Development of a conceptual model of the hydrologic response of tropical Andean micro-catchments in Southern Ecuador
Bibliographic record
Abstract
Abstract. This paper presents a lumped conceptual model designed for simulating the rainfallrunoff response of mountain micro-catchments with natural vegetation located in the south of Ecuador. The conceptual model is mimicking the soil hydrology and consists of a maximum of three linear reservoirs in series. A two and three reservoir model structure were tested, respectively A GLUE uncertainty analysis was applied to assess the model performance. Simulation results of the discharge confirmed the applicability of the soil-based conceptual model structure for the selected study areas, during model calibration and validation. The three reservoir model best predicted the runoff, nevertheless the two reservoir model well captures the rainfall-runoff process of the micro-catchments with páramo vegetation. Although differences in climate regime, vegetation, and soil of the selected catchments runoff is strongly controlled by the precipitation and soil type, and the horizons contributing to runoff are defined by their antecedent wetness. Results confirm that the discharge is mainly controlled by lateral subsurface flow through the organic horizons, while during dry conditions the C-horizon and the bedrock mainly contribute to discharge. Lateral transport through the densely rooted top horizon and the litter layer occurs during storm events, being under those conditions the major discharge component. Overland flow is a local phenomenon, negligible in comparison to the other flow components.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".