RUSLE模型对黄土高原退耕植被恢复坡面土壤侵蚀的模拟效果分析
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作者单位:

1.西北农林科技大学;2.中国科学院水利部水土保持研究所

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基金项目:

黄土丘陵沟壑区小流域道路侵蚀与水文连通性的耦合作用机制


Simulation effect analysis of RUSLE model on slope soil erosion restored by reclaimed vegetation in Loess Plateau
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Affiliation:

1.NORTHWEST A&2.F UNIVERSITY;3.Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources

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Coupling mechanism of road erosion and hydrological connectivity in a small watershed in the Loess Hilly-gully region

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    摘要:

    为了探究RUSLE模型在黄土高原退耕植被恢复坡面土壤侵蚀模拟的效果,本研究基于陕北安塞区坊塌小流域内10个径流小区于2016-2022年的降雨产流产沙监测资料,通过RUSLE模型各因子不同常用算法之间的变换组合,模拟了144种因子组合式下各退耕植被恢复坡面的土壤侵蚀量,并采用纳什效率系数NSE和均方根误差RMSE来作为评价模型有效性的判断指标。结果表明:利用RUSLE模型144种因子组合进行土壤侵蚀模拟,退耕植被恢复坡面的NSE系数范围为-38.47~ 0.19,均方根误差RMSE的范围为1.92~12.65 t/(hm2·a),RUSLE模型在退耕植被恢复坡面上的模拟效果较差,现有各因子算法难以适应退耕植被恢复坡面上的土壤流失量的评估,需要对RUSLE模型各因子进一步改进。

    Abstract:

    In order to explore the effect of RUSLE model on soil erosion simulation on the slope of reclaimed vegetation on the Loess Plateau, this study is based on the monitoring data of rainfall production and sediment loss of 10 runoff plots in Fangta small watershed of Ansai District, Northern Shaanxi Province during 2016-2022. Through the transformation combination of different commonly used algorithms of various factors of RUSLE model, In this paper, the soil erosion of the restored slope under the combination of 144 factors was simulated, and Nash efficiency coefficient NSE and root mean square error RMSE were used to evaluate the effectiveness of the model. The results show that: The RUSLE model was used to simulate soil erosion with 144 factors. The NSE coefficient of the returned farmland vegetation restoration slope ranged from -38.47 to 0.19, and the root mean square error RMSE ranged from 1.92 to 12.65 t/(hm2·a). The simulation effect of RUSLE model on the returned farmland vegetation restoration slope was poor. The existing factor algorithms are difficult to adapt to the evaluation of soil loss on the slope of vegetation restoration, so it is necessary to further improve the factors of RUSLE model.

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  • 收稿日期:2023-04-20
  • 最后修改日期:2023-06-08
  • 录用日期:2023-06-09
  • 在线发布日期: 2024-01-16
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