Treffer: Urban flood numerical simulation: Research, methods and future perspectives.

Title:
Urban flood numerical simulation: Research, methods and future perspectives.
Authors:
Luo, Pingping1,2 (AUTHOR), Luo, Manting1,2 (AUTHOR) 2020129065@chd.edu.cn, Li, Fengyue3 (AUTHOR), Qi, Xiaogang1,3 (AUTHOR) xgqi@xidian.edu.cn, Huo, Aidi1,2 (AUTHOR), Wang, Zhenhong1,2 (AUTHOR), He, Bin4 (AUTHOR), Takara, Kaoru5 (AUTHOR), Nover, Daniel6 (AUTHOR), Wang, Yihe7 (AUTHOR)
Source:
Environmental Modelling & Software. Oct2022, Vol. 156, pN.PAG-N.PAG. 1p.
Database:
GreenFILE

Weitere Informationen

Urban flooding has become an increasingly frequent and fatal natural hazard and numerical modeling techniques play a vital role in its prediction and management. We review urban flood numerical simulations by systematically summarizing the calculation methods of surface runoff, drainage systems, and coupled models. Following the review, accuracy and computational efficiency are found to be the two key areas hindering the quality improvement of urban flood models, so an investigation of the key trends in the improvement of model accuracy and computational efficiency is conducted. It is found that the 1D-2D coupling model, finite volume method, unstructured meshing method, and hybrid parallel computing applications are the most effective strategies. Furthermore, the complex coupling of models and the lack of validation data are still crucial challenges in the development of urban flood modeling. This result can be used as a guideline for hydrologists in choosing the proper method of urban flood numerical simulation according to the task. • The methods and applicability of urban flood numerical simulation are summarized to provide support for related research. • We propose that the urban flood model can be improved in two aspects: accuracy and calculation speed model. • The future development of urban flood numerical simulation tends to be modular with a complex coupling model. [ABSTRACT FROM AUTHOR]

Copyright of Environmental Modelling & Software is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)