How likely are widespread floods in US river basins? Seeking answers using a stochastic, wavelet-based approach
Bibliographic record
Abstract
Flooding can affect large regions leading to high economic and societal costs. Estimating regional flood risk is crucial for developing adaptation strategies, public awareness policies, and protection structures. Yet, estimating regional flood hazard is not trivial because of the few large flood events observed. Here, we derive regional flood hazard estimates for large river basins in the United States by using a stochastic streamflow generator. This allows us to increase the number of flood events available for the analysis and to investigate the simultaneous occurrence of flooding in different parts of a river basin. We propose the continuous, stochastic simulation approach (PRSim.wave), which combines a non-parametric spatio-temporal model based on the wavelet transform with the parametric kappa distribution. The model reproduces the temporal and distributional characteristics of streamflow at individual sites and retains the spatial dependencies between sites even for spatial extremes. We use PRSim.wave to generate long and spatially consistent time series of daily discharge for a large set of catchments in the conterminous United States. For each catchment, we extract flood events from the simulated series using a peak-over-threshold approach to derive a spatial dataset of flood occurrences. Using this dataset, we estimate how probable it is that a certain percentage of stations within a specific river basin is jointly flooded. We show that: (1) there are strong regional differences in the likelihood of joint and potentially widespread flooding and (2) there are spatial differences in regional flood hazard estimates which could not be derived from observed data only. We deem our approach a valuable tool for water managers and policy makers to make informed decisions on the risk of widespread flooding.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".