How Probable Is Widespread Flooding in the United States?
Bibliographic record
Abstract
Abstract Widespread flooding can cause major damages and substantial recovery costs. Still, estimates of how susceptible a region is to widespread flooding are largely missing mainly because of the sparseness of widespread flood events in records. The aim of this study is to assess the seasonal susceptibility of regions in the United States to widespread flooding using a stochastic streamflow generator, which enables simulating a large number of spatially consistent flood events. Furthermore, we ask which factors influence the strength of regional flood susceptibilities. We show that susceptibilities to widespread flooding vary regionally and seasonally. They are highest in regions where catchments show regimes with a strong seasonality, that is, the Pacific Northwest, the Rocky Mountains, and the Northeast. In contrast, they are low in regions where catchments are characterized by a weak seasonality and intermittent regimes such as the Great Plains. Furthermore, susceptibility is found to be the highest in winter and spring when spatial flood dependencies are strongest because of snowmelt contributions and high soil moisture availability. We conclude that regional flood susceptibilities emerge in river basins with catchments sharing similar streamflow and climatic regimes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".