Spatial and Temporal Variability of Canadian Seasonal Streamflows
Bibliographic record
Abstract
Abstract Wavelet and cross-wavelet analysis are used to identify and describe spatial and temporal variability in Canadian seasonal streamflows, and to gain insights into the dynamical relationship between the seasonal streamflows and the dominant modes of climate variability in the Northern Hemisphere. Results from applying continuous wavelet transform to mean seasonal streamflows from 79 rivers selected from the Canadian Reference Hydrometric Basin Network (RHBN) reveal striking climate-related features before and after the 1950s. The span of available observations, 1911–99, allows for depicting variance and covariance for periods up to 12 yr. Scale-averaged wavelet power spectra are used to simultaneously assess the temporal and spatial variability in each set of 79 seasonal streamflow time series. The most striking feature, in the 2–3-yr period and in the 3–6-yr period—the 6–12-yr period is dominated by white noise and is not considered further—is a net distinction between the timing and intensity of the temporal variability in autumn, winter, and spring–summer streamflows. It is found that the autumn season exhibits the most intense activity (or variance) in both the 2–3- and the 3–6-yr periods. The spring–summer season corresponds to the least intense activity for the 2–3-yr period, but it exhibits more activity than winter for the 3–6-yr period. Cross-wavelet analysis is provided between the seasonal streamflows and three selected climatic indices: the Pacific–North America (PNA), the North Atlantic Oscillation (NAO), and the sea surface temperature series over the Niño-3 region (ENSO3). The wavelet cross-spectra reveal strong climate–streamflow activity (or covariance) in the 2–6-yr period starting after 1950 whatever the climatic index and the season. Prior to 1950, local and weaker 2–6-yr activity is revealed in central and western Canada essentially in winter and autumn, but overall a non-significant streamflow–climate relationship is observed prior to 1950. Correlation analysis in the 2–6-yr band between the seasonal streamflow and the selected climatic indices revealed strong positive correlations with the ENSO in the spring–summer and winter seasons for the post-1950 period for both eastern and western Canada. A similar correlation pattern is revealed in the west with the NAO, while in the east moderate negative NAO correlations are observed only in the autumn season prior to 1950. After 1950 strong NAO correlations emerge for all the seasons. The cross-wavelet spectra and the correlation analysis in the 2–6-yr band suggest the presence of a change point around 1950 in the east and west seasonal streamflows.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".