Infiltration and soil water dynamics in a tropical dry forest: it may be dry but definitely not arid
Bibliographic record
Abstract
Abstract Studies of hydrological processes in tropical dry forests are less than 1% of published forest hydrology literature. The strong dry‐wet seasonality typical of the tropical dry forest ecoregion is similar to those that characterize semi‐arid regions. In semi‐arid systems, infiltration is often limited by low hydraulic conductivities ( K ) and extreme levels of soil water repellency, which when combined with high rainfall intensities, result in infiltration excess (Hortonian) overland flow (HOF) as a dominant runoff mechanism. Given that little is known about the surface runoff hydrology of tropical dry forests, we tested the hypothesis that our knowledge about controls on runoff in semi‐arid systems is transferrable to tropical dry forest hillslopes. Our results show that tropical dry forest soils do develop a strong water repellency during the dry season; however, this does not persist through the wet season. In our period of study, surface K ranged from 9 to 164 mm/h and was greater than the rainfall intensity for more than 75% of the rain events. In our period of study, rainfall intensities were generally low with more than half of the storm events falling between 0.2 and 4.2 mm/h. The low rainfall intensities, high K and lack of repellent surfaces during the wet season result in the percolation of >70% of the annual rainfall through the upper 30 cm of soil, indicating that subsurface flow, not HOF, is the primary runoff mechanism. These findings show that in spite of similar climate and vegetation regimes, hydrological knowledge from semi‐arid catchments is not transferrable to tropical dry forests. Copyright © 2014 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".