Multisite Downscaling of Monsoon Precipitation over the Godavari River Basin under the RCP 4.5 Scenario
Bibliographic record
Abstract
Climate change is considered to be the greatest challenge faced by mankind in the twenty first century. Distribution and circulation of the waters of the earth become increasingly difficult to determine because of additional uncertainty related to anthropogenic emissions. Climate change and its potential hydrological effects are increasingly contributing to the uncertainty which creates problem for water management planner to decide future demand and availability of water. Projecting the impact of climate change on hydrological cycle at river basin level is necessary to quantify the possible changes in the hydrological components. In the present study, Statistical downscaling is applied to monthly monsoon precipitation over Godavari river basin, India. Mean sea level pressure, specific humidity and 500 hPa geopotential height are used as explanatory predictors. 1 degree*1 degree gridded rainfall data over Godavari river basin are collected from Indian Meteorological Department (IMD). Future scenario of monsoon rainfall over different IMD grid points over the basin is projected by applying the statistical downscaling to The Norwegian Earth System Model (NorESM1-M) and Canadian Earth System Model (CanESM2) simulations under the Representative Concentration Pathways 4.5 (RCP 4.5) scenario of Fifth Coupled Model Inter-Comparison Project (CMIP 5). Downscaling procedure is applied to all 25 IMD grid points over the basin to find out the special distribution of monsoon rainfall for the future scenarios. Results indicate that the monsoon rainfall over the entire basin is showing an increasing trend.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".