Detection of trends in hydrological extremes for Canadian watersheds
Bibliographic record
Abstract
Abstract The potential impacts of climate change can alter the risk to critical infrastructure resulting from changes to the frequency and magnitude of extreme events. As well, the natural environment is affected by the hydrologic regime, and changes in high flows or low flows can have negative impacts on ecosystems. This article examines the detection of trends in extreme hydrological events, both high and low flow events, for streamflow gauging stations in Canada. The trend analysis involves the application of the Mann–Kendall non‐parametric test. A bootstrap resampling process has been used to determine the field significance of the trend results. A total of 68 gauging stations having a nominal record length of at least 50 years are analysed for two analysis periods of 50 and 40 years. The database of Canadian rivers investigated represents a diversity of hydrological conditions encompassing different extreme flow generating processes and reflects a national scale analysis of trends. The results reveal more trends than would be expected to occur by chance for most of the measures of extreme flow characteristics. Annual and spring maximum flows show decreasing trends in flow magnitude and decreasing trends in event timing (earlier events). Low flow magnitudes exhibit both decreasing and increasing trends. Copyright © 2010 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 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".