Evaluation of leaf‐to‐canopy upscaling methodologies against carbon flux data in North America
Bibliographic record
Abstract
Despite the wide acceptance of the “big‐leaf” upscaling strategy in evapotranspiration modeling (e.g., the Penman‐Monteith model), its usefulness in simulating canopy photosynthesis may be limited by the underlying assumption of homogeneous response of carbon assimilation light‐response kinetics through the canopy. While previous studies have shown that the separation of the canopy into sunlit and shaded parts (i.e., two‐leaf model) is typically more effective than big‐leaf models for upscaling photosynthesis from leaf to canopy, a systematic comparison between these two upscaling strategies among multiple ecosystems has not been presented. In this study, gross primary productivity was modeled using two‐leaf and big‐leaf upscaling approaches in the Boreal Ecosystem Productivity Simulator for shrublands, broadleaf, and conifer forest types. When given the same leaf‐level photosynthetic parameters, the big‐leaf approach significantly underestimated canopy‐level GPP while the two‐leaf approach more closely predicted both the magnitude and day‐to‐day variability in eddy covariance measurements. The underestimation by the big‐leaf approach is mostly caused by its exclusion of the photosynthetic contributions of shaded leaves. Tests of the model sensitivity to a foliage clumping index revealed that the contribution of shaded leaves to the total simulated productivity can be as high as 70% for highly clumped stands and seldom decreases below ∼40% for less‐clumped canopies. Our results indicate that accurate upscaling of photosynthesis across a broad array of ecosystems requires an accurate description of canopy structure in ecosystem models.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".