Effects of Irrigation in India on the Atmospheric Water Budget
Bibliographic record
Abstract
Abstract The effect of large-scale irrigation in India on the moisture budget of the atmosphere was investigated using three regional climate models and one global climate model, all of which performed an irrigated run and a natural run without irrigation. Using a common irrigation map, year-round irrigation was represented by adding water to the soil moisture to keep it at 90% of the maximum soil moisture storage capacity, regardless of water availability. For two focus regions, the seasonal cycle of irrigation matched that of the reference dataset, but irrigation application varied between the models by up to 0.8 mm day−1. Because of the irrigation, evaporation increased in all models, but precipitation decreased because of a strong decrease in atmospheric moisture convergence. A moisture tracking scheme was used to track individual evaporated moisture parcels through the atmosphere to determine where these lead to precipitation. Up to 35% of the evaporation moisture from the Ganges basin is recycling within the river basin. However, because of a decreased moisture convergence into the river basin, the total amount of precipitation in the Ganges basin decreases. Although a significant fraction of the evaporation moisture recycles within the river basin, the changes in large-scale wind patterns due to irrigation shift the precipitation from the eastern parts of India and Nepal to the northern and western parts of India and Pakistan. In these areas where precipitation increases, the relative precipitation increase is larger than the relative decrease in the areas where precipitation decreases. It is concluded 1) that the direct effects of irrigation on precipitation are small and are not uniform across the models; 2) that a fraction of up to 35% of any marginal evaporation increase (for example, due to irrigation) will recycle within the river basin; and 3) that when irrigation is applied on a large scale, the dominant effect will be a change in large-scale atmospheric flow that decreases precipitation in eastern India and increases it in western and northern India.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".