Anthropogenic and climatic drivers of the 2022 mega-flood in Pakistan
Bibliographic record
Abstract
The convergence of climatic and anthropogenic factors that triggered the August 2022 mega-flood in Southern Pakistan caused 1486 fatalities and approximately $30 billion in economic damages. After a multi-year drought, the pre-monsoon rainfall was 111% higher than the long-term average of 1951–2021, increasing soil moisture by 30% in the Indus Basin floodplains. Monsoon rains were 547% above average, with record-breaking cumulative weekly rainfall in July (200 mm) on already saturated soils. Upstream drainage catchments (e.g., Chenab, Jhelum, and Ravi) received 33% and 41% more rain in pre-monsoon and monsoon periods, respectively. August 2022’s streamflow at Sukur Barrage, just upstream of the floodplains, was 170% larger than the historical average, due to the compounding effects of rain-on-snow and warmer temperatures accelerating snowmelt. The magnitude of multi-day consecutive rainfall events is projected to increase in Southern Pakistan by 2099 in a high-emission scenario (SSP5-8.5), making the catastrophic 2022 flood a forewarning of elevated future flood risks.
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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 | 0.000 | 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".