Hotspots of Global Water Resource Changes and Their Causes
Bibliographic record
Abstract
Abstract In recent decades, terrestrial water storage anomaly (TWSA) has experienced systematic shifts. Despite these observations, debates continue regarding the hotspots where terrestrial water storage changes dramatically and their causes. This study aims to address these controversies. Utilizing four TWSA products, this research analyzes TWSA's changing patterns and identifies hotspots of significant shifts from 1982 to 2019. The study employed the Bayesian Three‐Cornered Hat method to synthesize the best‐quality TWSA from original four TWSA products and the trends consistent method to identify regions with highly consistent trends. Subsequently, the elasticity coefficient method was used to reveal the causes of TWSA's dramatic changes in hotspots. Results show that TWSA has a declining trend over 66.1% global terrestrial areas during 1982–2019, with an average rate of −0.5 mm/y. The study identified six regions where marked changes in TWSA occurred, including Northern China, Southern Canada, Northern India, Central‐Southern Europe, Southwestern Africa, and Northeastern South America. Attribution analysis reveals that the leaf area index is the predominant factor affecting TWSA changes, dominating in 40.3% of global regions. Potential evapotranspiration (PET) follows closely, dominating in 39.8% of global regions. Meanwhile, only 13.1% and 6.8% of global regions are primarily influenced by precipitation and cropland density respectively. The dominant factor varies in different latitudes. Vegetation greening primarily controls TWSA changes in the high‐latitude regions of the Northern Hemisphere. This study identified hotspots of TWSA changes and investigated the causes of these variations. Those results will offer direction for prioritizing areas in future water resource management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".