Two-Way Calibration-Validation of SWAT Model for a Small Prairie Watershed with Short Observed Record
Bibliographic record
Abstract
The Soil Water Assessment Tool (SWAT) model was calibrated for a small watershed with extensive irrigation, low runoff, and a short (2004-2007) period of record in the semi-arid region of the Canadian prairies. The watershed is located on a segment of a river. The net flow between the upstream (inlet) and downstream (outlet) locations of the reach comprises only a fraction of the streamflow at the outlet. This type of watershed is rarely investigated and generally different from the pear shaped single outlet watersheds presented in SWAT literature. The objectives of this study were to determine if SWAT could be calibrated and validated on this unique watershed using one- and two-way calibration-validation methods, and to determine the effect of a variable climatic record on calibrated and validated parameters. To study the effect of a variable climatic record on the calibrated parameters for this watershed a two-way calibration verification scheme that involved two scenarios was used. Scenario 1 involved calibration using 2004 (average precipitation) and 2005 (above average precipitation) and validation on 2006 and 2007 (both below average precipitation). For scenario 2, the calibration and validation periods were reversed. A third scenario utilized the period of 20042006 for calibration and reserved the 2007 data for validation. The objective criteria used to evaluate model performance included decomposition of the Root Mean Square Error (RMSE) into systematic and unsystematic error, the Nash-Sutcliffe efficiency coefficient (EF) and the coefficient of determination (R2) as model best fit objective functions. The calibration results were satisfactory for scenarios 1 and 3. The calibration of scenario 2 and the validation results for all three scenarios were unsatisfactory. The objective criteria calculated separately for each year in the calibration records showed the calibration results were satisfactory for 2005 only, which is the only wet year in the observed record with above average precipitation. Thus, calibrated parameters were mainly influenced by the 2005 wet season. Therefore, SWAT model calibration on a mixed record of wet and dry conditions will reflect the hydrological processes dominant during wet conditions rather than those observed during dry ones. Although we conclude that the SWAT model could not be successfully validated on this unique watershed, a longer record may result in successful calibration and validation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".