Estimating actual evapotranspiration using water budget and soil water reduction methods
Bibliographic record
Abstract
Studies on estimation of actual evapotranspiration on disturbed lands are scarce and yet such data are essential in hydrologic modeling. Our study compared the variability of estimates of actual evapotranspiration (AET) from a reclaimed site in northern Alberta using the simplified water budget (WB) and soil water reduction (SWR) methods. The AET estimates from the simplified water budget equation (AET1) required field soil water content, precipitation and runoff. The AET estimates from the soil water reduction method (AET2) required daily potential evapotranspiration (PET), field capacity (FC) water content, minimum field-measured water content, and field water content. Soil water was measured using neutron moisture gauge every 2 wk during the growing season for a 2-yr period. The average AET1 and AET2 estimates for 2001 were 1.9 and 1.4 mm per day, respectively, where as those for 2002 were 2.1 and 1.2 mm per day, respectively. The paired t-tests to compare AET1 against AET2 indicated significance differences (P ≤ 0.05) in 5 out of 11 measurement dates, especially during high rainfall periods. Overall AET2 estimates were more variable than AET1 estimates. Therefore, the larger variability of AET2 estimates imply less reliability of spatially averaged AET2 estimates for use in regional and global circulation models. Key words: Extractable water, reclaimed land, water budget, soil water reduction, potential evapotranspiration
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".