A Global Data Set for Economic Losses of Extreme Hydrological Events During 1960‐2014
Bibliographic record
Abstract
Abstract A comprehensive data set of extreme hydrological events (EHEs)—floods and droughts, consisting of 2,171 occurrences worldwide, during 1960‐2014 was compiled, and then their economic losses were normalized using a price index in U.S. dollar. The data set showed a significant increasing trend of EHEs before 2000, while a slight post‐2000 decline. Correspondingly, the EHE‐caused economic losses increased obviously before 2000 followed by a slight decrease; the post‐2000 decline could be partially attributed to the decreases in drought and flood‐prone area or climate adaptation practices. Spatially, Asia experienced most EHEs (969), corresponding to the largest share of economic losses (approximately $868 billion for floods and $50 billion for droughts, respectively), while Oceania had the least EHEs (102) and the least economic losses (approximately $19 billion for floods and $45 billion for droughts). The five countries with the highest EHE‐caused economic losses were China, United States, Canada, Australia, and India. Countries that suffered the highest flood‐caused economic losses were China, United States, and Canada. This data set provides a quantitative linkage between climate science and economic losses at a global scale, and it is beneficial for the regional climatic impact assessments and strategical development for mitigating climate change impacts.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".