Discharge regime and simulation for the upstream of major rivers over Tibetan Plateau
Bibliographic record
Abstract
The hydrological regimes for the major river basins in the Tibetan Plateau (TP), including the source regions of the Yellow (UYE), Yangtze (UYA), Mekong (UM), Salween (US), Brahmaputra (UB), and Indus (UI) rivers, were investigated through a land surface model and regression analyses between climate variables and runoff data. A hydrologic modeling framework was established across the TP to link the Variable Infiltration Capacity (VIC) land surface hydrology model with a degree‐day glacier‐melt scheme (VIC‐glacier model) at a 1/12° × 1/12°. The model performance was evaluated over the upper basins of the six rivers. The heterogeneity and scarcity of the meteorological stations are the major limitation for hydrological modeling over the TP. The relative contributions to streamflow from rainfall, snowmelt, and glacier melt for the six basins were quantified via the model framework and simulation. The results suggest that monsoon precipitation has a dominant role in sustaining seasonal streamflow over southeastern regions, contributing 65–78% of annual runoff among the UYE, UYA, UM, US, and UB basins. For the UI, the runoff regime is largely controlled by the glacier melt and snow cover in spring and summer. The contribution of glacier runoff is minor for the UYE and UM (less than 2% of total annual flow), and moderate for the UYA and US basins (5–7% of yearly flow), while glacier melt makes up about 12% and 48% of annual flow for the UB and UI basins, 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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".