Multidimensional Urban Waterlogging Risk Assessment Based on a Refined Inundation Model

Haiyan Yang, Titong Jiang (), Zhe Wang and Xiaobo Sun
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Haiyan Yang: Beijing Climate Change Research and Talent Training Base, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Titong Jiang: Beijing Climate Change Research and Talent Training Base, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Zhe Wang: Beijing Climate Change Research and Talent Training Base, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Xiaobo Sun: Ecological and Municipal Infrastructure Planning & Design Institute (Beijing), CAUPD Beijing Planing & Design Consultants Ltd., Beijing 100044, China

Sustainability, 2024, vol. 17, issue 1, 1-22

Abstract: To enhance the scientific and accurate assessment methods for urban waterlogging risk in City B and to promote sustainable urban development, this paper conducts a detailed evaluation of waterlogging risk from three dimensions: pedestrian safety, road traffic, and waterlogging-prone areas. After considering existing monitoring technologies and the constructed waterlogging model, the paper identifies standing water depth, standing water duration, and standing water velocity as the key indicators for waterlogging risk assessment and utilizes scenario simulation methods to evaluate waterlogging risk across these dimensions. Additionally, the paper employs boundary conditions of 2-h short-duration rainfall with a 5-year return period and 24-h long-duration rainfall with a 50-year return period for the assessment. The evaluation results indicate that, for pedestrian safety, under both short and long-duration rainfall conditions, low-risk areas represent the largest proportion of risk areas, reaching 6.36% and 10.83% of the total area, respectively. In the road traffic assessment, the proportions of severely congested roads under short- and long-duration rainfall conditions are 27.06% and 57.15%, respectively. In the evaluation of waterlogging-prone areas, high-risk areas account for the largest proportion of risk areas under both short- and long-duration rainfall conditions, reaching 0.64% and 1.42% of the total area, respectively.

Keywords: multidimensional waterlogging research; waterlogging model; risk assessment of waterlogging; scenario simulation method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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