HOW WILL CLIMATE CHANGE IMPACT THE STORM MAGNITUDE AND THROUFALL IN SEVERAL FOREST AREAS IN IRAN?
Bibliographic record
Abstract
Abstract. Across all forest systems, the primary driver of throughfall (TF) amount is rainfall amount (Pg) though no work has addressed the sensitivity of the TF response to projected shifts in Pg due to climate change. We assessed how climate change may impact TF sensitivity to variability in Pg for eleven typical forest sites across the main climate types of Iran using a nondimensional relative sensitivity coefficient. The Coupled Model Intercomparison Project phase 5 (CMIP5) HadGEM2-ES product was used under two emission scenarios (Representative Concentration Pathway (RCP) 2.6 and 8.5) to project yearly precipitation and Pg for the measurement sites during 2020–50. There was a strong linear relationship between TF and Pg at all sites [TF = 0.66 (Pg) – 0.30; R2 = 0.91; n = 639]. The sensitivity coefficient ranged from 0.96–5.3 across the eleven forest sites. Large sensitivity coefficient differences were found between small (< mean annual Pg) and large (> mean annual Pg) storms for arid plantations. To buffer expected shifts in storm size due to climate change, it may be suitable to incorporate TF sensitivity when choosing landscaping and urban greening. Shifts in Pg and increased small storm frequency are predicted for 2020–50 per CMIP5 HadGEM2-ES low and high emission scenarios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".