Observed Interannual Variability and Projected Scenarios of Drought using Drought Indicators
Bibliographic record
Abstract
The observation-based analysis of drought development in the Uttar Pradesh region in India showed that, despite the area being relatively large, agricultural drought exhibits high spatial variability. However, the lack of net radiation data hinders the capacity to provide reliable estimates of evapotranspiration (ET), affecting the assessment of drought occurrence since its propagation across the hydrological system becomes very sensitive to the estimation of ET. The most prominent precipitation deficits occur during the monsoon season (June to October), showing that changes in the large-scale circulation are responsible for the impact of severe drought. El Niño-Southern Oscillation (ENSO) modulates the variability of drought with a warm phase favoring drought development with the strongest influence between August and October. The climate change projections under RCP4.5 and RCP8.5 scenarios suggest the intensification of drought events in the Uttar Pradesh region in the mid-century, with the Chambal River of the Ganges River basin being the most affected area in terms of precipitation and temperature. The projected scenarios correspond to an increase of 1.7°C for mean temperature, and 3.5°C for minimum and maximum temperature in the 2050 horizon, and a decrease of 400 to 800 mm for annual precipitation was projected under both RCPs.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".