Reconstructing sixty year (1950-2009) daily soil moisture over the Canadian Prairies using the Variable Infiltration Capacity model
Bibliographic record
Abstract
The Variable Infiltration Capacity (VIC) land surface macroscale hydrology model was used to reconstruct 60 years (1950-2009) of daily soil moisture values for three soil layers (0–20 cm, 20–100 cm, and 0–100 cm) over the three Canadian Prairie Provinces with a total area of 1,964,000 km2. VIC was applied over a grid of 4,393 points with a resolution of 0.25° × 0.25°, and was driven by observed daily maximum and minimum air temperature and precipitation from 1,167 meteorological stations. The model was first calibrated using observed hydrographs from seven catchments with drainage areas varying from 3,750 to 7,870 km2. Special attention was given to modelling of rainfall-runoff processes over the non-contributing drainage area of the Prairies. VIC was then validated over these seven catchments at different periods and over an additional five catchments with drainage areas ranging from 36,500 to 131,000 km2. An estimation procedure to determine model parameters was developed and applied to catchments where hydrographs are not available for the standard calibration process. In situ soil moisture measurements from six Alberta sites were also used for model validation. VIC performed well over both calibration and validation catchments. The results clearly demonstrate that incorporating non-contributing drainage areas into runoff calculations could substantially improve the ability of VIC to simulate surface and sub-surface runoff in regions where poor drainage network development is a dominant feature of drainage basins. The VIC reconstructed 60–year average of the soil moisture in the top 1 m shows some expected climatological features of the Prairies. For example, the reconstructed soil moisture climatology portrays the dry Palliser Triangle region and the Prairie Dry Belt in the southern Prairies. The VIC simulated soil moisture was used to calculate the daily Soil Moisture Anomaly Percentage Index (SMAPI) for the three soil layers; SMAPI can be used as an index of agricultural drought severity taking into account climatology. The value of the calculated SMAPI in quantifying and documenting prairie drought events is demonstrated through an intensive examination of the April 2002 drought case.
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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) | 0.001 | 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".