particular at the beginning of the growing season. This study emphasizes that CSSI and ¹³⁷Cs techniques are complementary for establishing land sediment redistribution. Their combined use could provide key decision support knowledge for optimised decision-making of land managers to ensure the sustainability of agro-ecosystem management.