Land use change effects on runoff generation in a humid tropical montane cloud forest region
Bibliographic record
Abstract
Abstract. While tropical montane cloud forests (TMCF) provide critical hydrological services to downstream regions throughout much of the humid tropics, catchment hydrology and impacts associated with forest conversion in these ecosystems remain poorly understood. Here, we compare the annual, seasonal and event-scale streamflow patterns and runoff generation processes of three neighbouring headwater catchments in central Veracruz (eastern Mexico) with similar pedological and geological characteristics, but different land cover: old-growth TMCF (MAT), 20 yr-old naturally regenerating TMCF (SEC) and a heavily grazed pasture (PAS). We used a 2 yr record of high resolution rainfall and stream flow data (2008–2010) in combination with stable isotope and chemical tracer data collected for a series of storms during a 6-week period of increasing antecedent wetness (wetting-up cycle). Our results showed that annual and seasonal streamflow patterns of the MAT and SEC were similar. In contrast, the PAS showed a 10% higher mean annual streamflow, most likely because of a lower rainfall interception. During the wetting-up cycle, storm runoff ratios increased at all three catchments (from 11 to 54% for the MAT, 7 to 52% for the SEC and 3 to 59% for the PAS). With the increasing antecedent wetness, hydrograph separation analysis showed progressive increases of pre-event water contributions to total stormflow (from 35 to 99% in the MAT, 26 to 92% in the SEC and 64 to 97% in the PAS). At all three sites, rainfall-runoff responses were dominated by subsurface flow generation processes for the majority of storms. However, for the largest and most intense storm (typically occurring once every 2 yr), sampled under wet antecedent conditions, the event water contribution in the PAS (34% on average) was much higher than in the forests (5% on average), indicating that rainfall infiltration capacity of the PAS was exceeded. This result suggests that despite the high permeability of the volcanic soils and underlying substrate in this TMCF environment, the conversion of forest to pasture may lead to important changes in runoff generation processes during large and high intensity storms. On the other hand, our results also showed that 20 yr of natural regeneration may be enough to largely restore the original hydrological conditions of this TMCF.
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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 | 0.000 |
| 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) | 0.001 | 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".