An empirical method for approximating canopy throughfall
Bibliographic record
Abstract
Abstract Rainfall replenishes surface and subsurface water but is partially intercepted by a canopy. However, it is challenging to quantify the rainfall passing through the canopy (i.e. throughfall). This study derives simple‐to‐use empirical equations relating throughfall to canopy and rainfall characteristics. Monthly throughfall is calculated by applying a mass balance model on weather data from Singapore; Vancouver, Canada; and Stanford, USA. Regression analysis is then performed on the calculated throughfall with three dependent variables (i.e. maximum canopy storage, average rainfall depth and time interval between two consecutive rainfall in a month) to derive the empirical equations. One local equation is derived for each location using data from that particular location, and one global equation is derived using data from all three locations. The equations are further verified with calculated monthly throughfall from other weather data and actual throughfall field measurements, giving an accuracy of about 80–90%. The global equation is relatively less accurate but is applicable worldwide. Overall, this study provides a global equation through which one can quickly estimate throughfall with only information on the three variables. When additional weather data are available, one can follow the proposed methodology to derive their own equations for better estimates. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".