Integrated Hydrological Modeling of Climate Change Impacts in a Snow‐Influenced Catchment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it