IMPACT OF CLIMATE CHANGE ON RAINFALL IN THE IRRIGATED INDUS BASIN: A CASE STUDY IN THE LOWER CHENAB CANAL SYSTEM
Bibliographic record
Abstract
Impact of climate change on the water resources is considerable for the future policy making.Climate change impact on the irrigated Indus basin is also significant as it is in the upper Indus basin.In hydrological cycle, rainfall is the most important component and has significant contribution in the crop water requirement.Recharge in the aquifer is not a hidden phenomenon during the monsoon period in the irrigated Indus basin.Impact of climate change on the rainfall was studied using the Hadley Climate model version 3 (HadCM3).HadCM3 provides the A2 and B2 scenario and its impact on the future climatic parameters.Statistical downscaling model (SDSM) was used for downscaling the rainfall in the selected area of the Faisalabad irrigation zone.NCEP predictors was used for the assessment of the downscaled data using SDSM.Percentage change in the rainfall was observed for the midcentury (2040-2069) as compared to the base period .Results reveled the increase in the rainfall during the Rabi season.While significant decrease in the rainfall was observed during the monsoon season.Maximum percentage decrease in the rainfall was observed 6.42% and 61.9% in the month of November under A2 and B2 scenarios, respectively.Similarly, maximum percentage increase in the rainfall was observed 10.4.6% and 101.4% in the month of November under A2 and B2 scenarios, respectively.Decrease in the rainfall was observed in the months of monsoon and in April.While the increase in the rainfall was observed in the remaining period.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".