Sediment source fingerprinting has been successfully deployed to provide information on the surface and subsurface sources of sediment in many catchments around the world. However, there is still scope to reexamine some of the major assumptions of the technique with reference to the number of fingerprint properties used in the model, the number of model iterations and the potential uncertainties of using more than one sediment core collected from the same floodplain sink. We investigated the role of subsurface erosion in the supply of fine sediment to two sediment cores collected from a floodplain in a small degraded catchment in the Eastern Cape, South Africa. The results showed that increasing the number of individual fingerprint properties in the composite signature did not improve the model goodness-of-fit. This is still a much debated issue in sediment source fingerprinting. To test the goodness-of-fit further, the number of model repeat iterations was increased from 5000 to 30,000. However, this did not reduce uncertainty ranges in modelled source proportions nor improve the model goodness-of-fit. The estimated sediment source contributions were not consistent with the available published data on erosion processes in the study catchment. The temporal pattern of sediment source contributions predicted for the two sediment cores was very different despite the cores being collected in close proximity from the same floodplain. This highlights some of the potential limitations associated with using floodplain cores to reconstruct catchment erosion processes and associated sediment source contributions. For the source tracing approach in general, the findings here suggest the need for further investigations into uncertainties related to the number of fingerprint properties included in un-mixing models. The findings support the current widespread use of <5000 model repeat iterations for estimating the key sources of sediment samples.
- Sediment source fingerprinting
- uncertainty analysis
- Eastern Cape
- gully erosion
- mass balance modelling