Patterns of Trend in Canadian Streamflow
Bibliographic record
Abstract
Numerous studies have investigated spatial patterns of trend in streamflow. However, there is no consensus on the best methods to detect streamflow trends and the spatial extent or field significance of those trends. This paper looks at two of the contentious issues in trend analysis and assessment of field significance: serial correlation and cross-correlation. Both serial and crosscorrelation are known to reduce the accuracy of trend detection. The Trend-Free Pre-Whitening method was found to effectively remove the influence of serial correlation from a time series, enabling a more accurate assessment of site significance. An additional bootstrap test was developed to preserve the existing cross-correlation and to allow for a more reliable assessment of field significance. These methods and several others were applied to the annual mean, maximum and minimum streamflow data for 213 stations in the Canadian Reference Hydrometric Basin Network. These analyses revealed relatively consistent results for upward and downward trends for the three streamflow regimes. Mapping the results of these trend analyses revealed several interesting patterns, including three wide bands of trend that stretch across the country. A three band of downward trend stretches from Pacific to Atlantic at latitudes between approximately 50° and 58°; this band is particularly noticeable for the mean and maximum flows. A band of upward trend lies to the north of the downward band and stretches from northern B.C. and the Yukon through the Northwest Territories and into Nunavut; this band is noticeable for mean, maximum and minimum flows. A second band of upward trend lies to the south of the downward band, at latitudes between 44° and 50°; this band is noticeable for the same three flow variables.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".