Unveiling the Climate Change Impact and Suitability Assessment of CMIP5 and CMIP6 Emission Scenarios for the Mahanadi Reservoir Project Complex, Chhattisgarh
Bibliographic record
Abstract
Evaluation of future temperature and precipitation is essential for managing water supplies, reducing the impact of natural disasters, and expanding agricultural opportunities. In the present study, recently released Coupled Model Intercomparison Project Phase 6 (CMIP6) and CMIP5 were analyzed concerning the Mahanadi Reservoir Project (MRP), Complex, Chhattisgarh. The Statistical Downscaling Model (SDSM) projected climatic variables of shared socioeconomic pathways and representative concentration pathways (SSPs-RCPs) such as SSPs 245, SSPs 585, and RCP 4.5, and RCP 8.5, respectively, for two different timescales (2023–2060, 2061–2099). In recognition of subsequent timescales, the chosen GCM CCCmaCanESM2 was found to be the most efficient among CMIP5, whereas MPI-ESM1-2-HR was used for CMIP6. Higher temperatures and less precipitation are predicted in high-emission scenarios (SSP5-8.5 and RCPs 8.5) compared to mid-emission scenarios (SSP2-4.5 and RCPs 4.5). Therefore, the findings of this study could be utilized to forecast the hydrological cycle and analyze the sustainability of the environment. Furthermore, this study will be relevant for future water resource management and adaptation efforts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".