Multi-model ensemble projection of mean and extreme streamflow of Brahmaputra River Basin under the impact of climate change
Bibliographic record
Abstract
Abstract The streamflow of Brahmaputra River Basin is vital for sustainable socioeconomic development of the Ganges delta. Frequent floods and droughts in the past decades indicate the susceptibility of the region to climate variability. Although there are multiple studies investigating the basin's future water availability, most of those are based on limited climate change scenarios despite the wide range of uncertainties in different climate model projections. This study aims to provide a better estimation of projected future streamflow for a combination of 18 climate change scenarios. We develop a hydrologic model of the basin and simulate the future water availability based on these climate change scenarios. Our results show that the simulated mean annual, mean seasonal and annual maximum streamflow of the basin is expected to increase in future. By the end of the 21st century, the projected increase in mean annual, mean dry season, mean wet season, and annual maximum streamflow is about 25, 178, 11, and 22%, respectively. We also demonstrate that this projected streamflow can be expressed as a multivariate linear regression of projected changes in temperature and precipitation in the basin and would be very useful for policy makers to make informed decision regarding climate change adaptation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".