Assessing hydroclimatic impacts of climate change in snowy catchments using a physically based hydrological model
Bibliographic record
Abstract
Study Region This study focuses on 34 snowy catchments in Southern Quebec, Canada, characterized by diverse physiographic and hydrometeorological conditions. The region is particularly vulnerable to climate change due to its cold, snow-dominated hydrology and significant seasonal variability in temperature and precipitation. Study Focus The study evaluates future hydroclimatic changes using the Water flow and balance Simulation Model (WaSiM), a physically based distributed hydrological model. Hydroclimatic variables, including precipitation, snow water equivalent (SWE), streamflow, evapotranspiration, soil moisture, and groundwater recharge, were analyzed for reference (1981–2010) and future (2070–2099) periods. New Hydrological Insights for the Region The findings reveal significant shifts from snowfall to rainfall, reduced snow accumulation, and earlier snowmelt, leading to altered seasonal streamflow patterns, increased winter low flows, and earlier peak flows. Groundwater recharge and evapotranspiration are projected to rise during colder months, while surface runoff is expected to decline. In addition to analyzing individual variables, the study highlights how climate change alters the relationships between key hydrological processes, such as those linking groundwater recharge, soil moisture and evapotranspiration. These interdependencies underscore the importance of adopting a holistic approach to assess climate change impacts on the water cycle.
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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".