Exploring the provenance of information across Canadian hydrometric stations: implications for discharge estimation and uncertainty quantification
Bibliographic record
Abstract
Accurate discharge values form the foundation of effective water resource planning and management. Unfortunately, these data are often perceived as absolute and deterministic by users, modelers, and decision-makers, despite the inherent subjectivity and uncertainty in the data preparation processes. This study is undertaken to examine the discharge estimation methods used by the Water Survey of Canada (WSC) and their impacts on reported discharge values. First, we explain the hydrometric station network, essential terminologies, and fundamental concepts of rating curves. Subsequently, we examine WSC's standard operating procedures (SOPs), including shift, temporary shift, and override, in discharge estimation. Based on WSC's records of ∼ 1800 active hydrometric stations for discharge monitoring, we evaluated sample rating curves and their correlation with stage and discharge measurement. We investigate under-ice measurements, ice condition periods and frequency, and extreme values in contrast to rating curves. Employing an independent workflow, we demonstrate that 69 % of existing records align with the rating curve and temporary shift concept, while the remaining 31 % follow alternative discharge estimation methods (override). Selected example stations illustrate discharge estimation methods over time. We also demonstrate the impact of override and temporary shifts on commonly assumed uncertainty models. Given the practices of override and temporary shifts within WSC, there is a need to explore innovative methods for discharge uncertainty estimation. We hope our research helps in the critical challenge of estimating and communicating uncertainty in published discharge values.
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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 | 0.031 | 0.215 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".