Re-conceptualizing the Soil and Water Assessment Tool to Predict Subsurface Water Flow Through Macroporous Soils
Why this work is in the frame
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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 km 2 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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it