Evaluating the impact of projected CO2, temperature, and rainfall change on groundwater resources in a rice–wheat dominated cropping region of northwestern India
Bibliographic record
Abstract
Abstract Increasing CO2 concentration, temperature rise, and changes in rainfall due to climate change are expected to influence groundwater resources in irrigated agricultural regions. A simulation study using AquaCrop and MODFLOW models was undertaken to assess the combined effects of increasing CO2 concentrations, temperature, and rainfall changes on groundwater behavior in a rice–wheat cropping region of northwest India. Simulations were carried out for the 2016–2099 period under two scenarios: increasing CO2 concentrations corresponding to different RCPs (Scenario-I) and at a constant CO2 concentration of 369.4 ppm (Scenario-II). The results indicate that elevated CO2 negates the effect of rising temperature on evapotranspiration (ET) and water demand, and thus, lower ET is simulated under Scenario-I than Scenario-II for different RCPs during the future periods. The lower projected ET resulted in lower rice (2.3%–6.3%) and wheat (1.4%–16.1%) irrigation demand under Scenario-I than under Scenario-II. Of all RCPs, the lowest groundwater level (GWL) decline of 9.2, 20.5, and 24.4 m from the reference GWL (18.85 m) at the end of the early, mid-, and end-century periods, respectively, is projected under RCP8.5 and Scenario-I. Simulation results indicate that CO2 concentration plays an important role while assessing climate change effects on groundwater in irrigated agricultural systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".