Assessment of climate change impact on rainfall for studying water availability in upper Mahanadi catchment, India
Bibliographic record
Abstract
Abstract The paper deals with the projected rainfall for eight rain gauge stations located in the upper Mahanadi catchment in Chhattisgarh state of India and corresponding changes on the water availability in few reservoirs of the catchment. Rescaled predictors obtained from NCEP were used and statistically tested for selection of best-fit set of predictors using percentage reduction methods. The calibrated and validated models were used to generate multiple series for early, mid and late century periods, i.e. for 2020–2035 (FP-1), 2046–2064 (FP-2) and 2081–2099 (FP-3) under CMIP5 climatic forcing conditions of RCP2.6, RCP4.5 and RCP8.5 using predictors data of CanESM2, Canadian GCM. The comparisons of future predicted rainfall with the base period (1981–2003) showed mixed trends, viz. declining trend at five stations, both declining and increasing trend at two stations, and increasing trend at one station. The predicted reduced rainfall during August and September attribute to a significant impact on paddy cultivation and industrial development. The analysis of future catchment rainfall on five important reservoirs in this region indicated a reduction of 12–29% seasonal rainfall with respect to the base period rainfall; while for one reservoir not much variation (–7 to 5%) in the rainfall was noted, possibly due to the large catchment area.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".