Assessing the hydrological impacts of climate change in the headwater catchment of the Tarim River basin, China
Bibliographic record
Abstract
Two statistical downscaling models were used to downscale regional climate change scenarios, on the basis of the outputs of three general circulation models (GCMs) and three emission scenarios. Driven by these climate change scenarios, a distributed macro-scale hydrological model (the Variable Infiltration Capacity (VIC) model) was applied to assess the impact of climate change on hydrological processes in the headwater catchment (HC) of the Tarim River basin, China. The results showed that the HC tends to experience warmer and drier conditions under the combined climate change scenarios. The predictions show a decreasing trend of the runoff in the HC, driven by the combined climate change scenarios. The results predicted an increasing trend for winter runoff however, which was consistent with the forecasts from most previous studies on other locations such as the region of St Lawrence tributaries (Quebec, Canada) and the Willamette River Basin (Oregon, USA). There was an inconsistent intra-annual distribution of the changes in precipitation and runoff in the HC; these inconsistencies may be explained by increasing snowmelt runoff resulting from higher air temperature. It was concluded that uncertainties within different GCM outputs are more significant than emission scenarios in the assessment of the potential impact of climate change.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".