Spatiotemporal variation of snow cover over the Tibetan Plateau based on MODIS snow product, 2001–2014
Bibliographic record
Abstract
ABSTRACT In this paper, we investigate the spatiotemporal characteristics and trends of snow cover fraction (SCF) in the Tibetan Plateau (TP) and seven upstream river basins (Yellow, Yangtze, Mekong, Salween, Brahmaputra, Indus and Yarkant) by employing the Moderate Resolution Imaging Spectroradiometer (MODIS) data for 2001–2014. The possible linkage between the SCF, and temperature and precipitation changes over the TP and individual basins is also investigated. Results suggest that the distribution of snow cover over the TP exhibit a large spatiotemporal heterogeneity with high SCFs mainly concentrating on the southern and western edges which is strongly linked to the moist air supplies. The distribution of snow cover is highly dependent on the elevations, with a higher SCF and a later onset of snow melt at the higher elevation zones than at the lowers. There is an elevation threshold existing for separating two distinct snow cover regimes, which are 4000 m for the western basins and 5000 m for the southeastern basins. The snow cover over the TP has slightly decreased by about 1.1% during 2001–2014, with dramatic reductions mostly lying in the heavy snowy regions and some light increases occurring in the areas with annual mean SCFs mostly less than 10%. The reduction rates of snow cover increase with the rising of altitudes for the TP average, and the basins of Indus, Yarkant, Salween and Brahmaputra. At the same time, the Yellow and Yangtze basins exhibit larger increasing rates of snow cover at the higher elevations zones. The SCF variations are linked to the temperature and precipitation changes. Precipitation tends to be the major factor impacting the snow cover changes in the TP during 2001–2014.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".