Implementing a plant hydraulics parameterization in the Canadian Land Surface Scheme Including biogeochemical Cycles (CLASSIC) v.1.4
Bibliographic record
Abstract
Drought conditions caused by soil moisture stress and/or high vapour pressure deficit pose a challenge to many terrestrial ecosystem models (TEMs). The Canadian LAnd Surface Scheme Including biogeochemical Cycles (CLASSIC) employs an empirical approach to link soil moisture stress with stomatal conductance. Such soil moisture-based empirical approaches typically perform poorly during drought. Here, we implemented an explicit plant hydraulics parameterization, i.e., Stomatal Optimization based on Xylem hydraulics (SOX), in CLASSIC, thereby connecting the soil-plant-atmosphere continuum through plant hydraulic traits. Performance of the resulting CLASSIC$_{SOX}$ was evaluated against carbon and water fluxes measured with eddy covariance at eight boreal forest flux tower sites in North America. Compared to CLASSIC, CLASSIC$_{SOX}$ better simulated gross primary productivity (GPP) across all sites, i.e., coefficient of determination (R$^{2}$) increased (0.51 to 0.59), root mean square error (RMSE) and bias decreased (1.85 to 1.54 g C m$^{-2}$ d$^{-1}$) and (-0.99 to -0.58 g C m$^{-2}$ d$^{-1}$), respectively. Under drought conditions, identified using the Palmer drought severity index, GPP simulated with CLASSIC$_{SOX}$ improved compared to CLASSIC, i.e., R$^{2}$ increased (0.51 to 0.60), and RMSE and bias decreased (1.79 to 1.46 g C m$^{-2}$ d$^{-1}$) and (-0.97 to -0.53 g C m$^{-2}$ d$^{-1}$), respectively. In contrast, CLASSIC$_{SOX}$ simulated evapotranspiration worsened, i.e., R$^{2}$ decreased (0.61 to 0.42), RMSE increased (0.54 to 0.62 mm d$^{-1}$), and bias changed direction (0.09 to -0.09 mm d$^{-1}$). As evaporation is a highly parameterized process in CLASSIC, it likely needs to be re-parameterized to account for the SOX transpiration behaviour.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".