Version 1
: Received: 30 August 2022 / Approved: 31 August 2022 / Online: 31 August 2022 (08:55:36 CEST)
How to cite:
Zou, L.; Wang, Z.; Lu, Q.; Wu, S.; Chen, L.; Qin, Z. The Rain-induced Urban Waterlogging Risk and its Evaluation: A Case Study in the Central City of Shanghai. Preprints2022, 2022080540. https://doi.org/10.20944/preprints202208.0540.v1
Zou, L.; Wang, Z.; Lu, Q.; Wu, S.; Chen, L.; Qin, Z. The Rain-induced Urban Waterlogging Risk and its Evaluation: A Case Study in the Central City of Shanghai. Preprints 2022, 2022080540. https://doi.org/10.20944/preprints202208.0540.v1
Zou, L.; Wang, Z.; Lu, Q.; Wu, S.; Chen, L.; Qin, Z. The Rain-induced Urban Waterlogging Risk and its Evaluation: A Case Study in the Central City of Shanghai. Preprints2022, 2022080540. https://doi.org/10.20944/preprints202208.0540.v1
APA Style
Zou, L., Wang, Z., Lu, Q., Wu, S., Chen, L., & Qin, Z. (2022). The Rain-induced Urban Waterlogging Risk and its Evaluation: A Case Study in the Central City of Shanghai. Preprints. https://doi.org/10.20944/preprints202208.0540.v1
Chicago/Turabian Style
Zou, L., Lei Chen and Zhengkun Qin. 2022 "The Rain-induced Urban Waterlogging Risk and its Evaluation: A Case Study in the Central City of Shanghai" Preprints. https://doi.org/10.20944/preprints202208.0540.v1
Abstract
Waterlogging induced by rain in urban areas has a potential risk impact on property and safety. This paper focuses on the impact of rain on waterlogging and evaluates the waterlogging risk in the central city of Shanghai. A simplified waterlogging depth model is developed in different areas with different drainage capacity and rainfall in consumption of simplifying the effect of complex terrain characteristics and hydrological situation. Based on urban waterlogging depth and its classification collection, a Rain-induced Urban Waterlogging Risk Model(RUWRM) is further established to evaluate waterlogging risk in the central city. The results show that waterlogging depth is closely linked with rainfall and drainage, with a linear relationship between them. More rainfall leads to higher waterlogging risk, especially in the central city with imperfect drainage facilities. Rain-induced urban waterlogging risk model can rapidly gives the waterlogging rank caused by rainfall with a clear classification collection. The results of waterlogging risk prediction indicate that it is confident to get the urban waterlogging risk rank well and truly in advance with more accurate rainfall prediction. This general study is a contribution that allows the public, policy makers and relevant departments of urban operation to assess the appropriate management to reduce traffic intensity and personal safety or strategy to lead to less waterlogging risk.
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
