Comparison of terrestrial evapotranspiration estimates using the mass transfer and Penman‐Monteith equations in land surface models
Bibliographic record
Abstract
The mass transfer (MT) equation and the Penman‐Monteith (PM) equation are two common approaches used in various land surface models for simulating evapotranspiration (ET). Yet assessments are rarely conducted to determine how well these structurally differing equations simulate ET across various biomes and climatic environments with different canopy upscaling strategies. We evaluated the capacity of models to estimate ET using the MT equation with the one‐leaf strategy in the Community Land Model version 4 and the PM equation in the Dynamic Land Model using the one‐leaf and two‐leaf upscaling approaches for 22 selected eddy covariance flux towers representing 10 typical plant functional types. Overall, across half‐hourly, daily, monthly, and seasonal scales, the MT equation performed less robust than the PM equation in forests. The former had 8–15% higher root‐mean‐square error and 1–4% lower index of agreement and a large uncertainty in warm and wet seasons for several sites. It leaves a doubt about its application of estimating ET across regional to global scales. Considering the net radiation available on the surface of leaf/soil and adopting the two‐leaf approach made the PM equation closer to the EC measurements on average but still could not capture the variation during the cold season. We suggest that further improvements in simulation of ET require seasonal variation of some key parameters and quantification of spatial heterogeneity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".