Comparison of two modeling approaches for water transfer in the soil-vegetation-atmosphere continuum
Bibliographic record
Abstract
As part of a R&D project aiming at providing vineyard managers with a computer-based decision support system for optimizing irrigation, we discuss the validity of two modeling approaches for water transfers in the soil-plant-atmosphere continuum. The two models are an empirical ‘bucketlike’ model and a mechanistic model based on the 1D Richards’ equation. The practical context implying that soil-water parameters are poorly estimated, both models are compared in terms of accuracy and parameterization cost. The models are coupled to the same canopy growth, radiation absorption, evaporation and transpiration models, and the predawn leaf water potential (PLWP) is used as the indicator of soil-water deficit. The parameters of both models are estimated with pedotransfer functions from the single texture and the only additional input for the 1D model is root repartition versus depth. A multi-factor sensitivity analysis relative to the input parameters of the soil-vegetation-atmosphere coupling shows that the hard-to-obtain rooting depth is the key factor of the PLWP sensitivity. It also shows that the computation of PLWP with the 1D model is robust against root distribution uncertainty, so that root distribution can be fixed to a mean value. The accuracy of the two models is evaluated in vineyards varying in soil type, rooting depth and irrigation procedure in the Languedoc region (France), by comparing simulated to measured values of vine transpiration and PLWP. Provided the calibration of rooting depth, both models give results coherent with field measurements, with no significant improvement when the more physical mechanistic model is used.
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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 | 0.002 |
| 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 | 0.001 |
| 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".