Effects of Averaging and Separating Soil Moisture and Temperature in the Presence of Snow Cover in a SVAT and Hydrological Model for a Southern Ontario, Canada, Watershed
Bibliographic record
Abstract
Abstract The energy and water balances at the earth's surface are dramatically influenced by the presence of snow cover. Therefore, soil temperature and moisture for snow-covered and snow-free areas can be very different. In computing these soil state variables, many land surface schemes in climate models do not explicitly distinguish between snow-covered and snow-free areas. Even if they do, some schemes average these state variables to calculate grid-mean energy fluxes and these averaged state variables are then used at the beginning of the next time step. This latter approach introduces a numerical error in that heat is redistributed from snow-free areas to snow-covered areas, resulting in a more rapid snowmelt. This study focuses on the latter approach and examines the sensitivity of soil moisture and streamflow to the treatment of the soil state variables in the presence of snow cover by using WATCLASS, a land surface scheme linked with a hydrologic model. The model was tested for the 1993 snowmelt period on the Upper Grand River in Southern Ontario, Canada. The results show that a more realistic simulation of streamflow can be obtained by keeping track of the soil states in snow-covered and snow-free areas.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".