Land use planning based on soil and water assessment tool model in a mountainous watershed to reduce runoff and sediment load
Bibliographic record
Abstract
Soil erosion is a major environmental threat to the sustainability and productive capacity of soils. This study aimed to identify optimal land use types for Zayandehrood watershed in central Iran for the first time which is large and mountainous to minimize runoff production and soil loss. Two different types of land use data for two scenarios were developed using soil and water assessment tool (SWAT) in combination with Sequential Uncertainty Fitting Program (SUFI-2) at the subbasin level with uncertainty analysis to explicitly quantify hydrological components on a daily time step. In the first scenario, the current land use map of the study area was used, and the second scenario was constructed using an optimal land use map obtained from a land evaluation study. Promotion of the land uses in the second scenario resulted in a noticeable reduction in discharge and sediment productions in the watershed. The simulated mean discharge values by the scenarios 1 and 2 were approximately 14 658 and 13 290 m3 yr−1, respectively. The mean annual sediment yield simulated by the scenario 1 (approximately 122 220 t yr−1) decreased to that of the scenario 2 (94 440 t yr−1). This study provides a strong basis for reducing runoff and sediment yields in central Iran; however, its general analytical framework could be applied to other parts of the world that are facing similar challenges.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".