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Purpose: Soils are important regulators of Critical Zone processes that influence the development of geochemical signals used for sediment fingerprinting. In this study, pedological knowledge of tropical soils was incorporated into sediment source stratification and tracer selection in a large Brazilian catchment. Materials and methods: In the Ingaí River basin (~ 1200 km2), Brazil, three source end-members were defined according to the interpretation of soil and geological maps: the upper, mid, and lower catchment. A tributary sampling design was employed, and sediment geochemistry of three different size fractions was analyzed (2–0.2 mm; 0.2–0.062 mm, and < 0.062 mm). A commonly used statistical methodology to element selection was compared to a knowledge-based approach. The mass balance un-mixing models were solved by a Monte Carlo simulation. Modeled source contributions were evaluated against a set of artificial mixtures with known source proportions. Results and discussion: For the coarse fraction (2–0.2 mm), both approaches to element selection yielded high errors compared to the artificial mixtures (23.8% and 17.8% for the statistical and the knowledge-based approach, respectively). The knowledge-based approach provided the lowest errors for the intermediate (0.2–0.062 mm) (10.9%) and fine (< 0.062 mm) (11.8%) fractions. Model predictions for catchment outlet target samples were highly uncertain for the coarse and intermediate fractions. This is likely the result of the spatial scale of the source stratification not being able to represent sediment dynamics for these fractions. Both approaches to element selection show that most of the fine sediments (median > 90%) reaching the catchment outlet were derived from Ustorthents in the lower catchment. Conclusions: The different element selection methods and the artificial mixtures provide multiple lines of evidence for evaluating the fingerprint approaches. Our findings highlight the importance of considering pedogenetic processes in source stratification, and demonstrate that different sampling strategies might be necessary to model specific sediment fractions. [ABSTRACT FROM AUTHOR]

Water erosion negatively affects soil fertility, soil structure, and water availability to plants. Moreover, off-site erosion effects contribute to the sedimentation and eutrophication of water courses. The Grande River is one of the main tributaries of the Paraná River, and an important source of hydroelectric power in Brazil. The Upper Grande River Basin covers an area of 15,705 km 2 , mostly occupied by rangelands. Shallow and little permeable Cambisols are the predominant soil class in the basin, which, combined with the intensive and highly concentrated summer rainfall, characterize an erosion-prone scenario. The aim of this study was to model the soil losses and the sediment yield in the Upper Grande River Basin. It also sought to quantify the sediment delivery to the two main hydroelectric power plant reservoirs in the basin: Camargos/Itutinga and Funil. Geographical Information Systems (GIS) were used to apply the Revised Universal Soil Loss Equation (RUSLE) and the Sediment Delivery Distributed model (SEDD) in the study area. The models were calibrated using sediment transport data obtained from a river gauging station located in a subwatershed. RUSLE predictions estimated that the average soil losses in the Upper Grande River Basin were of 22.35 t ha − 1 yr − 1 , and that bare soils, eucalypt and agriculture suffered the highest erosion rates among the identified land use classes. The average specific sediment yield in the basin was of 1.93 t ha − 1 yr − 1 . According to the model calibration, the specific sediment yield predictions showed an error of 0.01 t ha − 1 yr − 1 , or 0.6%. Agriculture and eucalypt forests, which compose approximately 10% of the study area, contribute to more than 40% of the sediment yield in the basin. The model predictions estimated that 1.45 million t yr − 1 of sediments are delivered to the Camargos/Itutinga power plant reservoir, whereas the Funil power plant reservoir receives a sediment input of 1.68 million t yr − 1 . Although model calibration yielded small errors in relation to the observed sediment measurements, the relative lack of available data has impaired a more thorough validation of the employed models. Nevertheless, the results indicate that the RUSLE/SEDD approach may be useful for analyzing sediment transport in Brazilian watersheds, where limited input data is available. [ABSTRACT FROM AUTHOR]