Impacts of severe land use changes on the hydrology of snow dominated catchments in southern Quebec
Bibliographic record
Abstract
The present study evaluates the hydrological response to severe land use changes using the distributed physically-based hydrological model HYDROTEL over 89 catchments in Eastern North America. Additionally, two catchments were selected to assess the impacts on past extreme flood events. Streamflow simulations were generated using three regional climate simulations from the Canadian Regional Climate Model version 5 (CRCM5) for the 1986–2015 period. The simulations were based on current land use (EVAL) and two extreme land use scenarios with only grass (GRASS) and only forest (FOREST) land use. The FOREST and GRASS simulations include the feedback of land-use to climate. The Multivariate Bias Correction (MBCn) algorithm was applied to correct biases in EVAL simulation, with the same coefficients used for GRASS and FOREST simulations to ensure a consistent climate baseline and maintain the spread of the simulations. HYDROTEL’s land-use inputs were adjusted to match those from the CRCM5 simulations. The results indicated significant changes in both the timing and magnitude of spring peak flows associated with snowmelt: the FOREST scenario showed earlier, reduced peak flows, while the GRASS scenario led to delayed, larger peaks. Additionally, the catchments were categorized by size-large (>1000 km 2 ), medium (500–1000 km 2 ), and small (<500 km 2 )-to evaluate size-related impacts. A model comparison was conducted between HYDROTEL and the physically-based distributed model WaSiM on two diverse catchments, revealing that HYDROTEL demonstrated higher sensitivity to snowmelt and accumulation processes (because of its multi-land use snow model structure), while WaSiM showed greater sensitivity to evapotranspiration (due to its Richards-equation-based soil module). Finally, it was found that the RCM-simulated land use-climate feedback is considerably more important than the hydrological model land-use change effect on the spring flood modification.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".