Analysis and Calibration of Empirical Relationships for Estimating Evapotranspiration in Qatar: Case Study
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it