Regional spatial variability of root-zone soil moisture in arid regions and the driving factors — a case study of Xinjiang, China
Bibliographic record
Abstract
Soil moisture is a key element of the hydrological cycle, and it significantly impacts the surface water and energy fluxes. However, a knowledge gap exists on the spatial variability of root-zone soil moisture at the regional scale in arid and hyperarid regions. Thus, soil moisture measurements at 142 sites were taken in Xinjiang (northwest China), and the relationships between soil moisture and 19 environmental factors were analyzed. The results showed that both absolute gravitational soil water content (SWC) and relative extractable water (REW) increased with increasing soil depth in the 0–100 cm soil profile. It generally decreased in the order of cropland > forestland > grassland > shrubland > bare land. Semivariograms suggested that SWC had moderate spatial dependence over a large range of 473–558 km, and REW was more randomly distributed at the regional scale in Xinjiang. Redundancy analysis suggested that environmental factors could explain 47.5%–50.9% of the variability of soil moisture, which was more strongly driven by land surface factors (p < 0.01) than by climatic factors (p > 0.05). Soil properties and other local variables explained, respectively, 40.7% and 32.3% of the variability of soil moisture in the 0–100 cm soil profile. Soil properties independently accounted for 12.8% and 28.1% of the variability in soil moisture in the 0–50 and 50–100 cm soil layers, respectively. Soil texture, field capacity, wilting point, organic carbon, bulk density, land use, and normalized difference vegetation index were the dominant factors influencing soil moisture variations.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".