Rainfall intensity switches ecohydrological runoff/runon redistribution patterns in dryland vegetation patches
Bibliographic record
Abstract
Effectively managing net primary productivity in drylands for grazing and other uses depends on understanding how limited rainfall input is redistributed by runoff and runon among vegetation patches, particularly for patches that contrast between lesser and greater amounts of vegetation cover. Due in part to data limitations, ecohydrologists generally have focused on rainfall event size to characterize water redistribution processes. Here we use soil moisture data from a semiarid woodland to highlight how, when event size is controlled and runoff and interception are negligible at the stand scale, rainfall intensity drives the relationship between water redistribution and canopy and soil patch attributes. Horizontal water redistribution variability increased with rainfall intensity and differed between patches with contrasting vegetation cover. Sparsely vegetated patches gained relatively more water during lower intensity events, whereas densely vegetated ones gained relatively more water during higher intensity events. Consequently, range managers need to account for the distribution of rainfall event intensity, as well as event size, to assess the consequences of climate variability and change on net primary productivity. More generally, our results suggest that rainfall intensity needs to be considered in addition to event size to understand vegetation patch dynamics in drylands.
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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".