Short Time-Interval Rainfall Disaggregation for Continuous Hydrologic Simulation
Bibliographic record
Abstract
Traditionally design storms have been used to design and analyze urban drainage systems and hydraulic structures.Design storms can be developed with the desired temporal resolution to accommodate urban hydrology needs, but because the temporal distribution is generally arbitrary the application of complex disaggregation techniques is unwarranted.Continuous hydrologic simulation is recommended as an alternative to the traditional design storm approach for the design and analysis ofhydrologic and hydraulic structures for reasons discussed in James (1994) and James and Robinson (1982).Continuous simulation models require long-term rainfall records (preferably more than 50 years) to generate the long-term statistical response of the hydrologic system required for accurate design and analysis of engineering systems and the evaluation of ecological and sustainability issues.Accurate hydrologic simulation of small urban catchments requires the use of a rainfall time series with a fine temporal resolution.Studies have shown that when the response time of a watershed is shorter than the total duration of rainfall excess, the runoff rate is observed to depend on the depth of rainfall and the intensity distribution (Ball1994; Woolhiser and Goodrich 1988;Hjelmfelt 1981).But for fully developed hydrographs Ball (1994) found the temporal pattern of rainfall excess to have little influence over the peak discharge.Thus, for short duration storms, coarse time resolution rainfall data may smooth the Burian, S. and S.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".