Sediment Source Identification in a Southern Brazilian Watershed: Utilizing Geochemical Properties and Spectral Signatures with Mixing Models

 

One of the main impacts of water erosion within a watershed is the downstream deposition of sediments in watercourses and decrease in water quality, esigning and implementing effective soil and water conservation practices to address these impacts requires a soil conservation practices. Increasingly, researchers are using sediment source fingerprinting methods which use physical, biological, and geochemical attributes of the soil and sediments as tracers (Tiecher et al., 2015). Identifying sediment sources enables targeted corrective measures, but tracer selection and fingerprinting feasibility are ongoing debates among experts (Lizaga et al., 2020; Owens et al., 2022).

This study focuses on identifying sediment sources to develop erosion mitigation plans in a 33.3 km² rural river basin, in southern Brazil, crucial for supplying the municipality of Pelotas. Three primary sediment sources were identified: annual crops, perennial forage (pastures), and gutters (river channels). Samples were collected from the surface horizon (0-20 cm) of agricultural land and perennial pastures. Gutter samples were collected from the underground horizon, where active erosion processes were taking place. In total, 116 source samples were obtained. Nine sediment samples were collected from six sites across the study area every two months during 2021-2022, forming three collections for each sub-area of the river basin (A1, A2, and A3). Traditional fingerprinting methods, utilizing geochemical tracers, total organic carbon, and color coefficient tracers in the visible spectrum, were employed to analyze the soil of the contributing area and the sediments. The FingerPro (v1.1; Lizaga, 2018) mixture model was applied to evaluate the contributions of sediment sources to the collected sediment.

This communication presents preliminary results of 37 tracers: 22 geochemical elements, 14 color coefficients, and total organic carbon. Data processing, using FingerPro, was conducted separately by sub-area and sediment collection. Tracer selection involved a-two sequential statistical tests: 1) Kruskal-Wallis (KW) selects tracers with significant differences between at least two sources and 2) Discriminant Function Analysis (DFA) selects optimal tracers that effectively discriminate between the three sediment sources.

The results obtained demonstrated that the selected tracers for each sub-area varied considerably. For example, the tracer selection procedure for sub-area A1 resulted only in total organic carbon as a viable tracer while the number tracers selected for the other two sub-areas were seven and five, for A2 and A3, respectively. Notably, the varying sets of tracers being selected for each sub-area indicate that the heterogeneity in soil properties is an important consideration in sediment source fingerprinting studies. Combining samples from the whole river basin may distort sediment dynamics. Tailored approaches are crucial for accurate understanding and management.

Acknowledgements

This study was made possible by the generous support of Brazil-CAPES through a doctoral scholarship (Finance Code 001).

References

Lizaga et al. 2020. Consensus ranking as a method to identify non-conservative and dissenting tracers in fingerprinting studies

Lizaga. 2018. fingerPro 1.1.

Owens, P. N. 2022. Sediment source fingerprinting: are we going in the right direction?.

Tiecher et al. 2015. Combining visible-based-color parameters and geochemical tracers to improve sediment source discrimination and apportionment