黄土高原小流域次降雨侵蚀产沙分段预报模型研究  

PIECEWISE PREDICTION MODEL FOR EROSION AND SEDIMENT YIELDING OF INDIVIDUAL RAINFALL EVENTS IN SMALL WATERSHED ON LOESS PLATEAU

作　　者：于国强[1] 李占斌[1,2] 鲁克新[1] 张霞 

机构地区：[1]西安理工大学西北水资源与环境生态教育部重点实验室,西安710048 [2]中国科学院水利部水土保持研究所黄土高原土壤侵蚀与旱地农业国家重点实验室,陕西杨凌712100 [3]陕西省环境科学研究设计院,西安710054

出　　处：《土壤学报》2010年第4期604-610,共7页Acta Pedologica Sinica

基　　金：国家科学自然基金项目(40771124);国家科技支撑项目(2006BAD09B02);西安理工大学优秀博士学位论文基金(106-210911)联合资助

摘　　要：建立具有广泛适用性的流域尺度侵蚀产沙预报模型是土壤侵蚀和水土保持研究的前沿领域。本文以黄土高原丘陵沟壑区第一副区岔巴沟流域为例,建立了流域侵蚀产沙人工神经网络模型,并运用缺省因子检验法分析各因子对流域侵蚀产沙的敏感程度;建立了基于敏感因子与分形信息维数的流域次降雨侵蚀产沙分段预报模型,并加以验证。研究表明,人工神经网络模型具有较高的精度,能够很好地定量描述流域水沙耦合关系;径流侵蚀功率和径流深对流域次降雨侵蚀产沙的敏感程度与流域地貌形态的复杂程度有关;以分形信息维数为界限,分段引入径流侵蚀功率和径流深,当Di>0.8308时,采用径流侵蚀功率预测精度要高于采用径流深的预测精度,当Di<0.8140时,采用径流深预测精度要高于采用径流侵蚀功率的预测精度。该侵蚀产沙分段预报模型的建立和方法的提出具有一定的合理性和可靠性,对其他侵蚀产沙模型有一定的借鉴之处。It is a frontier in the field of soil erosion control and soil-water conservation to establish a model of extensive applicability for prediction of soil erosion and sediment yield on a watershed scale. With Chabagou Watershed of the Loess Plateau as a case for study, an artificial neural network model was established for sediment yield; analysis was done of various factors for their sensitivities to sediment yielding with the default factor method; and then based upon sensitiveness factors and fractal information dimension, a piecewise prediction model for erosion and sediment yield of individual rainfall events was established and verified. Results show that the artificial neural network model demonstrated capability of describing quantitatively the coupling relationship between runoff and sediment with sufficient high accuracy. The sensi- tivity of sediment yielding of individual rainfall events to runoff depth and runoff erosion power is related to complexity of the landform. With fractal information dimension as boundary, runoff depth and runoff erosion power were introduced piecewise into the prediction model. The prediction based on runoff erosion power was higher than that based on runoff depth in accuracy, when Di 〉0. 8308, and it was the other way round when Di 〈0. 8140. The findings demonstrate that the establishment of the pieeewise prediction model and the proposition of the method for prediction of erosion and sediment yield are reasonable and reliable, and of some value as reference for establishment of other models for prediction of erosion and sediment yield.

关 键 词：岔巴沟流域 人工神经网络 分形信息维数 分段预报模型 

分 类 号：S157.1[农业科学—土壤学]