Chapter II Regional Estimation of Short-Duration Rainfall Distribution Using Available Daily Rainfall Data.
Bibliographic record
Abstract
The objective of this chapter is to propose methods for estimating the distribution of hourly rainfall based on at-site daily rainfall measurements, and from available regional information. The methods consist of deriving time-resolution independent mathematical models to describe the distribution of rainfalls for different time scales at a single site, and for the whole study region. The proposed models are ba'led on the theory of multifractal multiplicative cascades which describes the transfer mechanism of rain flux from large time scales (e.g. days or longer) to smaller time intervals (e.g. hours or shorter). In the present study, using rainfall data from a network of nine recording raingages in the Montreal region (Quebec, Canada) an empirical investigation is carried out to demonstrate how well rainfall measurements at different time resolutions can be described by such multiplicative cascade processes. Results of the data analysis in this study have provided some credence to this hypothesis since it was found that the probability distributions of rainfalls over a wide range of time scales can be described by one particular theoretically-derived functional form. Further, the good agreement between computed and observed hourly rainfall distributions have indicated the adequacy ofthe proposed methods. It is expected that a straightforward extension of the method suggested in this study could be used to obtain rainfall distributions appropriate to other time intervals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".