Abstract
Effective forecasting and response to meteorological hazards are crucial for safeguarding life, property, and supporting sustainable socioeconomic development. With the rising frequency and severity of meteorological hazards worldwide, this study proposes an enhanced risk assessment framework for urban infrastructure exposed to extreme weather events, with a focus on cascading impacts to critical services such as electricity, communication, and transportation networks (roads and subways). A disaster-loss model is developed to quantify infrastructure vulnerability at various spatial and temporal scales under heavy rainfall conditions, accounting for secondary effects. The model's performance is validated through empirical analysis of a 15-year rainfall event in Dongguan City, China, occurring from September 7-8, 2023. Results indicate the model's ability to predict real-event outcomes with approximately 70% accuracy. This model offers valuable insights for disaster prevention and mitigation strategies, aiding decision-makers in optimizing emergency resource allocation, enhancing disaster response efficiency, and issuing timely public risk warnings to minimize losses.
| Original language | English |
|---|---|
| Article number | 104943 |
| Number of pages | 18 |
| Journal | International Journal of Disaster Risk Reduction |
| Volume | 114 |
| Early online date | 30 Oct 2024 |
| DOIs | |
| Publication status | Published - Nov 2024 |
Bibliographical note
Copyright © 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license(https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Data Access Statement
Data will be made available on request.Funding
This research work is supported by the National Natural Science Foundation of China (No. 72304064), Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515110339) and Guangdong Provincial Key Laboratory of Intelligent Disaster Prevention and Emergency Technologies for Urban Lifeline Engineering (2022) (Grant No. 2022B1212010016). Thanks to Dongguan Water Bureau for providing data for this study.
Keywords
- Cascading failures
- Heavy rain
- Infrastructure
- Prevention and mitigation
- Refined risk assessment