Re-conceptualizing the Soil and Water Assessment Tool to Predict Subsurface Water Flow Through Macroporous Soils
Bibliographic record
Abstract
More water and nutrients from artificially-drained agricultural land reach surface waters by leaching through macropores than by percolating through the soil matrix. However, the Soil and Water Assessment Tool (SWAT) describes water flows poorly in land with subsurface drainage because it does not partition water between macropore and matrix transport processes. We produced a new percolation algorithm to distinguish the macropore flow pathway, which was integrated in the SWAT-MAC model and used to predict water flows in a 30 km2 agricultural subwatershed in southern Quebec, Canada. Partitioning of subsurface flow between macropore and matrix components was reasonable, compared to a chemical-based hydrograph separation of streamflow in this subwatershed. The macropore flow algorithm also improved water allocation between the annual surface runoff and subsurface flow in the SWAT-MAC model. We predict more macropore flow into tile drains under fine-textured soils than coarse-textured soils, which is consistent with experimental observations. However, macropore flow was underestimated in the non-growing season and over-predicted during the growing season, which can be adjusted in the macropore flow algorithm by accounting for dynamic macropore connectivity or effective macroporosity. There are too few observations of regional-specific effects of soil moisture and management practices on macropore flow to correct the algorithm at this time. We conclude that the percolation algorithm of SWAT-MAC represents the macropore flow pathway and improves the description of water movement through agricultural soils with subsurface drainage systems, which are important for transferring water and nutrients to downstream aquatic systems in cold, humid temperate regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".