Copula based Assessment of Climate Extremes across India: Past and Future
Bibliographic record
Abstract
Climate change significantly influences the global hydrological cycle and consequently affects climatic extremes. The present study is focussed upon varying patterns of climate extremes using observed daily precipitation (1989-2019), daily temperature from Global Meteorological Forcing Dataset (GMFD) (1985-2016) and simulated daily meteorological forcing data (2025-2055 and 2065-2095) of 21 GCMs attained from the statistically downscaled dataset, NEX-GDDP (NASA Earth Exchange Global Daily Downscaled Projections) under RCP4.5 and RCP8.5 scenario across India. The copula method was employed to estimate the joint return period based on different climate extreme indices. Here, we found that R20, R95p and CWD attain an increasing trend and CDD mostly shows a decreasing trend in major segments of country in future. Based upon the 10-year joint return periods (1989-2019), it is found that parts of north-western, north-eastern, southern, western region and Western Ghats are highly prone towards floods and a large portion of the country is susceptible to co-occurrence of floods and droughts. Moreover, the study shows that many regions with less vulnerability towards precipitation extremes would become more vulnerable in future. Furthermore, TXx, TNx, TX90p, TN90p, TNn and TXn are found to be significantly increasing in future except increasing during 2065-2095 under RCP4.5 predominantly across the country. And, TX10p and TN10p follows a significantly decreasing trend in future across the except exhibiting a decreasing trend during 2065-2095 under RCP4.5, throughout the country. With the projected increase in hot days/nights, the frequency of concurrence of extreme number of hot days (TX90p) and nights (TN90p) within a year would increase in the future across the country. The present study provides useful information on the regional distribution of climate extremes and how they might change in the future. This information can further contribute to facilitate an effective planning strategy to improve resilience towards climate extremes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".