A global view of future socioeconomic impact of floods based on hybrid simulations
Bibliographic record
Abstract
<!--!introduction!--> Floods are generally considered as one of the most significant extreme events in terms of casualties and losses. Although they are a direct consequence of weather and climate extremes, a full understanding of the influence of climate change on their socioeconomic impact requires the consideration of the human component in terms of exposure and vulnerability. At the same time, a global view of flood risk based on large catalogues of physically consistent events is of key interest to environmental research, climate science, economics, and financial risk management. However, the lack of flexibility to account for socioeconomic variables and the high computational cost to produce large global event sets of flood impact for future climate scenarios make the use of regional hydrological models unpractical. As an alterative, in this work we use the global flood modeling framework developed by Carozza and Boudreault (C&B) (Carozza & Boudreault, 2021). The C&B model applies statistical and machine learning methods to relate historical flood occurrence and impact data with climatic, watershed, and socioeconomic factors for 4,734 basins at Pfafstetter level 5 globally. The model is climate‐consistent, global, fast, flexible, and ideal for applications that do not necessarily require high‐resolution flood mapping. After training with observational data in the period 1986-2017, the climate variables are replaced with bias-corrected output from the NCAR CESM Large Ensemble (40 members) to project flood impact up to 2060. We produce a variety of event sets to assess how climate change and future socioeconomic growth may affect flood impact.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".