A modified trapezoid framework model for partitioning regional evapotranspiration
Bibliographic record
Abstract
Abstract While evapotranspiration (ET) is normally measured as one hydrologic component, evaporation (E), and transpiration (T) result from different physical‐biological processes. Using a two‐source model, a trapezoid framework has been widely applied in recent years. The key to applying the trapezoid framework model is the determination of the dry/wet boundaries of the land surface temperature‐fractional vegetation coverage trapezoid (LST‐fc). Although algorithms have been developed to characterize the two boundaries, there remains a significant uncertainty near the wet boundary which scatters in a discrete and uneven manner. It is therefore difficult to precisely locate the wet boundary. To address this problem, a Wet Boundary Algorithm (WBA) was developed in this study with the algorithm applied in the region of Huang‐Huai‐Hai plain of China, using the Pixel Component Arranging and Comparing Algorithm (PCACA) to retrieve ET from MODerate‐resolution Imaging Spectroradiometer (MODIS) Data. The eddy covariance (EC) measurements from Yucheng station was used to verify the modified model where the root mean square error (RMSE) of 17.8 W/m2, Bias of −7.2 W/m2 for latent heat flux (LE) simulation in 28 cloudless test days. The ratio of transpiration to evapotranspiration (T/ET) varied between 0.48 and 0.81 over the Huang‐Huai‐Hai plain. The spatial and temporal distribution of ET revealed that agriculture practices have a significant influence on the hydrological cycle, where crop growth promotes the magnitude of ET. Likewise, harvesting activities significantly reduce ET. The proposed WBA algorithm significantly reduces the uncertainty of the trapezoid ET model caused by wet edge positioning. The analysis of the impact of agricultural activities on ET provide a better understanding how human activities change the hydrological cycle at regional scales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".