(Peer-Reviewed) Urban Stormwater Modeling with Local Inertial Approximation Form of Shallow Water Equations: A Comparative Study
Weiqi Wang 王玮琦 ¹, Wenjie Chen 陈文杰 ¹, Guoru Huang 黄国如 ¹ ² ³
¹ School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, China
中国 广州 华南理工大学土木与交通学院
² State Key Laboratory of Subtropical Building Science, School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, China
中国 广州 华南理工大学土木与交通学院 亚热带建筑科学国家重点实验室
³ Guangdong Engineering Technology Research Center of Safety and Greenization for Water Conservancy Project, Guangzhou, 510640, China
中国 广州 广东省水运工程设计与绿色施工工程技术研究中心
Abstract
This study focused on the performance and limitations of the local inertial approximation form model (LIM) of the shallow water equations (SWEs) when applied in urban flood modeling. A numerical scheme of the LIM equations was created using finite volume method with a first-order spatiotemporal Roe Riemann solver. A simplified urban stormwater model (SUSM) considering surface and underground dual drainage system was constructed based on LIM and the US Environmental Protection Agency Storm Water Management Model.
Moreover, a complete urban stormwater model (USM) based on the SWEs with the same solution algorithm was used as the evaluation benchmark. Numerical results of the SUSM and USM in a highly urbanized area under four rainfall return periods were analyzed and compared.
The results reveal that the performance of the SUSM is highly consistent with that of the USM but with an improvement in computational efficiency of approximately 140%. In terms of the accuracy of the model, the SUSM slightly underestimates the water depth and velocity and is less accurate when dealing with supercritical flow in urban stormwater flood modeling. Overall, the SUSM can produce comparable results to USM with higher computational efficiency, which provides a simplified and alternative method for urban flood modeling.
Emerging optical techniques for sorting and detection of chiral particles
Yuzhi Shi, Chengfeng Li, Xiaolei Lin, Wenwen Xue, Chengxing Lai, Tao He, Qinghua Song, Zhanshan Wang, Yulan Wang, Din Ping Tsai, Xinbin Cheng, Haidong Zou
Opto-Electronic Advances
2026-06-08
Phonon-assisted absorption photoconductive switch
Zhao Wang, Lixin Zhang, Lu Cheng, Danwen Zhang, Yu Lu, Naiji Zhang, Xin Zhang, Duanyang Chen, Zhan Sui, Hongji Qi, Wei Zheng
Opto-Electronic Science
2026-05-25
Photonic spiking reinforcement learning for intelligent routing
Shuiying Xiang, Yonghang Chen, Ling Zheng, Zhicong Tu, Xintao Zeng, Mengting Yu, Shuai Wang, Yahui Zhang, Xingxing Guo, Weitao Pan, Yue Hao
Opto-Electronic Science
2026-05-25
Massively parallel and programmable photonic differential equation solver
Jiahao Wang, Wen Chen, Zhou Zhou, Dongyu Hu, Zile Li, Peng Chen, Yan-qing Lu, Shuang Zhang, Cheng-Wei Qiu, Shaohua Yu, Guoxing Zheng
Opto-Electronic Advances
2026-05-15
Femtosecond laser rapid customization of high-performance anti-reflection windows
Yulong Ding, Xiang Jiang, Cong Wang, Xianshi Jia, Linpeng Liu, Weina Han, Zheng Gao, Shiyu Wang, Nai Lin, Dejin Yan, Ji'an Duan
Opto-Electronic Science
2026-04-23
Ppt-level volatile organic compounds detection via microsecond-pulse-enhanced mid-infrared photoacoustic
Senyu Wang, Liang Zhao, Hongyu Luo, Xiangyu Zhao, Jianfeng Li, Wei Wang, Hao Lei, Mingrui Jiang, Jinlong Wan, Binxing Zhao, Bincheng Li, Yong Liu
Opto-Electronic Science
2026-04-23