Hargreaves Equation as an All-Season Simulator of Daily FAO-56 Penman-Monteith ETo
Bibliographic record
Abstract
This work showed the Hargreaves equation (HG) can be modified into a highly efficient computational replacement for the FAO-56 Penman-Monteith equation and its auxiliary functions for the computation of daily reference evapotranspiration (ETo) estimation when only available data are daily temperature data. By modifying all the constants (i.e., '0.0023', '0.5' and '17.8') in HG and adding a new constant, a modified HG (HG1234) gave almost identical estimates of daily ETo as FPM, with modified coefficient of efficiency, 𝐸1 = 0.99, 𝑟2 = 1.00, and MAE of 0.00 mm/d for a weather station in Accra, Ghana. HG1234 and ten other less drastic modifications of HG were compared against FPM at simulated average wind speeds, 𝑢2, of 0.5 m/s, 2.0 m/s and 4.0 m/s for daily estimates of ETo. In general the uncalibrated HG predicted FPM with very low 𝐸1 at 𝑢2 other than the global average of 2 m/s, and the more drastic the modification of HG the higher its efficiency at simulating FPM ETo at all wind velocities. Thus although HG was not originally developed for daily ETo estimation, modifying it to HG1234 can turn it into a very efficient and much faster simulator of FPM daily ETo estimation that would be very useful in applications, such as areal ETo estimation research using satellite images, where fast and frequent re-evaluations of FPM ETo for millions of points are necessary.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".