Separating Wet and Dry Years to Improve Calibration of SWAT in Barrett Watershed, Southern California
Bibliographic record
Abstract
Hydrological models often perform poorly in simulating dry years in regions with large inter-annual variability in rainfall. We calibrated the Soil and Water Assessment Tool (SWAT) model to dry and wet years separately, using the semi-arid Barrett watershed on the west coast of USA as an example. We used hydrological and meteorological data from 1980–2010 to calibrate the SWAT model parameters, compared the monthly runoff results simulated by SWAT using a traditional calibration for the entire runoff series with results using a calibration with the wet and dry year series, and analyzed differences in the most sensitive parameters between the wet and dry year series. The results showed that (1) the SWAT model calibrated to the entire runoff series produced significant differences in simulation efficiency between the wet years and dry years, with lower efficiency during the dry years; (2) the calibration with separate wet and dry years greatly enhanced the SWAT model’s simulation efficiency for both wet and dry years; (3) differences in hydrological conditions between wet and dry years were represented by changes in the values of the six most sensitive parameters, including baseflow recession rates, channel infiltration rates, Soil Conservation Service (SCS) curve number, soil evaporation, shallow aquifer flow, and soil water holding capacity. Future work can attempt to determine the physical processes that underlie these parameter changes and their impact on the hydrological response of the semi-arid watersheds.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".