Identifying Runoff Production Mechanisms for Dam Safety Applications in the Colorado Front Range
Bibliographic record
Abstract
Hydrologic analyses are used in dam safety evaluations to determine the flow a dam must pass without failing. Many current guidelines model flood runoff solely by an infiltration-excess mechanism. Saturation-excess runoff and subsurface stormflow mechanisms are known to be important for common events in forested regions, but few studies have analyzed their role in extreme events. The objectives of this study are to determine the active runoff mechanisms for large historical storms, design storms in the Colorado Front Range, and propose methods to model these mechanisms that dam safety consultants can use. Hydrologic models are developed for five basins to simulate historical flood events in 1976 and 2013 as well as various design storms. The model results (and available in-situ soil moisture observations) show that the entire soil layer approached saturation during the 2013 storm, which had a long duration and low rainfall intensities. Thus, saturation-excess runoff was likely the dominant mechanism. In contrast, the modeled soil layer does not approach saturation for the 1976 storm, which had a short duration and high rainfall intensities, so infiltration-excess runoff was likely the dominant mechanism. Similarly, infiltration-excess runoff dominates for short duration (2-h) design storms, while saturation-excess dominates for longer (6-h and above) design storms in the Front Range basins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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".