Significance analysis of soil erosion factors in loess hilly gully region
HAO Shanshan, LI Menghua, MA Yongqiang, SHI Yun
1. Collage of Resources and Environmental Science, Ningxia University, 750021, Yinchuan, China;
2. Ningxia(China-Arab) Key Laboratory of Resource Assessment and Environment Regulation in Arid Region, 750021, Yinchuan, China
摘要为探究黄土丘陵区土地利用类型、地形及降雨等因素对土壤侵蚀的影响程度，以彭阳县1995、2005、2015年土地利用、降雨量、DEM（5 m）等为数据源，选取高建堡、虎沟等11个小流域作为试验区，采用修正通用土壤流失方程（RUSLE）的算法，计算各试验区降雨侵蚀力、坡度坡长等5个土壤侵蚀影响因子和土壤侵蚀模数；基于BP神经网络模型（BPNN），构建以各试验区降雨侵蚀力、坡度坡长等因子为输入变量，侵蚀模数为输出变量的关系模型，预测、验证模型的有效性。结果如下：1）BP神经网络模型能够有效预测土壤侵蚀影响因子的显著性；2）研究区小流域尺度上，地形因子对土壤侵蚀的显著性最强，土壤可蚀性因子的显著性最弱；3）在时间尺度上，小流域土壤侵蚀影响因子显著性略有差异，降雨侵蚀力因子的显著性通过降雨量体现出来；水土保持措施因子和植被覆盖与管理因子的显著性与退耕还林（草）工程等生态建设项目的实施有关，在2005年体现出对土壤侵蚀的抑制性；4）2015年的显著性预测结果适用于以生态自然恢复的区域，土壤侵蚀影响因子的显著性表现为：SL > P > R > C > K。研究结果表明，基于BP神经网络模型预测土壤侵蚀影响因子显著性的方法适用于黄土丘陵区，可为后续小流域综合治理提供科学依据。
Abstract：[Background] The terrain of loess hilly gully region is complex and varied, and soil erosion is serious. Soil erosion is a nonlinear system, influenced by various uncertain factors, such as soil, vegetation, terrain and others, the result of erosion is very complicated.[Methods] In order to explore the effects of land use types, topography, rainfall and other soil erosion factors in the loess hilly gully region, take Pengyang county as an example, based on the data source in 1995, 2005, 2015, such as land use, annual rainfall, DEM (5 m), etc. In the ecological zoning of Pengyang county, 11 small watershed areas such as Gaojianpu and Hugou were selected as experimental zones. Firstly, the revised universal soil loss equation (RUSLE) was adopted to calculate the soil erosion modulus and five influence factors, such as rainfall erosion force, slope and slope length in each small watershed. Then the BP neural network method was applied to construct the relational model. The five influence soil erosion factors in various small watershed as input variables, soil erosion modulus as the output variable, and finally the validity of the model was predicted and verified.[Results] 1) BP neural network model effectively predicted the significance of soil erosion influencing factors. 2) In the small watershed scale of the study area, topographic factors had the strongest significance to soil erosion, while soil erodibility factors had the weakest significance to soil erosion. 3) On the time scale, there was a slight difference between soil erosion influnence factors in small watershed. The rainfall erosion factor was reflected by rainfall, the significance of soil and water conservation measures factor and the vegetation cover and management factor were related to the implementation of ecological construction projects such as the project of returning cultivated land to forest (grass). In 2005, soil and water conservation measures factor and vegetation cover and management factors showed the inhibition of soil erosion. 4) The significance prediction results of 2015 were applicable to the study area with ecological natural restoration, and the significance of soil erosion influencing factors was as follows:SL > P > R > C > K.[Conclusions] The results show the method based on BP neural network model to predict the significance of soil erosion impact factor is applicable to loess hilly region, and it can provide scientific basis for the follow-up comprehensive governance of small watershed.
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2019, Vol. 17 
