济南市小清河黄台桥以上流域城市洪涝过程模拟与风险分析 ^*

Urban flood simulation and risk analysis in upstream Huangtaiqiao area of the Xiaoqinghe River basin, Jinan city

摘要

为探究济南市小清河黄台桥以上流域洪涝过程演化规律及洪涝风险，构建SWMM和InfoWorks ICM-2D耦合洪涝模型，采用保留精英策略的遗传算法率定SWMM模型主要参数，模拟分析不同降雨重现期下管网排水能力、地表淹没特征及风险分布特征. 结果表明：改进后的遗传算法可使模型精度在短时间内达到较优水平，显著提升参数率定效率；该耦合模型在研究区展现出良好的适用性. 随降雨重现期增大，超载管道数与溢流节点数显著增加，10 a重现期时地下管网容量基本饱和，超过10 a后内涝风险快速上升；流域淹没总面积从5 a的573.38 hm²增至100 a的956.26 hm²，高水深区集中于二环南路、经十路、腊山立交等主干道；洪涝风险等级随重现期增大而不断提高，高风险区面积不断增加，流域对短重现期降雨有一定抗风险能力，但极端降雨情况下需重点开展内涝防范工作.

Abstract

To investigate evolution laws of flood processes and status of flood risk in upstream Huangtaiqiao area of the Xiaoqinghe River basin, Jinan city, a coupled flood model integrating SWMM and InfoWorks ICM-2D was developed. The main parameters of the SWMM model were calibrated using a genetic algorithm with an elite retention strategy. The model was used to simulate and analyze pipeline network drainage capacity, surface inundation status, and hazard distributions under different rainfalls with various return periods. The improved genetic algorithm could quickly optimize model accuracy to a high level in a short time, significantly improving efficiency of parameter calibration. The coupled model exhibited good applicability in the study area. With an increase of rainfall return period, number of overloaded pipes and overflow nodes in the pipeline network increased significantly. Capacity of underground pipeline-network was basically saturated at 10 a return period; the water-logging risk rose rapidly when return period exceeded 10 years. The total inundated area of the river basin increased from 573.38 hm ² at a 5 a return period to 956.26 hm ² at a 100 a return period, with areas of high water depth concentrated on major roads such as Erhuan Road (S), Jingshi Road, and Lashan Interchange. The flood risk level continuously increased with extension of return period, and area of high-risk areas continued to increase - with return periods from 5 a to 100 a, the area of relatively high-risk zones and high-risk zones increased by 151.11 hm ² and 263.50 hm ², respectively. This study area showed a certain risk resistance capacity against short-return-period rainfall, but targeted water-logging prevention measures should be prioritized under extreme rainfall conditions.

关键词

洪涝模拟 / 淹没水深 / 洪涝风险 / 遗传算法 / 小清河流域

Key words

flood simulation / inundation depth / flood risk / genetic algorithm / Xiaoqinghe River basin

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基金资助

国家自然科学基金重点资助项目(52239003)