Runoff generation in a steep, tropical montane cloud forest catchment on permeable volcanic substrate
Bibliographic record
Abstract
Most studies to date in the humid tropics have described a similar pattern of rapid translation of rainfall to runoff via overland flow and shallow subsurface stormflow. However, study sites have been few overall, and one particular system has received very little attention so far: tropical montane cloud forests (TMCF) on volcanic substrate. While TMCFs provide critical ecosystem services, our understanding of runoff generation processes in these environments is limited. Here, we present a study aimed at identifying the dominant water sources and pathways and mean residence times of soil water and streamflow for a first‐order, TMCF catchment on volcanic substrate in central eastern Mexico. During a 6‐week wetting‐up cycle in the 2009 wet season, total rainfall was 1200 mm and storm event runoff ratios increased progressively from 11 to 54%. With the increasing antecedent wetness conditions, our isotope and chemical‐based hydrograph separation analysis showed increases of pre‐event water contributions to the storm hydrograph, from 35 to 99%. Stable isotope‐based mean residence times estimates showed that soil water aged only vertically through the soil profile from 5 weeks at 30 cm depth to 6 months at 120 cm depth. A preliminary estimate of 3 years was obtained for base flow residence time. These findings all suggest that shallow lateral pathways are not the controlling processes in this tropical forest catchment; rather, the high permeability of soils and substrate lead to vertical rainfall percolation and recharge of deeper layers, and rainfall‐runoff responses appeared to be dominated by groundwater discharge from within the hillslope.
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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 | 0.000 | 0.001 |
| 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 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".