Developing <scp>SWAT‐S</scp> to strengthen the soil erosion forecasting performance of the <scp>SWAT</scp> model
Bibliographic record
Abstract
Abstract Soil erosion is an important cause of global land degradation, and accurate monitoring of it is essential. The Soil and Water Assessment Tool (SWAT), a distributed hydrological model, is an advanced technique for predicting soil erosion at watershed scale. However, as the erosion framework was established in gently sloping land, SWAT is limited in predicting soil erosion in some highland and mountainous regions. Therefore, this study suggested a method to integrate the sediment transport theoretical formula that can reflect the morphology of gully regions into SWAT to obtain SWAT‐S to enhance the calculation performance of sediment load, and the SWAT‐S was evaluated according to the coefficient of determination (R2), Nash‐Sutcliffe coefficient (NSE), Percent‐Bias (P‐BIAS) and root mean square errors (RMSE)‐observations SD ratio (RSR) in the Yanhe basin on the Chinese Loess Plateau. The results showed that SWAT‐S is more successful in reproducing the monthly sediment load, with R2, NSE, |P‐BIAS| and RSR were changed by 5.08%, 17.65%, −2.92% and −10.00% in the calibration, as well as by 1.18%, 10.39%, 45.45% and −18.75% in the validation of the SWAT‐S compared to SWAT. Meanwhile, SWAT‐S estimates 2.66 × 106 t more sediment than SWAT during the June–September flood season and better matches observed data. In total, the revised SWAT can improve the performance of sediment estimation, which is beneficial for the wider application of the model in more regions of the world.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".