ASSESSMENT OF CLIMATE CHANGE IMPACTS ON PRECIPITATION AND TEMPERATURE: A CASE STUDY ON THE KRISHNA RIVER BASIN
Bibliographic record
Abstract
In this study, the statistical downscaling model (SDSM) is employed for downscaling the precipitation (PREC), maximum temperature (Tmax), and minimum temperature (Tmin) over the Krishna River Basin (KRB). The Canadian Earth System Model, version 2 (CanESM2) General Circulation Model (GCM) outputs were considered as predictor variables. First, the SDSM was calibrated using the data for a 30-year period (1961-1990) and subsequently validated with the data for a 15-year period (1991-2005). Upon perceiving a satisfactory performance, the SDSM was then used for projecting the predictand variables (PREC, Tmax, and Tmin) for the 21st century considering three representative concentration pathway (RCP) scenarios viz. RCP2.6, RCP4.5, and RCP8.5. The future period was divided into three 30-year time slices named epoch-1 (2011-2040), epoch-2 (2041-2070), and epoch-3 (2071-2100), respectively. The period 1976-2005 was considered as baseline period and all the future results were compared with this data. The results were analysed at various temporal scales, i.e., monthly, seasonal, and annual. The study has reveals that the KRB is going to become wetter during all the seasons. The results are discussed for the worst-case scenario, i.e., RCP8.5 epoch-3. The average annual maximum and minimum temperature is expected to increase. The extreme event analysis is also carried out considering the 90th and 95th percentile values. It is noticed that the extreme (90th and 95th percentiles) are going to increase. There are extreme events that go beyong extreme values. The outcome of this study can be used in flood modeling for the KRB and also for the modeling of future irrigation demands along with the planning of optimal irrigation in the KRB culturable area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".