Understanding storm surge inundation risk is essential for developing countermeasures and adaptation strategies for tackling climate change. Consistent assessment of storm surge inundation risk that links probability of hazard occurrence to distribution of economic consequence are scarce due to the lack of historical data and uncertainty of climate change, especially at local scales. This paper proposes a simulation-based method to construct exceedance probability (EP) curves for representing storm surge risk and identifying the economic impact of climate change in the coastal areas of Ise Bay, Japan. The region-specific exceedance probability curves show that risk could be different among different districts. The industry-specific exceedance probability curves show that manufacturing, transport and postal activities, electricity, gas, heat supply and water, and wholesale and retail trade are the most affected sectors in terms of property damage. Services also need to be of concern in terms of business interruption loss. Exceedance probability curves provide complete risk information and our simulation-based approach can contribute to a better understanding of storm surge risk, improve the quantitative assessment of the climate change-driven impacts on coastal areas, and identify vulnerable regions and industrial sectors in detail.

A key issue in assessing the spatial distribution of flood risk is considering risk information derived from multiple flood sources (river flooding, drainage inundation, etc.) that may affect the risk assessment area. This study proposes a method for assessing spatial flood risk that includes flooding and inundation in small-basin areas through multivariate design rainfall. The concept of critical rainfall duration, determined by the time of concentration of flooding, is used to represent the characteristics of flooding from different sources. A copula method is adopted to capture the correlation of rainfall amounts in different critical rainfall durations to reflect the correlation of potential flooding from multiple flood sources. Rainfalls for different return periods are designed based on the copula multivariate analysis. Using the design rainfalls as input, flood risk is assessed following the rainfall–runoff–inundation–loss estimation procedure. A case study of the Otsu River Basin, Osaka Prefecture, Japan, was conducted to demonstrate the feasibility and advantages of this method. Compared to conventional rainfall design, this method considers the response characteristics of multiple flood sources, and solves the problem of flood risk assessment from multiple flood sources. It can be applied to generate a precise flood risk assessment to support integrated flood risk management.

The application of risk management strategies is a common approach in emergency response scenarios. However, scant knowledge exists regarding its utilization in the specific context of an outbreak, both theoretically and practically. This study delves into the realm of risk management during the COVID-19 pandemic, focusing on four key measurements: risk avoidance (RA), risk reduction (RD), risk transfer (RT), and risk retention (RR). Using 800 valid responses collected from 31 provinces across China between August 1 and September 30, 2020, this study investigates spatial disparities in individuals’ intentions towards risk management. To achieve this, an extended version of the Theory of Planned Behavior (E-TPB) is applied. The Structural Equation Model’s path analyses revealed several findings: (1) discernible spatial disparities in RR, RA, and RD intentions between large and small cities; (2) RD and RR intentions were significantly associated with attitude, subjective norm, perceived behavioral control, and risk perception; (3) RA and RT intentions were significantly associated with attitude and risk perception; (4) risk perception exihibiting both direct and indirect effects on RA and RR intentions. This study contributs to the urban studies literature by extending the theoretical framework of risk management in the context of COVID-19. It enhances the measurement tools employed in the TPB model and scrutinizes spatial disparities in the adoption of preventative measures against COVID-19. The findings underscore the importance for local policymakers to consider geographical differences when formulating effective strategies for COVID-19 prevention.