Precipitation assessment and hydrological implications of climate change in the high-altitude Indus basin
Bibliographic record
Abstract
In this PhD dissertation, the direct measurements of precipitation from different sources are integrated with the indirect estimates of precipitation at the major glacier zones to appraise spatial and altitudinal distribution of precipitation in the high-altitude Indus basin. These data are further adjusted for measurement errors and high-quality reference data of spatially distributed precipitation is developed to reconcile precipitation distribution. The reference data of temperature are developed using elevation and latitude dependent regression models. Performance of 27 widely used gridded precipitation products is evaluated at subregional scale. The best performing product is bias-corrected using the reference data of precipitation and temperature. Similarly, precipitation estimates of 75 GCM outputs are evaluated and two best performing GCMs representing warm-wet and cold-dry extremes under three RCPs (2.6, 4.5 & 8.5) are bias-corrected. The historical and future datasets developed therein are analysed to detect climate change and variability at sub-regional scale. A fully-distributed physically-based energy-balance Variable Infiltration Capacity (VIC) hydrological model is forced with these datasets to simulate the hydrologic regime of the study area at sub-basin scale. River inflows are analysed for change and variability in water availability, shifts in seasonality and annual cycle of river water, and changes in future hydrological extremes at Kabul-Nowshera, Indus-Tarbela, Jhelum-Mangla and Chenab-Marala rim stations.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".