Assessing the Temporal Dynamics of Terrestrial Water Storage in Ten Large River Basins in China
Bibliographic record
Abstract
Understanding the temporal variations of terrestrial water storage (TWS) in large river basins is crucial for water resource management and ecosystem protection. Yet, the variations of TWS in large river basins are often been assessed in term of temporal trends with limited studies on the stationarity of TWS. In this study, we investigated the temporal trends and stability of TWS in then large river basins in China from 2004 to 2014 using the corrected Gravity Recovery and Climate Experiment (GRACE) gravity satellite data. Key findings are: (1) the average TWS in China showed a significant downward trend during the study period; and (2) the TWS in ten large river basins was non-stationary across China. Water surpluses were observed in the Northeast and the Southeast, while water deficits were found in the Northwest and the Southwest. This study provides policy-makers with critical information to design adaptive strategies and plans for basin-scale water resources management.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".