Validation of ET Estimates from the Canadian Prairie Agrometeorological Model for Contrasting Vegetation Types and Growing Seasons
Bibliographic record
Abstract
The objective of this study was to establish the ability of the prairie agrometeorological model (PAMII) to simulate daily evapotranspiration (ET). Specifically, modelled ET estimates from PAMII were validated using daily ET estimates from eddy-covariance systems at West Nose Creek (barley field located northwest of Calgary, Alberta) and a FluxNet site (short-grass prairie located west of Lethbridge, Alberta). Additionally, PAMII was validated for three contrasting growing seasons at the grassland site to establish the model’s ability to quantify the effect of different growth conditions on ET. PAMII performed well and was able to capture the day-to-day variability of the ET at both sites. PAMII successfully captured the increase (decrease) in accumulated growing season ET for the wet (dry) growing seasons at the short-grass prairie site. Moreover, the optimal reference minimum stomatal resistance term was significantly lower for the barley crop (50 s m–1) than the corresponding value for the short-grass prairie (80 s m–1). At the grassland site the optimal stomatal resistance term varied markedly depending on the growing conditions; the optimal value for the wet year was 60 s m–1 compared to 90 s m–1 for the dry year. That is, no single value worked best for all years, and our findings caution against using a single value for the reference minimum stomatal resistance. In summary, PAMII captured the salient features of the ET variability at both sites and for contrasting at the grassland site. However, our research has identified several areas where future versions of the PAMII model might be improved.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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".