Hydrological projections in the upper reaches of the Yangtze River Basin from 2020 to 2050
Bibliographic record
Abstract
Understanding the impact of climate change on runoff is essential for effective water resource management and planning. In this study, the regional climate model (RCM) RegCM4.5 was used to dynamically downscale near-future climate projections from two global climate models to a 50-km horizontal resolution over the upper reaches of the Yangtze River (UYRB). Based on the bias-corrected climate projection results, the impacts of climate change on mid-twenty-first century precipitation and temperature in the UYRB were assessed. Then, through the coupling of a large-scale hydrological model with RegCM4.5, the impacts of climate change on river flows at the outlets of the UYRB were assessed. According to the projections, the eastern UYRB will tend to be warm-dry in the near-future relative to the reference period, whereas the western UYRB will tend to be warm-humid. Precipitation will decreases at a rate of 19.05-19.25 mm/10 a, and the multiyear average annual precipitation will vary between - 0.5 and 0.5 mm/day. Temperature is projected to increases significantly at a rate of 0.38-0.52 °C/10 a, and the projected multiyear average air temperature increase is approximately 1.3-1.5 ℃. The contribution of snowmelt runoff to the annual runoff in the UYBR is only approximately 4%, whereas that to the spring runoff is approximately 9.2%. Affected by climate warming, the annual average snowmelt runoff in the basin will be reduced by 36-39%, whereas the total annual runoff will be reduced by 4.1-5%, and the extreme runoff will be slightly reduced. Areas of projected decreased runoff depth are mainly concentrated in the southeast region of the basin. The decrease in precipitation is driving this decrease in the southeast, whereas the decreased runoff depth in the northwest is mainly driven by the increase in evaporation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".