Reliability of Design Storms used to Size Urban Stormwater System Elements
Bibliographic record
Abstract
The objective of this study was to generate hydrographs using design storms and real rain data recorded over a 30 y time period. In the first step rainfall characteristics were investigated using Intensity-Duration-Frequency (IDF) relationships. IDF curves were developed, called UM IDF curves, and when compared to the official Hamilton IDF curves showed a striking similarity for events with low return periods, and some variation for 50 and 100 y return periods. From the UM IDF curves three new design storms (SCS type 2, AES Canadian and Chicago Storm), referred to as UM Design Storms, were developed for comparison with the Hamilton design storms. The differences in intensity-duration of the less frequent storms (observed in the IDF comparison) were also reflected in the design storm volume and peak discharge. All the different design distributions, for a wide range of return periods, were entered into the rainfall-runoff model, PCSWMM, for event simulation of design storms. PCSWMM was also run for continuous simulation of the coarse observed data. Keeping the geomorphic conditions and loss parameters constant, differences were quantified in terms of computed flow and storage capacities for two design applications. The designs derived from the coarse observed data were considered to be optimal and used to decide suitability or otherwise of the designs based on other synthetic distribution patterns, and it was found that almost all the Reliability of Design Storms to Size Stormwater System Elements application designs obtained from design storm hyetographs were overdesigned. In other words, for every return period, the flow capacity and detention storage required to accommodate the design storm hydrographs was greater than what would be required if the same applications were designed for historic storms in continuous simulation. Given the coarse continuous data these results are to be expected, and should be reexamined when continuous at a fine time step become available.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".