Unlocking the Future of Agricultural Water: Predicting Spring Soil Thermal and Hydrological Dynamics in a Warming Manitoba
Bibliographic record
Abstract
Abstract. This study integrated a process-based model (RZ-SHAW) to predict the future winter soil thermal and hydrological states in Manitoba, a region characterized by thick seasonal freezing layers, with a focus on conditions relevant to the late winter and early spring planting season. Historically, saturated frozen ground has led to dominant surface runoff pathways. However, ongoing warming trends under the high-emission CMIP6 SSP5-8.5 scenario are projected to alter these dynamics significantly. To investigate this, we utilized observed soil temperature and soil volumetric water content data from a network of 120 soil hydro and thermal monitoring stations across Manitoba (2017-2022) to calibrate and validate the RZ-SHAW model. Our projections to 2100 indicate a statistically significant increase in average winter soil temperature, rising by approximately 6.0 °C to 6.3 °C, and a statistically significant increase in average winter volumetric liquid water content of around 5.1 cm³/cm³. These findings suggest a future with warmer winter soils and higher liquid water content, fundamentally altering spring thaw dynamics and moisture availability. This underscores the need for adaptive strategies in response to shifting hydrological patterns due to climate change, emphasizing the region's broader impacts on agriculture and ecosystem health.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".