基于Meta分析的黄土高原坡面典型水土保持措施减流减沙效应研究OA
A meta-analysis of the runoff and sediment reduction effects of typical soil and water conservation measures on slopes of the Loess Plateau
[目的]黄土高原是我国生态环境最脆弱、水土流失最严重的区域.评估水土保持措施效益并明晰驱动机制对区域生态安全具有重要意义.[方法]本研究基于次降雨事件,采用 Meta 分析整合降雨量、坡度、坡长和措施类型等多源数据,结合相关性分析、冗余分析与机器学习预测模型,揭示了不同水土保持措施的减流减沙效果差异.[结果]1)植被与工程措施中,减流效益最高的分别为灌木(50.07%)和鱼鳞坑(36.93%);减沙效益最高的则为灌木(76.41%)和梯田(83.28%).农业措施中,减流和减沙效益最高的分别是垄作(78.62%)和等高耕作(40.63%).2)降雨量与工程措施减沙效益呈极显著负相关(P<0.001);坡度与植被措施减流效益呈极显著正相关(P<0.001),而与工程措施减流、农业措施减沙分别呈显著(P<0.05)和极显著负相关(P<0.001);坡长与植被措施减沙、工程措施减流效益呈高度显著(P<0.01)负相关.冗余分析表明,在减流效益方面,植被、工程和农业措施条件下贡献率最高的因子分别为措施类型、坡长和措施类型,而在减沙效益方面则分别是坡长、降雨量和坡度.3)基于机器学习模型预测各水土保持措施的减流减沙效益,其中 XGBoost对植被措施减流预测精度最高(R2=0.74),多层感知机对工程措施减沙预测最优(R2=0.69),随机森林对农业措施减流预测误差最小(R2=0.51).[结论]本研究从相关性与因子贡献角度系统阐明不同水土保持措施效益差异的主导驱动因素,可为黄土高原水土保持措施优化配置提供科学依据.
[Objective]The Loess Plateau,widely acknowledged as one of the most ecologically fragile regions in China with the most severe soil erosion,serves as a critical area for soil and water conservation.Assessing the effectiveness and underlying driving mechanisms of conservation measures is essential for enhancing regional ecological security and sustainability.[Methods]This study was based on the observation of individual rainfall events.Meta-analysis was adopted to integrate multi-source data,including precipitation(P),slope gradient(S),slope length(L),and measure type(M).Furthermore,correlation analysis,redundancy analysis,and machine learning prediction models were combined to reveal the differences in the effects of different soil and water conservation measures and their dominant factors.[Results]1)Among vegetation and engineering measures,shrubs(50.07%)and fish-scale pits(36.93%)had the highest runoff reduction efficiency(RR),while shrubs(76.41%)and terraces(83.28%)achieved the highest sediment reduction efficiency(SR).For agricultural measures,ridge tillage achieved the highest RR(78.62%),whereas contour tillage showed the highest SR(40.63%).2)Correlation analysis showed P was extremely significantly negatively correlated with engineering SR(P<0.001);S was extremely significantly positively correlated with vegetation RR(P<0.001),but significantly(P<0.05)and extremely significantly(P<0.001)negatively correlated with engineering RR and agricultural SR,respectively;L had highly(P<0.01)significant negative correlations with vegetation SR and engineering RR.Redundancy analysis indicated M,L,M were the top contributors to RR of the three measure types,and L,P,S dominated their SR.3)Machine learning models predicted the RR and SR of soil and water conservation measures.XGBoost achieved the highest accuracy for vegetation RR(R2=0.74),MLP(Multi-Layer Perceptron)for engineering SR(R2=0.69),and Random Forest the smallest error for agricultural RR(R2=0.51).[Conclusion]This study systematically elucidates the differential effectiveness and key driving mechanisms of various soil and water conservation measures,thereby providing a scientific basis for their optimal allocation on the Loess Plateau.
金洋;张馨予;张帆;张守红
林木资源高效生产全国重点实验室,北京林业大学水土保持学院,100083,北京||山西吉县森林生态系统国家野外科学观测研究站,042200,山西临汾林木资源高效生产全国重点实验室,北京林业大学水土保持学院,100083,北京||山西吉县森林生态系统国家野外科学观测研究站,042200,山西临汾林木资源高效生产全国重点实验室,北京林业大学水土保持学院,100083,北京||山西吉县森林生态系统国家野外科学观测研究站,042200,山西临汾||北京市水土保持工程技术研究中心,100083,北京林木资源高效生产全国重点实验室,北京林业大学水土保持学院,100083,北京||山西吉县森林生态系统国家野外科学观测研究站,042200,山西临汾||北京市水土保持工程技术研究中心,100083,北京
农业科技
水土保持措施Meta分析机器学习减流减沙黄土高原
soil and water conservation measuresmeta-analysismachine learningrunoff and sediment reductionLoess Plateau
《中国水土保持科学》 2026 (3)
17-30,14
国家重点研发计划项目"黄土高原小流域山水林田湖草沙综合治理与生态系统服务协同提升技术及示范"(2023YFF1305101) The National Key Research and Development Program"Integrated Management and Ecosystem-Service Enhancement Technologies for Mountain-Water-Forest-Field-Lake-Grass-Sand Systems in Small Watersheds of the Loess Plateau"(2023YFF1305101)
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