Effectiveness of Soil and Water Conservation Practices Under Climate Change in the Gorganroud Basin, Iran
Bibliographic record
Abstract
Assessing the effectiveness of conservation practices under changed climatic conditions has proven to be invaluable in selecting the adaptation practices. Conservationists are concerned that past effective practices may no longer be effective in the future climate change. This research is aimed at assessing the effectiveness of soil and water conservation practices under future climate change, with respect to sediment yield leaving a watershed. For this purpose, the Soil and Water Assessment Tool, SWAT, was applied to simulate various climate change scenarios with three soil and water conservation practices to assess possible changes in stream flow, and sediment yield of the Gorganroud watershed in the northern part of Iran. Study results demonstrated that the impact of climate change in the increase of watershed sediment yield is more than the stream flow and varies from 35.9 to 47.7% for the period 2040–2069. Implementing conservation practices under climate change can reduce the sediment yield of watershed up to 7.2% and for the sub‐basin scale up to 46.4%. Range management practices were found to be the most effective practice in the decrease of sediment at the sub‐basin scale and porous gully plugs and terrace construction, the most effective at the watershed scale. The results indicate that soil and water conservation practices will be more effective at reducing sediment yields under anticipated future climates. Though, implementation of each conservation practice solely was not sufficient to compensate for climate change‐driven increases in sediment yield. This study provides valuable information for watershed managers and decision makers regarding selection of soil and water conservation practices for adaptation to climate change.
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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 | 0.001 | 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 | 0.000 |
| 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 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".