SABAE‐HW: An Enhanced Water Balance Prediction in the Canadian Land Surface Scheme Compared with Existing Models
Bibliographic record
Abstract
The Canadian Land Surface Scheme (CLASS) is a numerical model pioneered at the Canadian Atmospheric Environment Service by Verseghy (1991) and Verseghy et al. (1993) to evaluate the vertical transfer of energy and water between the atmosphere and three surface soil layers. This article introduces SABAE‐HW (soil atmosphere boundary, accurate evaluations of heat and water), a new model built using the modeling framework of CLASS version 2.6 that allows a user to specify depth and number of soil layers. The physically based calculations of heat and moisture transfer are adequately extended in the new code to fit the desired refined mesh. The generalized minimal residual (GMRES) iterative method is used to resolve new soil heat flux terms. Moreover, a water table lower boundary condition is added to allow eventual coupling with groundwater models. The results of SABAE‐HW are validated for some synthetic runs under real‐like seasonal weather conditions and various soil types. Then the model prediction capability is confirmed for a location within the Assiniboine Delta Aquifer using North American Regional Reanalysis (NARR) atmospheric data. Intercomparisons of results to simulation outputs from CLASS, SHAW, HYDRUS‐1D, and HELP models demonstrate the capability of SABAE‐HW to predict reasonable moisture profiles and water budget terms. Under warm weather conditions, SABAE‐HW and SHAW yield similar water content profiles and bottom drainages. Also, in the freezing–thawing situations, detailed moisture profiles by SABAE‐HW are contrasted to both CLASS and SHAW solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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) | 0.001 | 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 teacher head, 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".