Environmental Modeling of a Claypan Watershed using HSPF
Bibliographic record
Abstract
The Hydrological Simulation Program -FORTRAN (HSPF) is a comprehensive, continuous model designed to simulate watershed hydrology and water quality.Its performance in simulating surface runoff, sediment, and pesticide loss from Goodwater Creek, a 72.8 km 2 (28 mi 2 ) USDA agricultural research watershed, was evaluated.The watershed is located in Central Missouri in the Central Claypan Major Land Resource Area (MLRA 113) and has a nearly level to gently sloping surface.The low permeability of the claypan layer coupled with the nearly level slope causes unique hydrologic problems.In this evaluation, most of the model parameters related to hydrology, sediment, and pesticide transport were carefully selected from previous studies.Some of the model parameters were directly calculated and others were calibrated, based on the detailed field data on hydrology, water quality, and field operations-such as cultivation and chemical application -from a 36 ha (89 ac) research field within the Goodwater Creek watershed.For accurately simulating watershed responses, field activities were considered by using the SPEC-ACTION block in HSPF.The calibration revealed that some model parameters, most notably infiltration index, take numerical values outside recommended ranges in order to define the claypan watershed behavior accurately.With a proper calibration, however, the HSPF model simulated runoff, sediment yield, and chemical loss from the Goodwater Creek watershed well.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".