Warming spring air temperatures, but delayed spring streamflow in an Arctic headwater basin
Bibliographic record
Abstract
This study will use the Mann–Kendall (MK) non-parametric trend test to examine timing changes in spring (early May to the end of June) streamflow records observed by the Water Survey of Canada during 1985–2011 in an Arctic headwater basin in the Western Canadian Arctic. The MK test shows a general delay in the five timing measures of springtime streamflow, which are based on the 5 percentile (Q5), 10 percentile (Q10), 50 percentile (Q50), 90 percentile (Q90), and 95 percentile (Q95) dates of spring runoff, respectively. However, much stronger trend signals were clearly noted for the high percentiles than that for the low and middle percentiles, indicating different effects of hydroclimate processes working on the timing of springtime streamflow. In contrast, the earlier snowmelt onset derived from daily mean temperatures was found over the 27-year study period. In addition, multiple relationships were correlated between these five timing measures of spring runoff and five hydroclimate indicators (total snowfall, snowmelt onset, spring temperature fluctuation, spring rainfall, and spring rainfall timing) in order to identify possible causes on the changes of springtime streamflow timing. The results indicate that the differences are due to the contradictory effects of winter–spring air temperature changes, temperature fluctuation during the melting period, and spring rainfall to spring runoff. The earlier snowmelt onset, which is attributed to the winter–spring warming, and spring temperature fluctuation that works in the opposite way, result in the minor timing changes of Q5, Q10, and Q50. The increase in spring rainfall and its delayed timing have a significant impact on the dates of Q90 and Q95. Moreover, the decreased total snow accumulation over the winter season only has a minor influence on the timing of springtime streamflow.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".