Response of soil erosion and non-point source pollution to different rainfall, vegetation and land preparation measures in Miyun reservoir area during 2010–2023
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».