| 摘要: |
| 黄土高原的土壤侵蚀与水土流失程度都很严重，对其进行水土流失的预报有着重要的生态意义和经济意义。利用SCS-CN（soil conservation service curve number）模型进行地表产流预测。针对黄土高原特定的气候及下垫面条件，以陕西省榆林市绥德韭园沟典型小流域为研究区域，借助韭园沟流域次降雨径流资料，优化影响降水产流关系的相应参数（初损率和降雨强度）。结果表明：①使用反算法来优化初损率，确定初损率为0.075，模型效率系数为0.208；②使用MATLAB结合粒子群算法来进一步优化初损率，确定初损率为0.13，模型效率系数为0.504，相比于反算法提高了142%，模型预报精度得到了很大提高；③在黄土丘陵沟壑区引入雨强因子修正降雨量函数，改进后模型效率系数为0.652，确定性系数为0.753，利用雨强修正函数后的SCS模型相比于标准SCS模型，确定性系数和模型效率系数分别提高了101%和534%。通过预测流域径流深与实测流域径流深的比较，模型模拟精度较为理想，可用于黄土高原不同小流域场次降雨的产流预报。 |
| 关键词: SCS-CN模型 雨强因子 初损率 模型改进 |
| DOI： |
| 分类号:P333 |
| 基金项目:国家重点研发计划资助项目（2016YFA0601703，2016YFC0401005，2016YFC0402808）；江苏省水利科技项目（2016046） |
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| Improvement and runoff prediction of SCS-CN model |
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XU Zan1, WU Lei2, WU Yongxiang1, XU Rongrong1
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1.Nanjing Hydraulic Research Institute, Nanjing;2.College of Water Resources and Architectural Engineering, Northwest Agriculture and Forestry University, Yangling
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| Abstract: |
| The Loess Plateau is one of the regions with the most severe water loss and soil erosion, which has the important ecological and economic significance for the prediction of the soil and water loss in the region. Integrating such factors as the climate, early soil moisture, soil type, and underlying surface, and featuring advantages of easy calculation, few parameters and easy access to the data needed, the SCS-CN (Soil Conservation Service Curve Number) has been extensively applied in the prediction of the surface runoff yield in the small watershed. Considering the specific climate and underlying surface of the Loess Plateau, the typical small watershed of Jiuyuangou, Suide County, Yulin, Shanxi Province was taken as the study region, then by virtue of rainfall runoff data in the Jiuyuangou area, the corresponding parameters (initial abstraction rate and rainfall intensity) affecting the runoff generation relation of the rainfall were optimized. The result showed that: ①The optimization of initial abstraction rate was conducted by use of the back-calculation method to confirm that λ was 0.075 and the coefficient of efficiency for the model was 0.208; ②The further optimization of initial abstraction rate was conducted by using MATLAB combined with the particle swarm optimization, and it was confirmed that λ was 0.13 and the coefficient of efficiency for the model was 0.504, increasing by 142% compared with the back-calculation method; and the prediction accuracy of this model was greatly improved; ③The factors of rainfall intensity were introduced in the loess hilly and gully region for the amendment of rainfall function, and after the improvement, the coefficient of efficiency for the model was 0.652 and the deterministic coefficient was 0.753. Compared with the standard SCS model, after the use of the modification function of rainfall intensity the deterministic coefficient R2 and the coefficient of efficiency of the model were increased by 101% and 534%, respectively. The comparison between the predicted and the measured runoff depths in the watershed shows that the simulated accuracy for the model is relatively ideal, and the model can be used for the prediction of the runoff generation of the rainfall in different small watersheds of the Loess Plateau. |
| Key words: SCS-CN model factors of rainfall intensity initial abstraction rate model improvement |
