Historical changes in overtopping probability of dams in the United States
Bibliographic record
Abstract
With concerns about aging dams and nonstationary changes in hydrologic extremes (e.g., flooding), questions arise about whether existing dams may be at risk of failure and pose threats to society. Here, we analyzed 33 dams across the United States to investigate temporal trends in dam overtopping probabilities of annual maximum dam water levels. These dams were selected because of the availability of public domain long-term time series of uncontrolled water levels (50 years or longer). We applied updated stationary frequency analyses using generalized extreme value distributions on 30-year rolling periods from 1973 to 2022. The results revealed an overall increasing trend in the number of dams exhibiting critical overtopping probabilities (i.e., low, moderate and high) alongside a decline in the number of non-critical overtopping probabilities (i.e., very low) over time. This approach uncovered overtopping probabilities that traditional analyses based solely on dam water levels could not reveal. We identified six dams having the greatest overtopping probability, with several being located near large population centers, posing potential risks to the downstream communities. All six dams are classified as large and high-hazard potential. This study provides insights into dam management and risk assessment, emphasizing the need for proactive measures to mitigate potential threats.
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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 | 0.000 | 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 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".