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21 June 2024 Urban waterlogging risk assessment and drainage parameters design of Xicen Science and Technology Innovation Center based on satellite remote sensing data
Tian Liang, Hanwei Yang, Qiang Meng, Kun Luo, Jiayan Fang, Qingyan Sheng
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Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131671S (2024) https://doi.org/10.1117/12.3029748
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China
Abstract
Under global warming, the increase in extreme rainfall events has led to a significant rise in the frequency of urban waterlogging disasters, and major cities are facing severe risks of waterlogging. In this paper, the revised “annual maximum method” was utilized to develop the rainfall intensity formula suitable for Xicen Science and Technology Innovation Center (XICEN). Based on satellite remote sensing data and SCS-CN model, we evaluated the urban waterlogging risks under five extreme rainfall scenarios with return periods of 5a, 10a, 20a, 50a, and 100a through the inversion method of critical rainfall. The results indicate that when the hourly rainfall exceeds 50mm, waterlogging may affect the entire study area. Furthermore, as return period extends, the risk of waterlogging also increases. Under the scenario of 5a return period, the waterlogging risk is mainly categorized as mid-low or low risk; however, under the scenario of 100a return period, the waterlogging risk of XICEN exceeds mid-high levels. These findings can serve as references for urban waterlogging disaster response and future construction planning.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Tian Liang, Hanwei Yang, Qiang Meng, Kun Luo, Jiayan Fang, and Qingyan Sheng "Urban waterlogging risk assessment and drainage parameters design of Xicen Science and Technology Innovation Center based on satellite remote sensing data", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131671S (21 June 2024); https://doi.org/10.1117/12.3029748
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KEYWORDS
Rain
Risk assessment
Remote sensing
Satellites
Climate change
Design
Data modeling
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