Climate Change Impacts on the Hydrology of the Brahmaputra River Basin
Bibliographic record
Abstract
Climate change (CC) is impacting the hydrology in the basins of the Himalayan region. Thus, this could have significant implications for people who rely on basin water for their lives and livelihoods. However, there are very few studies on the Himalayan river basins. This study aims to fill this gap by presenting a water balance for the Brahmaputra River Basin using the Soil and Water Assessment Tool (SWAT). Results show that snowmelt contributed about 6% of the total annual flow of the whole Brahmaputra, 21% of the upper Brahmaputra, and 5% of the middle Brahmaputra. The basin-wide average annual water yield (AWY) is projected to increase by 8%, with the maximum percentage increase in the pre-monsoon season. The annual snowmelt is projected to decrease by 17%, with a marked decrease during the monsoon but an increase in other seasons and the greatest percentage reduction in the upper Brahmaputra (22%). The contribution of snowmelt to AWY is projected to decrease while rain runoff will increase across the entire Brahmaputra and also in the upper and middle Brahmaputra. The impact assessment suggests that the upper Brahmaputra will be most affected by CC, followed by the middle Brahmaputra. The results can be used to support future water management planning in the basin taking into account the potential impact of CC.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".