Sediment Source Identification in a Southern Brazilian Watershed: Utilizing Geochemical Properties and Spectral Signatures with Mixing Models
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».