Climate Change Impact on Flow Discharge of Kunhar River Catchment using Snowmelt Runoff Model
Bibliographic record
Abstract
Most of the runoff in Kunhar Catchment is generated from the snowmelt which makes it more sensitive to climate change. The Snowmelt-Runoff Model (SRM), a simple degree-day model, has been applied around the world under different climatic regions to evaluate the hydrological effects of climate change.The current study encompasses the use of Snowmelt-Runoff Model (SRM) to forecast stream flows in Kunhar river catchment depicting three varying climate change scenarios i.e. a) an increase in +2°C temperature b) an increase in +2°C temperature along with 20% increase in precipitation, and c) a 20% increase in precipitation. The study area was distributed with respect to varying altitude with 500 m elevation interval. Moderate resolution Imaging Satellite (MODIS) daily snow product MOD10A1 is used to map snow cover. SRM model was simulated and later climate change run was evaluated. SRM Model was calibrated for 2003 – 2004 and validated for 2005 – 2006 with an average coefficient of 0.93 R2 and average seasonal volume difference Dv of 1.46%. The stimulated results for scenario “b” show an increase of flow discharge by 27%. Whereas, there was 21% and 6% increment in discharge simulated for scenarios “a” and “c” respectively.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".