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 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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".