Snow-detonated floods: Assessment of the U.S. midwest march 2019 event
Bibliographic record
Abstract
Iowa and the Nishnabotna watershed (Iowa), Midwest U.S. Historically, Iowa and the Midwest have faced floods during the summer season. Some historical floods on record are the 2008 and 2013 floods. In March 2019, a meteorological bomb cyclone set the conditions for an unexpected major snow-related flood. This study (1) presents a comprehensive analysis of the March 2019 flood and asses the early-spring peak flows trends, (2) explores the use of a parsimonious hydrological model with a snow component, and (3) validates the model performance for the last 20 years. The March 2019 event was an extreme flood event that set records on at least 10% of the USGS gauges in Iowa. Moreover, the early spring peak flow analysis showed a significant increasing trend between February and April. In this period, the trend is positive for most gauges, with more than a 30% increase at an annual rate of 4% of the mean yearly peak flow. These findings showed the relevance of snow-detonated floods and their regional understanding. Considering the results' significance, we provided evidence that HLM and a conceptual snow component can represent, forecast, and provide insights regarding snow-detonated events.
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".