Fog Precipitation Plays a Significant Role in Providing Moisture to the Caspian High Latitude Forests of Northern Iran
Bibliographic record
Abstract
ABSTRACT Fog precipitation is likely essential for the mountainous Hyrcanian forests of northern Iran, which is experiencing water stress. It is a source of moisture that is often overlooked. The impact of fog on some temperate deciduous forests, such as these Hyrcanian stands, is believed to be significant, but there are still limited quantitative assessments of this contribution. This study directly addresses this knowledge gap by quantifying fog precipitation within a pure natural stand of oriental beech ( Fagus orientalis Lipsky) at 2000 m asl during the growing seasons (foliated periods) of 2022 and 2023. The study site had a tree density of 217 trees ha −1 , mean height of 19.1 m and DBH of 41.0 cm. The site was equipped with 50 throughfall, 10 open field rainfall and 6 stemflow collectors. Rainfall and rainfall‐fog events were visually separated. During the measurement period, 76 rainfall and rainfall‐fog events were recorded. Rainfall‐fog events accounted for 80% of all events. The cumulative amounts of rainfall and rainfall‐fog events were 23.1 and 287.4 mm, respectively. Half of the events (all rainfall‐fog events) exhibited negative interception. Despite having the same number of events, negative I events generated higher amounts of cumulative throughfall (238.7 mm) and stemflow (8.1 mm) and lower amount of cumulative interception value (−40.1 mm). Fog precipitation was estimated 20.3 mm in our measurement site during two leaf‐out seasons. This fog capturing potential of the beech forests during the growing season was equal to roughly 100 m 3 ha −1 per growing season. Our findings demonstrated that the Hyrcanian beech forests play a crucial role in enhancing water availability, particularly during dry periods, through effective fog capturing potential. Integrating fog water inputs into regional water resource management and forest conservation strategies is needed for ensuring the sustainability of these valuable ecosystems.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".