Wavelet analysis of variability in annual Canadian streamflows
Bibliographic record
Abstract
Wavelet analysis is used to identify and describe variability in annual Canadian streamflows and to gain insights into the dynamical link between the streamflows and the dominant modes of climate variability in the Northern Hemisphere. Results from applying continuous wavelet transform to mean annual streamflows from 79 rivers selected from the Canadian Reference Hydrometric Basin Network (RHBN) reveal striking climate‐related features before the 1950s and after the 1970s. The span of available observations, 1911–1999, allows for depicting variance for periods up to 12 years. Scale‐averaged wavelet power spectra are used to simultaneously assess the interannual and spatial variability in 79 annual streamflow time series. The most striking feature, in the 2–3 year band and in the 3–6 year band (the 6–12 year band is dominated by white noise (since 1950) and is not considered further) is a net distinction between the timing and intensity of the interannual variability in western, central, and eastern streamflows, which is shown to be linked to the regional climatology. It is found that for the 2–3 year band, the Canadian streamflows are depicted mainly by the Pacific North America (PNA) during 1950–1999, and the Northern Hemisphere Annular Mode (NAM) only prior to 1950, and the North Atlantic Oscillation (NAO) after 1970. Similarly, in the 3–6 year band, the streamflows are depicted mostly by the NAO, the sea surface temperature anomalies over the Niño‐3 region (ENSO3) and the PNA during the period 1950–1999, and the NAM prior to 1950. Furthermore, strong local correlations between teleconnection patterns and western, central, and eastern streamflows are also revealed in both the 2–3 and 3–6 year bands with striking changes around 1950 and 1970. The correlation analysis in the 2–3 year and 3–6 year bands revealed the presence of two change points in the west and east streamflows occurring around 1950 and 1970.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".