Assessment of Soil Erosion Dynamics Using the GIS-Based RUSLE Model: A Case Study of Wangjiagou Watershed from the Three Gorges Reservoir Region, Southwestern China
Bibliographic record
Abstract
The adjacent agricultural watershed is a vital component of the Three Gorges Reservoir Region (TGRR); however, it is affected by serious soil erosion. Assessing soil erosion dynamics in such watersheds is useful for identifying its causes and tendencies to develop, in turn providing scientific information for soil and water conservation at the regional scale. In the present study, the spatial and temporal patterns of soil erosion of a small agricultural watershed in central TGRR were investigated from 2002 to 2014 using the Revised Universal Soil Loss Equation (RUSLE) model, combined with Geographic Information Systems (GIS). The trends and processes of the overall soil erosion intensity were analyzed using spatial overlay analysis and the Markov transition matrix model, respectively. The spatial distribution of soil erosion rates within this watershed was relatively consistent during the study period. Erosion intensity was moderate, with a mean soil loss of 35.1 t·ha−1·year−1. Precipitation was a dominant factor influencing the intensity of soil erosion. Moreover, most erosion intensities shifted closely to middle grades from 2002 to 2008, and declined from 2008 to 2014, indicating that soil erosion in the Wangjiagou watershed has recently decreased. These results suggest that recently implemented integrated soil management practices were responsible for the recently observed erosion patterns.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".