Climate change, growing season water deficit and vegetation activity along the north–south transect of eastern China from 1982 through 2006
Bibliographic record
Abstract
Abstract. Considerable work has been done to examine the relationship between environmental constraints and vegetation activities represented by the remote sensing-based normalized difference vegetation index (NDVI). However, the relationships along either environmental or vegetational gradients are rarely examined. The aim of this paper was to identify the vegetation types that are potentially susceptible to climate change through examining their interactions between vegetation activity and evaporative water deficit. We selected 12 major vegetation types along the north–south transect of eastern China (NSTEC), and tested their time trends in climate change, vegetation activity and water deficit during the period 1982–2006. The result showed significant warming trends accompanied by general precipitation decline in the majority of vegetation types. Despite that the whole transect increased atmospheric evaporative demand (ET0) during the study period, the actual evapotranspiration (ETa) showed divergent trends with ET0 in most vegetation types. Warming and water deficit exert counteracting controls on vegetation activity. Our study found insignificant greening trends in cold temperate coniferous forest (CTCF), temperate deciduous shrub (TDS), and three temperate herbaceous types including the meadow steppe (TMS), grass steppe (TGS) and grassland (TG), where warming exerted more effect on NDVI than offset by water deficit. The increasing growing season water deficit posed a limitation on the vegetation activity of temperate coniferous forest (TCF), mixed forest (TMF) and deciduous broad-leaved forest (TDBF). Differently, the growing season brownings in subtropical or tropical forests of coniferous (STCF), deciduous broad-leaved (SDBF), evergreen broad-leaved (SEBF) and subtropical grasslands (STG) were likely attributed to evaporative energy limitation. The growing season water deficit index (GWDI) has been formulated to assess ecohydrological equilibrium and thus indicating vegetation susceptibility to water deficit. The increasing GWDI trends in CTCF, TCF, TDS, TG, TGS and TMS indicated their rising susceptibility to future climate 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".