A Comparison of DRAINMOD and SWAT for Surface Runoff and Subsurface Drainage Flow Prediction at the Field Scale for a Cold Climate
Bibliographic record
Abstract
Tile drainage reduces surface runoff, soil erosion and improves crop yields, but contributes to the loss of nutrients from agricultural fields. Therefore, it is important to accurately predict the field-scale hydrology in order to better manage water resources and ensure environmental sustainability. In this study, two widely used models, DRAINMOD and the Soil and Water Assessment Tool (SWAT), were calibrated and validated for hydrology of two tile-drained agricultural fields in the Pike River watershed of Southern Quebec. The hydrologic performance of DRAINMOD and SWAT was compared for cold-climate conditions and evaluated at seasonal and monthly time scales. Three years of hydrologic data served to calibrate and validate the model, with the year 2002/03 being used for calibration and 2004 for validation. Model predictions of surface runoff and subsurface drainage flow were compared with the measured surface runoff and subsurface drainage flow values from the two instrumented study sites. The comparison of two models was established based on their prediction accuracy. In the calibration period, DRAINMOD overestimated cumulative subsurface drainage outflow by 5 %, and SWAT underestimated cumulative subsurface drainage outflow by 26%. In the validation period, DRAINMOD was found more successful than SWAT in subsurface drainage flow prediction with R2 greater than 0.82 for both sites. Also, the prediction error statistics indicated clearly that the DRAINMOD performed well in predicting subsurface drainage outflows with different soil type in cold climatic conditions. However, the SWATs performance model in simulating total monthly surface runoff was better than DRAINMOD during the validation period with R2 greater than 0.88. The results of this study showed that DRAINMOD was better than SWAT at simulating field scale hydrology in most cases. Additionally, the calibration of SWAT required more effort and input data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".