Detecting significant decreasing trends of land surface soil moisture in eastern China during the past three decades (1979–2010)
Bibliographic record
Abstract
Abstract Understanding the historical trends and driving mechanism of China's soil moisture change is an important step in combating climate change. Using the time series satellite‐derived Essential Climate Variable Soil Moisture (ECV_SM) product, we detected a significant decrease trend in land surface soil moisture in eastern China over a 32 year period (1979–2010). Theoretical sensitivity analysis suggested that soil moisture is regulated collectively by precipitation (P), potential evapotranspiration (PET), land surface conditions such as land cover/use changes, landscape features, irrigation and urban expansion, (m), and the water balance between input and output water supplies O (the input water supplies minus the output). The change in spatial pattern and temporal trend of P/PET is highly consistent with the corresponding change in soil moisture. The magnitude of soil moisture variation is also well correlated with that of P/PET (R2 = 0.43; p < 0.001). Therefore, P/PET is believed to be the dominant factor in determining the temporal trends of soil moisture change. Among the 29 drainage basins with significant decreasing trend of soil moisture change, the areas of forest cover increased by 36.08% and the average topographic slope was twice steeper than that of other regions. Therefore, besides the climate factor (P/PET variable), land surface conditions (such as land cover changes and topographic) also played important roles in regulating the trend of regional soil moisture 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".