摘要
暴雨洪水管理模型作为动态模拟的城市暴雨积涝模型,被广泛应用于城市积涝负荷估算研究,并在很多小尺度研究中取得了很好的效果。随着中国城市发展不断加快,城市积涝研究的空间尺度也随之变大,如何科学合理地构造模型,并且保证模型的精度成为研究中的一个重要问题。基于地理国情监测数据,分析暴雨洪水管理模型应用于城市区域暴雨积涝模拟时地理国情监测数据对模型精度的影响,并在此基础上,在汇水区划分、管网概化、节点选取3个方面对其参数进行优化。以陕西省延安市为例,利用优化后的模型对3场真实降雨相关数据进行分析,给出对延安市内涝风险的区域划分。研究结果表明,溢流节点和超载管段基本符合街道淹没情况,为延安市内涝风险防治提供了科学参考。
As a dynamically simulated urban storm water accumulation model,the storm water management model(SWMM)has been widely used in urban accumulation load estimation and has achieved good results in many small-scale studies.In China,as urban development continues to accelerate,spatial scale of studies of urban accumulation has become larger and larger.How to construct the model scientifically and reasonably and how to ensure the accuracy of the model have become important issues of study.Therefore,the impact of geographic national condition monitoring data on the model accuracy when SWMM was applied to urban area storm accumulation simulation was analyzed.In terms of watershed division,pipe network generalization,and node selection,the parameters in the simulation of three real rainfalls were optimized.Taking Yan’an City as an example,data of three real rainfalls were analyzed and simulated by using the optimized model,and the regional division of water logging risk in the city was given.Results show that the overflow node and the overloaded pipe section well agree with the street inundation.The simulation shows that the overflow point and the overload section of pipelining could meet the demand from street inundation.The results provide a scientific reference for the prevention and control of water logging risk in Yan’an City.
作者
张春森
于振
吴满意
ZHANG Chun-sen;YU Zhen;WU Man-yi(College of Geomatics,Xi'an University of Science and Technology,Xi'an 710000,China;First Topographic Survey Team,Ministry of Natural Resources,Xi'an 710054,China)
出处
《科学技术与工程》
北大核心
2020年第2期453-459,共7页
Science Technology and Engineering
基金
陕西省自然科学基金(2018JM5103)。
关键词
地理国情监测
暴雨洪水管理模型
模型精度
城市暴雨积涝
geographical national monitoring
storm water management model
model accuracy
urban storms
