Wavelet analysis of inter‐annual variability in the runoff regimes of glacial and nival stream catchments, Bow Lake, Alberta
Bibliographic record
Abstract
Abstract Continuous wavelet analyses of hourly time series of air temperature, stream discharge, and precipitation are used to compare the seasonal and inter‐annual variability in hydrological regimes of the two principal streams feeding Bow Lake, Banff National Park, Alberta: the glacial stream draining the Wapta Icefields, and the snowmelt‐fed Bow River. The goal is to understand how water sources and flow routing differ between the two catchments. Wavelet spectra and cross‐wavelet spectra were determined for air temperature and discharge from the two streams for summers (June–September) 1997–2000, and for rainfall and discharge for the summers of 1999 and 2000. The diurnal signal of the glacial runoff was orders of magnitude higher in 1998 than in other years, indicating that significant ice exposure and the development of channelized glacial drainage occurred as a result of the 1997–98 El Niño conditions. Early retreat of the snowpack in 1997 and 1998 led to a significant summer‐long input of melt runoff from a small area of ice cover in the Bow River catchment; but such inputs were not apparent in 1999 and 2000, when snow cover was more extensive. Rainfall had a stronger influence on runoff and followed quicker flow paths in the Bow River catchment than in the glacial catchment. Snowpack thickness and catchment size were the primary controls on the phase relationship between temperature and discharge at diurnal time scales. Wavelet analysis is a fast and effective means to characterize runoff, temperature, and precipitation regimes and their interrelationships and inter‐annual variability. The technique is effective at identifying inter‐annual and seasonal changes in the relative contributions of different water sources to runoff, and changes in the time required for routing of diurnal meltwater pulses through a catchment. However, it is less effective at identifying changes/differences in the type of the flow routing (e.g. overland flow versus through flow) between or within catchments. Copyright © 2003 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.002 |
| 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".