城市暴雨内涝模拟模型优化与精度验证

Optimization and Verification of a Novel Hydrodynamic Model for Urban Flood Simulation

本文采用"Vegetation-Impervious Surface-Soil"模型和线性光谱混合分解方法,获取像元中不透水面、植被、土壤覆盖信息,用于计算SCS模型产流参数综合CN(Curve Number)值;基于土地利用类型,采用经验值与数值实验逐步求精相结合的方法,确定水动力汇流模型参数曼宁系数,并用实测积水数据验证两次参数修正的模拟效果。以上海中心城区为例进行验证,研究结果表明:(1)将采用V-I-S模型得到的不透水面、植被、土壤信息设定CN值,能够降低积水分布的极值化现象,提高SCS产流模型产流量和产流分布精度;(2)采用经验法和数值模拟逐步求精法,按土地利用类型设定曼宁系数,使各时段最大积水深度高于原模型,说明曼宁系数是汇流模型的敏感参数。

This paper aims to optimize the performance of a previously developed hydrodynamic model for urban flood simulation. Our major task includes calibration of two key parameters in the runoff generation and flood routing modules, and verification of the precision of the model output. In the runoff producing module, we focused on optimization of Curve Number （CN） values. To achieve this purpose, the method of Linear Spectral Mixture Analysis （LSMA） was employed to extract terrestrial information of vegetation coverage, soil categories and impervious land use from Landsat TM images, based on which a specific CN value could be defined for each unit in the hydrodynamic model. As for the flood routing module, we reset the manning coefficient via integrating previous empirical value and findings from calibration experiments conducted in this study. Verification experiments show both the calibration of CN values and manning coefficient promotes the model＇s simulation precision. Using the Vegetation-Impervious Surface-Soil （V-I-S） raster layers, in which the CN values incorporate more accurate information of vegetation coverage and soil categories, as input for the hydrodynamic model, are able to lower the extreme abnormal values of simulated water depth, and provide more reasonable estimation of water volume and inundation area. After resetting the manning coefficient for different land uses, the simulated maximum water depth increased notably （almost 100 mm）, compared with previous model outputs without calibration of this parameter. Through our calibration study, it is safe to say that manning coefficient is a sensitive and critical parameter and deserves further attention in the extension research for optimization of the flood routing module.