Evaluating climate change impacts on the hydrology of watershed in northwestern China using a stepwise‐clustered downscaling approach
Bibliographic record
Abstract
ABSTRACT In this study, a stepwise‐clustered downscaling model (SCDM) is advanced for transferring atmospheric simulation outputs to acquire high‐resolution climate projections at a large‐scale watershed system. SCDM can operate different temporal resolutions of atmospheric variables with continuous and discrete complexities. SCDM coupling with hydrological model is used for evaluating climate change impacts on hydrology of the Kaidu watershed in northwestern China. The daily and monthly series of large‐scale atmospheric simulation outputs for the Kaidu watershed are extracted from the ensemble of GCMs during past, recent and future periods. Results reveal that (1) SCDM is capable of downscaling climate projections for different stations, and can help understand the spatial heterogeneity of climate change, (2) the performance of SCDM is more acceptable for temperature than precipitation, (3) increase trends of Tmin and Tmax (minimum and maximum temperatures) from recent to future are projected. Besides, results from multiple downscaled climate change projections are used for driving a daily climate‐streamflow hydrological model. Results disclose that the streamflow would increase because temperature change will cause more glacier melt in future.
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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 | 0.001 |
| 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) | 0.001 | 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".