Information on sediment sources in river catchments is required for effective sediment control strategies, to understand sediment, nutrient and pollutant transport, and for developing soil erosion management plans. Sediment fingerprinting procedures are employed to quantify sediment source contributions and have become a widely used tool. As fingerprinting procedures are naturally variable and locally dependant, there are different applications of the procedure. Here, the auto-evaluation of different fingerprinting procedures using virtual sample mixtures is proposed to support the selection of the fingerprinting procedure with the best capacity for source discrimination and apportionment. Surface samples from four land uses from a Central Spanish Pyrenean catchment were used i) as sources to generate the virtual sample mixtures and ii) to characterise the sources for the fingerprinting procedures. The auto-evaluation approach involved comparing fingerprinting procedures based on four optimum composite fingerprints selected by three statistical tests, three source characterisations (mean, median and corrected mean) and two types of objective functions for the mixing model. A total of 24 fingerprinting procedures were assessed by this new approach which were solved by Monte Carlo simulations and compared using the root mean squared error (RMSE) between known and assessed source ascriptions for the virtual sample mixtures. It was found that the source ascriptions with the highest accuracy were achieved using the corrected mean source characterisations for the composite fingerprints selected by the Kruskal Wallis H-test and principal components analysis. Based on the RMSE results, high goodness of fit (GOF) values were not always indicative of accurate source apportionment results, and care should be taken when using GOF to assess mixing model performance. The proposed approach to test different fingerprinting procedures using virtual sample mixtures provides an enhanced basis for selecting procedures that can deliver optimum source discrimination and apportionment.

Information on sediment contribution and transport dynamics from the contributing catchments is needed to develop management plans to tackle environmental problems related with effects of fine sediment as reservoir siltation. In this respect, the fingerprinting technique is an indirect technique known to be valuable and effective for sediment source identification in river catchments. Large variability in sediment delivery was found in previous studies in the Barasona catchment (1509 km2, Central Spanish Pyrenees). Simulation results with SWAT and fingerprinting approaches identified badlands and agricultural uses as the main contributors to sediment supply in the reservoir. In this study the <63 μm sediment fraction from the surface reservoir sediments (2 cm) are investigated following the fingerprinting procedure to assess how the use of different statistical procedures affects the amounts of source contributions. Three optimum composite fingerprints were selected to discriminate between source contributions based in land uses/land covers from the same dataset by the application of (1) discriminant function analysis; and its combination (as second step) with (2) Kruskal-Wallis H-test and (3) principal components analysis. Source contribution results were different between assessed options with the greatest differences observed for option using #3, including the two step process: principal components analysis and discriminant function analysis. The characteristics of the solutions by the applied mixing model and the conceptual understanding of the catchment showed that the most reliable solution was achieved using #2, the two step process of Kruskal-Wallis H-test and discriminant function analysis. The assessment showed the importance of the statistical procedure used to define the optimum composite fingerprint for sediment fingerprinting applications.

The study and quantification of soil redistribution is a complex and difficult task and even a non-solved question at catchment scale both in field and numerical simulation studies. In this study we tackle this topic by coupling two different predicting models and a sound field-based dataset to assess the potential soil redistribution in a Mediterranean rain-fed agricultural and mountainous catchment (La Reina gully catchment, Cinco Villas region, NE Spain): the enhanced Modified-RMMF-2014 version of the “Modified Revised Morgan, Morgan and Finney” model (Morgan, 2001; López-Vicente and Navas, 2010) of soil erosion and the IC (Index of Connectivity; Borselli et al., 2008) model of sediment connectivity. In a cereal experimental plot (1.9 ha; 1 x 1 m of cell size), located in the lowlands of the La Reina gully catchment, we firstly ran the IC model under six different scenarios of runoff pathways and results were compared with field observations of soil redistribution. The best performance was obtained with the IC model when the map of geomorphic features (rills, ephemeral gullies and fan deposits) was used in the simulation. Predicted rates of both models were correlated at 613 control points and three areas where identified at the plot: erosive-, stable- and depositional- prone areas, affecting 30%, 22% and 48% of the soil surface, respectively. The average erosion rates in each area were 3.3, 1.5 and 1.1 Mg ha-1 yr-1 with standard deviation values of 20.3, 19.0 and 8.3 Mg ha-1 yr-1. Then, the IC model was run at La Reina gully catchment (231 ha; 5 x 5 m) and the IC values were analysed following the first approach. Stable areas and those mainly affected by processes of soil loss and deposition were identified. Results showed clear differences in the index of connectivity along the catchment though the extension of the areas with predominant processes of soil loss was under predicted (11% of the catchment area). Further research should be focused on the adjustment of the IC model to catchment scale. Our approach offers a simple and alternative method to assess spatially distributed processes of soil redistribution at catchment scale that can be of interest in ungauged catchments where calibration task of numerical models is difficult to be done.