Effect of Elevation on Variation in Reference Evapotranspiration under Climate Change in Northwest China
Bibliographic record
Abstract
Through its effects on water and energy cycles, elevation plays an important role in modulating the spatial distribution of climatic changes in mountainous regions. A key hydrological indicator, reference evapotranspiration (ET0) reflects the maximum amount of water transferred to the atmosphere from the land surface. The current scarcity of information regarding elevation’s impact on variation in ET0 under climate change limits our understanding of the extent to which elevation modulates interactions between ET0 and climate change and of the attendant processes involved. Drawing upon long-term (1960–2017) meteorological observations from 84 stations in Northwest China (NWC), we examined (i) spatial and temporal variations in ET0; (ii) the sensitivity and contribution of air temperature (T), sunshine duration (SD), relative humidity (RH), and wind speed (WS) to ET0; (iii) the existence of a relationship between elevation and ET0 trends; and (iv) the major factor in controlling this relationship by using attribution analysis. Overall, annual ET0 in NWC showed a declining trend between 1960 and 2017, though at a change point in 1993, the trend shifted from a decline to a rise. A significant correlation between temporal change in ET0 and elevation confirmed the existence of a relationship between elevation and ET0 variation. The effect of elevation on changes in ET0 depended mainly on the elevation-based tradeoff between the contributions of T and WS: WS was the primary factor contributing to the decrease in ET0 below 2000 m, and T was the dominant factor contributing to the increase of ET0 above 2000 m. The rate of reduction in WS declined as elevation increased, thereby diminishing its contribution to variation in ET0. The present study’s results can serve to guide agricultural irrigation in different elevation zones under NWC’s evolving climatic conditions.
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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 | 0.002 |
| 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 | 0.001 | 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".