Integrated Hydrological Modeling of Climate Change Impacts in a Snow‐Influenced Catchment
Bibliographic record
Abstract
Abstract The potential impact of climate change on water resources has been intensively studied for different regions and climates across the world. In regions where winter processes such as snowfall and melting play a significant role, anticipated changes in temperature might significantly affect hydrological systems. To address this impact, modifications have been made to the fully integrated surface‐subsurface flow model HydroGeoSphere (HGS) to allow the simulation of snow accumulation and melting. The modified HGS model was used to assess the potential impact of climate change on surface and subsurface flow in the Saint‐Charles River catchment, Quebec (Canada) for the period 2070 to 2100. The model was first developed and calibrated to reproduce observed streamflow and hydraulic heads for current climate conditions. The calibrated model was then used with three different climate scenarios to simulate surface flow and groundwater dynamics for the 2070 to 2100 period. Winter stream discharges are predicted to increase by about 80, 120, and 150% for the three scenarios due to warmer winters, leading to more liquid precipitation and more snowmelt. Conversely, the summer stream discharges are predicted to fall by about 10, 15, and 20% due to an increase in evapotranspiration. However, the annual mean stream discharge should remain stable (±0.1 m 3 /s). The predicted increase in hydraulic heads in winter may reach 15 m and the maximum decrease in summer may reach 3 m. Simulations show that winter processes play a key role in the seasonal modifications anticipated for surface and subsurface flow dynamics.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".