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Record W6958482668 · doi:10.6084/m9.figshare.27936250

Hydrometric data discretization into environmental flow components: a new, practical approach to explore concentration-discharge relationships

2024· article· en· W6958482668 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsWatershedStreamflowWater qualityHydrology (agriculture)Watershed areaSTREAMSBaseflowFlow (mathematics)

Abstract

fetched live from OpenAlex

The intricate dynamics of streamflow and water quality are often explored through the lens of concentration-discharge (C-Q) relationships. Interpreting C-Q relationships and using mathematical models to explain them can, however, be challenging due to hysteresis effects, low-frequency data, or noisy data. To address these challenges, this study introduces a noise-filtering approach by aggregating discrete data collected over several years into bins corresponding to distinct environmental flow components (EFCs). Covering a spectrum from extreme low flows to small and large floods, this method simplifies C-Q analysis by focusing on watershed hydrochemical responses to varying flow conditions. The objectives of the study were to explore the variability of stream water quality across a gradient of EFCs, categorize watersheds based on their median dominant export behaviour type (e.g. dilution, mobilization, chemostatic), and evaluate the predictability of export behaviour type from watershed characteristics. The study relied on 30 watersheds located in Québec, Canada, ranging in size from 11 to 2610 km2, with daily streamflow data and at least monthly water quality data spanning at least two years over the 1989-2020 period. EFC-specific summary statistics for dissolved organic carbon (DOC), total nitrogen (TN), and total phosphorus (TP) concentrations were computed and used to assess C-Q relationships and classify the general tendencies of watershed export behaviour. Results reveal nuances in watershed export behaviour depending on the specific water quality parameter and flow components assessed. Some differences in watershed hydrobiogeochemical behaviours could be explained by differences in watershed physiographic characteristics. The EFC-based approach to C-Q analysis provides watershed scientists and managers with a simple and comprehensive tool to utilize existing data, enabling a deeper understanding of watershed nutrient export dynamics. Les dynamiques de la qualité de l’eau et du débit des cours d’eau sont souvent explorées à travers les relations concentration‑débit (C‑D). Cependant, l’interprétation des relations C‑D et l’utilisation de modèles mathématiques pour les expliquer peuvent être complexes en raison des effets d’hystérésis, des données à faible fréquence ou des données bruitées. Pour relever ces défis, cette étude introduit une approche de filtrage du bruit en agrégeant des données discrètes collectées sur plusieurs années dans des compartiments correspondant à des composantes distinctes des flux environnementaux (CFE). En couvrant un spectre allant des débits extrêmement faibles aux petites et grandes crues, cette méthode simplifie l’analyse C‑D en se concentrant sur les réponses hydrochimiques des bassins versants à des conditions d’écoulement variables. Les objectifs de l’étude étaient d’explorer la variabilité de la qualité de l’eau des cours d’eau selon un gradient de CFE, de catégoriser les bassins versants en fonction de leur type de comportement dominant d’exportation médian (par exemple, dilution, mobilisation, chimio‑statique) et d’évaluer la prévisibilité du type de comportement d’exportation à partir des caractéristiques des bassins versants. L’étude s’est appuyée sur 30 bassins versants situés au Québec, Canada, dont la superficie varie entre 11 et 2 610 km2, avec des données quotidiennes sur le débit et au moins des données mensuelles sur la qualité de l’eau couvrant au moins deux ans entre 1989 et 2020. Des statistiques récapitulatives spécifiques aux CFE pour les concentrations de carbone organique dissous (COD), d’azote total (TN) et de phosphore total (TP) ont été calculées et utilisées pour évaluer les relations C‑D et classifier les tendances générales des comportements d’exportation des bassins versants. Les résultats révèlent des nuances dans les comportements d’exportation des bassins versants en fonction des paramètres spécifiques de qualité de l’eau et des composantes de débit évaluées. Certaines différences dans les comportements hydrobiogéochimiques des bassins versants pourraient être expliquées par des différences dans leurs caractéristiques physiographiques. L’approche de l’analyse C‑D basée sur les CFE offre aux scientifiques et aux gestionnaires des bassins versants un outil simple et complet pour exploiter les données existantes, leur permettant de mieux comprendre la dynamique des exportations de nutriments dans les bassins versants. A simple noise-filtering approach using low-frequency data to help watershed managers classify nutrient export behavior based on key river flow ranges was proposed.The predictability of export behavior from watershed characteristics was evaluated and could be useful for policy makers to inform targeted watershed best management practices.The newly suggested noise-filtering approach allows nutrient export dynamics to be assessed in management settings with limited data and could be applied in regions where extensive monitoring networks are not available. A simple noise-filtering approach using low-frequency data to help watershed managers classify nutrient export behavior based on key river flow ranges was proposed. The predictability of export behavior from watershed characteristics was evaluated and could be useful for policy makers to inform targeted watershed best management practices. The newly suggested noise-filtering approach allows nutrient export dynamics to be assessed in management settings with limited data and could be applied in regions where extensive monitoring networks are not available.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.145
GPT teacher head0.291
Teacher spread0.146 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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