Sediment Source Identification in a Southern Brazilian Watershed: Utilizing Geochemical Properties and Spectral Signatures with Mixing Models
Bibliographic record
Abstract
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.AcknowledgementsThis study was made possible by the generous support of Brazil-CAPES through a doctoral scholarship (Finance Code 001).ReferencesLizaga et al. 2020. Consensus ranking as a method to identify non-conservative and dissenting tracers in fingerprinting studiesLizaga. 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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".