Development and Application of ETCalc, a Unique Online Tool for Estimation of Daily Evapotranspiration
Bibliographic record
Abstract
Various empirical methods that use meteorological data have been developed for estimating evapotranspiration. However, there are currently no online tools available for the estimation of daily evapotranspiration based on user-provided daily data. Here, we introduce ETCalc (https://etcalc.hydrotools.tech), a free, unique online tool that integrates eight methods (i.e. Penman-Monteith, Thornthwaite, Blaney – Criddle, Turc, Priestley – Taylor, Hargreaves, Jensen – Haise and Abtew) for estimation of daily potential evapotranspiration, reference evapotranspiration and, by employing user-defined crop (or cover) coefficients, daily actual evapotranspiration, based on user-provided daily meteorological data. ETCalc has been developed in response to the effort of the Canadian federal government to encourage easier and open access to science and is applicable to any area for which basic meteorological data are available and hence, its suitability is not restricted to particular geographical areas. Through a streamlined interface, ETCalc allows for uploading of user-provided data, tabular and graphical inspection of the input and output data, as well as export of the output data and of the associated metadata. The use of ETCalc is exemplified using 10-year daily meteorological data from Charlottetown, Prince Edward Island, Canada for comparing the output from each of the ETCalc methods and for the calculation of the precipitation deficit.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.020 |
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".