Warming soil temperature and increasing baseflow in response to recent and potential future climate change across northern Manitoba, Canada
Bibliographic record
Abstract
This study investigates the impacts of climate change on the hydrology and soil thermal regime of ten sub-arctic watersheds (northern Manitoba, Canada) using the Variable Infiltration Capacity (VIC) model. We utilize statistically downscaled and bias-corrected forcing datasets based on 17 general circulation model (GCM) - representative concentration pathways (RCP) scenarios from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to run the VIC model for three 30-year periods: a historical baseline (1981–2010), and future projections (2021–2050: 2030s and 2041–2070: 2050s), under representative concentration pathways (RCPs) 4.5 and 8.5. The CMIP5 Multi-Model Ensemble (MME) mean-based VIC simulations indicate a 15–20% increase and 10% decrease in the projected annual precipitation and snowfall, respectively over the southern portion of the basin and >20% rainfall increase over the higher latitudes of the domain by the 2050s. Snow accumulation is projected to decline across all sub-basins, particularly in the lower latitudes. Projected uncertainties in major water balance components (i.e., evapotranspiration, surface runoff, and streamflow) are more substantial in the wetland and lake-dominated Grass and Gunisao watersheds than their eight counterparts. Future warming increases soil temperatures >2.5°C by the 2050s, resulting in 40–50% more baseflow. Further analyses of soil temperature trends at three different depths show the most pronounced warming in the top soil layer (1.6°C 30-year-1 in the 2050s), whereas baseflow increases substantially by 19.7% and 46.3% during the 2030s and 2050s, respectively. These results provide crucial information on the potential future impacts of warming soil temperatures on the hydrology of sub-arctic watersheds in north-central Canada and similar hydro-climatic regimes.
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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 | 0.001 | 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) | 0.002 | 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".