Response of soil erosion and non-point source pollution to different rainfall, vegetation and land preparation measures in Miyun reservoir area during 2010–2023
Bibliographic record
Abstract
Soil erosion and non-point source pollution are critical global environmental issues, with profound implications for ecosystems, agricultural productivity, and water quality. These problems are especially exacerbated in regions subjected to intense rainfall, where their impacts can be particularly severe. In China, the suburban areas of Beijing have experienced considerable challenges associated with both soil erosion and non-point source pollution. Under different rainfall types, the impact mechanisms of rainfall, vegetation, and land preparation on soil erosion and non-point source pollution are highly complex and have not yet been fully understood. This study is based on soil erosion (runoff, sediment yield) and non-point source pollution (TN, Total nitrogen; TP, Total phosphorus; COD, Chemical Oxygen Demand) data from 130 erosive rainfall events (Classified as light, moderate, heavy and extreme rainfall based on 24-h precipitation) across 16 runoff plots from 2010 to 2023. The runoff plots consist of different vegetation and land preparation measures. The characteristics of soil erosion and non-point source pollution under four different rainfall types and different soil conservation measures were compared. Additionally, the impacts of rainfall, vegetation, and land preparation on soil erosion and non-point source pollution under different rainfall types were explored. The results indicate that the frequency of extreme rainfall events accounts for only 16.9 % of erosive rainfall, yet the runoff, sediment yield, TN, TP, and COD they generate account for 40.7 %, 35.0 %, 37.9 %, 33.4 %, and 41.9 % of the total, respectively. Vegetation and land preparation measures have a significant effect on reducing runoff, sediment yield, TN, TP, and COD. The primary factor influencing runoff, TN, TP, and COD was maximum 30-min rainfall intensity (I 30 ), with correlation coefficients of 0.33, 0.20, 0.30, and 0.28, respectively ( p < 0.01). As rainfall intensity increases, the contribution of vegetation to soil erosion and non-point source pollution increases from 0.7 % under light rainfall to 41.1 % under extreme rainfall. The combined effect of vegetation and land preparation increases from 1.7 % to 14.4 % under extreme rainfall. Under the same rainfall conditions, the contribution of vegetation and land preparation to soil erosion is significantly higher than that to non-point source pollution. The study identifies the mechanisms by which rainfall, vegetation, and land preparation influence soil erosion and non-point source pollution under varying rainfall conditions. These findings offer valuable insights for soil conservation and non-point source pollution management, particularly in areas experiencing extreme rainfall events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".