Evaluating the Suitability of Application of Hydrological Models in a Mixed Land Use Watershed
Bibliographic record
Abstract
This project compared the suitability of two hydrological models for the Flint River watershed (FRW). The Flint River flows into Wheeler Lake, which drains to the Tennessee River, a major source of water in northern Alabama. Two very widely used hydrological models, the Soil and Water Assessment Tool (SWAT) and the Storm Water Management Model (SWMM), were selected for this study. Both models were calibrated and validated for FRW. The calibration parameters were selected based on past research studies which used the same hydrologic models. The calibration parameters for SWMM and SWAT included basin, subbasin, soil, groundwater, channel and land use parameters. During calibration, both models were run at daily and monthly time steps, where simulated streamflows were compared with observed streamflows (years 2004-2013) and various statistical parameters were computed. While comparing simulated and observed monthly streamflows, SWAT showed better performance (r = 0.86-0.97, R 2 = 0.73-0.93, bias = 12.2%, RMSE = 5.6 m 3 /s-8.9 m 3 /s) than SWMM (r = 0.70, R 2 = 0.50, RMSE = 2 m 3 /s-56 m 3 /s, bias = 6.2%-8.4%). However, both models showed better performance for monthly streamflows than for daily streamflows. The evaluation determined that SWAT provides a more suitable model than SWMM when applied to a mixed land use watershed like FRW.
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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 | 0.003 | 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 | 0.000 |
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 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".