Quantifying rainfall and cloud water interception in upland forests of Norfolk Island
Bibliographic record
Abstract
Abstract The higher elevation (>200 m ASL) forests of Norfolk Island are regularly immersed in the clouds and scientific and anecdotal evidence suggests that in addition to rainfall, water is likely to be collected as cloud droplets are intercepted by the forest canopy. This water is likely to be important for the local hydrology and ecology, yet it has never been quantified. To address this, a field measurement campaign was established to measure hydrological inputs to the forest floor at two elevated (290 and 310 m ASL) forest sites in the Norfolk Island National Park over a 524‐day period. Instrumentation included throughfall and stemflow measurement systems and recording rain gauges in the open in nearby clearings. Sites exhibited very high stem density and basal area and delivery of water to the forest floor was dominated by stemflow because of the funnelling characteristics of the dominant palm and pine trees. Both sites showed similar hydrological behaviour with stemflow and throughfall of around 48% and 32% of total atmospheric inputs, respectively. Stemflow contributions of 48% far exceed observations from the literature on cloud‐affected ecosystems which are typically less than 10%. Rainfall rarely occurred in the absence of low‐level cloud and some cloud immersion events lasted for many days with hydrologic inputs continuing for extended periods despite rainfall not being observed in the open. Cloud water interception accounted for approximately 20% of total water input at both sites which is equivalent to 25% extra water on top of rainfall measured in the open. From an island‐wide perspective, the calculated additional hydrological input is only small due to the limited spatial extent of upland forest; however, the additional water is likely to be very important to local hydrological processes and the unique plants, insects and animals which inhabit the upland forests of Norfolk Island.
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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".