Validation and Use of a Semidistributed Hydrological Modeling System to Predict Short-Term Effects of Clear-Cutting on a Watershed Hydrological Regime
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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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".