Regional streamflow trend detection with consideration of both temporal and spatial correlation
Bibliographic record
Abstract
Abstract It is known that serial correlation within time series at sites and cross‐correlation among sites in a specific region will influence the ability of statistical tests to assess the field significance of trends over the region. However, serial and/or cross‐correlation has been ignored in field trend‐analyses. This study attempts to develop a methodology that takes into account both serial and cross‐correlation in the assessment of the field significance of trends. The regional average Mann–Kendall (RAMK) statistic is used to represent the regional properties of trends at a regional scale. The null distribution of the RAMK statistic is derived on the basis that the joint probability distribution of m independent normal variables is also normally distributed. The variance of the RAMK statistic is then modified by serial and cross‐correlation. The applicability of the method was demonstrated by applying it to assess the field significance of trends in annual mean, annual maximum, and annual minimum daily streamflow from 1967 to 1996 in ten major homogeneous climate regions of Canada. The results indicate that the method developed provides more accurate assessment of the field significance of trends than that without consideration of serial and cross‐correlation. At the significance level of 0.10, annual mean daily flow increased significantly in the region of Yukon and northern BC mountains whereas it decreased significantly in the Pacific and the Prairie regions. Annual maximum daily flow decreased significantly across southern Canada, except in the Pacific region. Annual minimum daily flow decreased significantly in the Pacific region and in southeastern Canada, with the exception of the region of Great Lakes and St Lawrence river basin, whereas it increased significantly in the region of Yukon and northern BC mountains. Copyright © 2002 Royal Meteorological Society
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".