Importance of soil heating, liquid water loss, and vapor flow enhancement for evaporation
Bibliographic record
Abstract
Field measurements conducted by Cahill and Parlange (1998) are reanalyzed to verify if their conclusion that daytime peak values of 60–70 W m−2 of latent heat flux divergence occurred in the 7–10 cm soil layer of a drying Yolo silt loam when maximum values of surface latent heat flux are estimated to have been about 100 W m−2. The new analyses, as similar to theirs as possible, are validated using a numerical simulation of coupled soil moisture and heat flow based on Philip and de Vries (1957) as a test bed. The numerical simulation is extended to include the flow of air induced by diurnal soil heating and evaporative water loss to verify the flux divergence calculations reported in Parlange et al. (1998) that explained the findings of Cahill and Parlange (1998). It is shown that the conclusions of both of these papers are in error, so that the original version of the Philip and de Vries (1957) theory is consistent with their field measurements after all and the effects of airflow associated with soil heating and liquid water loss (and low-frequency barometric pressure variations also considered) are negligible in practice. In an additional investigation, enhancement of diffusive vapor flow (first postulated by Philip and de Vries (1957)) and discussed extensively in the literature since is shown to have negligible effects on cumulative evaporation under field conditions.
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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 | 0.000 |
| 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) | 0.001 | 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".