Time-scale effects on runoff simulation and parameters sensitivity using SWAT model
Bibliographic record
Abstract
Study region Jianxi Watershed (JXW) and Shanmei Reservoir Watershed (SRW), located in southeastern coastal China. Study focus The SWAT model is a widely used hydrological simulation tool. However, the impact of different model development methods and the variation of sensitive parameters across time scales on simulation performance remains insufficiently studied. This study focused on the effects of time-scale on SWAT model runoff simulation performance by applying independent and unified development methods for model setup. Runoff simulations were conducted at annual, monthly, and daily time scales. The simulation performance differences between the two methods were compared, variations in sensitive parameters across time scales were analyzed, and their underlying mechanisms were explored to optimize SWAT model development and improve simulation reliability. New hydrological insights for the region (1) The simulation results of the SWAT model with the independent development method were all better than that of the unified development method. (2) The sensitive parameters of the models at the annual, monthly, and daily time scales exhibited significant differences. There were just 4 common parameters across the different time scales for the 2 regions, which were the surface runoff parameter CN2, the groundwater-related parameters ALPHA_BF and RCHRG_DP, and the evapotranspiration-related parameter ESCO. (3) The values of the four common sensitive parameters were varied with the time-scale, which was a key factor contributing to the superior simulation performance of the independent development method over the unified method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".