Developing a decision support tool for assessing land use change and BMPs in ungauged watersheds based on decision rules provided by SWAT simulation
Bibliographic record
Abstract
Abstract. Decision making on water resources management at ungauged, especially large-scale watersheds relies on hydrological modeling. Physically based distributed hydrological models require complicated setup, calibration, and validation processes, which may delay their acceptance among decision makers. This study presents an approach to develop a simple decision support tool (DST) for decision makers and economists to evaluate multiyear impacts of land use change and best management practices (BMPs) on water quantity and quality for ungauged watersheds. The example DST developed in the present study was based on statistical equations derived from Soil and Water Assessment Tool (SWAT) simulations and applied to a small experimental watershed in northwest New Brunswick. The DST was subsequently tested against field measurements and SWAT simulations for a larger watershed. Results from DST could reproduce both field data and model simulations of annual stream discharge and sediment and nutrient loadings. The relative error of mean annual discharge and sediment, nitrate–nitrogen, and soluble-phosphorus loadings were −6, −52, 27, and −16 %, respectively, for long-term simulation. Compared with SWAT, DST has fewer input requirements and can be applied to multiple watersheds without additional calibration. Also, scenario analyses with DST can be directly conducted for different combinations of land use and BMPs without complex model setup procedures. The approach in developing DST can be applied to other regions of the world because of its flexible structure.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".