Advancing the realistic simulations of N2O emissions in cold climate watersheds using Soil and Water Assessment Tool
Bibliographic record
Abstract
Nitrous Oxide (N2O) emissions in Soil and Water Assessment Tool (SWAT) is heavily dependent on soil temperature and moisture. However, SWAT has been known to highly under-estimate soil temperature which limits movement of water and nutrients throughout the soil profile and does not replicate freeze-thaw cycles which is of paramount importance in the N2O emissions. Thus, we integrated modules developed by individual researchers pertaining to energy balanced snow melt, rain-on-snow, energy balanced soil temperature and N2O emission into a single SWAT model and termed in SWAT Cold Climate N2O (SWAT-CCN2O). SWAT-CCN2O was then tested for flows, sediments, soil temperature and N2O emission simulation in a representative watershed in Ontario, Canada, the Speed River basin. Compared with the unaltered SWAT model, SWAT-CCN2O was able to significantly capture the pre-spring snowmelt induced flows. A more realistic simulation of soil temperature (soil temperatures did not go below -4oC) and a satisfactory simulation of sediments and N2O emissions were observed in the basin, which highlights the potential to use SWAT-CCN2O for streamflow and N2O simulation in cold climatic catchments. This version of SWAT is made publicly available for further improvements and applications in similar watersheds.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".