Analysis of annual hydrological droughts: the case of northwest Ontario, Canada
Bibliographic record
Abstract
Two important parameters of hydrological droughts are the longest duration and the greatest severity (in standardized form) over a desired return period (say T years), referred to as critical drought. The long-term mean of the annual flow sequences has been used as the truncation level for defining hydrological drought. Two well-known approaches—time series simulation and a probability theory-based approach—were used to estimate drought parameters. The drought episodes are treated as runs of deficits, and so the theory of runs forms a major tool for analysis. The sample estimates of the mean, coefficient of variation (or standard deviation), skewness, lag-1 serial correlation, and/or information on the probability distribution of flow sequences, are the basic input parameters in both approaches. The applicability of both approaches was tested for deducing drought parameters across Canada, with emphasis on northwest Ontario, a region bordering Lake Superior. Natural annual flow sequences in this region can be treated as normal independent sequences in the stochastic sense. The results of the probabilistic approach yielded marginally better results than the simulation approach. A main advantage of the probabilistic approach turned out to be parsimony with only two parameters, viz. drought probability at the truncation level and return period for normal independent annual flow sequences. Furthermore, estimates of the greatest standardized severity can be taken as equal to the longest duration, thus eliminating the need for severity analysis. The regional variation of droughts in northwest Ontario was portrayed through a map plotting the values of drought potential index (DPI). In northwest Ontario, a 100-year drought may persist continuously for 6 years and a 25-year drought for 4 years. The DPI map indicated proneness to drought along the Ontario—Manitoba border in the northwest Ontario region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".