黄土高原丘陵沟壑区临界地貌侵蚀产沙特征

深入研究流域临界地貌形态对侵蚀产沙的作用机制至关重要,也是建立流域侵蚀产沙预测模型的关键所在。该研究以黄土高原丘陵沟壑区岔巴沟为研究对象,开展了流域次降雨侵蚀产沙的BP人工神经网络(back propagation artificial neutral network,BPANN)模型与多重线性回归(multiple linear regression,MLR)模型比较研究,定量分析了流域侵蚀产沙的敏感因子及影响程度,并使用分形信息维数临界值和相对应的变量建立临界地貌侵蚀产沙预测模型,阐明临界地貌侵蚀产沙特征。结果表明,在侵蚀产沙模数预测方面,BPANN模型较MLR模型具有更好预测性能,能够有效表征综合条件下侵蚀产沙的动态变化过程。径流深和径流侵蚀功率对侵蚀产沙以及水文响应的影响程度受地貌形态因素制约。当分形信息维数大于地貌临界值时,采用径流侵蚀功率的预测精度高于径流深;相反,分形信息维数小于地貌临界值时,采用径流深的预测精度高于径流侵蚀功率。以分形信息维数为界,分别引入径流深或径流侵蚀功率所建立的临界地貌侵蚀产沙预测模型,具有较高的预测精度,具有一定的可行性、可靠性和普适性。

Characteristics of erosion and sediment yield under critical landform in hill-gully area of Loess Plateau

Abstract: Small watersheds are typically used as basic units for the comprehensive control of soil and water loss in the Loess Plateau. Study on the mechanism of the critical topography on erosion and sediment is very important to the establishment of prediction model for watershed-scale erosion and sediment. The research presented was conducted in the Chabagou watershed, which was located in the hill-gully area of the Loess Plateau, China. A back propagation artificial neural network (BPANN) model for watershed-scale erosion and sediment yield was established, whose accuracy was then compared to that of the multiple linear regression (MLR) model. The sensitivity degree of various factors to erosion and sediment yield was quantitatively analyzed. Based on the sensitive factors and the fractal information dimension, the critical geomorphic prediction model for erosion and sediment yield of individual rainfall event was established and further verified. The results revealed that the BPANN model performed better than the MLR model in terms of predicting the erosion modulus, and the former was able to effectively characterize dynamic changes in sediment yield under comprehensive condition of the factors. The sensitivity of runoff erosion power and runoff depth to the erosion and sediment yield associated with individual rainfall event was found to be related to the complexity of surface topography. The characteristics of such a hydrological response were thus closely related to topography. When surface topography was complex, the erosion modulus was more sensitive to runoff erosion power; conversely, when surface topography was simple, the erosion modulus was more sensitive to runoff depth. The developed sensitivity method based on BPANN was employed in order to select the main predictive (sensitive) factors for erosion and sediment yield; as the influence of these factors gradually increased, this quantitative method was increasingly helpful. Therefore, when the fractal information dimension was greater than the topographic threshold, the accuracy of prediction using runoff erosion power was higher than that using runoff depth. In contrast, when the fractal information dimension was smaller than the topographic threshold, the accuracy of prediction using runoff depth was higher than that using runoff erosion power. The characteristics of erosion and sediment transport, i.e. the hydrological response of a watershed, were closely related to topography. Sheet (inter-rill) erosion, which exhibited rainfall erosion characteristics, was more prone to occur when topography was simple. In contrast, rill and gully erosion, which exhibited the dual characteristics of rainfall and sediment transport, were more inclined to occur when topographic thresholds were exceeded. The fractal information dimension was used as a model boundary; when the value of the fractal information dimension was greater than the selected topographic threshold, the accuracy of predictions using runoff erosion power was higher than that using runoff depth. In contrast, when the value of the fractal information dimension was smaller than the topographic threshold, the accuracy of predictions using runoff depth was higher than that using runoff erosion power. Therefore, the piecewise prediction model for watershed-scale erosion and sediment yield of individual rainfall event, in which runoff erosion power and runoff depth are introduced using the fractal information dimension as a boundary, can be considered feasible and reliable, and has a high prediction accuracy. Considering the observed watershed differences and the relatively insufficient fractal dimension values, further comprehensive analysis and comparison should be carried out in order to establish piecewise erosion prediction models for other watersheds. However, as the present piecewise erosion prediction model takes account of watershed-scale surface topography, runoff depth and runoff erosion power, as well as the relationships between these factors, it can be used as a basis to establish and popularize other erosion models.