Étude d'impact hydrologique de changements d'utilisation du sol sur le bassin de la rivière Raisin par simulations historiques avec le modèle à bases physiques semi-spatialisé HYDROTEL.
Bibliographic record
Abstract
The impact of various land-use changes on the hydrology of the Raisin River in Eastern Ontario was studied using simulation with the physically based semi-distributed hydrological model HYDROTEL. First, we have simulated the impact of the real land-use change which happened on the watershed between 1964 and 1978, on daily flow and soil water content. The increasing importance of cash crop in the agricultural landscape, in particular corn, and the increase in the forested area resulted in a decrease in the variability of hydrological conditions. Second, we have simulated a series of theoretical land-use scenarios, such as a totally forested watershed, a watershed entirely covered by cash crops, and a forested watershed with wetlands, in order to discriminate between the influence of particular land uses found on the watershed in the last thirty years. Cash crop is the land use which produced the most extreme conditions of all land uses tested, with spring flood happening earlier and with more intensity and low flow being the lowest. Following the two simulation exercises, it appears that from 1964 to 1978, the increasing forested area compensated for the drying effect of cash crop on soil. Land-use parameterisation is gaining importance in global-scale models but in this watershed-scale study, the parameters which have the most effect quantitatively on flow, are the snow melt factors and snow melt thresholds associated with forested or open field environments. Bioclimatic calendars of root depth, leaf area index and albedo associated with the various land uses were secondary. On the other hand, they are important for baseflow and for fall high flows and on soil water content at the end of the summer. Regarding the studied time period, meteorological fluctuations have more important impact on hydrology at the watershed scale than do land-use changes. On the other hand, land use plays a role of tempering, dampening, climatic effects and it is necessary to take that impact into account, because it is the only one for now on which it is possible to have some control.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".