Validation and Use of a Semidistributed Hydrological Modeling System to Predict Short-Term Effects of Clear-Cutting on a Watershed Hydrological Regime
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Bibliographic record
Abstract
The Gestion Intégrée des Bassins versants à l'aide d'un Système Informatisé (GIBSI), a semidistributed hydrological modeling system, was evaluated for its ability to simulate the impact of deforestation on the hydrological regime of the Famine River watershed (728 km2), a subwatershed of the Chaudière River, Québec, Canada. Annual, spring and summer, and low-water runoff, as well as peak flows, were estimated for both a base-case scenario and a deforestation scenario using 31 annual meteorological series. GIBSI simulated an average increase of annual runoff after clear-cutting of 57% (268 mm) and the proportion of runoff to precipitation increased from 40% to 63%. The average increase in spring runoff was 25%, while in summer it was 138%. For summer low-flow periods, GIBSI simulated an average increase in runoff of 102%. For spring and summer peak-flow rates, hydrographs generated by GIBSI showed that average spring peak flows were increased after deforestation by 26% while summer peak flows were increased by 101%. Differences between spring and summer runoffs as well as peak-flow rates are due to changes in the degree of saturation of the soil and actual evapotranspiration between the two scenarios. Hence, while land-use changes have a substantial impact on summer runoff and low flows, they have little impact on extreme peak-flow events, especially during spring (less than 10% or more than 90% nonexceeding probability). This suggests that land use has a limited role in controlling these extreme events. The simulation results obtained by GIBSI were consistent with those found in the literature. Therefore, GIBSI offers potential as a management tool for investigating prevention and reduction measures of deforestation effects on the hydrological regime of a watershed.
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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 | 0.000 |
| 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) | 0.000 | 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 it