Streamflow response to land use/land cover change in the tropical Andes using multiple SWAT model variants
Bibliographic record
Abstract
Study region: Tropical watershed in the Colombian Andes, the Chico River (CR) watershed. Study focus: Hydrological models are widely used to project the impacts of LULC (Land Use/Land Cover) change on water budget. However, their ability to produce reliable predictions depends on how accurately they represent the role of vegetation in the watershed’s water balance. We analyze how different representations of Leaf Area Index (LAI) affect streamflow responses to LULC change using the Soil and Water Assessment Tool (SWAT) model. We also examine streamflow response to 100 % pasture cover (PAS), 100 % forest cover (FOR), and a control scenario using the original SWAT model, SWAT-T, and a proposed new variant (SWAT-Tb), which improves LAI bimodal representation for tropical regions. New hydrological insights for the region: SWAT-T and SWAT-Tb reproduce observed LAI and streamflow in the CR watershed. However, SWAT-T restricts LAI simulation to unimodal seasonality, while the original SWAT reproduces streamflow but not LAI seasonality. Results using SWAT-T (unimodal LAI) and SWAT-Tb (constant and bimodal LAI) show streamflow increases during the dry seasons for the FOR scenario and decreases for the PAS scenario. Conversely, the original SWAT, with its default LAI representation, tends to underestimate and overestimate streamflow changes in the FOR and PAS scenarios, respectively. Our results highlight that an unrealistic LAI representation can mislead LULC change impact assessments on streamflow in the tropical Andes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".