Trends in Extreme Precipitation Events in the Indus River Basin and Flooding in Pakistan
Bibliographic record
Abstract
In the absence of a sufficiently dense network of climate stations covering all topographic regions of the Indus River basin and delivering high quality data over the last 30 years or more, daily precipitation data were obtained from the National Centers for Environmental Prediction-Department of the Enviornment (NCEP-DOE) Reanalysis 2 dataset for the period 1979 to 2011. The daily precipitation data were transformed into time series of frequency of extreme precipitation events of 1-day and 10-day durations defined in terms of 90th and 99th percentile threshold exceedances. The non-parametric Mann-Kendall trend test was applied to determine whether statistically significant changes in precipitation extremes occurred over time, in due consideration of autocorrelation in the data.Extreme precipitation showed a high spatial variability, with the highest daily and 10-day precipitation totals, and thus highest 90th and 99th percentiles, in the southeastern lowlands at the foot of the Himalayas and the lowest in the Karakorum. Significantly decreasing trends in extreme precipitation were observed in the western part of the Indus River basin; significantly increasing trends were mainly detected in the very high mountainous regions in the east (Transhimalaya and Himalayas) and in the north (Hindu Kush and Karakorum) of the Indus basin. High precipitation rates are not common in the arid climate of these high mountainous regions. Future flood management plans need to consider the increasing trends in extreme precipitation events in these areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".