Shrub tundra ecohydrology: rainfall interception is a major component of the water balance
Bibliographic record
Abstract
Abstract As shrubs expand across the Arctic, they alter all cycles in the Earth system, including the water cycle. However, the coupling of shrubs with the water cycle during summer remains poorly understood. Rainfall interception, a major cause of divergent hydrological responses between vegetated and non-vegetated environments, is particularly poorly constrained. We quantified shrub rainfall interception and redistribution in birch and alder in the Western Canadian Arctic using networks of throughfall and stemflow gauges. We find that rainfall interception losses are a major component of the water budget, as effective rainfall was reduced by 15%–30% in the birches. Underneath alders, effective rainfall was almost as large or larger than gross rainfall, but they also left a rain shadow. The spatial variability in throughfall was substantial underneath both shrub species. Stemflow was a small but non-negligible component, as the alders concentrated ∼15% of rainfall to their few vertical stems, compared to the ∼8% the birches funnelled along their numerous, predominantly skewed stems. The substantial small-scale variability in effective rainfall may create islands in which conditions for certain biogeochemical processes are particularly favourable. On larger scales, rainfall interception reduces the water yield and thus the runoff received by downstream ecosystems such as lakes. The interception losses are predicted to increase with shrub density in a way that also depends on climatic conditions, with large losses in many coastal environments. The extent to which shrub expansion leads to drier Arctic ecosystems is, however, unclear because of the complex interplay between many ecohydrological processes. Shrub rainfall interception is one major, previously overlooked piece of this puzzle.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".