Is the annual maximum leaf area index an important driver of water fluxes simulated by a land surface model in temperate forests?
Bibliographic record
Abstract
In land surface models, vegetation is often described using plant functional types (PFTs), a classification that aggregates plant species into a few groups based on similar characteristics. Within-PFT variability of these characteristics can introduce considerable uncertainty in the simulation of water fluxes in forests. Our objectives were to (i) compare the variability of the annual maximum leaf area index (LAImax) within and between PFTs and (ii) assess whether this variability leads to significant differences in simulated water fluxes at a regional scale. We classified our study region in southwestern Quebec (Canada) into three PFTs (evergreen needleleaf, deciduous broadleaf, and mixed forests) and characterized LAImax using remotely sensed MODIS-LAI data. We simulated water fluxes with the Canadian Land Surface Scheme (CLASS) and performed a sensitivity analysis. We found that within-PFT variability of LAImax was 1.7 times more important than variability between PFTs, with similar mean values for the two dominant PFTs, deciduous broadleaf forests (6.6 m2·m−2) and mixed forests (6.3 m2·m−2). In CLASS, varying LAImax within the observed range of values (4.0–7.5 m2·m−2) led to changes of less than 2% in mean evapotranspiration. Overall, LAImax is likely not an important driver of the spatial variability of water fluxes at the regional level.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".