Analysis and Calibration of Empirical Relationships for Estimating Evapotranspiration in Qatar: Case Study
Bibliographic record
Abstract
Knowledge of evapotranspiration (ETo), which is the process of water loss from vegetated soils due to evaporation and transpiration, is important in real-time irrigation management and water-resource allocation, particularly in water-scarce regions. In this study, several methods used in estimating evapotranspiration, including the Blaney-Criddle, Hargreaves-Samani, Jensen-Haise, Linacre, and Turc methods were calibrated and validated against the Penman-Monteith model, which is considered as the standard method of estimating evapotranspiration. The paper utilizes data from the Doha International Airport meteorological station over a period of 30 years (January 1985–December 2014). ETo values were estimated using the different methods. These values were then compared to those obtained by the Penman-Monteith method. Using appropriate indicators, the Turc method was found to be the best for estimating ETo over Doha (R2=0.9519, RMSE=1.4511 mm day−1, and MAE=1.1633 mm day−1). The Turc method comes in handy for estimating ETo over Qatar as it utilizes only three meteorological parameters (mean temperature, relative humidity, and solar radiation), which are easily measurable over that area.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".