EGU26-6254, updated on 13 Mar 2026
https://doi.org/10.5194/egusphere-egu26-6254
EGU General Assembly 2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Poster | Tuesday, 05 May, 08:30–10:15 (CEST), Display time Tuesday, 05 May, 08:30–12:30
 
Hall X3, X3.63
Predictive Methodology for Cascading Disasters/Events Induced by Extreme Rainfall in Urban Areas
Seunghee Oh1 and Yoon-Seop Chang2
Seunghee Oh and Yoon-Seop Chang
  • 1Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea, (seunghee5@etri.re.kr)
  • 2Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea, (ychang76@etri.re.kr)

Climate change has increased the intensity and frequency of hazard events, resulting in disaster risks that exceed historical experience. In highly urbanized countries such as South Korea, heavy and extreme rainfall has become a major climate-related hazard, leading to concentrated human and economic losses. While extreme rainfall can be partially anticipated through meteorological radar observations, official forecasts, and pattern-based prediction models, such hazard-focused approaches are insufficient to fully assess disaster risk in urban areas. This is because actual impacts are strongly influenced by exposure, vulnerability, and cascading effects, which may evolve into complex disasters.

In line with the IPCC and UNDRR disaster risk framework, this study emphasizes the need to anticipate secondary hazards and cascading risk events that may develop into complex disasters under extreme rainfall conditions. To address this challenge, a scenario generation method for extreme rainfall–induced complex disasters is proposed. The method integrates three key components: (1) regional exposure and vulnerability characteristics, including population distribution, industrial activities, transportation networks, and critical infrastructure; (2) secondary hazard and impact information derived from historical disaster records; and (3) interrelationships and correlations among different hazard and disaster types.

Using a weighted analytical framework, the proposed approach generates representative scenarios with high likelihood as well as extreme scenarios with lower likelihood but potentially high impacts. These scenarios support a risk-informed understanding of possible disaster pathways and provide actionable prior information for preparedness planning, emergency response, and scenario-based training. The results contribute to strengthening disaster risk reduction and enhancing urban resilience against climate-related extreme rainfall–induced complex disasters.

This work was supported by Electronics and Telecommunications Research Institute(ETRI) grant funded by the Korean government [26ZR1300, Development of Technology for the Urban Extreme Rainfall Response Platform].

How to cite: Oh, S. and Chang, Y.-S.: Predictive Methodology for Cascading Disasters/Events Induced by Extreme Rainfall in Urban Areas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6254, https://doi.org/10.5194/egusphere-egu26-6254, 2026.