Hydrometric data discretization into environmental flow components: a new, practical approach to explore concentration-discharge relationships
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".