Predicting evapotranspiration change in a successionnal forest without eddy covariance measurements
Bibliographic record
Abstract
In this study we developed a minimalist conceptual model to predict actual evapotranspiration (AET) during summer from soil water depletion data. The approach was applied to three experimental sites in Western Canada which represent a forest succession (different stand ages in a similar environment). The objective was to gain insights into the role of forest stand age, interannual climate variability, and the Horton index (rate of vaporization to wetting) in controlling summer AET. We used continuous soil moisture and precipitation data over 20 years to drive the model at different temporal resolutions. The results were compared with observed AET measurements (eddy-covariance data). The results show that easy-to-measure soil water depletion can be used to make predictions of summer AET for half-hourly and daily time scales. The approach could predict the differences from 5 to 35% of AET among the differently aged stands. The results are sensitive to the canopy architecture, active root depth and stand age. Moreover, we find that the model is reliable despite summer dry-wet transitions due to sporadic rainfall events. Finally, the interannual variability of the Horton index at each stand implies different root water uptake strategies during landscape succession. If the model should prove successful for a wider range of forest landscapes, envisioned applications include estimates at sites where eddy covariance data are inexistent or impossible (e.g. complex terrain) or for large-scale water balance assessments using remote-sensing techniques.
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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) | 0.000 | 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".