Quantifying Topographic Effects on Carbon and Water Fluxes Over Mountainous Areas
Bibliographic record
Abstract
Abstract Relative to flat surfaces, mountain terrains modify solar radiation absorbed by vegetation on sloping surfaces, causing changes in mass and energy fluxes, including gross primary productivity (GPP) and evapotranspiration (ET). However, these changes are generally ignored in regional and global ecosystem models and their magnitudes have not been systematically evaluated. In this study, we first validated the Biosphere‐atmosphere Exchange Process Simulator (BEPS) model against measured GPP and ET over mountainous sites, and then applied it to a mountainous region (Fujian Province, China). In BEPS, the topographic effects are systematically considered in the following steps: (1) the satellite‐derived leaf area index (LAI) is projected to sloping surfaces, (2) canopy radiative transfer is modeled relative to the normal to the slope, and (3) the modeled fluxes are reprojected from sloping to horizontal surfaces. Step (1) decreases LAI as sloping surfaces are larger than the corresponding horizontal surfaces, but Step (3) increases fluxes in the opposite way. Because of the nonlinear relationships between fluxes and LAI, GPP and ET simulations without considering the topographic effects are always underestimated, especially on sunlit slopes. The underestimation increases with increasing slope, and for slopes greater than 40°, GPP is underestimated by 11% and ET by 33%, suggesting that existing global GPP and ET products could have been significantly underestimated in mountainous regions.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".