Assessment of evapotranspiration models applied to a watershed of Canadian Prairies with mixed land-uses
Bibliographic record
Abstract
Using meteorological data collected in the summer of 1996 and 1997, three evapotranspiration (ET) models, the Penman–Monteith (PM), the modified Penman for non-saturated surface, and the two-source models were applied to estimate hourly ET from different land-use covers of the Paddle River Basin (area = 265 km2). By assuming closed canopy conditions, the PM model could estimate the ET of coniferous forest and agricultural land because the contribution of soil evaporation under these land covers is not significant. For mixed forest and pasturelands that are partially vegetated, however, the amount of soil evaporation is significant and so PM underestimated the total ET. The modified Penman model mainly underestimated hourly ET during daytime when the atmosphere is unstable, but overestimated ET during early morning and late afternoon (stable atmosphere), particularly for pastureland. By re-establishing a relationship for the relative evaporation, the ET simulated by the modified Penman model improved. Both PM and the modified Penman are assessed with respect to the comprehensive, two-source model's simulated ET because the latter agrees favourably with the ET obtained from the basin water balance. The water balance ET is reliable because it is based on the streamflow, surface temperature, net radiation and soil moisture simulated by the semi-distributed hydrological model, Semi-distributed Physically based Hydrologic Model–Remote Sensing, DPHM-RS (host to the two-source model), all of which have been demonstrated to agree well with the observed data. Copyright © 2000 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".