The effects of spatial aggregation of complex topography on hydroecological process simulations within a rugged forest landscape: development and application of a satellite-based topoclimatic model
Bibliographic record
Abstract
We evaluated the effects of topographic complexity on landscape carbon and hydrologic process simulations within a rugged mixed hardwood forest by developing and applying a satellite-based hydroecological model at multiple spatial scales. The effects of topographic variability were evaluated by aggregating raster-based digital elevation model and satellite-derived leaf area index inputs across eight different spatial resolutions from 30 m (62 208 pixels) to 2160 m (12 pixels). Our modeling analysis showed that the effect of topography was the strongest on solar radiation and temperature, intermediate on soil water and evapotranspiration, and ambiguous on soil respiration. Spatial aggregation of model inputs smoothed heterogeneous spatial patterns of modeled output variables relative to fine-scale results. Model outputs varied nonlinearly with different levels of spatial aggregation, while spatial variability of model inputs and outputs were dampened at increasingly coarse aggregation levels. Biases in spatially aggregated model predictions were generally less than ±10%, except for solar radiation, which showed biases of up to +50% at coarser spatial scales. The large positive bias in the solar radiation implies that overestimation of biophysical variables that are sensitive to solar radiation (e.g., photosynthesis and net primary production) may be considerable in rugged forested landscapes unless subgrid scale effects are accounted for.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".