Assessing urban pluvial waterlogging resilience based on sewer congestion risk and climate change impacts

Junhao Wu , Zihan Liu , Tianxiang Liu , Wen Liu , Wenli Liu , Hanbin Luo

Abstract

Urban waterlogging is a critical issue in urban governance, as without proper preventive measures, stagnant water can lead to damage or even casualties. Identifying waterlogging risk points is often desired in urban waterlogging management. Evaluation strategies should be tailored due to the dynamic features of climate warming and high uncertainty in rainfall patterns. An urban pluvial waterlogging resilience (UPWR) analysis framework is proposed as the basis and the tool for dynamically evaluating the spatial distribution of urban waterlogging resilience. UPWR combines hydrological modelling with drainage capacity calculation, extending the original urban waterlogging analysis into a spatial–temporal resilience analysis paradigm for urban waterlogging management in a future climate. An area in Wuhan city, which has suffered from severe waterlogging disaster events for years, was selected for a case study to test the effectiveness and applicability of the proposed approach. This study found that (1) as the drainage network congestion situation intensifies, the UPWR grade decreases in 14% of the study areas; (2) from 2030 to 2100, the UPWR in Wuhan shows a worsening trend under each Representative Concentration Pathway (RCP), and the UPWR under the RCP45 scenario is the worst; and (3) the analysis results can be used to predict future UPWR changes and determine in advance which areas require special maintenance. The novelty of this research lies in the following: (a) the two levels of precipitation and drainage are combined to construct an UPWR assessment framework, providing a quantitative spatial distribution that lays the foundation for exploring urban waterlogging management solutions; and (b) consideration is given to the dynamic change in climate and urban resistance to waterlogging when predicting the future UPWR via a continuous time series, which can provide a basis for urban waterlogging control planning.

https://www.sciencedirect.com/science/article/pii/S0022169423011721