Development of a Modified Rational Equation for Arid-Region Runoff Estimation
Bibliographic record
Abstract
In practice, the United States (U.S.) Soil Conservation Service (SCS) curve number (CN) method is commonly used in distributed, continuous-time hydrologic models to estimate direct runoff. However, for the standard SCS-CN method, the determination of initial abstraction (Ia) as a fraction of potential maximum retention (S) after runoff starts is very subjective and thus highly debatable. The objective of this study was to develop a Modified Rational Equation (MoRE) that combines the advantages (e.g., simplicity and global acceptance) of Rational Equation and those (e.g., easy parameterization and extensive verification in U.S.) of the standard SCS-CN method as well as that can be used to more accurately predict direct runoff depth. The MoRE has two variables, rainfall intensity and relative saturation, but it does not require Ia be determined. A verification using laboratory and field data indicated that the MoRE can more accurately reproduce the observed direct runoffs than the standard SCS-CN method as well as its improved version.
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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 | 0.000 | 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 | 0.000 | 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 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".