Differences in Spatiotemporal Variability of Potential and Reference Crop Evapotranspirations
Bibliographic record
Abstract
Potential evapotranspiration (ETp) and reference crop evapotranspiration (ETo) are two key parameters in hydrology, meteorology, and agronomy. ETp and ETo are related to each other but have different meanings and applications. In this study, the ETp and ETo were distinguished and calculated with the Penman and FAO56 PM equations using the weather data of 551 stations in China from 1961 to 2018. The differences in their spatiotemporal variations were examined with an MMK test, an R/S test, and wavelet analysis. The monthly ETp and ETo were close but the ETp was always larger than the ETo, with values ranging from 1 to 356 mm and 2 to 323 mm, respectively. Their differences varied in different months and sub-regions. The maximum monthly difference transferred from south to north and then back to the south in a yearly cycle, showing spatiotemporal heterogeneity. The annual values of the ETp and ETo were also close, but the ETp was significantly higher than the ETo. The increasing future trends of ETp but decreasing trends of ETo were tested at most sites in China. Although the primary periods were almost the same, their spatial distribution was slightly different. In conclusion, ETp is different from ETo and they should be applied carefully. This study performs a thorough comparison and reveals the underlying basis of and discrepancy between ETp and ETo.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".